A Hierarchical Bayesian Method for Time Domain Structure Damage Detection

2019 ◽  
Author(s):  
Wei Feng ◽  
Qiaofeng Li ◽  
Qiuhai Lu
Keyword(s):  
Damage Detection ◽  
Time Domain ◽  
Structural Damage ◽  
Engineering Practice ◽  
Hierarchical Bayesian ◽  
Structure Damage ◽  
Virtual Forces ◽  
Hierarchical Bayesian Method ◽  
External Forces ◽  
Local Stiffness

Abstract A time domain structural damage detection method based on hierarchical Bayesian framework is proposed. Due to local stiffness reductions, the responses of damaged structures vary from those in undamaged status under the same external excitation. In this paper, the responses of damaged structures are assumed as the result of a summation of known external forces and unknown virtual forces exerted on corresponding undamaged structures. The damages can thus be detected, located, and quantified by the identification of associated virtual forces. A hierarchical Bayesian formulation considering all undetermined damage-related variables is adopted for the identification of virtual forces. The reasonable values of the variables and their uncertainties are depicted by their posterior distributions, sampled by Markov chain Monte Carlo method. Compared with traditional Bayesian formulations, manual choice of prior parameters is avoided and less prior information is required. The proposed virtual force indicator provides a more intuitive perspective for damage detection tasks and is potentially more operable in engineering practice. These advantages are illustrated by simulation of a cantilever beam under various damage conditions.

Structure Damage Detection via the Behavior of Substructure

1991 ◽  
Author(s):  
J. H. Wang ◽  
C. S. Liou
Keyword(s):  
Damage Detection ◽  
Mechanical System ◽  
Dynamic Behavior ◽  
Structural Damage ◽  
Structure Damage

Abstract A mechanical system generally consists of many substructures. However, it is impossible to observe the dynamic behavior of any substructure directly when the whole structure is in operation. A method was proposed in this work to determine the FRFs of a substructure by using the measured FRFs of the whole structure and the priorly known FRFs of another substructure With this method, one can detect the structural damage more easily by observing the change of the FRFs of the damaged substructure.

Temperature effect on electromechanical admittance–based concrete structural health monitoring

2019 ◽  
Vol 19 (3) ◽  
pp. 661-692 ◽  
Author(s):  
Demi Ai ◽  
Chengxing Lin ◽  
Hui Luo ◽  
Hongping Zhu
Keyword(s):  
Numerical Analysis ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Temperature Effect ◽  
Health Monitoring ◽  
Structural Damage ◽  
Shield Tunnel ◽  
Engineering Practice ◽  
Damage Evaluation ◽  
Structural Health

Concrete structures in service are often subjected to environmental/operational temperature effects, which change their inherent properties and also inflict a challenge to their extrinsic monitoring systems. Recently, piezoelectric lead zirconate titanate (PZT)-based electromechanical admittance technique has been increasingly growing into an effective tool for concrete structural health monitoring; however, uncertainty in the changes of monitoring signals induced by temperature impact on concrete/PZT sensor would inevitably cause interference to structural damage detection, which adversely hinder its application from laboratory to engineering practice. This article, aiming at exploring the temperature effect on the electromechanical admittance–based concrete damage evaluation, primarily covered a series of theoretical/numerical analysis with rigorously experimental verifications. Three aspects of comparative studies were performed in theoretical/numerical analysis: (1) thermal-dependent parameters were inclusively evaluated in contribution to the electromechanical admittance characteristics via PZT-structure interaction models; (2) three-dimensional finite element analysis in multi-physics coupled field was employed to qualitatively assess the singular temperature effect on the electromechanical admittance behaviors of free-vibrated PZT, surface-bonded PZT/inside-embedded PZT coupled healthy concrete cubes; and (3) depending on the modeling of surface-bonded PZT-/inside-embedded PZT-cracked concrete cube, thermal effect on damage evaluation was addressed via quantification on the electromechanical admittance variations. In the experimental study, rigorous validation tests were carried out on a group of lab-scale concrete cubes, where surface-bonded PZT/inside-embedded PZT transducers were simultaneously employed for electromechanical admittance monitoring in view of thermal difference between concrete surface and its inner part. Correlation coefficient deviation value-based effective frequency shifts algorithm was also employed to compensate the temperature effect. Moreover, temperature effect was further testified on the monitoring of a full-scale shield-tunnel segment structure. Experimental results indicated that temperature triggered different behaviors of electromechanical admittance signatures for surface-bonded PZT/inside-embedded PZT transducers and contaminated the electromechanical admittance responses for damage detection. Structural damage severity level can be disadvantageously amplified by temperature increment even if under the same damage scenarios.

Time domain force localization and reconstruction based on hierarchical Bayesian method

2020 ◽  
Vol 472 ◽  
pp. 115222 ◽  
Author(s):  
Wei Feng ◽  
Qiaofeng Li ◽  
Qiuhai Lu ◽  
Bo Wang ◽  
Chen Li
Keyword(s):  
Time Domain ◽  
Bayesian Method ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Method

A hybrid learning strategy for structural damage detection

2020 ◽  
pp. 147592172096694
Author(s):  
Lorena Andrade Nunes ◽  
Rafaelle Piazzaroli Finotti Amaral ◽  
Flávio de Souza Barbosa ◽  
Alexandre Abrahão Cury
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Real Time ◽  
Health Monitoring ◽  
Time Domain ◽  
Structural Damage ◽  
Machine Learning Techniques ◽  
Hybrid Classifier ◽  
Detection Techniques ◽  
Structural Health

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised ( k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

scholarly journals Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 911 ◽  
Author(s):  
Sheng Li ◽  
Xiang Zuo ◽  
Zhengying Li ◽  
Honghai Wang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Fiber Optic ◽  
Learning Algorithm ◽  
Support Vector ◽  
Engineering Practice ◽  
Fiber Optic Gyroscope ◽  
Deep Convolutional Neural Networks ◽  
Bridge Model

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

Local variation detection in MDOF system using wavelet based transmissibility and its application in cracked beam

2015 ◽  
Vol 23 (14) ◽  
pp. 2307-2327 ◽  
Author(s):  
XZ Li ◽  
XJ Dong ◽  
ZK Peng ◽  
WM Zhang ◽  
G Meng
Keyword(s):  
Damage Detection ◽  
Frequency Band ◽  
Information Acquisition ◽  
Structural Damage ◽  
Local Variation ◽  
Frequency Bands ◽  
Numerical Studies ◽  
Novel Method ◽  
Local Stiffness ◽  
Crack Position

Since the local stiffness or damping variation happens when damage occurs in engineer structures, it is useful to detect the local variation as a way for structural damage inspection. As a vibration based approach, transmissibility has attracted considerable interest because of its convenience and effectiveness in damage detection. However, using the traditional Fourier transform, it should be very careful to select the frequency bands in transmissibility calculation. Inappropriate choice of frequency band could cause a complete inaccurate result. For unknown damage detection, it is difficult to select the frequency band which eigen-frequency should be included. This paper proposes a novel method using wavelet based transmissibility for local variation detection. Benefiting from the ability in subtle information acquisition of wavelet transform, it is useful in reducing the influence of frequency bands to the indicators. Analytical derivation using wavelet balance method and numerical studies of a multiple degree of freedom (MDOF) system are carried out to verify the effectiveness of the proposed method. In the last section, the method is applied for detecting crack position in cantilever beam with analysis of its sensitivity to frequency bands and measurement inaccuracies.

scholarly journals An Improved Time-Domain Damage Detection Method for Railway Bridges Subjected to Unknown Moving Loads

2020 ◽  
Author(s):  
Mahdi Shahbaznia ◽  
Morteza Raissi Dehkordi ◽  
Akbar Mirzaee
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Time Domain ◽  
Structural Damage ◽  
Model Updating ◽  
Numerical Models ◽  
Critical Parameters ◽  
Moving Loads ◽  
Railway Bridges ◽  
Updating Procedure

There is considerable interest in structural health monitoring (SHM) and damage detection of bridges and considerable progress has been made in this field in recent years. However, several challenges such as sensitivity to low levels of damage and identification without the knowledge of the moving load remain and need to be precisely investigated by researchers. The current work addresses such challenges and proposes an efficient response sensitivity-based model updating procedure in time-domain for damage identification of railway bridges subjected to unknown moving loads. The bridge is modelled as an Euler-Bernoulli beam and the train is modelled as a set of sprung masses passing over the beam. Structural damage is considered as a reduction in the modulus of elasticity of the elements. Sensitivity analysis and Tikhonov regularization methods are adopted and used to solve the inverse problem of the model updating. To verify the efficiency of the model, two numerical models with multiple damage scenarios subjected to unknown moving loads are analyzed. In addition, the efficiency of the proposed method in the presence of measurement noise is also verified. Numerical results reveal that the proposed model-updating procedure simultaneously identifies structural damages as well as the unknown moving loads with an acceptable accuracy. The effect of critical parameters such as mass and speed of the moving vehicle on the accuracy of identification results is investigated as well. Based on the findings of this research, the proposed method can be adopted and applied to online and long-term health monitoring of real bridge structures.

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