Improved modal truncation error in the directly analytical method for damage identification of frame structures

The damage identification is made by the numerical simulation analysis of a five-storey-and-two-span RC frame structure, using improved and unimproved direct analytical method respectively; and the fundamental equations were solved by the minimal least square method （viz. general inverse method）. It demonstrates that the feasibility and the accuracy of the present approach were impoved significantly, compared with the result of unimproved damage identification.

Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

Delamination is a prevalent type of damage in composite laminate structures.Its accumulation degrades structural performance and threatens the safety and integrity of aircraft.This study presents a method for the quantitative identification of delamination identification in composite materials,leveraging distributed optical fiber sensors and a model updating approach.Initially,a numerical analysis is performed to establish a parameterized finite element model of the composite plate.Then,this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations.The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors.Finally,a multi-island genetic algorithm is employed for global optimization to identify the size and location of the damage.The efficacy of the proposed method is validated through numerical examples and experiment studies,examining the correlations between damage location,damage size,and strain responses.The findings confirm that the model updating technique,in conjunction with distributed fiber optic sensors,can precisely identify delamination in composite structures.

Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Influence of System Uncertainties on Structural Damage Identification through Ambient Vibrations of Steel Structures

The practical difficulties presented by forced vibration testing of large steel structures, such as tall buildings, transmission lines or bridges, led to an increased interest in structural monitoring through ambient vibrations, which usually allows the proper identification of modal properties, natural frequencies, damping and modes of vibration. Changes in these modal properties constitute an indication of structural damage, which may then be assessed on the basis of experimental evidence. The authors proposed an approach to determine the so-called damage damping and stiffness matrices, which are essential to identify the location and intensity of damage. No restrictions were introduced on the damping matrix of the system. The approach requires ambient vibration data of all relevant coordinates used in the structural model, which are processed employing the SSI method. In practice, the identification method is seriously hampered by ambient factors such as temperature or humidity. In general those effects must be filtered out in other to obtain a reliable diagnosis of damage, approach that demands long term monitoring. In this paper, an alternative approach is explored, based on the introduction of error damping and stiffness matrices. Data on both matrices is generated on the basis of observed variations of structural member stiffness and damping caused by ambient factors. The influence of this uncertainty on the identified spectral properties is assessed by simulation.

Comparison of adaptive structural damage identification techniques in nonlinear hysteretic vibration isolation systems

Early structural damage identification to obtain an accurate condition assessment can assist in the reprioritization of structural retrofitting schedules in order to guarantee structural safety. Nowadays, seismic isolation technology has been applied in a wide variety of infrastructure, such as buildings, bridges, etc., and the health conditions of these nonlinear hysteretic vibration isolation systems have received considerable attention. To effectively detect structural damage in vibration isolation systems based on vibration data, three time-domain analysis techniques, referred to as the adaptive extended Kalman filter （AEKF）, adaptive sequential nonlinear least-square estimation （ASNLSE） and adaptive quadratic sum-sqnares error （AQSSE）, have been investigated. In this research, these analysis techniques are compared in terms of accuracy, convergence and efficiency, for structural damage detection using experimental data obtained through a series of laboratory tests based on a base-isolated structural model subjected to E1 Centro and Kobe earthquake excitations. The capability of the AEKF, ASNLSE and AQSSE approaches in tracking structural damage is demonstrated and compared.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

A Review of Structural Health Monitoring Techniques as Applied to Composite Structures

Structural Health Monitoring(SHM)is the process of collecting,interpreting and analysing data from structures in order to determine its health status and the remaining life span.Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties.However,composite materials are prone to develop damage when subjected to low to medium impacts(i.e.1-10 m/s and 11-30 m/s respectively).Hence,the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of high importance.Despite the availability of several SHM methods for the damage identification in composite structures,no single technique has proven suitable for all circumstances.It must be noted that the amount of techniques available nowadays is too extensive to be comprehensively reviewed in a single paper.Therefore,the focus will be on techniques that can serve as a starting point for studies focusing on damage detection,localisation,assessment and prognosis on certain kinds of structures.Thus,the line of thought behind the search and the structure of this review is a result of objectives beyond the scope of the paper itself.Nevertheless,it was considered that,once the above was understood,an updated synopsis such as this could also be useful for other researchers in the same field.

Remote Intelligent Identification System of Structural Damage

The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies. Key words RBFNN - inteligent identification - structural damage - Brower/Server (B/S) model CLC number TP 183 Foundation item: Supported by the Natural Science Foundation of Hubei Province in China (2001ABB0778), The Science and Technology Foundation for Wuhan Young Scholar (20015005039)Biography: RAO Wen-bi (1967-), female, Ph. D, associate professor, research direction: artificial intelligence

Damage Identification of General Overhead Travelling Crane Structure Based on Model Updating by Sensitivity

A model based damage identification was proposed by facilitating parameter sensitivity analysis and applied to a general overhead travelling crane.As updating reference data,experimental modal frequency was obtained by operational modal analysis(OMA)under ambient excitation.One dimensional damage function was defined to identify the damage by bending stiffness.The results showed that the model updating method could locate the damage and quantitatively describe the structure.The average error of eigenvalues between updated model analysis and the experimental results was less than 4% which proved the accuracy reliable.The comparison of finite element analysis and the test results of the deflection under the capacity load further verified the feasibility of this method.

Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by Empirical Mode Decomposition Method

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element Model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The Empirical Mode Decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

Output only modal identification and structural damage detection using time frequency & wavelet techniques

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 ambient/random 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.

A SINGULAR VALUE DECOMPOSITION BASED TRUNCATION ALGORITHM IN SOLVING THE STRUCTURAL DAMAGE EQUATIONS

The structural damage identification through modal data often leads to solving a set of linear equations. Special numerical treatment is sometimes required for an accurate and stable solution owing to the ill conditioning of the equations. Based on the singular value decomposition (SVD) of the coefficient matrix, an error based truncation algorithm is proposed in this paper. By rejection of selected small singular values, the influence of noise can be reduced. A simply-supported beam is used as a simulation example to compare the results to other methods. Illustrative numerical examples demonstrate the good efficiency and stability of the algorithm in the nondestructive identification of structural damage through modal data.

Research on mode localization of reticulated shell structures

Reticulated shell structures （RSSs） are characterized as cyclically periodic structures. Mistuning of RSSs will induce structural mode localization. Mode localization has the following two features： some modal vectors of the structure change remarkably when the values of its physical parameters （mass or stiffness） have a slight change; and the vibration of some modes is mainly restricted in some local areas of the structure. In this paper, two quantitative assessment indexes are introduced that correspond to these two features. The first feature is studied through a numerical example of a RSS, and its induced causes are analyzed by using the perturbation theory. The analysis showed that internally, mode localization is closely related to structural frequencies and externally, slight changes of the physical parameters of the structure cause instability to the RSS. A scaled model experiment to examine mode localization was carried out on a Kiewit single-layer spherical RSS, and both features of mode localization are studied. Eight tests that measured the changes of the physical parameters were carried out in the experiment. Since many modes make their contribution in structural dynamic response, six strong vibration modes were tested at random in the experimental analysis. The change and localization of the six modes are analyzed for each test. The results show that slight changes to the physical parameters are likely to induce remarkable changes and localization of some modal vectors in the RSSs.

Damage identification of steel truss bridges based on deep belief network

To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage.The hidden layer of the DBN is trained through a layer-by-layer pre-training.Finally,the backpropagation algorithm is used to fine-tune the entire network.The method is validated using a numerical model of a steel truss bridge.The results show that under the influence of noise and modeling uncertainty,the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

An Improved Damage Identification Method Based on Mode Information

This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated to improve the accuracy of damage locating and severity estimation by eliminating the erratic assumptions and limits in the existing algorithm. The damage prediction accuracy is numerically assessed for each algorithm when applied to a two-dimensional frame structure for which pre-damage and post-damage modal parameters are available for only a few modes of vibration. The analysis results illustrate the improved accuracy of the new algorithm when compared to the existing algorithm.

Analysis of Concrete Crack Treatment Technologies in Buildings

With the continuous development of civil engineering,concrete crack treatment technology has become an important research field.This paper proposes treatment techniques for different types of cracks,including the prevention and repair of surface cracks,the reinforcement and grouting of structural cracks,and the design and construction of controlled cracks through the analysis of the causes and classification of concrete cracks.The methods and suggestions proposed in this paper are practical and can improve the quality and safety of buildings.

基于双重注意力机制-改进Inception模块的CNN模型识别框架结构损伤

针对传统深度学习方法的网络隐含层和参数异常庞大且训练时间较长的特点,提出了一种基于双重注意力机制和改进Inception模块的卷积神经网络(convolutional neural network,CNN)模型来识别框架结构损伤。首先,通过局部最大值同步挤压变换将结构的振动响应信号转化为二维时频图并作为卷积神经网络的输入,然后基于改进Inception模块搭建二维卷积神经网络,最后通过双重注意力机制增强相关度高的损伤特征从而成功识别结构的损伤位置和损伤程度。通过IASC-ASCE SHM Benchmark结构I阶段数值模拟数据和卡塔尔大学看台模拟器数据集验证所提方法的有效性,研究结果表明:该方法不仅可以减少模型参数的个数和加快模型收敛速度,而且在面对框架结构多类别损伤识别问题时具有较高的准确率和较强的抗噪性能。

基于Kriging代理模型的结构损伤识别新方法

提出一种基于Kriging代理模型的损伤识别方法。利用初始样本建立结构响应与结构损伤参数之间的关系,代替原结构响应与结构物理参数之间的关系,有效减少损伤识别过程中反复调用有限元软件对结构进行网格划分和有限元计算的次数,提高了识别效率。采用加点准则对代理模型进行修正,保证代理模型可以准确反映结构响应与结构损伤参数之间的关系。通过一个管梁结构的数值算例验证了所提方法的有效性。最后,将该方法应用于某导弹发射台模型的损伤识别中,实验结果表明,该方法可应用于工程实际。

基于Mindlin板理论的偏移损伤成像数值仿真研究

提出了一种应用散射Lamb波的偏移技术对板结构中多部位损伤进行实时识别.基于Mindlin板理论,推导了板结构中弥散性弯曲波频率-波数域的快速偏移方法.首先对由线性传感器阵列激励和接收到的入射和散射波场在波数-频率域分别进行延拓,然后根据Huygens原理,结合波场延拓的时间一致性原理施加成像条件,对损伤进行成像识别.数值仿真研究采用基于Mindlin板理论的有限差分法模拟结构中含不同形状及尺寸损伤时的散射波场.对模拟散射波场进行偏移成像的结果表明该方法不仅能够识别多部位损伤的位置,还具有识别损伤程度的能力,其快速计算的优点满足在线结构健康监测系统对实时性的要求.