Vibration-based Structural Damage Research Articles

Vibration-based structural damage detection using FRF and modal constant curvature

Vibration-based structural damage detection using FRF and modal constant curvature

A Cepstrum-Informed neural network for Vibration-Based structural damage assessment

Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer stacking. This study introduces a novel Cepstrum-Informed Attention-Based Network (CIABN) developed to model power cepstral coefficients of structural acceleration responses, guided by cepstrum-based physical properties to facilitate efficient structural damage assessment. The CIABN integrates three key components: a unique input–output mapping based on weighted cepstral coefficients, a novel cepstral positional encoding mechanism, and a multi-head self-attention mechanism. The unique input–output mapping enables appreciable model generalization in overall structural characteristics, with the weighted cepstral coefficients serving as informative and compact data for efficient neural network modeling. The developed cepstral positional encoding scientifically guides the model to capture the coefficient indices, and the underlying trend of cepstral coefficients primarily governed by overall structural characteristics. The multi-head attention mechanism enables computationally efficient parallel analysis of interdependencies among coefficients, facilitating the development of a lightweight network. The effectiveness and superiority of the method have been validated using both simulated and experimental structural data.

FocusedDFT: A DFT implementation for vibration-based structural damage detection

This paper introduces focusedDFT, a novel Discrete Fourier Transform (DFT) implementation optimized for short signals with low frequency components, particularly where a single frequency component is of interest. The results suggest focusedDFT as a viable and efficient solution for vibration-based damage detection methods, offering improved execution times compared to traditional DFT approaches.

Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer

Abstract Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm – a Transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

SigBERT: vibration-based steel frame structural damage detection through fine-tuning BERT

PurposeThis article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.Design/methodology/approachSigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.FindingsThe experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.Originality/valueSigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

Structural damage identification by using physics-guided residual neural networks

In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Vibration-based structural damage localization through a discriminant analysis strategy with cepstral coefficients

Over the past decades, Vibration-Based Methods (VBMs) have consistently exhibited exceptional effectiveness in the field of Structural Health Monitoring when it comes to assessing structural damage in both civil and mechanical structures. Recently, the progress made in data-driven strategies for localizing structural damage through the VBMs has resulted in substantial benefits. These advanced strategies not only enable an efficient decision-making process but also provide precise identification of repair locations for monitored structures. Importantly, they achieve this without the inherent complexity and computational burden typically associated with traditional model-based methods. In this study, an innovative data-driven method for localizing and quantifying structural damage is proposed. The method is developed on the basis of the principles of Linear Discriminant Analysis (LDA) and a newly devised modeling strategy that utilizes the power cepstral coefficients extracted from the structural acceleration response. The developed LDA model is able to highlight the local structural characteristics (i.e., mode shape-related information) embedded in the cepstral coefficients in the LDA latent space. Based on the highlighted local characteristics of the cepstral coefficients, a statistical pattern recognition strategy is proposed to effectively conduct the quantification and localization of structural damage. The proposed damage localization method is designed to function in a completely unsupervised-learning manner, which eliminates the requirement for the model to have access to any prior knowledge or information regarding structural damage during the training phase. The proposed method has been validated by both numerical simulations and experimental data.

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

AbstractIn this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

Vibration-based structural damage detection strategy using FRFs and machine learning classifiers

In this paper, a damage detection strategy for beam-type structures based on frequency response functions (FRFs) is presented. Five aluminum beams of equal nominal dimensions are used in an experimental program under laboratory conditions to obtain experimental FRF data from both undamaged and damaged conditions. Damages are induced by creating rectangular notches on the beams using saw-cutting. A stochastic finite element model of the beam is developed in MATLAB to construct several training datasets. The damages are modeled by reducing the cross-sectional area at the corresponding damaged elements. Simple damage indexes are proposed as damage-sensitive features. Decision Tree, Support Vector Machines, and Artificial Neural Networks classifiers are trained in the first stage to perform damage detection and localization. Single and multiple damages located in a single zone and more than one zone simultaneously are considered. In the second stage, experimental data not used for training are used for validation. The results from the first stage suggest that the proposed damage indexes can effectively detect and locate structural damage in beams. Among all classifiers, Artificial Neural Networks is the classifier that best performed. High accuracy is achieved to identify the presence of damage (99.3%) and detecting its location on the beam for some of the training datasets (from 80.0% to 97.1%). In the second stage of validation, accuracy, as expected, decreased. However, misclassifications occur mainly for FRF samples in the impact zone, which indicates that the proposed strategy can be efficient to detect damages at locations other than the excitation zone.

An FDD-based modal parameter-less proportional flexibility-resembling matrix for response-only damage detection

Modal flexibility-based methods are effective tools for vibration-based structural damage detection, including in the output-only case. These methods are typically characterized by two stages: first, the modal parameters are identified, thus obtaining a certain number of modes; second, these modal parameters are used to assemble the modal flexibility matrix. This paper proposes a method for estimating a matrix that approximates a proportional flexibility matrix, termed proportional flexibility-resembling (PFR) matrix, and shows that this matrix can be used for damage detection and localization purposes. This matrix is obtained through signal processing operations to be executed after applying the first steps of the frequency-domain decomposition (FDD) technique—i.e., after the singular value decomposition of the spectral density matrix. The defining aspect of the PFR matrix is that, differently from the traditional formulation of modal flexibility and proportional flexibility matrices, it can be assembled without the need of an explicit identification of the modal parameters. In fact, the matrix is estimated by processing all first singular vectors and also all first singular values in a selected frequency range. In the proposed method, the typical two stage approach of traditional modal flexibility methods is avoided, and the intervention of an operator is limited to setting the values of a few parameters in the initial phase of the process. Numerical simulations and experimental data from a testbed structure were used to show the effectiveness of the proposed approach, and the analyses were performed by considering structures with different damage scenarios and damping properties.

Railway track support condition assessment — Methodology validation using numerical simulations

A novel methodology to evaluate railway track support conditions is currently being developed. This methodology is based on modal analysis of the multi-element system composed by the railway infrastructure and an instrumented railway vehicle. It belongs to the group of vibration-based structural damage identification methods and is focused on observing the characteristic frequencies of this multi-element system, which can be related with changes in the physical properties of the railway infrastructure. As opposed to other vehicle-based track monitoring methods, the proposed methodology should enable condition assessment of the railway infrastructure subgrade, an element that is often neglected during railway monitoring operations. Since this methodology is vehicle-based, it can be used to assess the entire extension of a railway infrastructure to gather data regarding the support conditions of the track. It can also assess the evolution of track support conditions over time by comparing different rides over the same railway stretch. An important aspect of this methodology that still lacks validation is the topic of characterizing track support conditions of a railway infrastructure through its natural frequencies. The suitability of the theoretical model behind this methodology is another aspect that needs to be assessed. Numerical simulations tests using the multibody simulation software Simpack® are currently being performed to address both topics. This paper describes some of the simulations performed in this context, including a description of the numerical models used. The obtained results support the selected theoretical model and the overall validity of the proposed methodology.

Development of Structural Damage Detection Method Working with Contaminated Vibration Data via Autoencoder and Gradient Boosting

Vibration-based structural damage detection is one of the most promising venues for building smart and automated structural health monitoring applications; however, its applicability is impeded by a large amount of collected vibration data, and the performance could be undermined by degraded data. Therefore, this study develops a robust framework, dubbed AutoBoost-SDD, that can effectively handle contaminated vibration data and provide reliable monitoring results within reasonable computational resources. The proposed method consists of three key components. Firstly, multi-domain feature extraction techniques are utilized to convert high-dimensional raw data into informative feature vectors. Secondly, the auto-encoder deep learning architecture is leveraged to refine feature vectors of contaminated data. Finally, a tree-based boosting machine learning algorithm, namely LightGBM, is employed to assess the structures’ operational states using learned output from the second step. The viability and performance of the proposed framework are illustrated via three case studies involving numerical data of a 5-degree of freedom system, a 2D frame structure, and experimental data of a large-scale 18-story frame structure from the literature. The results show that the AutoBoost-SDD framework is able to provide reasonable detection results despite the presence of various contaminations, including noisy, missing, and anomalous data.

Outlier analysis combined with Gaussian mixture model for structural damage detection

Damage diagnostic techniques based on changes in natural frequency has two major problems, limiting their applicability to practical structures. The limitations are low sensitivity to smaller-size damage and the influence of ambient and operational factors (temperature, humidity, wind, traffic, etc.). The change in natural frequency due to environmental and operational factors may be even larger than the change due to damage. A reliable vibration-based structural damage detection distinguishing damage and other confounding factors is needed to avoid false alarm of damage. This paper presents an enhanced approach combining the gaussian mixture model (GMM) and outlier analysis for damage detection considering the effect of changing environmental and operational conditions. In the proposed work, GMM is first applied to classify the feature vectors (first two natural frequencies) into different clusters. The natural frequencies obtained under similar environmental and operational conditions were classified into the same cluster by satisfying the same probability distribution. Outlier analysis is then performed for each different cluster using the Mahanabolis Distance (MD) measure. The MD is a measure of the distance between a point and a distribution. In the current study, the MD between the current data sample and the reference data of each mixture is computed and the minimum MD among all the clusters is used as the damage index (DI). The threshold to conclude the presence of damage (DI > threshold) is established based on Generalized Extreme Value Distribution using the datasets of the healthy structure with various environmental and operational conditions. The proposed method is verified using benchmark examples of numerically simulated simply supported beam and experimental wooden bridge structure. The results of the investigations concluded that the proposed DI considering the patterns in the data using GMM proves better in decoupling the damage and other confounding factors following the conventional outlier analysis of damage detection.

Vibration-Based Structural Damage Identification Using P-CNN and Time-Frequency Hybrid Index under the Conditions of Uncertainties and Incomplete Measurements

The accuracy of vibration-based structural damage identification is affected by uncertainties in vibration measurements and finite element modeling. Moreover, a sufficient number of sensor measurements are not practical in large-scale structures. In this regard, the frequency domain [frequencies and mode shapes (FMS)] and time domain [acceleration cross-correlation function (ACCF)] indexes are put into a parallel convolutional neural network (P-CNN, a new convolutional neural network architecture with dual-channel) to locate and quantify structural damage. First, this approach is verified by a numerical model of a simply-supported beam, and the performance is evaluated by comparing it with methods using FMS and ACCF indexes input to the conventional 2D-CNN, respectively. The comparative results demonstrate that the proposed method can identify the damage with the lowest error, and the errors are less than 5%. In addition, three sparse measurement conditions are adopted to investigate the effect of the number of measuring points on damage identification. It is found that under the influence of uncertainties, even if only four sensors are used, this approach can identify damage accurately. Second, an engineering example of a continuous rigid frame bridge is adopted to validate the feasibility of the proposed method. The results show that when the number of sensors accounts for 65% and 47% of test sensors, respectively, it performs well in locating and quantifying structural damage. Therefore, this method can not only reduce the number of sensors and eliminate uncertainties effects, but also improve the performance of the 2D-CNN through information fusion and complementarity, which is also beneficial to minimize the cost of sensor layout in actual structures.

A data-driven sensor placement strategy for reconstruction of mode shapes by using recurrent Gaussian process regression

Current Optimal Sensor Placement (OSP) strategies for bridges mostly rely on data from a finite element model rather than from the real structure due to high cost in placing massive sensors for data collection. For large-scale bridges, however, it is difficult to formulate a precise model and thus the OSP strategies building upon a finite element model inevitably suffer from modelling errors. Besides, the finite element model cannot account for real measurement noise. Premised on the fact that it is not expensive to make in-situ trial measurements with a few sensors on a target bridge before deploying a structural health monitoring (SHM) system on it, a data-driven OSP strategy is proposed in this study which aims at accurately reconstructing mode shapes (to facilitate vibration-based structural damage detection) by using only a few vibration sensors to be included in the SHM system. The proposed OSP strategy is also applicable for the upgrade of a long-term SHM system currently deployed on a bridge, by using historical data collected from the current SHM system. To precisely reconstruct mode shapes, a two-stage OSP strategy in terms of Recurrent Gaussian Process Regression (RGPR) is developed, and its performance is validated on a simulation model and a real bridge. In the first stage, the greedy algorithm is leveraged to temporarily deploy sensors on the structure and train accurate RGPR models using the collected data, which are used to afford spatially complete mode shape data for optimization later. Starting from a few sensors temporarily deployed on the bridge, a one-by-one sensor adding procedure is performed to configure increasing sensors until the target is achieved. In the second stage, Cuckoo Search (CS) algorithm is pursued to obtain the globally optimal sensor placement solution, from which the temporarily deployed sensors can be re-configured to the optimum positions. Both the best sensor quantity and positions are obtained by the proposed OSP strategy.

An automated vibration-based structural damage localization strategy using filter-type feature selection

Damage detection techniques have been widely explored over the last years driven by the advances of computational intelligence technologies. To understand the structure’s dynamic behavior, some studies use modal-based methods, which are traditionally applied to reflect changes caused by damage. More recently, strategies using raw vibration measurements have gained notoriety and are proving to be a promising approach. In this context, the aim of this paper is to present a novel automated methodology for damage localization based on the extraction of features from dynamic data using domain knowledge and on an unsupervised filtering process. Feature extraction is performed simultaneously in time, frequency, and quefrency domains to diversify information retrieval. In machine learning (ML), this filtering procedure is called feature selection (FS) and is applied herein with the goal to decrease redundancy and increase the relevance of the feature set by eliminating a portion of it in view of a predefined criterion. The main concept is that the proposed method can customize its features concerning the structure, providing generality about any type of geometry, material, and excitation it encounters. The damage-sensitive index is computed through an outlier analysis, where percentile intervals are created based on the filtered features from the structure’s healthy state. Thus, any anomalies surrounding this initial state should be automatically located. The method was successfully tested in five applications, two of them being full-scale bridges, which shows a promising performance for real-world monitoring situations. Some of its significant achievements are single and multiple damage location, structural reinforcement detection, quantification of severity in progressive damage scenarios, and robustness to noise due to varying traffic and/or environmental conditions. Such a reliability is tied to an efficient default parameters setup, enabling an automated damage localization algorithm.

Vibration-based structural damage detection using 1-D convolutional neural network and transfer learning

This paper presents a novel vibration-based structural damage detection approach by using a one-dimensional convolutional neural network (1-D CNN) and transfer learning (TL). The CNN can effectively extract structural damage information from the vibration signals. However, the CNN training needs enough samples, while some damage samples (scenarios) obtained from real structures are limited, which will compromise the CNN ability to detect structural damage. As a solution, the numerical models have potential to provide sufficient CNN training samples; meanwhile, the state-of-the-art TL technique can significantly shorten the network training time and improve the accuracy. Therefore, this paper proposes a new method to detect the damage of a bridge model. The 1-D CNN is firstly trained with the samples of the single damage scenarios of the numerical bridge model. And then it is transferred to the complex scenarios of multi-damage (double or triple simultaneously), random size structures, and experimental model. The results demonstrate that: with the TL, the accuracy of damage detection is increased by about 47% at most, and the convergence speed is increased by at least 50%; in particular, the TL can inhibit over-fitting, and for the real bridge case, the accuracy also increased by 44.4%. It is demonstrated that: the TL can effectively improve the damage detection accuracy and convergence effect, and the application of this method to the random size structures also proves its generalization.

Investigation into vibration-based structural damage identification and amplitude-dependent damping ratio of reinforced concrete bridge deck slab under different loading states

Investigation into vibration-based structural damage identification and amplitude-dependent damping ratio of reinforced concrete bridge deck slab under different loading states

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.