基于随机森林的桁架结构损伤程度预测

为适应桁架结构健康监测技术的发展,进一步优化桁架结构损伤程度预测效果,提出一种基于随机森林的桁架结构损伤程度预测算法。算法建立并优化桁架结构损伤数据库,对多个损伤信号分量提取频域特征,通过孤立森林算法和缺失森林算法对数据库进行异常检测和数据补全,并采用主成分分析法对其进行特征降维。经实验验证,算法能够针对不同损伤程度的桁架结构敏感度做出有效判定,与极端随机树、AdaBoost、Bagging等回归算法相比,均方误差显著降低,为桁架结构健康监测提供了有效的预测手段。     

基于改进FOA的风机叶片结构损伤检测

为解决风轮叶片多损伤情况下的结构损伤识别问题,提出一种基于单元模态应变能变化率和改进果蝇优化算法的分步损伤判别方法。该方法首先采用单元模态应变能变化率对损伤进行初步判定,然后利用果蝇优化算法对损伤位置和程度进行精确识别。由于传统果蝇优化算法极易陷入局部最优问题,则对其进行了改进。结果表明:采用单元模态应变能变化率可以有效识别出可能的损伤单元,在此基础上用改进果蝇优化算法可以更精确地识别结构损伤位置和程度,同时采用改进果蝇优化算法的识别精度明显优于简单果蝇优化算法。     

基于自构形神经网络的桥梁结构损伤诊断

桥梁结构损伤识别问题为高度非线性的复杂系统,单依靠数学与力学模型求解法难以对桥梁结构进行准确的损伤诊断,采用多层前馈人工神经网络的自构形学习算法进行桥梁结构损伤识别,仿真结果表明该算法诊断效率高,可应用于机械设备质量检测系统,实现产品质量检测自动化.     

基于量子粒子群优化算法的结构损伤识别

提出一种以广义柔度矩阵为损伤指标,基于量子粒子群优化算法的结构损伤识别方法.该方法根据结构损伤前后广义柔度矩阵差与结构物理参数变化关系,将结构广义柔度矩阵识别问题转化为优化问题,进而采用系统辨识能力较强的量子粒子群优化算法搜索目标函数最优值,从而达到损伤位置和损伤程度同时识别的双重效果.最后通过简支梁数值模拟对该方法的有效性进行了验证.     

基于压电晶体传感器的椭圆损伤定位算法

压电晶体传感器作为激励部件可以在结构中产生高频弹性应力波,其同裂纹等局部损伤发生相互作用将产生波动的能量耗散、波形反射以及波形干涉等现象.利用附在金属板上的压电激励器在结构产生的高频弹性应力波,并通过嵌入或附着在结构中的压电传感器进行接收,提出了一种基于TDOA模型的椭圆损伤定位算法.利用该算法可以获得损伤部位坐标的显式表达式,并能够对损伤区域的大小做出估计.仿真计算结果和实验证明该算法能够提供实时的"主动"损伤检测手段,同时可以准确快速地对结构中可能存在的损伤进行定位.     

基于时间反转的直升机结构损伤成像算法

复合材料在直升机结构中大量应用,为了提高直升机服役过程中的安全性,在直升机尾桁结构上采用基于时间反转的导波结构健康监测方法进行损伤成像算法研究。阐述了基于时间反转的损伤成像方法步骤,利用Matlab软件平台编写了损伤成像算法程序,并利用在尾桁结构件上采集的信号数据进行损伤成像方法验证。实验选取多个模拟损伤测试点进行多次损伤诊断,均能得到误差不超过20 mm的损伤定位结果,可以满足直升机结构健康监测地面实验分析的需要。     

基于计算机视觉的建筑施工期临时结构损伤识别方法

建筑施工临时结构是施工现场的事故主要风险源。以往的基于振动的临时结构监测方法依赖于在预先分析确定的监测关键部位放置的加速度传感器。但由于临时结构存在构件搭设不规范、施工现场不确定性等因素,通过有限元分析等手段得到的监测关键部位可能与实际情况相差较大,存在不确定性。为此提出一种基于欧拉运动放大算法的临时结构损伤识别方法,充分利用计算机视觉技术的全域覆盖及监测高效的优点。采用数码摄像机采集临时结构的数字图像序列,经过基于相位的欧拉运动放大算法处理,获取运动放大后的数字图像序列;运用Canny边缘识别算法获取边缘图像序列并消除运动放大造成的噪声,通过基于形心的运动跟踪算法获取临时结构的位移时程数据,并利用快速傅里叶变换进行频谱分析;与预先建立的损伤动力指纹库进行对比判断临时结构的损伤状态。以存在10种损伤状态的门式脚手架为测试对象,证明该方法的可行性与适用性。与加速度传感器测量进行对比,该方法平均误差为0.95%,满足临时结构损伤状态识别的精度要求。     

微粒群算法原理及改进算法在土木工程中应用

微粒群算法（Particle swarm optimization,PSO）模拟鸟群捕食的过程,用于寻找空间中的最优解。对PSO算法的基本原理进行了介绍,对一些改进的PSO算法进行了总结,阐述了PSO算法在土木工程结构损伤检测中的应用。     

基于独立分量分析和支持向量机的结构损伤识别集成算法

针对模式识别最关键的两个环节:特征提取和分类器设计,提出了基于独立分量分析(ICA)和支持向量机(SVM)的损伤识别集成算法,首先应用ICA方法计算独立源信号和混合矩阵[A],利用混合矩阵与模态振型的对应关系,得到振型矩阵[Φ],将模态振型的变形矩阵[Φ]*作为特征参数输入至SVM分类器进行损伤识别,在冲击载荷作用下,对钢框架结构模型进行了振动试验,结果表明:ICA方法提取的模态振型是一种高效的损伤特征参数,基于ICA和SVM的集成算法能够成功识别结构损伤、损伤位置和损伤程度,从而为结构健康监测提供了一种行之有效的损伤识别方法。     

BP网络在光纤传感对复合材料损伤定位中的应用研究

为了对复合材料进行结构健康和损伤监测 ,减少由于复合材料裂缝、应力集中、疲劳等导致的事故 ,建立了光纤传感系统 ,引入神经网络算法 ,并介绍了其在损伤程度和位置分类中的实现。     

基于ANN的动态系统状态方程辨识建模仿真

对系统辨识原理、基于神经网络（ANN）的动态系统辨识进行了分析,针对动态系统辨识模型描述的复杂性,为简化ANN辨识建模的输入／输出关系的表达,提高算法的简洁性,采用了状态方程辨识模型,并给出了基于ANN的动态系统状态方程辨识模型。为比较分析不同网络结构的辨识建模效果及网络模型泛化能力,针对三种不同网络结构方案进行了辨识建模仿真研究。仿真结果最示,基于ANN的动态系统状态方程模型的辨识建模是有效的,并且简单合理的网络结构方案,可提高网络辨识模型的泛化能力。     

Identification of nonlinear dynamic systems using functional linkartificial neural networks

We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the input pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.     

Damage detection of truss bridge joints using Artificial Neural Networks

Recent developments in Artificial Neural Networks (ANNs) have opened up new possibilities in the domain of inverse problems. For inverse problems like structural identification of large structures (such as bridges) where in situ measured data are expected to be imprecise and often incomplete, ANNs may hold greater promise. This study presents a method for estimating the damage intensities of joints for truss bridge structures using a back-propagation based neural network. The technique that was employed to overcome the issues associated with many unknown parameters in a large structural system is the substructural identification. The natural frequencies and mode shapes were used as input parameters to the neural network for damage identification, particularly for the case with incomplete measurements of the mode shapes. Numerical example analyses on truss bridges are presented to demonstrate the accuracy and efficiency of the proposed method.     

基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。     

人工神经网络在木材损伤识别中的应用

利用人工神经网络(ANN)对含有不同缺陷程度的木材进行了定性和定量解析.通过选择合理的神经网络结构,建立有效的训练样本集,确定合理的参数及训练方法,对3种不同缺陷类型木材进行了解析,考察了网络的泛化能力.结果表明:该网络能够对不同缺陷程度的木材进行准确地识别,而且本研究为人工神经网络在木材缺陷损伤的定性和定量分析方面提供了一种有效的方法.     

Fast and improved backpropagation learning of multi-layer artificial neural network using adaptive activation function

In the conventional backpropagation (BP) learning algorithm used for the training of the connecting weights of the artificial neural network (ANN), a fixed slope−based sigmoidal activation function is used. This limitation leads to slower training of the network because only the weights of different layers are adjusted using the conventional BP algorithm. To accelerate the rate of convergence during the training phase of the ANN, in addition to updates of weights, the slope of the sigmoid function associated with artificial neuron can also be adjusted by using a newly developed learning rule. To achieve this objective, in this paper, new BP learning rules for slope adjustment of the activation function associated with the neurons have been derived. The combined rules both for connecting weights and slopes of sigmoid functions are then applied to the ANN structure to achieve faster training. In addition, two benchmark problems: classification and nonlinear system identification are solved using the trained ANN. The results of simulation-based experiments demonstrate that, in general, the proposed new BP learning rules for slope and weight adjustments of ANN provide superior convergence performance during the training phase as well as improved performance in terms of root mean square error and mean absolute deviation for classification and nonlinear system identification problems.     

Improving artificial neural networks’ performance in seasonal time series forecasting

In this study, an artificial neural network (ANN) structure is proposed for seasonal time series forecasting. The proposed structure considers the seasonal period in time series in order to determine the number of input and output neurons. The model was tested for four real-world time series. The results found by the proposed ANN were compared with the results of traditional statistical models and other ANN architectures. This comparison shows that the proposed model comes with lower prediction error than other methods. It is shown that the proposed model is especially convenient when the seasonality in time series is strong; however, if the seasonality is weak, different network structures may be more suitable.     

Implementing the induction-motor drive with four-switch inverter: An application of neural networks

This paper reports a four switch based three-phase voltage source inverter using space vector pulse width modulation (SVPWM), and designed with a three-layer feed forward back propagation based artificial neural network (ANN). The input–output samples, obtained using simulations in Matlab Simulink, were used for the extensive training of the neural network. Matlab interface with National Instruments’ NI-USB-6259 BNC was used for implementing the designed scheme with calculated weights and biases. The designed ANN based SVPWM model receives command voltage and reference speed as the inputs and generates pulse width modulated waves for the four-switch three-phase inverter bridge. The V/f ratio can be controlled by controlling the input parameters of the ANN generating PWM pulses. The simulations and experimental results, and harmonic analysis with the designed ANN structure are presented at different base speeds. The designed model was tested in under modulation, over modulation and unity modulation mode of operation and tuned to give minimum total harmonic distortion. Harmonic results at different modulation indexes are also presented. The ANN based implementation reduces the complexity of control system and overall cost reduction is achieved by the combination of FSTPI and ANN.     

A comparison of neural network,evidential reasoning and multiple regression analysis in modelling bridge risks

Artificial neural network (ANN), the evidential reasoning (ER) approach and multiple regression analysis (MRA) can all be utilized to model bridge risks, but their modelling mechanisms and performances are quite different and therefore need comparison. This study compares the modelling mechanisms of the three alternative approaches and their performances in modelling a set of bridge risk data. It is found that ANN outperforms the ER approach and MRA for the considered case study. The reason for this is analyzed. The advantages and disadvantages of the three alternative approaches are also compared.     

An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design responses which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained using grillage analysis program for different bridge deck configurations and the correlation between sectional parameters and design responses has been established. Subsequently, GA is employed for arriving at optimum configuration of the bridge deck system by minimizing the total cost. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on cost optimization of T-girder bridge deck system for different spans. The method presented in this paper, would greatly reduce the computational effort required to find the optimum solution and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error.