A Multiply Connected Belief Network approach for schedule risk analysis of metropolitan construction projects

Metropolitan construction projects may be distinguished from other types of projects due to their specific nature. Issues with regard to purchase of property obstacles, a lack of accurate layout drawings for some underground facilities and, therefore, following probable changes in designs make it vital to have specific studies on risk analysis of such engineering projects. However, conventional methods offered in literature of risk analysis fail to consider the interactional mechanisms of risks and their simultaneous occurrence. This paper exploits the Multiply Connected Belief Network approach in MATLAB environment to analyse the simultaneous occurrence of risk factors on schedule delay of a highway project. The method of inference is through Junction Tree Algorithm due to existence of multiply connections in the developed network of the project. The findings for schedule delay were tested in a different space using Monte Carlo simulation and had satisfactory results.     

计及需求响应的Elman-NN短期负荷预测模型研究

通过频谱分析研究了需求响应负荷的基本特性,并以此为依据建立了计及需求响应的Elman神经网络(Elman-NN)预测模型。Elman-NN具有处理动态信息能力强、训练时间短、全局寻优性强的优点。通过实际算例,对比在Elman-NN模型中计及需求响应因素前后的预测性能,结果显示计及需求响应因素可显著提高Elman-NN模型预测精度。本文证实了在模型中计及需求响应因素的重要作用,为需求响应负荷的预测研究奠定了必要的理论基础。     

A neural network based system for predicting earthmoving production

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN earth. To engender confidence in the results of neural computation, a validation function is implemented in NN earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN earth to provide default information, such as internal cost rate, fuel cost, and operator's cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN earth are illustrated in a sample application. In practice, NN earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.     

Predicting chaotic time series of PM10 concentration using artificial neural network

Respirable particulate matter (PM10) concentration at one residential site in Delhi, India was predicted using the neural network approach. The concepts of chaotic systems theory were utilized to build the neural network model. The embedding dimension was estimated to provide the inputs to the neural network. The model evaluation results indicated the importance of noise reduction before selecting the embedding dimension of the time series. The selection of a proper embedding dimension is considered to be essential for obtaining reliable predictions. The model’s performance shows the capability of neural networks in modelling the chaotic time series.     

基于神经网络的结构损伤识别研究进展

为提高结构损伤识别方法的精确性和适用性,将神经网络引入到结构损伤识别中。介绍了神经网络的由来、原理和研究意义,概述了国内外基于神经网络的结构损伤识别研究进展。通过分析可以看,出用于结构损伤识别的神经网络方法有着广阔的应用前景。论文针对进一步研究的方向提出了建议。     

面向站立姿态的操纵舒适性评价模型研究

良好的操纵舒适性不仅减少工作中的疲劳,而且提高工作效率.针对站立姿态下操纵舒适性评价的不确定性和模糊性,构建基于T-S模糊神经网络 (Takagi-Sugeno Fuzzy Neural Network, T-S FNN)的站立姿态操纵舒适性评价模型.通过实验,收集模型的训练和测试数据.选取20名被试者参与本次实验,实验要求每个被试者完成100项目操纵任务,共有4个面板位置,每个面板上有25个圆形贴纸,代表不同操纵位置.在实验过程中分别记录被试者的关节角度、脚底压力、人体尺寸、操纵目标位置及主观舒适性数据.选取90%的实验数据对模型进行训练,10%的实验数据对所提出的方法进行验证,并与BP神经网络模型预测的主观舒适性进行比较,结果表明:T-S FNN模型具有较小的均方根误差（1.2 VS 4.5）.最后随机选取15组不同操纵任务进行检验,结果表明:该方法的预测值和实际值相关性系数为0.962（P<0.01）,与快速上肢评估（RULA）计算结果的相关性系数为0.833（P<0.01）,与工作体位分析系统（OWAS）计算结果的相关性系数为0.694（P<0.01）,说明该方法能够良好的反应真实结果.     

遗传算法在故障诊断中的应用

通过遗传算法的全局寻优能力优化BP神经网络的初始参数,使其收敛速度快、并能收敛至全局最小点,再利用BP网络来进行精确求解;实验表明:将二者有机结合后能对各种故障进行可靠的分类,有效地提高了故障诊断的效率和准确度。     

非战争军事行动装备保障资源运输方式选择模型

装备保障资源运输是非战争军事行动装备保障的重要环节,采用神经网络法选择保障资源运输方式,用遗传算法优化保障资源运输路径,并结合实际的交通网络图给出了运输方式选择的案例分析,表明该模型的可行性。     

基于神经网络算法的复合材料板声发射源定位

使用Levenberg-Marquardt训练函数的神经网络算法在复合材料板上进行声发射定位,采用声波到达时间作为输入向量,训练得到了到达各个传感器的声波触发时间与板上坐标的良好的映射关系。考虑到波形衰减,用传统的固定门槛提取的到达时间容易与实际情况偏差较大,讨论了一种相对准确的适合工程使用的到达时间提取方法。     

用神经网络系统原理选择采矿方法

根据矿山具体条件，科学地、合理地选择采矿方法，是获得较好经济效益的关键。用传统的手段选择采矿方法存在一定的主观随意性，而用本文所述神经网络系统对所选采矿方法进行技术经济分析，然后判断该采矿方法的合理性，从而可找到符合矿山实情的采矿方法。