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.     

Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation

This paper presents a damage detection algorithm using a combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input in artificial neural networks (ANNs) for location and severity prediction of damage in beam-like structures. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever steel beams for the first three natural modes. Different damage scenarios have been introduced by reducing the local thickness of the selected elements at different locations along finite element model (FEM) of the beam structure. The necessary features for damage detection have been selected by performing sensitivity analyses and different input–output sets have been introduced to various ANNs. In order to check the robustness of the input used in the analysis and to simulate the experimental uncertainties, artificial random noise has been generated numerically and added to noise-free data during the training of the ANNs. In the experimental analysis, two steel beams with eight distributed surface-bonded electrical strain gauges and an accelerometer mounted at the tip have been used to obtain modal parameters such as resonant frequencies and strain mode shapes. Finally, trained feed-forward backpropagation ANNs have been tested using the data obtained from the experimental damage case for quantification and localisation of the damage.     

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.     

基于小波理论的悬臂梁裂纹定位研究

利用小波多分辨率分析,对含裂纹悬臂梁的裂纹进行定位研究,数值算例表明:多分辨率分析的高频重构部分的系数峰值清楚地指示出裂纹位置,并对不同消失矩的小波的识别效果进行了比较,发现如果只是进行裂纹定位,低阶消失矩的小波的指示效果更好。     

Detection of void and metallic inclusion in 2D piezoelectric cantilever beam using impedance measurements

The aim of current work is to improve the existing inverse methodology of void-detection based on a target impedance curve, leading to quick-prediction of the parameters of single circular void. In this work, mode-shape dependent shifting phenomenon of peaks of impedance curve with change in void location has been analyzed. A number of initial guesses followed by an iterative optimization algorithm based on univariate method has been used to solve the problem. In each iteration starting from each initial guess, the difference between the computationally obtained impedance curve and the target impedance curve has been reduced. This methodology has been extended to detect single circular metallic inclusion in 2D piezoelectric cantilever beam. A good accuracy level was observed for detection of flaw radius and flaw-location along beam-length, but not the precise location along beam-width.     

A Bayesian Probabilistic Approach for Crack Characterization in Plate Structures

Abstract: This article explores the possibility of using a Bayesian probabilistic approach for the detection of cracks in thin plate structures, utilizing measured dynamic responses at only a few points on the plate. Existing laser scanning or shearography based crack detection methods are applicable only when measurement at the region near the defect is possible. These types of techniques are important in providing information, in addition to that obtained through visual inspection, for the purpose of structural health monitoring. Because of the global nature of the vibration characteristics of structural systems, this article puts forward a crack detection approach that can be applicable with only a few sensors and when the sensor locations are not close to the crack. This kind of method is particularly valuable as it can be applied when visual inspection is not possible (e.g., part of the plate is obstructed and is not assessable by inspectors). Owing to the problems of measurement noise and incomplete measurement (i.e., only a limited number of measurement points are employed and high‐mode information is lost because of the digitization of the signal and measurement noise), the results of crack detection as an inverse problem contain uncertainties. To explicitly handle such uncertainty, the proposed crack detection method follows the Bayesian statistical system identification framework. Rather than pinpointing the crack parameters (i.e., the crack location, length, and depth), the posterior probability density function (PDF) of the crack parameters is calculated to quantify the confidence level of the identified results, which is extremely important for engineers when they make judgments about remedial works. This article reports the theoretical development of the modeling of a cracked plate and a crack detection method together with numerical verification of the proposed method. The case study results are very encouraging, and indicate that the proposed method is feasible.     

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.     

Wavelet‐Based Detection of Beam Cracks Using Modal Shape and Frequency Measurements

Abstract: When a structure is subjected to dynamic or static loads, cracks may develop and the modal shapes and frequencies of the cracked structure may change accordingly. Based on this, a new method is proposed to locate beam cracks and to estimate their depths. The fault‐induced modal shape and frequency changes of cracked structures are taken into account to construct a new hybrid crack detection method. The method includes two steps: crack localization and depth estimation. The locations of the cracks are determined by applying the wavelet transform to the modal shape. Using the measured natural frequencies as inputs, the depths of the cracks are estimated from a database established by wavelet finite element method. The effectiveness of the proposed hybrid two‐step method is demonstrated by numerical simulation and experimental investigation of a cantilever beam with two cracks. Our analyses also indicate that the proposed method performed reasonably well at certain level of noise.     

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.     

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.     

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。     

一带悬挑结构的计算分析与补强设计

哈药六厂某厂房外横墙因功能调整需由原设计的 4 90mm厚砖墙改为 6 2 0mm厚砖墙 ,为使本工程的外观造型与其近邻办公楼相协调 ,在该外横墙外侧需较原设计增设干挂石材幕墙 ,由于填充墙加厚及干挂石材幕墙等新增荷载在原设计中未予考虑 ,需对带悬挑的预应力边框架进行核算与补强加固。经核算 ,框架柱及框架梁的非悬挑区段在荷载增大后仍能满足相关设计标准的要求 ,只需对框架梁的悬挑区段进行补强加固。提出了通过在基础悬挑梁和一层顶悬挑梁间后置钢筋混凝土墙形成带边框深梁以增大悬挑结构抗力 ,减小悬挑结构裂缝和变形的加固思想和方法。可供同类工程加固改造设计时参考     

Higher Order Coherences for fatigue crack detection

Higher Order Coherences (normalized Higher Order Spectra) are the tools to identify the relationship between the different harmonic components in a signal. The vibration of a structure having a crack also generates several harmonics of the exciting frequency due to its breathing (closing and opening) behaviour which is a non-linear phenomenon. Presently two types of the HOC — namely, the Bicoherence and the Tricoherence, are used for the fatigue crack detection. This paper presents the observations made on the HOC on the numerically simulated experiment of a cantilever beam with and without cracks. The robustness of the HOC in the crack detection even for the noisy response vibration data has also been brought out.     

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.     

高位大悬挑混凝土结构支模平台施工技术

以某高层公共建筑工程为例,介绍了一种高位大悬挑混凝土结构支模平台施工技术,该技术采用型钢基本结构单元搭设钢平台,解决了型钢悬挑梁平台安全储备不足的问题。     

TMD等强度悬臂梁实际应用时的频率精确分析

为了确定实际应用中调谐质量阻尼器（TMD）刚度单元的梯形悬臂梁参数,建立了梯形悬臂梁频率的精确计算公式,计算了梯形等强度悬臂梁与三角形等强度悬臂梁频率的误差,利用梯形悬臂梁频率计算公式确定了TMD等强度悬臂梁的设计步骤与程序,给出了工程示例,并使用仿真软件ANSYS进行了验证。结果表明:悬臂梁自由端的宽度小于固定端宽度的12.65%时,梯形等强度悬臂梁与三角形等强度悬臂梁的频率相对误差小于5%;所用程序可以用于确定满足频率精度要求的TMD等强度悬臂梁的参数,对工程实际有较大指导意义。     

综采面多层坚硬顶板破断特征及其安全控制研究

祁东煤矿8_2煤层覆岩存在多层厚度较大坚硬岩层,初次跨落步距大,周期断裂悬顶长,严重危害工作面的安全生产。通过分析坚硬顶板的岩梁结构、力学特性,建立了初次来压双固支梁模型,判断出关键层的位置,获得了顶板初次垮落及周期垮落步距。并建立了有矸石支撑的周期跨落悬臂梁模型,根据平衡关系计算得到给定支架载荷下的合理悬顶长度。采用超前深孔预裂爆破技术弱化坚硬顶板,设计了合理爆破参数。开采实践表明,采用的爆破技术实现了对坚硬顶板的有效控制,减小了基本顶跨落步距,降低了来压程度,保证了工作面的安全正常开采。     

基于自振频率的悬臂梁损伤识别方法

梁的损伤识别是损伤检测理论中最基本、最具有代表性意义的工作.寻找基于频率的结构损伤识别和定位的参数,并证明这些参数仅与损伤的位置有关,而与结构损伤程度无关.然后建立悬臂梁损伤定位通用频率指纹库,并通过数值算例和具体试验验证该频率指标用于悬臂梁损伤定位的有效性、实用性及其准确性.最终找到一种比较实用的悬臂梁损伤定位方法,从而为结构损伤识别理论在工程实际的应用提供方便.     

钢筋混凝土悬臂梁的构造设计

介绍了即将颁布的《混凝土结构设计规范》(GB5 0 0 10 )中悬臂梁的构造设计 ,分析、探讨了钢筋混凝土悬臂梁细部构造问题 ,包括纵筋的截断、锚固、梁端小立柱、截面承载力的计算要点 ,提出了设计原则     

钢梁损伤的神经网络诊断分析

应用人工神经网络技术 ,以反映结构损伤程度的固有频率作为神经网络输入的特征参数 ,建立结构故障诊断模型 ,对建筑工程结构中常用的简支矩形截面钢梁裂纹深度和位置进行了诊断和预测研究。从实例可以看出 ,应用神经网络技术对工程结构进行裂纹损伤故障的综合诊断和预测分析是可行的。