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TCR-RoadNet:A Transformer-Enhanced Multi-Task Deep Learning Architecture for Real-Time Road Damage Detection and Segmentation 认领 引用
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作者 Olzhas Olzhayev Bakhytzhan Kulambayev Azizah Suliman 《Computers, Materials & Continua》 SCIE EI 2026年第9期1024-1053,共30页
Automated road damage detection is a critical component of intelligent transportation systems,enabling efficient infrastructure maintenance and improved traffic safety.However,existing approaches often suffer from lim... Automated road damage detection is a critical component of intelligent transportation systems,enabling efficient infrastructure maintenance and improved traffic safety.However,existing approaches often suffer from limited contextual understanding,insufficient segmentation accuracy,and suboptimal real-time performance.This study presents TCR-RoadNet,a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments.The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement(TCR)module to capture both fine-grained structural details and long-range spatial dependencies across feature scales.To further enhance performance,a Decoupled Detection Head(DDH)is employed to stabilize localization and classification learning,while a Classification Refinement Module(CRM)improves inter-class discrimination using region-based feature enhancement.In addition,a Boundary-Aware Segmentation Head is introduced to generate precise damage contours by incorporating edge-sensitive learning mechanisms.The model is evaluated on the RDD2022 dataset,complemented by an extended dataset containing additional road images collected from Kazakhstan to increase environmental diversity and robustness.Experimental results demonstrate that the proposed method achieves 0.9416 precision,0.9235 recall,and 0.8718 mAP@50,along with a segmentation performance of 0.8129 mean Intersection over Union(mIoU),while maintaining real-time inference at 57 FPS.Comparative analysis and ablation studies confirm that each architectural component contributes to consistent performance gains in both detection and segmentation tasks.Qualitative results further illustrate the robustness of the proposed framework under varying lighting,weather,and road conditions.The proposed approach offers a scalable and efficient solution for real-time road condition monitoring and intelligent infrastructure management systems. 展开更多
关键词 Road damage detection pavement defect segmentation transformer computer vision multi-task deep learning intelligent transportation systems
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Structural damage detection of shear frame model using parallel physics-informed neural network 认领 引用
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作者 Surendra BANIYA Damodar MAITY 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第1期15-35,共21页
Conventional numerical and data-driven methods for structural damage identification often struggle with problems such as poor generalization,sensitivity to noise,and limited accuracy in quantifying and localizing dama... Conventional numerical and data-driven methods for structural damage identification often struggle with problems such as poor generalization,sensitivity to noise,and limited accuracy in quantifying and localizing damage,especially under real-world uncertainties.This study introduces a novel parallel physics-informed neural network framework that addresses these limitations by integrating physical constraints directly into the learning process and employing a nondimensionalized formulation of the governing equations to improve training stability and convergence.The method is applied to simultaneously identify the location and severity of damage in numerical and experimental shear frame model using measured acceleration responses from the structure.In the numerical case,different damage scenarios are analyzed under white Gaussian noise and real earthquake excitations,across various noise levels.The model demonstrates high accuracy in identifying damaged locations even under high noise conditions,and outperforms traditional response surface methods by reducing false positive cases.Experimental validation using open-source shear frame data further confirms the model’s effectiveness.Despite the presence of modeling uncertainty and measurement,the model accurately identifies damage locations and estimates their severity in agreement with experimental observations.Overall,the model proves its efficacy in predicting damage locations and assessing the severity levels even under uncertainty and noise levels. 展开更多
关键词 structural health monitoring damage detection parallel physics-informed neural network deep learning
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Improving damage detection methods in structural health monitoring by introducing a hybrid innovative damage-sensitivity feature using time series analysis 认领 引用
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作者 Seyed Arman Hashemi Behnam Adhami Ali Golsoorat Pahlaviani 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第3期753-766,共14页
This study presents an approach for output-only,ambient vibration data time series,analysis-based damage detection in a benchmark structural model.A three-dimensional finite element model based on the benchmark labora... This study presents an approach for output-only,ambient vibration data time series,analysis-based damage detection in a benchmark structural model.A three-dimensional finite element model based on the benchmark laboratory model is simulated to show the efficacy of the suggested approach.Also,a new damage-sensitivity feature that is based on autoregressive time series models with exogenous inputs(ARX)is introduced by using output acceleration responses from sensors under the influence of ambient loads provided in the modeling.Minor localized damage close to supports,and more extensive damage that might occur throughout a bridge’s service lifetime are simulated with the finite element model to show the robustness and stability of the suggested damage feature.Next,numerous damage features proposed by other researchers are compared with the feature introduced in this study,utilizing the generated data and the damage scenarios that were created.The findings demonstrate that the proposed damage feature can reliably and accurately identify and locate minor damage close to supports(regarded as a challenge in identification studies)and offer a measure of the level of damage.In addition,the damage feature can accurately locate large-scale damage without producing false positive or negative results. 展开更多
关键词 damage detection benchmark structural model damage-sensitivity feature time series models
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Improved lightweight road damage detection based on YOLOv5 认领 引用 被引量：2
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作者 LIU Chang SUN Yu +2 位作者 CHEN Jin YANG Jing WANG Fengchao 《Optoelectronics Letters》 EI 2025年第5期314-320,共7页
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize... There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms. 展开更多
关键词 road surface damage detection convolutional neural network feature maps convolutional neural network cnn lightweight model yolov improved lightweight model spatial pyram
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Structural damage detection based on model reduction and response reconstruction 认领 引用
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作者 ZOU Yun-feng SU Yun-hui +2 位作者 LU Xuan-dong HE Xu-hui CAI Chen-zhi 《Journal of Central South University》 SCIE EI CAS CSCD 2025年第11期4439-4462,共24页
Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduct... Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduction and response reconstruction is presented.Based on the framework of two-step model updating including substructure-level localization and element-level detection,the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating.In the iteration process,the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item.Besides,model reduction is applied to reduce computational degrees of freedom(DOFs)in each detection step.A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method.The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods. 展开更多
关键词 damage detection model reduction response reconstruction two-step model updating sensitivity algorithm
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Target Identification Method for the Damage Detection of Composite Laminates 认领 引用
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作者 Kan Feng Yu Yao +2 位作者 Rong Li Xu Hu Zheng Li 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2025年第6期1025-1031,共7页
The advantages of guided wave detection,such as its ability to propagate over long distances and penetrate deeply,have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced pol... The advantages of guided wave detection,such as its ability to propagate over long distances and penetrate deeply,have led to its application in the field of anisotropic damage detection in carbon fiber-reinforced polymer(CFRP).Due to the anisotropy of CFRP,traditional guided wave-based detection methods have difficulty in precisely locating the defect.In this study,we proposed a novel deep learning-based detection method for CFRP by employing image recognition technology for guided wave field inspection.This method is capable of rapidly and accurately extracting defective features from the structure,thereby facilitating precise damage identification.To avoid time-consuming sample data generation by simulation for CFRP,the steady-state guided wave field of the aluminum plates was simulated instead.The isotropic wave field data were then stretched and applied for neural network training. 展开更多
关键词 Damage detection Local high-frequency deflection shapes Target identification CFRP
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An Automatic Damage Detection Method Based on Adaptive Theory-Assisted Reinforcement Learning 认领 引用
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作者 Chengwen Zhang Qing Chun Yijie Lin 《Engineering》 SCIE EI CSCD 2025年第7期188-202,共15页
Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real... Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring. 展开更多
关键词 Reinforcement learning Theory-assisted Damage detection Newton’s method Model updating Architectural heritage
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Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders 认领 引用 被引量：2
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作者 Edson Florentino de Souza Cássio Bragança +2 位作者 Diogo Ribeiro Túlio Nogueira Bittencourt Hermes Carvalho 《Railway Engineering Science》 EI 2025年第4期614-641,共28页
High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible ... High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible and cost-effective monitor-ing solution for detecting damage on railway bridges while minimizing train operation interruptions.Moreover,integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring(SHM)for bridges.Despite being increasingly used in traditional SHM applications,studies using autoencoders within drive-by methodologies are rare,especially in the railway field.This study presents a novel approach for drive-by damage detection in HSR bridges.The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors.Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes.Numerical simulations were performed on a 3D vehicle-track-bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach,considering several damage scenarios,vehicle speeds,and environmental and operational variations,such as multiple track irregularities and varying measurement noise.The results show that the pro-posed approach can successfully detect damages,as well as characterize their severity,especially for very early-stage dam-ages.This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges. 展开更多
关键词 Drive-by Indirect monitoring Damage detection High-speed railway bridges Autoencoders
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Damage Detection of the Pipes Conveying Fluid on the Pasternak Foundation Using the Matching Pursuit Method 认领 引用
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作者 Nahid Khomarian Ramazan-Ali Jafari-Talookolaei +1 位作者 Morteza Saadatmorad Reza Haghani 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第6期1122-1140,共19页
The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governin... The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation.This study proposes a novel method that uses the matching pursuit(MP)algorithm for damage detection.The governing equations of motion for the pipe are derived using Hamilton’s principle.The finite element method,combined with the Galerkin approach,is employed to obtain the mass,damping,and stiffness matrices.To identify damage locations through pipe mode-shape decomposition,an index called the“matching pursuit residual”is introduced as a novel contribution of this study.The proposed method facilitates damage detection at various levels and locations under different boundary conditions.The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes.Furthermore,the results of the numerical and experimental tests showcase the efficiency of the proposed method,highlighting that the MP signal approximation algorithm effectively detects damage in structures. 展开更多
关键词 Damage detection Matching pursuit Damaged pipe Galerkin method Finite element method
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A Residual Convolutional Autoencoder-Based Structural Damage Detection Approach for Deep-Sea Mining Riser Considering Data Fusion 认领 引用
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作者 JIANG Yufeng ZHENG Zepeng +4 位作者 LIU Yu WANG Shuqing LIU Yuchi YANG Zeyun YANG Yuan 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第6期1657-1669,共13页
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe... A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures. 展开更多
关键词 deep-sea mining riser structural damage detection residual convolutional auto-encoder data fusion principal component analysis
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Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders 认领 引用
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作者 Lorenzo Bernardini Francesco Morgan Bono Andrea Collina 《Railway Engineering Science》 EI 2025年第4期721-745,共25页
Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors... Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status.To do so,continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span.According to authors’best knowledge,this is the first case in which an unsupervised technique,which relies on the use of sparse autoencoders,is used to localize damages.The bridge considered in this work is a Warren steel truss bridge,whose finite element model is referred to an actual structure,belonging to the Italian railway line.To investigate damage detection and localization performances,different operational variables are accounted for:train weight,forward speed and track irregularity evolution in time.Two configurations for the virtual measuring channels were investigated:as a result,better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal. 展开更多
关键词 Drive-by Sparse autoencoder Steel truss railway bridge Continuous wavelet transform Damage detection Damage localization
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Statistical moment-based structural damage detection method in time domain 认领 引用 被引量：10
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作者 J.Zhang Y.L.Xu +2 位作者 J.Li Y.Xia J.C.Li 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2013年第1期13-23,共11页
A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection（SMBDD） method in the frequency domain,has been recently proposed.The aim of this study is to exte... A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection（SMBDD） method in the frequency domain,has been recently proposed.The aim of this study is to extend the SMBDD method in the frequency domain to the time domain for building structures subjected to non-Gaussian and non-stationary excitations.The applicability and effectiveness of the SMBDD method in the time domainis verified both numerically and experimentally.Shear buildings with various damage scenarios are first numerically investigated in the time domain taking into account the effect of measurement noise.The applicability of the proposed method in the time domain to building structures subjected to non-Gaussian and non-stationary excitations is then experimentally investigated through a series of shaking table tests,in which two three-story shear building models with four damage scenarios aretested.The identified damage locations and severities are then compared with the preset values.The comparative results are found to be satisfactory,and the SMBDD method is shown to be feasible and effective for building structures subjected to non-Gaussian and non-stationary excitations. 展开更多
关键词 damage detection statistical moment time domain non-Gaussian non-stationary experimental investigation
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A review of the research and application of deep learning-based computer vision in structural damage detection 认领 引用 被引量：19
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作者 Zhang Lingxin Shen Junkai Zhu Baijie 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2022年第1期1-21,共21页
Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detect... Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid. 展开更多
关键词 deep learning damage detection computer vision loss assessment
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Application of clan member signal method in structural damage detection 认领 引用 被引量：11
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作者 郭迅 B.F.Spencer 卢书楠 《Earthquake Engineering and Engineering Vibration》 SCIE EI 2007年第1期29-34,共6页
It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （C... It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members. 展开更多
关键词 damage detection health monitoring vibration based clan member signal method local damage
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Damage Detection for Simply Supported Bridge with Bending Fuzzy Stiffness Consideration 认领 引用 被引量：15
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作者 ZHOU Yu a b +1 位作者 DI Shengkui a XIANG Changsheng a 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第2期308-319,共12页
The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Line... The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines(RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature(DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration. 展开更多
关键词 rotational angle influence lines method simply supported bridge bending fuzzy stiffness damage detection
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Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone 认领 引用 被引量：6
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作者 Qiqiang Chen Xinxin Gan +2 位作者 Wei Huang Jingjing Feng H.Shim 《Computers, Materials & Continua》 SCIE EI 2020年第12期2201-2215,共15页
Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.... Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods. 展开更多
关键词 Road damage detection road damage classification Mask R-CNN framework densely connected network
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Output only modal identification and structural damage detection using time frequency & wavelet techniques 认领 引用 被引量：17
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作者 S.Nagarajaiah B.Basu 《Earthquake Engineering and Engineering Vibration》 SCIE EI 2009年第4期583-605,共23页
The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time vari... The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambientandom response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed. 展开更多
关键词 Time-frequency methods short time Fourier transform Hilbert transform wavelets modal identification：output only structural health monitoring damage detection
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DAMAGE DETECTION BASED ON OPTIMIZED INCOMPLETE MODE SHAPE AND FREQUENCY 认领 引用 被引量：4
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作者 Wei Chen Wenguang Zhao +1 位作者 Huizhen Yang Xuquan Chen 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2015年第1期74-82,共9页
For the purpose of structural health monitoring, a damage detection method combined with optimum sensor placement is proposed in this paper. The back sequential sensor placement （BSSP） algorithm is introduced to opt... For the purpose of structural health monitoring, a damage detection method combined with optimum sensor placement is proposed in this paper. The back sequential sensor placement （BSSP） algorithm is introduced to optimize the sensor locations with the aim of maximizing the 2-norm of information matrix, since the EI method is not suitable for optimum sensor placement based on eigenvector sensitivity analysis. Structural damage detection is carried out based on the respective advantages of mode shape and frequency. The optimized incomplete mode shapes yielded from the optimal sensor locations are used to localize structural damage. After the potential damage elements have been preliminarily identified, an iteration scheme is adopted to estimate the damage extent of the potential damage elements based on the changes in the frequency. The effectiveness of this method is demonstrated using a numerical example of a 31-bar truss structure. 展开更多
关键词 structural damage detection optimum sensor placement sensitivity analysis information matrix
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Experiment Verification of Damage Detection for Offshore Platforms by Neural Networks 认领 引用 被引量：3
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作者 刁延松 李华军 +1 位作者 石湘 王树青 《China Ocean Engineering》 SCIE EI 2006年第3期351-360,共10页
In the present work, damage detection for offshore platforms is divided into three steps. Firstly, the located direction of the damaged member is detemfined by the pmbabilistic neural network with input of the change ... In the present work, damage detection for offshore platforms is divided into three steps. Firstly, the located direction of the damaged member is detemfined by the pmbabilistic neural network with input of the change rate of normalized medal frequency. Secondly, the profile and layer of the damaged member is also determined by the pmbabilistic neural network with input of the normalized damage-signal index. Finally, the damage extent is determined by the back propagation neural networks with input of the squared change rate of modal frequency. So the size of the network and the training time can be reduced greatly. All these networks are trained with simulated data obtained from the finite element model of an experiment model. Then these trained neural networks are examined with data obtained from impulse tests on the experiment model. The experiment results show that the trained neural networks are able to detect the damaged member with reasonable accuracy. 展开更多
关键词 damage detection offshore platform probabilistic neural networks back-propagation neural networks
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Automatic Damage Detection of Engineering Ceramics Ground Surface Based on Texture Analysis 认领 引用 被引量：3
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作者 林滨 陈善功 +1 位作者 韩雪松 梁小虎 《Transactions of Tianjin University》 EI CAS 2013年第4期267-271,共5页
Ground textures seriously interfere with the exact identification of grinding damage. The common nondestructive testing techniques for engineering ceramics are limited by their difficulty and cost. Therefore, this pap... Ground textures seriously interfere with the exact identification of grinding damage. The common nondestructive testing techniques for engineering ceramics are limited by their difficulty and cost. Therefore, this paper proposes a global image reconstruction scheme in ground texture surface using Fourier transform (FT). The lines associated with high-energy frequency components in the spectrum that represent ground texture information can be detected by Hough transform (HT), and the corresponding high-energy frequency components are set to zero. Then the spectrum image is back-transformed into the spatial domain image with inverse Fourier transform (IFT). In the reconstructed image, the main ground texture information has been removed, whereas the surface defects information is preserved. Finally, Canny edge detection is used to extract damage image in the reconstructed image. The experimental results of damage detection for the ground texture surfaces of engineering ceramics have shown that the proposed method is effective. 展开更多
关键词 engineering ceramics damage detection ground texture Fourier transform
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