Damage detection method for large structures using static and dynamic strain data from distributed fiber optic sensor

A method for damage detection applicable to large slender steel structures such as towers of large-scale wind turbines, long-span bridges, and high-rise buildings is presented. This method is based on continuous strain data obtained by distributed fiber optic sensor (FOS) and neural network (NN) analysis. An analytical model for cracked beam based on an energy balance approach was used to train a NN. The continuous static strains and the natural frequencies obtained from the distributed FOSs were used as the input to the trained NN to estimate the crack depths and locations. An experimental study was carried out on a cracked cantilever beam to verify the present method for damage identification. The cracks were inflicted on the beam, and static and free vibration tests were performed for the intact case and the damage cases. The distributed FOSs were used to measure the continuous strains. The damage estimation was carried out for the 5 damage cases using the NN technique. It has been found that the identified crack depths and locations agree reasonably well with the inflicted cracks on the structure.     

利用基本模态振型对梁结构裂缝识别的小波方法研究

文章首先讨论了含裂缝梁结构的柔度矩阵的变化;其次,通过选择不同模态振型函数进行小波变换分析,总结了不同模态振型在梁结构裂缝检测中的不同规律;最后,文章以悬臂梁为例,选用Gaus2小波对悬臂梁基本振型进行小波变换,进而对悬臂梁中的裂缝进行识别,裂缝深度与Lipschitz指数进行拟合。     

Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform

Abstract: A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients.     

Pipe crack identification based on the energy method and committee of neural networks

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with the different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Crack detection is carried out for 16 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.     

某框架结构梁早期裂缝成因分析与防治

针对某框架梁跨中出现梁侧垂直裂缝和梁底水平裂缝,分别从设计、材料、施工、使用四个方面分析了裂缝产生的原因,提出采用表面处理法、粘贴碳纤维加固法等对梁加固修复,并对如何防止此类裂缝产生提出预防措施,对同类工程问题有一定的借鉴作用。     

不同模态振型在梁结构裂缝识别中的差异

采用Gaus2小波对多裂缝梁的不同模态振型进行小波变换,分析了不同模态振型在梁结构裂缝检测中的差别。同时,还采用最小二乘法算出各裂缝处的Lipschitz指数,进而分析了各模态振型函数在裂缝处的奇异性程度。结论表明:梁结构裂缝识别的效果主要由裂缝处的小波系数的模的大小决定。     

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.     

A hybrid approach to detect crack parameters using measured natural frequencies in thin walled angle section steel beams

Present study outlines a hybrid approach using Finite Element Method (FEM)–Response Surface Method (RSM)–Genetic Algorithm (GA) to predict the crack parameters, namely crack position and crack depth ratio with the help of only the measured natural frequencies of cracked thin walled beams. Numerical experimental trials of cracked beams have been conducted based on design of experiment (DOE) approach using an improved Finite element model. The improvement has been achieved by consideration of warping stiffness in cracked angle section beam. Thereafter, regression analysis has been conducted to construct Response Surface Function (RSF). Optimum crack parameters were then calculated using GA by minimizing an objective function which has been formed as the root mean square (RMS) of the residuals between RSFs and measured frequencies. Results of the study indicate that, the proposed approach performs with excellent accuracy and it does not require the response of an uncracked beam as benchmark information.     

Wavelet packet based damage detection in beam-like structures without baseline modal parameters

In this paper, a new approach for damage detection in beam-like structures is presented. The method can be used without the need for baseline modal parameters of the undamaged structure. Another advantage of the proposed method is that it can be implemented using a small number of sensors. In the proposed technique, the measured dynamic signals are decomposed into the wavelet packet decomposition (WPD) components, the power spectrum density (PSD) of each component is estimated and then a damage localisation indicator is computed to indicate the structural damage. The proposed method is firstly illustrated with a simulated beam and the identified damage is satisfactory with assumed damage. Then, the method is applied to a steel beam. The effect of damage location and the effects of wavelet type and the decomposition level are examined. The results show that the proposed method has great potential in crack detection of beam-like structures.     

Foreground–background separation technique for crack detection

Current level‐2 condition assessment methods for critical infrastructure assets mostly rely on human visual investigation of visible damages and patterns at the structure surface, which can be a costly, time‐consuming, and subjective exercise in reality. In this article, a novel method for crack detection is proposed via salient structure extraction from textured background. This method first extracts strong edges and distinguishes them from strong textures in a local neighborhood. Then, the spatial distribution of texture features is estimated to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two key steps are fused to calculate the final structure saliency map for generation of the crack masks. This method was validated on a data set with 704 images and the outcome revealed an average f‐measure of 75% in detecting the concrete cracks that is significantly higher than two other baseline methods.     

Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure. Therefore, the accurate determination of crack characteristics, such as location and depth, is one of the key engineering issues for assessment of the reliability of structures. This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization (PSO); these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm (PSO-GA-FA), Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm (PSO-GWO-FA), and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO). A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters. Thus, the location and depth of a crack in a beam can be predicted by measuring its natural frequency. Hence, the measured natural frequency can be used as the input parameter of the algorithm. In this paper, this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms. The results show that among the proposed triple hybrid algorithms, the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.     

A machine learning approach based on multifractal features for crack assessment of reinforced concrete shells

The geometric properties and spatial characteristics of crack patterns are significant indicators of the extent of damage on reinforced concrete structures. However, manual visual assessment is subjective and depends highly on the inspector's skills. The current study proposes an automated approach for the quantification of digitally documented crack patterns on reinforced concrete shell elements subjected to reversed cyclic shear loading. Multifractal analysis is proposed as a feature extractor for images depicting crack patterns and a set of artificial cracks is analyzed, to quantify how the properties of crack patterns vary as a function of cracking inclination. The results of the parametric study motivated the training of a multiclass classification model, which is used to provide damage level estimates for cracked reinforced concrete members. The training of the classifier is performed using experimental data of reinforced concrete shell elements under well‐defined and idealized two‐dimensional pure shear stress loading conditions. A dataset with 119 images from crack patterns of reinforced concrete shells is used for training. The multifractal features successfully translate the shape of the crack patterns into meaningful information about the extent of damage; achieving an overall test accuracy of 89.3%.     

Crack identification method for tapered cantilever pipe-type beam using natural frequencies

A method to detect location and size of a crack in tapered cantilever pipe-type beam using changes in natural frequencies is presented. The boundary conditions of cantilever beam are applied to a general solution for vibrating tapered beam. Then, an equivalent bending stiffness for cracked beam is used to obtain the natural frequencies. Numerical simulations are carried out to construct a set of training patterns of a neural network, and committee of neural networks is employed to identify the crack. Crack identifications are carried out for the 16 damage cases numerically and for the 3 damage cases experimentally. The identified crack locations and sizes agree reasonably well with the exact values.     

层状裂隙岩体蠕变柔量估计的合理方法

基于Taylor模型方法和粘弹性断裂力学理论，推导了层状裂隙岩体的单轴、剪切以及体积蠕变柔量，并合理考虑了裂隙相互作用效应的影响，分析了层状裂隙岩体单轴蠕变和剪切蠕变的各向异性。计算分析指出，裂隙岩体的单轴蠕变柔量随裂隙密度参数的增加而增大，随裂隙倾角的增加而减小。考虑裂隙相互作用效应时，层状裂隙岩体的蠕变柔量明显大于不考虑裂隙相互作用的情况。该方法为通过室内岩石流变实验了解实际工程中裂隙岩体的蠕变特性提供了一种可能。     

Operational Modal Parameter Identification from Power Spectrum Density Transmissibility

Abstract: Operational modal analysis subjected to ambient or natural excitation under operational conditions has recently drawn great attention. In this article, the power spectrum density transmissibility (PSDT) is proposed to extract the operational modal parameters of a structure. It is proven that the PSDT is independent of the applied excitations and transferring outputs at the system poles. As a result, the modal frequencies and mode shapes can be extracted by combing the PSDTs with different transferring outputs instead of different load conditions where the outputs from only one load condition are needed. A five‐story shear building subjected to a set of uncorrelated forces at different floors is adopted to verify the property of PSDTs and illustrate the accuracy of the proposed method. Furthermore, a concrete‐filled steel tubular half‐through arch bridge tested in the field under operational conditions is used as a real case study. The identification results obtained from currently developed method have been compared with those extracted from peak‐picking method, stochastic subspace identification, and finite element analysis. It is demonstrated that the operational modal parameters identified by the current technique agree well with other independent methods. The real application to the field operational vibration measurements of a full‐sized bridge has shown that the proposed PSDTs are capable of identifying the operational modal parameters (natural frequencies and mode shapes) of a structure.     

基于小波多分辨率分析的简支梁损伤识别方法研究

基于小波多分辨率分析的方法,根据移动载荷作用下简支梁动态响应特性,利用梁上一点振动信号对裂纹梁进行损伤识别。移动载荷经过梁上裂纹处时,必然导致梁响应时间历程曲线的奇异性,这种奇异性不能直接被观察到,但是可以通过小波多分辨率分析进行识别。利用有限元Ansys软件的瞬态分析方法进行裂纹梁移动载荷作用下的损伤识别数值模拟,通过梁上某一点挠度、速度和加速度的振动信号,利用小波多分辨率分析有效地识别出单个及多个裂纹,同时扩展到多个同向和相向移动载荷作用下裂纹梁的损伤识别。     

Identification of Instantaneous Modal Parameter of Time‐Varying Systems via a Wavelet‐Based Approach and Its Application

This work presents an efficient approach using time‐varying autoregressive with exogenous input (TVARX) model and a substructure technique to identify the instantaneous modal parameters of a linear time‐varying structure and its substructures. The identified instantaneous natural frequencies can be used to identify earthquake damage to a building, including the specific floors that are damaged. An appropriate TVARX model of the dynamic responses of a structure or substructure is established using a basis function expansion and regression approach combined with continuous wavelet transform. The effectiveness of the proposed approach is validated using numerically simulated earthquake responses of a five‐storey shear building with time‐varying stiffness and damping coefficients. In terms of accuracy in determining the instantaneous modal parameters of a structure from noisy responses, the proposed approach is superior to typical basis function expansion and regression approach. The proposed method is further applied to process the dynamic responses of an eight‐storey steel frame in shaking table tests to identify its instantaneous modal parameters and to locate the storeys whose columns yielded under a strong base excitation.     

钢筋混凝土梁式结构裂缝特征与损伤评估方法试验研究

服役中大量钢筋混凝土梁式桥的结构性能急待采取行之有效的方法对其进行评估 ,以便研究进一步采取的措施。本文基于四片板梁和四片T梁模型室内试验结果 ,并由开裂截面的几何关系和裂缝开展理论 ,经回归分析及结构非线性分析 ,建立了以裂缝特征统计参数推测结构配筋率的方法 ,达到有效评估结构性能的目的 ,将结构外观检查的定性评估升级为定量评估 ,具有重要的使用价值。该方法简便可行、费用低廉     

A semi-analytical analysis of the elastic buckling of cracked thin plates under axial compression using actual non-uniform stress distribution

An improved hybrid semi-analytical method for calculating elastic buckling load of a thin plate with a central straight through-thickness crack subject to axial compression is proposed. In the study, the actual non-uniform in-plane stress distribution is firstly conducted by using Muskhelishvili's complex variable formulation in conjunction with boundary collocation method. A deflection shape function, satisfying not only the outer boundary conditions but also the inner boundary conditions of the crack edges, is obtained by using domain decomposition method. Finally the buckling load of a cracked plate using Raleigh–Ritz energy method is calculated based on the actual in-plane stress distribution and the reasonable deflection shape function obtained. The effects of crack length, plate's aspect ratio are studied for thin plates with different boundary conditions. Results obtained from the proposed method are in good agreement with the existing numerical results and experimental ones. It is finally shown that the proposed method, based on a correct non-uniform in-plane stress distribution, is more accurate than the few existing analytical methods based on a uniform in-plane stress distribution.     

混凝土结构的非结构裂缝与控制

分析了高层建筑中一些特殊部位、易裂部位出现的混凝土塑性沉降裂缝、梁板结构温度裂缝的产生机理，并从设计和施工两方面提出了裂缝控制措施，以有效控制裂缝的危害程度。