Engineering › Civil and Structural Engineering

Structural Health Monitoring Techniques

Works Count: 113584

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Description

This cluster of papers covers advancements in structural health monitoring techniques, including vibration-based damage identification, wireless sensor networks, modal identification, structural damage detection, model updating, Bayesian system identification, deep learning applications, sensor networks, environmental effects, and nonlinear dynamics.

Keywords

Vibration-based Damage Identification; Wireless Sensors; Modal Identification; Structural Damage Detection; Model Updating; Bayesian System Identification; Deep Learning Applications; Sensor Networks; Environmental Effects; Nonlinear Dynamics

Method for Random Vibration of Hysteretic Systems

1976-04-01

Yi‐Kwei Wen

Based on a Markov-vector formulation and a Galerkin solution procedure, a new method of modeling and solution of a large class of hysteretic systems (softening or hardening, narrow or wide-band) … Based on a Markov-vector formulation and a Galerkin solution procedure, a new method of modeling and solution of a large class of hysteretic systems (softening or hardening, narrow or wide-band) under random excitation is proposed. The excitation is modeled as a filtered Gaussian shot noise allowing one to take the nonstationarity and spectral content of the excitation into consideration. The solutions include time histories of joint density, moments of all order, and threshold crossing rate; for the stationary case, autocorrelation, spectral density, and first passage time probability are also obtained. Comparison of results of numerical example with Monte-Carlo solutions indicates that the proposed method is a powerful and efficient tool.

Structural Health Monitoring with Piezoelectric Wafer Active Sensors

2014-01-01

Victor Giurgiutiu

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Optimal Estimation of Dynamic Systems

2004-04-27

John L. Crassidis , John L. Junkins

Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal … Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation.Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals receiv

Evaluation of ductility of structures and structural assemblages from laboratory testing

1989-09-30

R. Park

Definitions for the required and available ductility used in seismic design are discussed. Methods for estimating the yield deformation and the maximum available deformation are described and suggestions are made … Definitions for the required and available ductility used in seismic design are discussed. Methods for estimating the yield deformation and the maximum available deformation are described and suggestions are made for appropriate definitions. Examples are given of different imposed histories of inelastic displacement which have been used in the experimental testing of structures and structural assemblages in which cycles of quasi-static loading are applied. A quasi-static procedure for establishing the available ductility factor of a subassemblage by laboratory testing is recommended.

REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS

1999-11-01

Bart Peeters , Guido De Roeck

Factors Governing the Intelligibility of Speech Sounds

1947-01-01

N. French , John C. Steinberg

The characteristics of speech, hearing, and noise are discussed in relation to the recognition of speech sounds by the ear. It is shown that the intelligibility of these sounds is … The characteristics of speech, hearing, and noise are discussed in relation to the recognition of speech sounds by the ear. It is shown that the intelligibility of these sounds is related to a quantity called articulation index which can be computed from the intensities of speech and unwanted sounds received by the ear, both as a function of frequency. Relationships developed for this purpose are presented. Results calculated from these relations are compared with the results of tests of the subjective effects on intelligibility of varying the intensity of the received speech, altering its normal intensity-frequency relations and adding noise.

Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward

2003-11-01

Gyuhae Park , Hoon Sohn , Charles R. Farrar +1 more

In this paper we summarize the hardware and software issues of impedance-based structural health moni- toring based on piezoelectric materials. The basic concept of the method is to use high-frequency … In this paper we summarize the hardware and software issues of impedance-based structural health moni- toring based on piezoelectric materials. The basic concept of the method is to use high-frequency structural excitations to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. A brief overview of research work on experimental and theoretical stud- ies on various structures is considered and several research papers on these topics are cited. This paper concludes with a discussion of future research areas and path forward. Piezoelectric materials acting in the manner pro- duce an electrical charge when stressed mechanically. Con- versely, a mechanical strain is produced when an electrical field is applied. The direct piezoelectric effect has often been used in sensors such as piezoelectric accelerometers. With the converse effect, piezoelectric materials apply local- ized strains and directly influence the dynamic response of the structural elements when either embedded or surface bonded into a structure. Piezoelectric materials have been widely used in structural dynamics applications because they are lightweight, robust, inexpensive, and come in a variety of forms ranging from thin rectangular patches to complex shapes being used in microelectromechanical systems (MEMS) fabrications. The applications of piezoelectric mate- rials in structural dynamics are too numerous to mention and are detailed in the literature (Niezrecki et al., 2001; Chopra, 2002). The purpose of this paper is to explore the importance and effectiveness of impedance-based structural health mon- itoring from both hardware and software standpoints. Imped- ance-based structural health monitoring techniques have been developed as a promising tool for real-time structural dam- age assessment, and are considered as a new non-destructive evaluation (NDE) method. A key aspect of impedance-based structural health monitoring is the use of piezoceramic (PZT) materials as collocated sensors and actuators. The basis of this active sensing technology is the energy transfer between the actuator and its host mechanical system. It has been shown that the electrical impedance of the PZT material can be directly related to the mechanical impedance of a host structural component where the PZT patch is attached. Uti- lizing the same material for both actuation and sensing not only reduces the number of sensors and actuators, but also reduces the electrical wiring and associated hardware. Fur- thermore, the size and weight of the PZT patch are negligible compared to those of the host structures so that its attach- ment to the structure introduces no impact on dynamic char- acteristics of the structure. A typical deployment of a PZT on a structure being monitored is shown in Figure 1. The first part of this paper (Sections 2 and 3) deals with the theoretical background and design considerations of the impedance-based structural health monitoring. The signal processing of the impedance method is outlined in Section 4. In Section 5, experimental studies using the impedance approaches are summarized and related previous works are listed. Section 6 presents a brief comparison of the imped- ance method with other NDE approaches and, finally, sev- eral future issues are outlined in Section 7. 2. Theoretical Background

Past, present and future of nonlinear system identification in structural dynamics

2005-11-07

Gaëtan Kerschen , Keith Worden , Alexander F. Vakakis +1 more

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Category judgment: A range-frequency model.

1965-01-01

Allen Parducci

Modal Testing: Theory and Practice

1986-01-01

D. J. Ewins , H. Saunders

A survey of the technology of modal testing, a new method for describing the vibration properties of a structure by constructing mathematical models based on test data rather than using … A survey of the technology of modal testing, a new method for describing the vibration properties of a structure by constructing mathematical models based on test data rather than using conventional theoretical analysis. Shows how to build a detailed mathematical model of a test structure and analyze and modify the structure to improve its dynamics. Covers techniques for measuring the mode, shapes, and frequencies of practical structures from turbine blades to suspension bridges.

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Structural Control: Past, Present, and Future

1997-09-01

George W. Housner , Lawrence A. Bergman , T. K. Caughey +7 more

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of … This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Practical Issues in Structural Modeling

1987-08-01

Peter M. Bentler , Chih-Ping Chou

Practical problems that are frequently encountered in applications of covariance structure analysis are discussed and solutions are suggested. Conceptual, statistical, and practical requirements for structural modeling are reviewed to indicate … Practical problems that are frequently encountered in applications of covariance structure analysis are discussed and solutions are suggested. Conceptual, statistical, and practical requirements for structural modeling are reviewed to indicate how basic assumptions might be violated. Problems associated with estimation, results, and model fit are also mentioned. Various issues in each area are raised, and possible solutions are provided to encourage more appropriate and successful applications of structural modeling.

Model Updating In Structural Dynamics: A Survey

1993-10-01

John E. Mottershead , Michael I. Friswell

Neural networks for pattern recognition

1995-01-01

Jonathan A. Marshall

An introduction to structural health monitoring

2006-12-12

Charles R. Farrar , Keith Worden

The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to … The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

Detection of structural damage through changes in frequency: a review

1997-09-01

O. S. Salawu

Vibration of cracked structures: A state of the art review

1996-11-01

Andrew D. Dimarogonas

Damage detection from changes in curvature mode shapes

1991-03-01

Ashok Kumar Pandey , M. Biswas , Mahmod Samman

Simulation of Stochastic Processes by Spectral Representation

1991-04-01

Masanobu Shinozuka , George Deodatis

The subject of this paper is the simulation of one-dimensional, uni-variate, stationary, Gaussian stochastic processes using the spectral representation method. Following this methodology, sample functions of the stochastic process can … The subject of this paper is the simulation of one-dimensional, uni-variate, stationary, Gaussian stochastic processes using the spectral representation method. Following this methodology, sample functions of the stochastic process can be generated with great computational efficiency using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number N of the terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the temporally-averaged mean value and the autocorrelation function are identical with the corresponding targets, when the averaging takes place over the fundamental period of the cosine series. The most important property of the simulated stochastic process is that it is asymptotically Gaussian as N → ∞. Another attractive feature of the method is that the cosine series formula can be numerically computed efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in engineering mechanics and structural engineering. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity).

Analytical Methods in Vibrations

1967-01-01

Frank H. Jackson

Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician

1984-12-01

Donald B. Rubin

A common reaction among applied statisticians is that the Bayesian statistician's energies in an applied problem must be directed at the a priori elicitation of one model specification from which … A common reaction among applied statisticians is that the Bayesian statistician's energies in an applied problem must be directed at the a priori elicitation of one model specification from which an optimal design and all inferences follow automatically by applying Bayes's theorem to calculate conditional distributions of unknowns given knowns. I feel, however, that the applied Bayesian statistician's tool-kit should be more extensive and include tools that may be usefully labeled frequency calculations. Three types of Bayesianly justifiable and relevant frequency calculations are presented using examples to convey their use for the applied statistician.

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A Summary Review of Vibration-Based Damage Identification Methods

1998-03-01

Scott W. Doebling , Charles R. Farrar , Michael B. Prime

This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification … This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring

2006-02-15

Jerome P. Lynch

In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are … In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health.

Derivation of auditory filter shapes from notched-noise data

1990-08-01

Brian R. Glasberg , Brian C. J. Moore

Modal identification of output-only systems using frequency domain decomposition

2001-06-01

Rune Brincker , Lingmi Zhang , Palle Andersen

In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing … In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

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Outlier Treatment in Data Merging

1997-08-01

R. H. Blessing

Experience with a variety of diffraction data-reduction problems has led to several strategies for dealing with mismeasured outliers in multiply measured data sets. Key features of the schemes employed currently … Experience with a variety of diffraction data-reduction problems has led to several strategies for dealing with mismeasured outliers in multiply measured data sets. Key features of the schemes employed currently include outlier identification based on the values y median = median(| F i | 2 ), σ median = median[ σ (| F i | 2 )], and | Δ | median = median(| Δ i |) = median[|| F i | 2 -median (| F i | 2 )|] in samples with i = 1, 2 ..... n and n ≥ 2 measurements; and robust/resistant averaging weights based on values of | z i | = | Δ i |/max{ σ median , | Δ | median [ n /( n −1)] 1/2 }. For outlier discrimination or down-weighting, sample median values have the advantage of being much less outlier-based than sample mean values would be.

Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

1996-05-01

Scott W. Doebling , Charles R. Farrar , Michael B. Prime +1 more

This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report … This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.

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An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis

1996-10-01

D. E. Seaman , Roger A. Powell

Kernel density estimators are becoming more widely used, particularly as home range estimators. Despite extensive interest in their theoretical properties, little empirical research has been done to investigate their performance … Kernel density estimators are becoming more widely used, particularly as home range estimators. Despite extensive interest in their theoretical properties, little empirical research has been done to investigate their performance as home range estimators. We used computer simulations to compare the area and shape of kernel density estimates to the true area and shape of multimodal two—dimensional distributions. The fixed kernel gave area estimates with very little bias when least squares cross validation was used to select the smoothing parameter. The cross—validated fixed kernel also gave surface estimates with the lowest error. The adaptive kernel overestimated the area of the distribution and had higher error associated with its surface estimate.

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Coupling of substructures for dynamic analyses.

1968-07-01

Roy R. Craig , MERVYN C. C. BAMPTON

A method for treating a complex structure as an assemblage of distinct regions, or substructures, is presented. Using basic mass and stiffness matrices for the substructures, together with conditions of … A method for treating a complex structure as an assemblage of distinct regions, or substructures, is presented. Using basic mass and stiffness matrices for the substructures, together with conditions of geometrical compatibility along substructure boundaries, the method employs two forms of generalized coordinates. Boundary generalized coordinates give displacements and rotations of points along substructure boundaries and are related to the displacement modes of the substructures known as modes. All constraint modes are generated by matrix operations from substructure input data. Substructure normal-mode generalized coordinates are related to free vibration modes of the substructures relative to completely restrained boundaries. The definition of substructure modes and the requirement of compatibility along substructure boundaries lead to coordinate transformation matrices that are employed in obtaining system mass and stiffness matrices from the mass and stiffness matrices of the substructures. Provision is made, through a RayleighRitz procedure, for reducing the total number of degrees of freedom of a structure while retaining accurate description of its dynamic behavior. Substructure boundaries may have any degree of redundancy. An example is presented giving a free vibration analysis of a structure having a highly indeterminate substructure boundary.

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THE MODAL ASSURANCE CRITERION–TWENTY YEARS OF USE AND ABUSE

2003-01-01

Randall J. Allemang

This article reviews the development of the original modal assurance criterion (MAC) together with other related assurance criteria that have been proposed over the last twenty years. Some of the … This article reviews the development of the original modal assurance criterion (MAC) together with other related assurance criteria that have been proposed over the last twenty years. Some of the other assurance criteria that will be discussed include the coordinate modal assurance criterion (COMAC), the frequency response assurance criterion (FRAC), coordinate orthogonality check (CORTHOG), frequency scaled modal assurance criterion (FMAC), partial modal assurance criterion (PMAC), scaled modal assurance criterion (SMAC), and modal assurance criterion using reciprocal modal vectors (MACRV). In particular, the thought process that relates the original MAC development to ordinary coherence and to orthogonality computations will be explained. Several uses of MAC that may not be obvious to the casual observer (modal parameter estimation consistency diagrams and model updating are two examples) will be identified. The common problems with the implementation and use of modal assurance criterion computations will also be identified. The development of the modal assurance criterion 1-2 over twenty years ago has led to a number of similar assurance criteria used in the area of experimental and analytical structural dynamics. It is important to recognize the mathematical similarity of these varied criteria in order to be certain that conclusions be correctly drawn from what is essentially a squared, linear regression correlation coefficient. The modal assurance criterion is a statistical indicator, just like ordinary coherence, which can be very powerful when used correctly but very misleading when used incorrectly. This article will first review the historical development of the modal assurance criterion. Other similar assurance criteria will then be identified although the list is not intended to be comprehensive. Typical uses of the modal assurance criterion will be discussed and finally, typical abuses will be identified.

The location of defects in structures from measurements of natural frequencies

1979-04-01

P. Cawley , Robert Adams

A method of non-destructively assessing the integrity of structures using measurements of the structural natural frequencies is described. It is shown how measurements made at a single point in the … A method of non-destructively assessing the integrity of structures using measurements of the structural natural frequencies is described. It is shown how measurements made at a single point in the structure can be used to detect, locate and quantify damage. The scheme presented uses finite-element analysis, since this method may be used on any structure. The principle may, however, be used in conjunction with other mathematical techniques. Only one full analysis is required for each type of structure. Results are presented from tests on an aluminium plate and a cross-ply carbon-fibre-reinforced plastic plate. Excellent agreement is shown between the predicted and actual damage sites and a useful indication of the magnitude of the defect is obtained.

Vibration-based Damage Identification Methods: A Review and Comparative Study

2010-04-20

Wei Fan , Pizhong Qiao

A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based … A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam- or plate-type structures.

Finite Element Model Updating in Structural Dynamics

1995-01-01

Michael I. Friswell , John E. Mottershead

Vibration Based Condition Monitoring: A Review

2004-11-19

E. Peter Carden , Paul Fanning

Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting … Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.

Some windows with very good sidelobe behavior

1981-02-01

Albert H. Nuttall

Some of the windows presented by Harris [1] are not correct in terms of their reported peak sidelobes and optimal behavior. We present corrected plots of Harris' windows and also … Some of the windows presented by Harris [1] are not correct in terms of their reported peak sidelobes and optimal behavior. We present corrected plots of Harris' windows and also derive additional windows with very good sidelobes and optimal behavior under several different constraints. The temporal weightings are characterized as a sum of weighted cosines over a finite duration. The plots enable the reader to select a window to suit his requirements, in terms of bias due to nearby sidelobes and bias due to distant sidelobes.

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Vector median filters

1990-04-01

Jaakko Astola , P. Haavisto , Y. Neuvo

Two nonlinear algorithms for processing vector-valued signals are introduced. The algorithms, called vector median operations, are derived from two multidimensional probability density functions using the maximum-likelihood-estimate approach. The underlying probability … Two nonlinear algorithms for processing vector-valued signals are introduced. The algorithms, called vector median operations, are derived from two multidimensional probability density functions using the maximum-likelihood-estimate approach. The underlying probability densities are exponential, and the resulting operations have properties very similar to those of the median filter. In the vector median approach, the samples of the vector-valued input signal are processed as vectors. The operation inherently utilizes the correlation between the signal components, giving the filters some desirable properties. General properties as well as the root signals of the vector median filters are studied. The vector median operation is combined with linear filtering, resulting in filters with improved noise attenuation and filters with very good edge response. An efficient algorithm for implementing long vector median filters is presented. The noise attenuation of the filters is discussed, and an application to velocity filtering is shown.

Updating Models and Their Uncertainties. I: Bayesian Statistical Framework

1998-04-01

James L. Beck , Lambros S. Katafygiotis

The problem of updating a structural model and its associated uncertainties by utilizing dynamic response data is addressed using a Bayesian statistical framework that can handle the inherent ill-conditioning and … The problem of updating a structural model and its associated uncertainties by utilizing dynamic response data is addressed using a Bayesian statistical framework that can handle the inherent ill-conditioning and possible nonuniqueness in model updating applications. The objective is not only to give more accurate response predictions for prescribed dynamic loadings but also to provide a quantitative assessment of this accuracy. In the methodology presented, the updated (optimal) models within a chosen class of structural models are the most probable based on the structural data if all the models are equally plausible a priori. The prediction accuracy of the optimal structural models is given by also updating probability models for the prediction error. The precision of the parameter estimates of the optimal structural models, as well as the precision of the optimal prediction-error parameters, can be examined. A large-sample asymptotic expression is given for the updated predictive probability distribution of the uncertain structural response, which is a weighted average of the predictive probability distributions for each optimal model. This predictive distribution can be used to make model predictions despite possible nonuniqueness in the optimal models.

Linear Piezoelectric Plate Vibrations

1969-01-01

H. F. Tiersten

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

2016-11-10

Osama Abdeljaber , Onur Avcı , Serkan Kıranyaz +2 more

Engineering Applications of Correlation and Spectral Analysis.

1982-09-01

H. Dennis Tolley , Julius S. Bendat , Allan G. Piersol

Discusses engineering applications and recent developments based upon correlation and spectral analysis. Illustrations deal with applications to acoustics, mechanical vibrations, system identification, and fluid dynamics problems in aerospace, automotive, industrial … Discusses engineering applications and recent developments based upon correlation and spectral analysis. Illustrations deal with applications to acoustics, mechanical vibrations, system identification, and fluid dynamics problems in aerospace, automotive, industrial noise control, civil engineering and oceanographic fields, as well as similar problems in other fields. Tackles problems and solutions, assuming reader has required hardware and software to compute estimates of correlation, spectra, coherence, and phase functions.

A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications

2020-07-03

Onur Avcı , Osama Abdeljaber , Serkan Kıranyaz +3 more

Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and … Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

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Finite element analysis

1991-05-01

Yu-Hsuan Chan

In current design practice, structural analysis for reinforced concrete frames is generally based on the assumption that plane sections remain plane after loading and the material is homogeneous and elastic. … In current design practice, structural analysis for reinforced concrete frames is generally based on the assumption that plane sections remain plane after loading and the material is homogeneous and elastic. Therefore, linear elastic methods of analysis are normally adopted for the design of simple reinforced concrete beams and frames to obtain the member forces and bending moments that will enable the design and detailing of the sections to be carried out, despite the fact that reinforced concrete is not a homogeneous and elastic material (British Standard BS 8110:1985).

Hilbert–Huang Transform and Its Applications

2013-04-28

Norden E. Huang , Samuel S. P. Shen

The Hilbert-Huang Transform (HHT) represents a desperate attempt to break the suffocating hold on the field of data analysis by the twin assumptions of linearity and stationarity. Unlike spectrograms, wavelet … The Hilbert-Huang Transform (HHT) represents a desperate attempt to break the suffocating hold on the field of data analysis by the twin assumptions of linearity and stationarity. Unlike spectrograms, wavelet analysis, or the Wigner-Ville Distribution, HHT is truly a time-frequency analysis, but it does not require an a priori functional basis and, therefore, the convolution computation of frequency. The method provides a magnifying glass to examine the data, and also offers a different view of data from nonlinear processes, with the results no longer shackled by spurious harmonics — the artifacts of imposing a linearity property on a nonlinear system or of limiting by the uncertainty principle, and a consequence of Fourier transform pairs in data analysis. This is the first HHT book containing papers covering a wide variety of interests. The chapters are divided into mathematical aspects and applications, with the applications further grouped into geophysics, structural safety and visualization.

Kernel Smoothing

1995-01-01

M. P. Wand , M. C. Jones

Bridge reliability assessment method considering failure nonlinear dependence based on kernel recursive adaptive filtering and generalized Nataf transformation

2025-06-25

Qifan Zhao , Yuefei Liu , Xueping Fan | Structures

Rapid measuring the static flexibility matrix of bridge structures using an indirect method

2025-06-25

Xiaoshun Wu , Jiacheng Fu , Shiyu Xu +3 more | Journal of Civil Structural Health Monitoring

Vibration Feature Monitoring of Damaged Caisson-Foundation System based on Pseudo-Wave Excitation

2025-06-25

Ngoc-Lan Pham , Somi Jung , Jeong‐Tae Kim | Journal of Fisheries and Marine Sciences Education

Improved hyperelastic material characterization using measurement data pre-processing

2025-06-25

Zoltán Kovács , Andras Aninger , Szabolcs Berezvai | Journal of Theoretical and Applied Mechanics/Mechanika Teoretyczna i Stosowana

Generative design of steel-prestressed concrete hybrid wind turbine tower based on machine learning and multi-objective optimization

2025-06-25

Yu Cheng , Jiepeng Liu , Hongtuo Qi +2 more | Engineering Structures

Seismic performance prediction and interpretation of RC bridges under vehicle-bridge interaction: From VBI system simulation to ensemble learning surrogate models

2025-06-25

Liang Luo , Yan Li , Hanfang Dai +4 more | Engineering Structures

Cost-effective bridge health monitoring <i>via</i> automated operational modal analysis using low-cost adaptable and reliable accelerometers

2025-06-24

Erika Delgado Zhagui , Seyedmilad Komarizadehasl , Victor Torralba +2 more | Structure and Infrastructure Engineering

Semi-Supervised Anomaly Detection for the Identification of Damages in an Aerospace Sandwich Structure Based on Synthetically Generated Strain Data

2025-06-24

Florian Forsthuber , Christoph Kralovec , Martin Schagerl | Applied Sciences

The structural health monitoring (SHM) of safety relevant composite components is becoming increasingly relevant as it enables in-service diagnosis and data acquisition capabilities, contributing to the optimization and efficient operation … The structural health monitoring (SHM) of safety relevant composite components is becoming increasingly relevant as it enables in-service diagnosis and data acquisition capabilities, contributing to the optimization and efficient operation of the overall system and ultimately saving costs and resources. In this field, machine learning (ML) techniques are attracting growing attention due to their capability to recognize complex patterns, making them very suitable for the identification of damages in operating mechanical structures. However, the acquisition of sufficiently large amounts of labeled and representative data from both pristine and damaged structures is very costly. To address this, a ML-based SHM approach is proposed that identifies structural damage using only physics-based synthetic strain data generated from the structure’s numerical finite element model. It employs a semi-supervised anomaly detection approach, trained solely on synthetic pristine data, to identify deviations in experimental data indicating damage. The method is validated on an aircraft spoiler demonstrator made of a composite sandwich panel, instrumented with a strain gauge grid on its surface layer. The results show that the proposed SHM approach accurately classifies damaged and undamaged experimental data, independent of the prevailing load case, solely based on synthetic pristine strain data. It is also able to localize these damages in the form of a confidence area with respect to the sensor grid. This demonstrates the feasibility of using only synthetic pristine data for data-driven SHM of composite aerospace structures.

Study on stress intensity factor identification of a multi-cracked elastomer based on digital speckle pattern combined with transfer learning

2025-06-24

Wei Liu , Bowen Liu , Guowen Wang +3 more | Optics and Lasers in Engineering

Systematic critical review of structural health monitoring under environmental and operational variability: approaches for baseline compensation, adaptation, and reference-free techniques

2025-06-24

Nima Rezazadeh , Alessandro De Luca , Donato Perfetto +2 more | Smart Materials and Structures

Abstract Structural health monitoring (SHM) plays a pivotal role in ensuring the safety, reliability and service life of engineering structures. In smart structures, networks of active-response materials (e.g., piezoelectric films, … Abstract Structural health monitoring (SHM) plays a pivotal role in ensuring the safety, reliability and service life of engineering structures. In smart structures, networks of active-response materials (e.g., piezoelectric films, magnetostrictive patches, or fiber-optic cables), which convert mechanical and thermal stimuli into electrical or optical signals and act as the primary interface for continuous condition assessment. A persistent challenge is the influence of environmental and operational variability (EOV), particularly temperature changes, which can distort sensor measurements and either obscure or mimic genuine indicators of structural damage. Although numerous methodologies have been proposed to address this issue across various sensing platforms, a comprehensive comparative assessment across methodological categories remains lacking. This review critically examines 3 principal approaches developed to mitigate EOV: direct baseline compensation, adaptive and multi-baseline strategies, and reference-free techniques, including recent advances in transfer learning and hybrid physics-informed machine learning frameworks. A structured literature search spanning Scopus, Web of Science, IEEE Xplore and ScienceDirect underpins the analysis. Each approach is systematically evaluated, highlighting key benefits, limitations and suitability for varying operational scenarios. In addition, emerging trends, gaps and future research directions are identified, emphasising the need for hybrid models, real-time reference-free methodologies, robust uncertainty quantification and scalable population-based SHM solutions. The synthesis is intended to inform the design of next-generation smart, adaptable SHM systems regardless of sensing modality and their seamless integration into intelligent structures operating under complex real-world environmental conditions.

Prognostic digital twinning of lattice-tubular hybrid wind turbine towers exposed to deterioration

2025-06-24

Tao Zheng , Junlin Heng , Jing Song +3 more | Structures

ANN-based prediction of natural frequencies in MWCNT-reinforced GFRP hybrid composite plates with double delaminations

2025-06-24

Dhivya Elumalai , Mohit Gupta , Anuj Kumar Sharma | Asian Journal of Civil Engineering

Gaussian Process Regression (GPR)-based missing data imputation and its uses for bridge structural health monitoring

2025-06-23

Matteo Dalmasso , Marco Civera , Valerio De Biagi +1 more | Advances in Bridge Engineering

Abstract Structural health monitoring (SHM) apparatuses rely on continuous measurement and analysis to assess the safety condition of a target system. However, in field applications, the SHM framework is often … Abstract Structural health monitoring (SHM) apparatuses rely on continuous measurement and analysis to assess the safety condition of a target system. However, in field applications, the SHM framework is often hampered by practical issues. Among them, missing data in recorded time series is arguably the most common and most disruptive challenge that can arise. Therefore, imputing missing values is necessary to maintain the integrity and utility of the SHM data. This research work investigates the use of Gaussian Process Regression (GPR) for imputing missing data in ordered time series. In particular, this approach is here proposed and tested for Vibration-Based Monitoring (VBM) and ambient monitoring, with applications to modal parameters and air temperature. Both punctual missing-at-random (MAR) and prolonged missing-not-at-random (MNAR) gaps in the time histories of recorded natural frequencies are analysed. The performance of the proposed GPR-based approach is evaluated on real-life data from field tests on a well-known case study, the KW51 rail bridge. The method is first tested to actual missing values in the dataset. Then, the accuracy is tested using artificially removed data, and the imputed values are compared to the ground truth (i.e., the actual measured data). In the first case, the results show that the complete time series are deemed qualitatively similar to what would be expected by an expert user. The outcomes of the second part quantitatively demonstrate that GPR can accurately impute missing data in modal parameter time series, preserving the statistical properties of the data.

Analysis of Acoustic Emission Signal Characteristics and Influencing Factors of Wind-Induced Vibration Noise of Stay Cables

2025-06-23

Shenglin Li , Hui Zhang , Yu Xue +3 more | Experimental Techniques

Mode Shape Extraction with Denoising Techniques Using Residual Responses of Contact Points of Moving Vehicles on a Beam Bridge

2025-06-23

Guandong Qiao , Xiuli Du , Qi Wang +1 more | Applied Sciences

This work introduces a novel approach to extract beam bridge mode shapes using the residual response between consecutive contact points of vehicles passing through a bridge. A comprehensive investigation is … This work introduces a novel approach to extract beam bridge mode shapes using the residual response between consecutive contact points of vehicles passing through a bridge. A comprehensive investigation is conducted on several critical parameters, including window size, vehicle velocity, road roughness, and beam damping property, as well as the influence of traffic flow. To enhance the mode shape extraction performance using the approximate expression of the contact points’ displacements under noisy disturbance, two new signal denoising methods, CEEMDAN-NSPCA and CEEMDAN-IWT, are proposed based on complete ensemble empirical mode decomposition (CEEMDAN). CEEMDAN-NSPCA integrates CEEMDAN with principal component analysis and a coefficient-based filtering strategy. While CEEMDAN-IWT utilizes an improved wavelet thresholding technique with adaptive threshold selection. The numerical simulations demonstrate that both methods could effectively attenuate high-frequency noise with small amplitudes and retain low-frequency components. Among them, CEEMDAN-IWT exhibits superior denoising performance and greater stability, making it particularly suitable for robust modal identification in noisy environments.

DYMOS: A New Software for the Dynamic Identification of Structures

2025-06-23

Fabrizio Gara , Simone Quarchioni , Vanni Nicoletti | Buildings

Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. Some researchers develop their own … Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. Some researchers develop their own tools, but independent software tools remain scarce. The number of such independent software is limited, and the development of new ones with enhanced features, better performance, and varied user interfaces would be beneficial to spread the informed use of dynamic identification techniques, leading to more reliable and valuable results for structural engineering applications. This work introduces the new DYMOS software for OMA from ambient vibration test recordings. DYMOS includes various state-of-art algorithms and tools for vibration-based modal identification and for optimal sensor placement (OSP), allowing for customization of analysis parameters and procedures with the aim of reducing the gap between the needs of professional practice and research. Additionally, a new graphical tool is introduced for visualizing results in both buildings and bridges. By using CAD drawings as input, it streamlines model construction, making the process faster, more intuitive, and efficient. The article aims to describe DYMOS and to demonstrate its potential for OMA and OSP in civil engineering through the application on two real case studies dynamically tested.

A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning

2025-06-23

Bingkai Wang , Wenlei Sun , Hongwei Wang | Sensors

This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the … This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the long-time series forecast forecasting process. Based on the Bi-directional Long Short-Term Memory (Bi-LSTM) framework, the proposed method incorporates incremental learning via an error-supervised feedback mechanism, enabling the dynamic self-updating of the model parameters. The experience replay and elastic weight consolidation are integrated to further enhance the prediction accuracy. Ultimately, the experimental results demonstrate that the proposed incremental forecast method achieves a 24% and 4.6% improvement in accuracy over the Bi-LSTM and Transformer, respectively. This research not only provides an effective solution for long-time prediction of WTB health but also offers a novel technical framework and theoretical foundation for long-time series forecasting.

Deflection Prediction of Highway Bridges Using Wireless Sensor Networks and Enhanced iTransformer Model

2025-06-22

Cong Mu , Chen Chang , Jiuyuan Huo +1 more | Buildings

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of … As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R2, RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications.

High-precision cable force identification in bridges: a data-driven approach integrating digital twins and transfer learning

2025-06-22

Xingjun Gao , M. Wang , Gongfa Chen +2 more | Discover Mechanical Engineering

Accelerated dynamics model updating method based on multigrid reduced-order technique

2025-06-21

G. Zhao , Ping Xu , Xiangtao Ma +3 more | Computers & Structures

Erratum: An Iterative Augmented Unscented Kalman Particle Filter for Simultaneous State-Parameter-Input Estimation for Structural Systems Subjected to Gamma-Distribution Noise

2025-06-21

Tianhao Yu , Jingfeng Wang , Wanqian Wang | International Journal of Structural Stability and Dynamics

Frequency Analysis of Impulse Response in Linear Damped Harmonic Oscillators: A Comparative Study of Fourier and Laplace Transforms with Numerical Validation

2025-06-20

Yue Liu | Theoretical and Natural Science

Analyzing a systems response is a crucial topic in various fields such as electric engineering, quantum mechanics, and applied mathematics. Analyzing systems from frequency domain is a promising and important … Analyzing a systems response is a crucial topic in various fields such as electric engineering, quantum mechanics, and applied mathematics. Analyzing systems from frequency domain is a promising and important approach. Aside from the widely accepted numerical method to solve for the displacement function of a linear damped harmonic oscillator driven by an impulsive force, various mathematical transforms have been developed to provide new approaches. Two transforms Fourier Transform and Laplace Transform are examined for their accuracy and efficiency. They are used to compute the responses of three different impulses finite rectangular impulse, Gaussian impulse, and exponentially decaying impulse and compared to the accurate answer obtained by the numerical approach. The result states that none of the two transforms can handle all three impulses, and different transforms have different strengths and weaknesses. Overall, this paper proves the validity of using mathematical transforms to solve for linear systems and explores the limitations of Fourier Transform and Laplace Transform. This paper is also considered to be a useful and comprehensive guide for introductory readers who are interested in frequency analysis. Future research can focus on investigating the responses for different impulses using different transforms.

Predictive optimized model for seismic damage in reinforcement grider bridge

2025-06-20

Goushiya Sayyed , M. D. Ahirrao , V. D. Jaysingpure +1 more | Asian Journal of Civil Engineering

OSP-SAP: A MATLAB graphical user interface for optimal sensor placement using SAP2000

2025-06-20

Antonio S. López-Cuervo , Enrique García‐Macías , Rafael Castro‐Triguero +1 more | SoftwareX

Long-term validation of a model-based virtual sensing method for fatigue monitoring of offshore wind turbine support structures: Comparing as-designed with state-of-the-art foundation models

2025-06-20

Dominik Fallais , Carlos Sastre Jurado , Wout Weijtjens +1 more | Marine Structures

None

2025-06-20

Adrien Leygue | Comptes Rendus Mécanique

Background: The Data-Driven Identification(DDI) method is a model-free approach to the identification of the mechanical stress in parts subject to statically indeterminate stress states. Although the method has been applied … Background: The Data-Driven Identification(DDI) method is a model-free approach to the identification of the mechanical stress in parts subject to statically indeterminate stress states. Although the method has been applied in many studies, no theoretical analysis of its convergence has been proposed so far. Purpose: The aim of this manuscript is to propose a first study of the DDI properties in order to increase the confidence in the results and guide the selection of optimal parameters. Methods: A new formulation compared to the original one is proposed to explicitly define a minimization problem that is more amenable to analysis. The algebraic characteristics of the new formulation are studied to derive properties of interest. Results: A simple criterion for the uniqueness of the DDI estimate is derived. In the case of elastic material behavior, an estimate of the error on the identified stress field is proposed. These results are illustrated using a synthetic dataset. Conclusion: This work proposes a first analysis of the DDI and demonstrates the ability of the method to compute a model-free estimation of the stress field. The developed criteria and estimator open the door to further developments for the improvement of the method, the design of sample geometries and loading path and extension to other classes of material behavior.

New Experimental Single-Axis Excitation Set-Up for Multi-Axial Random Fatigue Assessments

2025-06-20

Luca Campello , Vivien Denis , Raffaella Sesana +2 more | Machines

Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over … Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over the years, a number of testing methodologies, which differ in terms of the testing machines, specimen geometry, and type of excitation, have been proposed. The aim of this paper is to describe a new testing procedure for random multi-axial fatigue testing. In particular, the paper presents the experimental set-up, the testing procedure, and the data analysis procedure to obtain the multi-axial random fatigue life estimation. The originality of the proposed methodology consists in the experimental set-up, which allows performing multi-axial fatigue tests with different normal-to-shear stress ratios, by choosing the proper frequency range, using a single-axis exciter. The system is composed of a special designed specimen, clamped on a uni-axial shaker. On the specimen tip, a T-shaped mass is placed, which generates a tunable multi-axial stress state. Furthermore, by means of a finite element model, the system dynamic response and the stress on the notched specimen section are estimated. The model is validated through a harmonic acceleration base test. The experimental tests validate the numerical simulations and confirm the presence of bending–torsion coupled loading.

Damage detection in structural health monitoring using BiLSTM-1DCNN hybrid network: a case study on a large-scale steel truss bridge

2025-06-20

Nhung Cam Nguyen , Trung Manh Vu | Engineering Computations

Purpose One of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage … Purpose One of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage in structures using time series data. Nevertheless, these techniques are restricted in their ability to capture the bidirectional temporal and spatial relationships among the data. To remedy these shortcomings, this study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. Design/methodology/approach This study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. The novelty of BiLSTM-1DCNN comes in its effective fusion of 1DCNN’s local feature extraction and BiLSTM’s capacity to capture long-term information from both directions (forward and backward). Findings Validation using a dataset from the Nam O steel truss bridge demonstrates that the proposed method surpasses conventional models, including 1DCNN (87.9%), LSTM (72.5%), BiLSTM (76.1%) and LSTM-1DCNN (91.1%), achieving an accuracy rate of 96.4%. This approach provides a significant advancement in SHM, paving the way for more accurate and reliable damage detection. Research limitations/implications The study’s limitations include potential overfitting due to the model’s complexity and the high computational resources required. The BiLSTM-1DCNN model’s performance is also influenced by the quality and quantity of the training data as well as the specific characteristics of the dataset. Additionally, while the model performs well in detecting damage in the Nam O steel truss bridge, its generalizability to other types of structures and environmental conditions remains untested. Practical implications The BiLSTM-1DCNN model offers significant practical benefits for structural health monitoring by enhancing damage detection accuracy. Its ability to effectively capture both local features and long-term temporal dependencies makes it a valuable tool for real-time monitoring and early detection of structural issues. This can lead to more timely maintenance actions, potentially reducing repair costs and improving safety. The model’s high accuracy in detecting damage can also support infrastructure management decisions, contributing to better resource allocation and enhanced overall safety of critical structures. Social implications The BiLSTM-1DCNN model’s advanced damage detection capabilities have positive social implications, such as improving the safety and reliability of public infrastructure like bridges. Enhanced structural health monitoring can reduce the risk of catastrophic failures, protecting lives and reducing accidents. By providing more accurate and timely assessments, the model can also lead to more efficient use of maintenance and repairs, ensuring that resources are allocated effectively. Originality/value The originality of the BiLSTM-1DCNN model lies in its innovative integration of one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This unique combination leverages 1DCNN’s strength in local feature extraction and BiLSTM’s capacity to capture temporal dependencies in both directions, addressing the limitations of existing methods. The model’s high accuracy in detecting structural damage, as demonstrated by its performance on the Nam O steel truss bridge dataset, highlights its value in advancing structural health monitoring technologies and offers a significant improvement over traditional approaches.

Rapid seismic response prediction of steel frames based on Graph Attention Network (GAT) with Mamba blocks

2025-06-20

Hong Peng , W. J. Zheng , Ying Yu +1 more | Structures

Fatigue Life Prediction of Five MW Wind Turbine Blade Based on Long-Term Wind Speed Simulation

2025-06-20

Rui Ding , Li Jun Cao | Processes

This paper studies the load spectrum and fatigue life estimation method of large wind turbine blades. A method based on the wind speed spectrum and stress response at each wind … This paper studies the load spectrum and fatigue life estimation method of large wind turbine blades. A method based on the wind speed spectrum and stress response at each wind speed is proposed to calculate the response spectrum of the dangerous part of the blade. The response of the blade at each wind speed is obtained by finite element analysis. Considering the limited wind speed data, it is not enough to cover the whole situation of blade life experience, which will bring great discreteness to the blade fatigue life estimation method proposed in this paper. In this paper, the Markov chain Monte Carlo method is used to predict enough wind speed data to reduce the discreteness of life estimation. Because the traditional linear fatigue damage accumulation theory fails to consider the impact of loads below the fatigue limit on fatigue damage, this paper uses a fatigue damage theory based on fuzzy theory, introduces appropriate membership functions, and fully considers the impact of loads below the fatigue limit on fatigue damage. The calculation results are more in line with the actual situation.

Comparative assessment and recommendations for code‐based identification of vertical stiffness irregularities in reinforced concrete frame buildings

2025-06-19

Aditya Laghate , G. Tamizharasi | Structural Concrete

Abstract The poor performance of stiffness irregular buildings during past earthquakes highlights the need to re‐evaluate building code provisions that define irregularity using story stiffness (e.g., IS 1893‐1, NZS 4203, … Abstract The poor performance of stiffness irregular buildings during past earthquakes highlights the need to re‐evaluate building code provisions that define irregularity using story stiffness (e.g., IS 1893‐1, NZS 4203, and NBCC 4) or interstory drift (e.g., NBC E.030 and Turkish Earthquake Code [TEC]). It is vital to assess the effectiveness of each provision in detecting irregularities so as to identify the most efficient approach. The spectrum of 2D reinforced concrete moment frame buildings is examined by altering (i) building configurations, (ii) locations of single‐story irregularity, and (iii) locations of successive‐story irregularity. When computing stiffness irregularities, it is required to account for the effect of infill walls as an equivalent infill strut. Based on elastic behavior, IS 1893‐1, NBC E.030, and TEC accurately detect irregularities in buildings that occur in the intermediate stories; yet, the standards do not apply to buildings with irregularities at the bottom or top stories. Nonlinear static and dynamic analyses show that irregularity locations significantly affect building responses. Additionally, dynamic amplification of displacement responses occurs rapidly (~1 to 166 times) in these locations. As a result, the damage is limited to all stories below the irregular story, and it often extends across one to three immediate upper stories. But critical columns have a high demand–capacity ratio around irregular stories (~0.5 to 1.2). It is preferable to detect stiffness irregularity using interstory drift, which involves one to three stories above and below the irregular story; building code recommendations are provided.

An engineering method to estimate the stress and stress state including the damping ratio differences of various modes

2025-06-19

Junjie Miao , Le Xie | Research Square (Research Square)

<title>Abstract</title> This paper is aimed at proposing a method. This method can solve the problem of the dynamics simulation where different modes have different damping ratios. Simultaneously, the method to … <title>Abstract</title> This paper is aimed at proposing a method. This method can solve the problem of the dynamics simulation where different modes have different damping ratios. Simultaneously, the method to achieve the stress state is also presented. A finite element model of the investigated object is established. It is validated by the modal test and the strain test. Subsequently, the single-mode-frequency-division loading (SMFDL) is applied. The square root of the sum of squares is used to calculate the equivalent stress. Phase angles and statistic stress components are combined to calculate the stress state. The final results show that SMFDL is rational and the calculated equivalent stress has enough accuracy. That method to attain the stress state gives acceptable errors. Dividing the excitation into more sections is a good approach to improve the accuracy of the stress state estimation.

Inverse Method for Material Characterization of a UAV Composite Wing Based on FEM and Dynamic Response

2025-06-19

Maciej Milewski , Artur Kierzkowski , Mateusz Kucharski +1 more | Eksploatacja i Niezawodnosc - Maintenance and Reliability

This work introduces a method for the inverse identification of composite material properties using dynamic response data and finite element modelling. The methodology combines numerical modal analysis, Design of Experiments … This work introduces a method for the inverse identification of composite material properties using dynamic response data and finite element modelling. The methodology combines numerical modal analysis, Design of Experiments (DoE), Response Surface Methodology, and a Multi-Objective Genetic Algorithm (MOGA) to determine material parameters without destructive testing. The approach was applied to a UAV composite wing, achieving high correlation between simulated and experimental modal characteristics, with natural frequencies deviations below 2%. Variations between the identified parameters and reference data are linked to inherent inconsistencies in composite manufacturing and the operational condition of the tested structure. Nevertheless, the proposed method proves to be a reliable and non-invasive tool for estimating mechanical properties, enhancing the predictive capabilities of numerical models. Its adaptability makes it a promising solution for future applications in structural health monitoring, damage assessment, and optimization of aerospace composite structures.

Magnitude-based damage analysis of high-speed railway track bridge system across canyon

2025-06-19

Lizhong Jiang , Wei Li , Liqiang Jiang | Soil Dynamics and Earthquake Engineering

Finite element model updating of existing bridge structures based on measured data and deep learning method

2025-06-19

Yizhao Ni , Yongpeng Ma , Hongwei Lin +2 more | Structures

Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies

2025-06-19

Oğuz Güneş , Burcu Güneş | Applied Sciences

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based … Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications.

Multi-channel synchrosqueezed wavelet transform based identification of modal parameters of a vibrating structure

2025-06-18

Arpita Ghosh , Shivank Mittal , Arunasis Chakraborty +1 more | Journal of Civil Structural Health Monitoring

A new strategy for monitoring 3D internal deformation in dams based on the simplified inverse absolute nodal coordinate formulation (iANCF S3D)

2025-06-18

Qunying Fan , Rui Pang , Bin Xu +1 more | Mechanical Systems and Signal Processing

Vibration-based damage detection of offshore wind turbine foundations under environmental and operational variations

2025-06-18

Yulong Zhang , John H G Macdonald , Song Liu +2 more | Mechanical Systems and Signal Processing

Buckling and free vibration modes of a multi-cracked variable stiffness Euler–Bernoulli beam in a Winkler foundation with piecewise constant stiffnesses

2025-06-18

F. Gómez-Silva , R. Zaera , J.A. Loya | Engineering Structures

Enhancing the Structural Health Monitoring (SHM) through data reconstruction: Integrating 1D convolutional neural networks (1DCNN) with bidirectional long short-term memory networks (Bi-LSTM)

2025-06-18

Tran Quang Minh , Thuc Ngo Van , Huan X. Nguyen +1 more | Engineering Structures

Distance minimization data-driven method for nonlinear analysis of wooden beams

2025-06-18

Biao Dong , Guolin Xu , Xiangjian Wang | Structures

Parametric analysis and selection of Gabor-based phase motion estimation in structural identification

2025-06-18

Mengzhu Li , Zhitao Yan , Zhu Mao +1 more | Mechanical Systems and Signal Processing

Analytical study on behaviour of self-centered concrete walls using novel multiobjective artificial neural network

2025-06-18

Amin Foyouzati , Alireza Khaloo | Innovative Infrastructure Solutions

An autoencoder-clustering-based framework of daily wind speed pattern recognition and prediction for mountainous valley bridges

2025-06-18

Rujin Ma , J. Q. Zhang , Nanxi Chen +4 more | Measurement

Towards a Digital Twin Approach for Structural Stiffness Assessment: A Case Study on the Cho’ponota L1 Bridge

2025-06-18

Fatih Yesevi Okur | Applied Sciences

In this study, a series of comprehensive experimental tests were conducted to assess the impact of permanent displacements observed during the construction of the Cho’ponota L1 Bridge in Uzbekistan and … In this study, a series of comprehensive experimental tests were conducted to assess the impact of permanent displacements observed during the construction of the Cho’ponota L1 Bridge in Uzbekistan and to evaluate the bridge’s structural suitability for service. The investigation included Operational Modal Analysis and static and dynamic vehicular load tests, conducted using two trucks with different weights under varying loading scenarios and speeds. A total of 28 static and 24 dynamic load cases were tested across the bridge’s four spans. Displacement measurements were acquired using geodetic instruments during the static tests, while acceleration data were recorded during dynamic tests using high-sensitivity accelerometers, from which Dynamic Amplification Factors were calculated. The results indicated that all displacement values remained within permissible safety limits, and no visible damage or cracking was detected. Beyond conventional analysis, the study proposed a test-assisted digital twin framework in which high-fidelity field data were integrated into a finite-element model. The initial numerical model was calibrated using modal properties obtained from OMA, and discrepancies were minimized through iterative updates to material parameters, especially concrete stiffness. The resulting validated digital twin accurately reflects the bridge’s current structural condition and can be used for future predictive simulations and performance-based evaluations. The findings underscore the effectiveness of combining non-destructive testing with digital twin methodology in diagnosing structural behavior and offer a replicable model for assessing bridges experiencing construction-related anomalies.

Smoothness-constrained Dynamic Parameter Estimation for InSAR Time Series

2025-06-18

Yuqing Wang , Wietske Brouwer , Freek van Leijen +1 more | EarthArXiv (California Digital Library)

Urban resilience and decision-making rely on continuous monitoring of key safety indicators. The increasing availability of interferometric SAR (InSAR) observations offers a valuable opportunity for near real-time stability monitoring, particularly … Urban resilience and decision-making rely on continuous monitoring of key safety indicators. The increasing availability of interferometric SAR (InSAR) observations offers a valuable opportunity for near real-time stability monitoring, particularly in the built environment. However, traditional InSAR time series methods use batch processing to estimate static displacement parameters, limiting early anomaly detection, computational efficiency, and use of ongoing SAR data. These methods also assume motion behavior remains constant over time. Here we introduce a new method-DYNamic parAMeter estimation of InSAR scaTterer motion in near-real timE (DYNAMITE) that enables instantaneous parameter estimation by capturing dynamic behavior in InSAR time series. The method uses a state-vector prediction model updated with new observations via recursive least squares, eliminating the need to store past data. It imposes a smoothness constraint on displacement based on an exponentially correlated velocity model assuming an Ornstein-Uhlenbeck process and uses normalized median amplitude dispersion as a quality metric. Smoothness is controlled by specifying the instantaneous velocity's standard deviation and decorrelation time. Results show the recursive approach matches batch methods in quality while better capturing dynamic behavior, supporting near real-time monitoring.

Analytical Investigation of Dynamic Response in Cracked Structure Subjected to Moving Load

2025-06-18

Shuirong Gui , H. Zeng , Zhijun Gui +5 more | Buildings

Under cyclic moving load action, tensile-dominant structures are prone to crack initiation due to cumulative damage effects. The presence of cracks leads to structural stiffness degradation and nonlinear redistribution of … Under cyclic moving load action, tensile-dominant structures are prone to crack initiation due to cumulative damage effects. The presence of cracks leads to structural stiffness degradation and nonlinear redistribution of dynamic characteristics, thereby compromising str18uctural integrity and service performance. The current research on the dynamic behavior of cracked structures predominantly focuses on transient analysis through high-fidelity finite element models. However, the existing methodologies encounter two critical limitations: computational inefficiency and a trade-off between model fidelity and practicality. Thus, this study presents an innovative analytical framework to investigate the dynamic response of cracked simply supported beams subjected to moving loads. The proposed methodology conceptualizes the cracked beam as a system composed of multiple interconnected sub-beams, each governed by the Euler–Bernoulli beam theory. At crack locations, massless rotational springs are employed to accurately capture the local flexibility induced by these defects. The transfer matrix method is utilized to derive explicit eigenfunctions for the cracked beam system, thereby facilitating the formulation of coupled vehicle–bridge vibration equations through modal superposition. Subsequently, dynamic response analysis is conducted using the Runge–Kutta numerical integration scheme. Extensive numerical simulations reveal the influence of critical parameters—particularly crack depth and location—on the coupled dynamic behavior of the structure subjected to moving loads. The results indicate that at a constant speed, neither crack depth nor position alters the shape of the beam’s vibration curve. The maximum deflection of beams with a 30% crack in the middle span increases by 14.96% compared to those without cracks. Furthermore, crack migration toward the mid-span results in increased mid-span displacement without changing vibration curve topology. For a constant crack depth ratio (γi = 0.3), the progressive migration of the crack position from 0.05 L to 0.5 L leads to a 26.4% increase in the mid-span displacement (from 5.3 mm to 6.7 mm). These findings highlight the efficacy of the proposed method in capturing the complex interactions between moving loads and cracked concrete structures, offering valuable insights for structural health monitoring and assessment.

A Novel Multiscale Feature Fusion Neural Network for Analyzing Uncertain Dynamic Characteristics of Laminated Structures

2025-06-17

Yuan Liu , Ying Hou , Xibin Cao +5 more | Polymer Composites

ABSTRACT Conventional Monte Carlo simulations (MCS) face numerous challenges in addressing the unpredictable dynamic features of composite structures, such as excessive data volume, low computational efficiency, and limited applicability. By … ABSTRACT Conventional Monte Carlo simulations (MCS) face numerous challenges in addressing the unpredictable dynamic features of composite structures, such as excessive data volume, low computational efficiency, and limited applicability. By fully considering the stochasticity of geometric and material parameters, a data‐driven framework was introduced to achieve highly efficient and highly accurate prediction of the key indicators of the dynamic characteristics of laminated plates employed in satellites, including the natural frequency, random response, and harmonic response. First, a multiscale feature fusion neural network (MFEFLN) was developed based on two‐dimensional convolutional neural networks (2DCNNs) and gated recurrent units (GRUs). The MFEFLN system utilized convolutional operations to extract multiscale and multi‐level features and employed GRU to learn sequential features. The MFEFLN was trained on a small sample set and applied to analyze uncertain dynamic characteristics. The results of MFEFLN were compared with those of MCS, back‐propagation (BP) neural network, generative adversarial network (GAN), long short‐term memory (LSTM), 2D CNN, and ADCNN. The results indicated that the MFEFLN system could rapidly and accuratly predict the dynamic characteristics. These results indicate that the MFEFLN system can serve as a robust and effective tool for uncertainty quantification and sensitivity analysis, while also offering an innovative methodology for the reliability assessment of satellite structures and design optimization.