Application of artificial neural networks to fracture analysis at the Äspö HRL, Sweden: fracture sets classification

This study investigates the potential of artificial neural networks (ANNs) to recognize, classify and predict patterns of different fracture sets in the top 450 m in crystalline rocks at the Äspö Hard Rock Laboratory (HRL), Southeastern Sweden. ANNs are computer systems composed of a number of processing elements that are interconnected in a particular topology which is problem dependent. ANNs have the ability to learn from examples using different learning algorithms; these involve incremental adjustment of a set of parameters to minimize the error between the desired output and the actual network output. Six fracture-sets with particular ranges of strike and dip have been distinguished. A series of trials were carried out using backpropagation (BP) neural networks for supervised classification, and the BP networks recognized different fracture sets accurately. Self-organizing neural networks have been used for data clustering analysis with supervised learning algorithms; (competitive learning and learning vector quantization), and unsupervised learning algorithms; (self-organizing maps). The self-organizing networks adapted successfully to different fracture clusters (sets). A set of trials has been carried out to investigate the effect of changing the network's topologies on the performance of the BP networks. Using two hidden layers with tan-sigmoid and linear transfer functions was beneficial for the performance of BP classification. ANNs improved fracture sets classification that was based on Kamb contouring method with constraint on areas between fracture clusters.     

Neural Networks in Structural Engineering

In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural networks. Unlike their expert system counterparts, neural networks can be trained based on observed information. These systems exhibit a learning and memory capability similar to that of the human brain, a fact due to their simplified modeling of the brain's biological function. This article presents an introduction to neural network technology as it applies to structural engineering applications. Differing network types are discussed. A back-propagation learning algorithm is presented. The article concludes with a demonstration of the potential of the neural network approach. The demonstration involves three structural engineering problems. The first problem involves pattern recognition; the second, a simple concrete beam design; and the third, a rectangular plate analysis. The pattern recognition problem demonstrates a solution which would otherwise be difficult to code in a conventional program. The concrete beam problem indicates that typical design decisions can be made by neural networks. The last problem demonstrates that numerically complex solutions can be estimated almost instantaneously with a neural network.     

基于改进PSO-BP的模糊PID烧结炉温度控制

采用改进粒子群算法整定优化PID参数,并在反馈回路中加入BP神经网络预测下一时刻温度,将超前温度信息作为改进粒子群算法适应度函数参数,提前调整PID控制器参数,从而给出超前的控制,以此来减弱烧结炉温度变化的滞后性.通过模糊推理在温度控制过程中在线调整PID控制器参数,加强温度控制的跟随性.试验结果表明,与传统PID控制...     

Neural Network Approaches to Aid Simple Truss Design Problems

Abstract: Diverse problems in engineering may be solved accurately with computers. In structural engineering, many solution techniques exist. Over the past few years, neural networks have evolved as a new computing paradigm, and many engineering applications have been studied. This paper describes configuring and training of a neural network for a truss design application and explores the possible roles for neural networks in structural design problems. The specific problem considered is a simple truss design where, given a geometry and a loading, economical cross-sectional areas of all the members are to be selected. For this problem, a two-layer neural network is trained using the back-propagation algorithm with patterns representing optimal designs for diverse loading conditions. The performance of the trained neural network is evaluated with a sample problem.     

基于ELM的大坝变形分析与预报模型

传统的诸如BP神经网络等学习方法训练时需要设置大量的参数,并且容易产生局部最优解。极限学习机(Extreme Learning Machine,ELM)可以随机选择输入权重以及隐藏层偏差且不需要调节,最终只产生唯一最优解。将ELM引入大坝变形分析建模中,建立了基于ELM的变形预报模型。实例表明,相比传统的逐步回归模型与BP神经网络模型,基于ELM的大坝变形预报模型在效率和精度上都有提高。     

基于微粒群算法和模糊神经网络的火灾探测研究

针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。     

Neural Networks in Civil Engineering: 1989–2000

The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.     

Enhancing Neural Network Traffic Incident-Detection Algorithms Using Wavelets

Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially.     

Machine learning methods to assist energy system optimization

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique( by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model( AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently,a hybrid optimization algorithm( HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model( trained using supervised learning technique) for different scenarios where solar energy potential,wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10%( with reasonable differences in the decision space variables). HOA can reach to Pareto solutions( similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning( SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions.Therefore,STML can be used along with the HOA,which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.     

高性能混凝土配合比设计中神经网络方法的应用

本文介绍了神经网络BP算法 ,并应用于实验室配制的高性能混凝土配合比中 ,结果证明该方法应用于高性能混凝土的配合比设计中是可行的。本文最后指出神经网络在高性能混凝土的配合比优化设计中 ,也将会有广阔的应用前景     

Predicting construction crew productivity by using Self Organizing Maps

A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.     

Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings

Abstract:   Recently, the authors presented a multiparadigm dynamic time-delay fuzzy wavelet neural network (WNN) model for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs. Compared with conventional neural networks, training of a dynamic neural network for system identification of large-scale structures is substantially more complicated and time consuming because both input and output of the network are not single valued but involve thousands of time steps. In this article, an adaptive Levenberg–Marquardt least-squares algorithm with a backtracking inexact linear search scheme is presented for training of the dynamic fuzzy WNN model. The approach avoids the second-order differentiation required in the Gauss–Newton algorithm and overcomes the numerical instabilities encountered in the steepest descent algorithm with improved learning convergence rate and high computational efficiency. The model is applied to two highrise moment-resisting building structures, taking into account their geometric nonlinearities. Validation results demonstrate that the new methodology provides an efficient and accurate tool for nonlinear system identification of high-rising buildings.     

BP神经网络在基坑工程变形预测中的应用

结合工程实例,针对基坑开挖过程的变形特点,应用BP神经网络和基于粒子群优化算法的BP神经网络对基坑支护结构的变形进行预测,并对两种方法预测结果进行比较分析。结果表明,基于粒子群优化算法的BP网络的泛化预测性能要优于BP网络,预测深基坑地下连续墙结构水平位移更有效。     

Variance‐based global sensitivity analysis for fuzzy random structural systems

There have been increasing advances in the sophisticated approaches like fuzzy randomness to handle different uncertainties in civil engineering; however, less attention has been paid to the formulation of a sensitivity analysis for fuzzy random structural systems. In this study, the main objective is to present the formulation of fuzzy Sobol sensitivity indices to quantify the influence of fuzzy random structural parameters. Meanwhile, uncertainty in derivation of limit states and acceptance criteria in collapse analysis is addressed briefly and treated using fuzzy model parameters. To show the application of the established sensitivity test, the collapse behavior of a steel moment frame subjected to sudden column removal is evaluated thoroughly. The proposed fuzzy sensitivity indices are determined for the problem and the overall influence of fuzzy acceptance criteria on the collapse assessment is shown using fragility analysis. The results show that the presented fuzzy sensitivity analysis can give detailed insight into the characteristics of fuzzy random systems, and the epistemic uncertainty in derivation of limit states can have significant effects on the reliability‐based collapse analysis. It is worth mentioning that to alleviate high computational demands in fuzzy probabilistic collapse analysis, a neural network metamodel is applied in conjunction with the genetic algorithm which is also of practical value to engineers and researchers.     

一种基于神经网络的电梯故障诊断方法

针对电梯控制系统的安全性问题,提出了一种基于GCAQBP学习算法的神经网络模型,并将该模型应用于电梯故障诊断系统中。使用该方法对电梯的几种典型故障进行诊断分析。结果表明,该方法具有较好的实用效果。     

基于MATLAB的桁架结构优化设计

介绍了基于BP神经网络的全局性结构近似分析方法 ,解决了结构优化设计问题中变量的非线性映射问题。在此基础上 ,利用改进的遗传算法 ,对桁架结构在满足应力约束条件下进行结构最轻优化设计。利用Matlab的神经网络工具箱 ,编程求解了三杆桁架优化问题。     

基于贝叶斯正则化BP神经网络的工程投标报价

研究了基于贝叶斯正则化的BP神经网络在市政工程投标报价中的应用,建立了报价预测评估模型,并对该模型的结构体系和算法作了分析,通过工程实例验证了模型的可靠性,进而提高报价的准确性。     

粒子群神经网络在大跨钢结构挠度监测中的应用

针对传统BP神经网络拓扑结构不确定、收敛效率低、容易陷入局部最优解的缺陷,引入粒子群(PSO)算法替代BP神经网络中基于误差函数梯度下降的学习训练规则,构建了粒子群神经网络(PSO-BP)模型,并以重庆某大型场馆安全监测项目为例,通过对比分析验证了粒子群神经网络模型在大跨钢结构挠度监测中的可行性以及与传统BP神经网络模型相比在效率和精度方面的优越性。     

BP神经网络方法在大坝基岩变形监测中的应用

结合工程实例,利用上游水位,下游水位和气温建立BP神经网络预报模型,应用Matlab神经网络工具箱,采用traingdx算法进行模型训练,对大坝基岩变形进行预测,结果表明,建立的BP神经网络预测大坝基岩变形的模拟值具有较高的精度。     

GA优化BP神经网络在多参量火灾探测中的应用

针对BP 神经网络的随机权重和阈值稳定性不高的问题,运用遗传算法(GA)对BP 神经网络的初始权重和阈值进行优化,提出了一种基于GA 优化BP 神经网络的多参量数据融合方法以实现火灾探测,提高火灾探测准确率和模型泛化性能,并利用该模型对标准明火和阴燃火中的温度、烟雾浓度和CO 浓度进行数据融合实现火灾探测。研究显示,相较单纯BP 神经网络,经GA 优化的BP 神经网络火灾探测算法能够更快速精确地实现火灾探测,探测精度有显著改善,火灾识别准确率提高至98.84%。