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.     

神经网络在民用建筑设计工程中的应用

经过多年的发展,人工神经网络在传感器信息处理、信号处理、自动控制、知识处理、运输与通信等领域都取得了较大的发展,但是在建筑设计工程方面却涉猎较少.本文简述神经网络的发展和基本原理(包括模糊联想记忆),重点研究分析神经网络(FAM网)在民用建筑设计工程中的应用.在此基础上,提出了建筑设计领域应用神经网络尚需进一步研究的问题.     

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.     

Comparison of Neural Networks and Gravity Models in Trip Distribution

Abstract:  Transportation engineers are commonly faced with the question of how to extract information from expensive and scarce field data. Modeling the distribution of trips between zones is complex and dependent on the quality and availability of field data. This research explores the performance of neural networks in trip distribution modeling and compares the results with commonly used doubly constrained gravity models. The approach differs from other research in several respects; the study is based on both synthetic data, varying in complexity, as well as real-world data. Furthermore, neural networks and gravity models are calibrated using different percentages of hold out data. Extensive statistical analyses are conducted to obtain necessary sample sizes for significant results. The results show that neural networks outperform gravity models when data are scarce in both synthesized as well as real-world cases. Sample size for statistically significant results is forty times lower for neural networks.     

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.     

人工神经网络在强夯工程中的运用分析与讨论

鉴于当前人工神经网络在岩土工程中的应用越来越广泛的情况下 ,本文分析和比较了多种人工神经网络模型对强夯问题的适用性和可靠性 ,并提出了几个人工神经网络在应用过程中应注意的问题 ,使之能够更好地指导强夯工程实践     

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.     

基于人工神经网络市政工程安全评价模型的研究

引对传统的市政工程安全评价方法存在的局限性，把市政工程看作一个复杂的人机，环境系统，将人工神经网络基本理论引入市政工程的安全评价中，建立较全面的市政工程安全评价指标体系，构建基于人工神经网络的非线性安全评价模型，并验证评价模型的可靠性。     

人工神经网络在区域短期大气环境贡量预测预警中的应用

通过运用人工神经网络的多层神经网络对某区大气污染物SO2浓度的实测值进行训练学习,建立模型,再用此模型对SO2浓度进行预测和预报,以达到对大气环境质量进行预测预警的作用。应用实例表明：人工神经网络应用于大气环境质量预测预警是比较理想的。     

管幕工程地表变形的人工神经网络预测研究

管幕工法是一种新型的地下空间暗挖技术.国内首次应用于上海市中环线浦西段的北虹路下立交工程.该工法为解决软土地区超大断面地下工程的施工变形问题开创了新的领域.管幕工法虽然可以减少对周边环境的影响,但并不能完全消除.作者在充分研究钢管幕顶进施工过程中引起的地表变形特征基础上,建立相应的预测模型,应用人工神经网络智能滚动预测方法,对管幕工程的地表变形进行预测研究.研究表明：人工神经网络的一步滚动预测可以满足实际的工程需要,但精度相对偏低.而多步滚动预测虽可以得到较高的预测精度,但在实际工程应用中还需解决量的优化问题.     

Tide Prediction Using Neural Networks

Prediction of tides at a subordinate station located in the interior of an estuary or a bay is normally done by applying an empirical correction factor to observations at some standard or reference station. This paper presents an objective way to do so with the help of the neural network technique. In complex field conditions this approach may look more attractive to apply. Prediction of high water and low water levels as well as that of continuous tidal curves is made at three different locations. The networks involved are trained using alternative training algorithms. Testing of the networks indicated satisfactory reproduction of actual observations. This was further confirmed by a high value of the accompanying correlation coefficient. Such a correlation was better than the one obtained through use of the statistical linear regression model. The training algorithm of cascade correlation involved the lowest training time and hence is found to be more suitable for adaptive training purpose.     

Environmental River Engineering

In the past, river engineering works have often caused channel instability and adversely affected the river's conservation and amenity value. Recent guidelines have advocated a more natural approach to river engineering practice which retains habitat diversity within the river system. While a more natural approach is desirable, geomorphological guidance is required to ensure that the advocated changes are feasible and sustainable, both in the long and short term. The key requirement for sound environmental river engineering is a basic understanding of the natural processes controlling channel shape and dimensions. Examples are given in the paper to illustrate how such knowledge can be used to (a) stabilize rivers, (b) design environmentally-acceptable and stable flood-alleviation schemes, and (c) restore previously canalized rivers. The basis of the geomorphological input in the assessment and design process is a river survey which determines the factors controlling channel characteristics and how it will respond to planned changes.     

工程质量控制的定性与定量分析

结合工程实践,针对工程质量控制,探讨有关定性分析与定量分析相结合的分析方法.     

刍议环境适应性与环境工程

随着国家对环境问题重视程度的不断提高,相关学者对其也展开了深入的研究。本文首先对环境的性质进行了介绍,并根据环境预防与治理措施,对三者之间的关系进行了深入探讨,以此为今后我国环境保护工作提供一定的参考依据。     

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.     

神经网络在结构优化设计中的应用

人工种经网络因其具有一些显著的特征而被广泛应用于许多领域，本文就人工种经网络在结构优化中的应用作了一些探讨，并编制了工程结构优化的神经网络计算程度，最后，对一实例进行了计算分析。     

Mathematical Models and Engineering Design

The role of mathematical models in engineering design is no longer that of simply automating techniques which were previously carried out manually. Throughout industry models are now becoming accepted as one of the main decision support systems to managers. This is certainly the case in engineering design for managing the environment. We are rapidly moving into the age of expert systems and hydro-informatics, where the primary aim of most models is decision support. In this paper the role of the models in modern practice is reviewed and illustrated by case histories.     

Neural Networks for Wave Height Interpolation

Information on the heights of ocean waves at a site can be collected by a variety of instruments—each involving different methods of data retrieval and synthesis. Typically, sensing of wave heights by satellite requires the wave information to be presented in the form of values that are averaged over space and time intervals. In order to use such data for operational applications, it then becomes necessary to derive the wave heights over shorter intervals from their values available over long durations. This paper attempts to do this by employing the technique of neural networks. A simple three–layered feedforward network trained with the supervised backpropagation technique was used. The values of monthly mean significant wave heights available at different grid locations around the Indian coastline were given as input to obtain the output of weekly mean wave heights at the same locations. Analysis of the results indicated usefulness of the neural network technique in wave height interpolation problems.     

Real-Time Flood Forecasting Using Neural Networks

Real-time forecasting of stream flows during storms provides an essential input to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of the underlying phenomena. The technique of neural networks therefore was applied to model it. Forecasting of flood values during storms with a lead time of one and more hours was made using a selected sequence of past flood values observed at a specific location. Training of the network was done with the help of three alternative methods, viz., error backpropagation, conjugate gradient, and cascade correlation. Resulting flood forecasts were found to be satisfactory—especially when warning time was the least.     

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.