Machine Learning in Engineering Analysis and Design: An Integrated Fuzzy Neural Network Learning Model

Applying neural network computing to structural engineering problems has received increasing interest, with particular emphasis placed on a supervised neural network with the backpropagation (BP) learning algorithm. In this article, we present an integrated fuzzy neural network (IFN) learning model by integrating a newly developed unsupervised fuzzy neural network (UFN) reasoning model with a supervised learning model in structural engineering. The UFN reasoning model is developed on the basis of a single-layer laterally connected neural network with an unsupervised competing algorithm. The IFN learning model is compared with the BP learning algorithm as well as with a counterpropagation learning algorithm (CPN) using two engineering analysis and design examples from the recent literature. This comparison indicates not only a superior learning performance in solved instances but also a substantial decrease in computational time for the IFN learning model. In addition, the IFN learning model is applied to a complicated engineering design problem involving steel structures. The IFN learning model also demonstrates superior learning performance in a complicated structural design problem with a reasonable computational time.     

StructNet: A Neural Network for Structural System Selection

Abstract: This paper describes StructNet, a computer application developed to select the most effective structural member materials given a building project's attributes. The system analyzes 15 parameters of a building project (e.g., available site space, budget, height) and determines the most appropriate structural system for the beam, column, and slab structural members. This paper first describes the process for selecting a structural system for a building. It was very important to understand this process before determining the best type and structure for the computer application. Then a comparison between a neural network approach and a rule-based expert-system approach for this application is presented. A discussion of the reasons for selecting a neural network approach is given. The StructNet application is described in detail, including the testing of the network. Along with the testing of the network is a discussion of how varying the learning rate and error limit affect the performance of the neural network application. The testing of the network shows that the program can reasonably select the same structural system types as the expert used to collect the training project data. Since the system will be used only as a preliminary tool to limit the number of possible structural systems for a project, the accuracy of the system is acceptable. However, additional experimentation needs to be conducted to determine the accuracy and practical use of this application. The final sections of the paper discuss the lack of adequate testing procedures for neural networks used in applications for unstructured or ill-defined decision making. The use of these types of networks and their relevance to the civil engineering computer field are also discussed.     

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.     

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.     

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

The application of neural networks to rock engineering systems (RES)

This paper proposes a new approach for applying neural networks in Rock Engineering Systems (RES) based on the learning abilities of neural networks. By considering the analysis of the coding methods for the interaction matrix in RES and the learning processes of neural networks such as the Back Propagation (BP) method, neural networks can provide a useful mapping from system inputs to system outputs for rock engineering, so that the influence of inputs on outputs can be obtained. Then the results of the neural network analysis can be presented in a similar way to the global interaction matrix used in RES to present the fully-coupled system results. The neural network procedures are explained first, with illustrative demonstrations for simultaneous equations. Then, the link with the RES type of analysis is explained, together with some demonstration examples for rock engineering data sets. The specific analysis procedure is presented and then wider rock engineering examples are given relating to the characteristics of rock masses and engineering parameters. The main presentation tools used in this neural network approach are the Relative Strength Effect (RSE) and the Global Relative Strength Effect (GRSE) matrix. There is discussion of the value of this approach and an indication of the likely areas of future development.     

Neural Networks as Inference and Learning Engines

Abstract: This paper presents an abridgment of a neural network constructive methodology and applications with real data. The neural network can be considered as the learning core and inference engine of an expert system that produces either different network designs or simulations as output, its input being data sequences. Basically, it consists of additive structural learning, limiting it by a cross-validation technique.
Considerations about uncertainty treatment in neural networks are also presented, including uncertainty in data, in neuron activation, in outputs, and combination of several uncertainty sources.
Applications include three different sets of data, all of them related to the energy field. First, river streamflow estimation is discussed. Then CO₂ concentration prediction from gas injection rate is studied. Finally, the program learns to imitate a feedwater control system in a nuclear reactor. All tests show good results, as can be seen when compared with other standard methods.     

Recurrent Neural Networks for Structural Optimization

This paper presents an improvement for an artificial neural network paradigm that has shown significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxation-type recurrent neural networks. The suggested improvement enhances the convergence performance and involves a technique that sets the values of weight parameters of the recurrent neural network algorithm. The complete procedure of solving an optimization problem with a single-layer, relaxation-type recurrent neural network is introduced. The discrete Hopfield network is employed to solve the weighted matching problem. A set of simulation experiments is performed to analyze the performance of the discrete Hopfield network. Simulation results confirm that the discrete Hopfield network locates a locally optimal solution after each relaxation once the weight parameters are specified as defined in the suggested technique.     

应用人工神经网络技术评估混凝土中的钢筋锈蚀量

将人工神经网络技术应用于锈蚀开裂后混凝土中钢筋锈蚀量的评估,在分析锈蚀开裂后影响钢筋锈蚀量的主要因素基础上,建立了评估钢筋锈蚀量的人工神经网络模型,并从网络结构优化和学习参数的角度探讨了神经网络模型的适应性。最后通过实际工程检测结果验证了该方法的实际可行性。     

神经网络在土木工程领域的应用

分析了神经网络在结构工程、建筑管理等土木工程领域的应用研究现状及其发展方向 ,以一个建筑管理领域的实际问题为例提出了将神经网络用于解决土木工程领域问题的一般方法和步骤     

人工神经网络在结构工程中的应用和发展

依据神经网络原理及其自身的特点，对其应用在结构优化设计、结构分析及可靠度分析等方面进行了综述和研究，并在此基础上分析了神经网络在结构工程中的研究方向。     

Grouping Pavement Condition Variables for Performance Modeling Using Self-Organizing Maps

Different modeling techniques have been employed for the evaluation of pavement performance, determination of structural capacity, and performance predictions. The evaluation of performance involves the functional analysis of pavements based on the history of the riding quality. The riding comfort and pavement performance can be conveniently defined in terms of roughness and pavement distresses. Thus different models have been developed relating roughness with distresses to predict pavement performance. These models are too complex and require parsimonious equations involving fewer variables. Artificial neural networks have been used successfully in the development of performance-prediction models. This article demonstrates the use of an artificial intelligence neural networks self-organizing maps for the grouping of pavement condition variables in developing pavement performance models to evaluate pavement conditions on the basis of pavement distresses.     

Structural Engineering Applications with Augmented Neural Networks

This paper presents an augmented neural network (ANN), a novel neural network architecture, and examines its efficiency and accuracy for structural engineering applications. The proposed architecture is that of a standard backpropagation neural network with augmented neurons, that is, logarithm neurons and exponent neurons are added to the network's input and output layers. The principles of augmented neural networks are (1) the augmented neurons are highly sensitive in the boundary domain, thereby facilitating construction of accurate mapping in the model's boundary domain, and (2) the network denotes each input variable with multiple input neurons, thus allowing a highly interactive function on hidden neurons to be easily formed. Therefore, the hidden neurons can more easily construct an accurate network output for a highly interactive mapping model. Experimental results demonstrate that the network's logarithm and exponent neurons provide a markedly enhanced network architecture capable of improving the network's performance for structural engineering applications.     

Effect of Representation on the Performance of Neural Networks in Structural Engineering Applications

Abstract: The pattern-mapping, pattern-classification, and optimization capabilities of neural networks have been used to solve a number of structural analysis and design problems. Most applications exploit the pattern-mapping capability and are based on the back-propagation paradigm for neural networks. There are a number of factors that influence the performance of these networks. This paper initially discusses these factors and the domain-dependent and -independent techniques presently available for improving performance. The paper then considers the effect of representation, selected for the input/output pattern pairs, on the performance of these networks and demonstrates that representations based on dimensionless terms, derived from dimensional analysis, lead to improved performance. It is shown that dimensional analysis provides a representational framework, with reduced dimensionality and embedded domain knowledge, within which effective learning can take place and that this representational change can be used to enhance the domain-independent and -dependent techniques presently available for improving performance of these networks.     

神经网络在结构工程健康监测中的应用探讨

建立结构损伤诊断子系统是建立大型工程结构智能健康监测专家系统的核心问题。人工神经网络技术可以实现结构损伤的自动识别与定位,具有广阔的应用前景。本文介绍基于人工神经网络的两级损伤识别策略,并对采用人工神经网络进行结构损伤诊断的网络输入参数与网络结构选择等关键问题进行了探讨。     

人工神经元网络在动测桩基承载力中的应用

本文将人工智能领域的分支人工神经元网络方法应用在动测桩领域中,使得大量专家的经验和动测桩基承载力技术有机地结合在一起,在确定桩基承载力时发挥特有的作用。     

Structural optimization for performance-based design in earthquake engineering: Applications of neural networks

This work addresses an approach to performance-based design in the context of earthquake engineering. The objective is the optimization of the total structural cost, under constraints related to minimum target reliabilities specified for the different limit states or performance requirements. The problem involves (1) the use of a nonlinear, time-stepping dynamic analysis to investigate the responses of relevance to the performances’ evaluation and (2) the integration of the responses into measures of damage accumulated during the earthquake. The random responses are deterministically obtained for different combinations of the design parameters and the intervening random variables, of which some are associated with the structure and some with the earthquake characteristics. The approach uses a neural network representation of the responses and, for each one, the variability associated with different earthquake records is accommodated by developing two networks: one for the mean response over the records, and another for the corresponding standard deviation. The neural network representation facilitates the estimation of reliability by Monte Carlo simulation, and the reliability achieved in each performance level, for a specific combination of the design parameters, is itself represented with a neural network. This is then used within an optimization algorithm for minimum total cost under reliability constraints. An application example uses a reinforced concrete, multi-storey plane structure with seismic demands corresponding to the city of Mendoza, Argentina.     

Representing Design Knowledge with Neural Networks

Neural networks have been used in a number of civil engineering applications because of their ability to implicitly learn an input–output relationship. Typically, the applications involve deriving an input–output relationship for problems that may be too complex to model mathematically, computationally expensive, or difficult to solve using the traditional procedural computing approach. Heuristic design knowledge used by structural engineers when performing structural design often falls in the latter category of being difficult to represent procedurally. Neural networks have been investigated for the representation of heuristic design knowledge, and the results of this investigation and the lessons learned regarding neural network training are presented.     

基于神经网络的高层建筑结构体系选择

高层建筑结构初步设计中很重要的一项就是要确定结构体系,结构体系的选择是受许多因素影响的。本文提出了一个基于神经网络的专家系统来帮助设计人员选择恰当的结构体系,它利用了人工智能领域中的神经网络来存贮专家的设计经验,结果表明此方法是很有效的。     

Recurrent Neural Networks for Uncertain Time‐Dependent Structural Behavior

Abstract: In this article, an approach is introduced which permits the numerical prediction of future structural responses in dependency of uncertain load processes and environmental influences. The approach is based on recurrent neural networks trained by time‐dependent measurement results. Thereby, the uncertainty of the measurement results is modeled as fuzzy processes which are considered within the recurrent neural network approach. An efficient solution for network training and prediction is developed utilizing α‐cuts and interval arithmetic. The capability of the approach is demonstrated by means of the prediction of the long‐term structural behavior of a reinforced concrete plate strengthened by a textile reinforced concrete layer.