Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.     

优化神经网络在多传感器火灾探测中的应用

针对BP神经网络在拟合过程中探测精度低、容易陷入局部最优的问题,提出一种基于遗传算法（GA）和模拟退火算法（SA）共同改进的BP神经网络模型,该网络模型可以有效提高火灾识别准确率,同时避免网络过拟合现象,使预测结果跳出局部最优从而达到全局最优。首先,通过GA改进隐藏层结构部分,然后通过SA改进连接权重部分,最后利用优化后的GA-SA-BP模型对火灾实验数据进行信息融合实现火灾探测。实验研究表明,对比单一BP神经网络,经GA和SA改进后的BP神经网络能够有效改善网络拟合能力,并提升火灾探测精度至98.91%。     

改进遗传神经网络的大坝渗流监测模型

在对遗传算法的适应度函数改进并修改选择方法的基础上,用改进的遗传算法优化BP神经网络权值,提出一种改进遗传神经网络的大坝渗流监测模型。结合实例分析表明:预测模型合理,训练精度与检测性能得到提高。     

基于遗传优化神经网络的市政管网水质模型研究

将遗传算法的全局搜索能力和BP神经网络的局部学习能力有机结合,得到一种快速高效的建立供水管网余氯的水质模型的新方法。验证结果表明,遗传算法优化后的神经网络模型所需要考虑的参数较少,应用方便,预测精度和效率较高,在城市给水系统水质模拟预测研究中有一定的参考应用价值。     

基于遗传算法与神经网络的桥梁结构健康监测系统研究初探

在前人研究工作的基础上，根据遗传算法和神经网络在处理复杂非线性问题时的各自特点，分别将其用于桥梁结构健康监测系统的不同部分，提出了建立基于遗传算法与神经网络的桥梁结构健康监测系统的基本设想。由于该项研究工作刚刚起步，文中的有些部分已经经过试验验证，而有些部分则尚处于理论研究阶段，需通过试验进一步证实。     

基于均匀设计及遗传神经网络的大坝力学参数反分析方法

将均匀设计理论、BP神经网络和遗传算法三者结合起来,应用于大坝力学参数反分析中。首先对基本遗传算法进行改进,使得改进后的遗传算法具有很好的全局和局部寻优能力,将它作为BP神经网络的学习算法,形成遗传神经网络。然后利用均匀设计方法设计大坝坝体和坝基的材料力学参数样本,通过有限元正分析得到坝体的计算位移样本,训练遗传神经网络映射坝体计算位移值与材料力学参数之间的复杂非线性关系。最后将实测位移值输入训练好的遗传神经网络,即可得到各参数的反演值。本文以清江隔河岩水电站重力拱坝为例,反演分析了坝体混凝土的弹性模量、线膨胀系数以及坝基主要岩体的弹性模量等参数。经检验、评价与对比验证,结果表明该方法可以大大地缩短反分析时间,提高反分析效率和准确性。     

Construction equipment productivity estimation using artificial neural network model

Estimating equipment production rates is both an art and a science. An accurate prediction of the productivity of earthmoving equipment is critical for accurate construction planning and project control. Owing to the unique work requirements and changeable environment of each construction project, the influences of job and management factors on operation productivity are often very complex. Hence, construction productivity estimation, even for an operation with well‐known equipment and work methods, can be challenging. This study develops and compares two methods for estimating construction productivity of dozer operations (the transformed regression analysis, and a non‐linear analysis using neural network model). It is the hypothesis of this study that the proposed neural networks model may improve productivity estimation models because of the neural network's inherent ability to capture non‐linearity and the complexity of the changeable environment of each construction project. The comparison of results suggests that the non‐linear artificial neural network (ANN) has the potential to improve the equipment productivity estimation model.     

神经网络在渤海湾富营养化模型中的研究

以环境污染、富营养化严重的渤海湾为研究对象,建立了基于混沌遗传BP网络的渤海湾富营养化预测模型,模型建立与验证的数据采用渤海湾的监测资料,研究表明,模型能较好地预测富营养化。     

基于优化神经网络模型的隧道变形预测研究

神经网络预测技术一直是隧道学术界和工程界关注的关键课题.传统的神经网络对初始权值的依赖性很大,不同的初始值会导致差异很大的预测结果,并且初始值选择有很大的随机性和盲目性,往往导致网络振荡或不收敛.根据经典遗传算法的优势,对神经网络初始值进行优化,完成PB训练样本并建立非线性预测模型,避免了神经网络对初始权值的依赖性过大而引起计算误差.研究表明,GA-BP神经网络遗传算法适用于预测高速公路隧道断层破碎带围岩变形量,与现场试验数据相吻合,验证了GA-BP神经网络遗传算法工程应用的可行性,提高了预测的精度及避免人为误差.隧道洞周变形主要集中在开挖后12天,在此时间内应加强监控量测频率,以避免隧道过大变形引起坍方事故.     

Innovative piled raft foundations design using artificial neural network

Studying the piled raft behavior has been the subject of many types of research in the field of geotechnical engineering. Several studies have been conducted to understand the behavior of these types of foundations, which are often used for uniform loading on the raft and piles with the same length, while generally the transition load from the upper structure to the foundation is non-uniform and the choice of uniform length for piles in the above model will not be optimally economic and practical. The most common method in identifying the behavior of piled rafts is the use of theoretical relationships and software analyses. More precise identification of this type of foundation behavior can be very difficult due to several influential parameters and interaction of set behavior, and it will be done by doing time-consuming computer analyses or costly full-scale physical modeling. In the meantime, the technique of artificial neural networks can be used to achieve this goal with minimum time consumption, in which data from physical and numerical modeling can be used for network learning. One of the advantages of this method is the speed and simplicity of using it. In this paper, a model is presented based on multi-layer perceptron artificial neural network. In this model pile diameter, pile length, and pile spacing is considered as an input parameter that can be used to estimate maximum settlement, maximum differential settlement, and maximum raft moment. By this model, we can create an extensive domain of results for optimum system selection in the desired piled raft foundation. Results of neural network indicate its proper ability in identifying the piled raft behavior. The presented procedure provides an interesting solution and economically enhancing the design of the piled raft foundation system. This innovative design method reduces the time spent on software analyses.     

基于改进遗传算法的给水管网优化设计

针对基于简单遗传算法的给水管网优化设计在供水安全性和经济性方面存在的不足,提出了适于管网优化设计的改进遗传算法。改进遗传算法采用罚函数技术和自适应策略,有效地解决了简单遗传算法管网优化设计中水力性能较差的问题。以典型环状给水管网为例的计算结果表明,改进遗传算法设计方案的经济性和供水安全性都优于简单遗传算法设计方案,充分说明改进遗传算法用于给水管网优化设计具有实用性和有效性。     

遗传算法在桁架结构优化设计中的应用

本文探讨了遗传算法在桁架结构优化设计中的应用,通过对经典遗传算法进行改进,建立了桁架结构的优化设计模型,其中采用精华模型保证了上一代种群中最优个体进入到当前种群中进化,而且采用非均匀变异算子,使得进化前期变异明显,后期趋于稳定,以利于收敛到最优解附近。另外针对遗传算法容易收敛到局部最优点的缺陷,提出了通过改变各变量相应初始界限值,进行两次遗传算法操作的方法进行运算,提高了工作效率,且结果更为精确。最后通过十杆平面桁架的数值例题来验证遗传算法应用的可行性和有效性。     

造价估测方法的研究和BP神经网络模型的应用

比较分析了现行的造价估测模型的特点及其存在的问题,突出BP神经网络模型进行造价估测的理论优势,引入工程分类思想,以学校类建筑为例,建立了BP神经网络估测模型并进行了造价估测.     

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...     

BP-人工神经网络在给水管网调整方案中的应用研究

根据天津开发区给水管网的现状分析和开发区近期供水规划,讨论了现状管网输配能力与未来供水需求的差距,基于管网运行的经济性和安全性,利用BP-人工神经网络的优化方法,提出了管网调整规划方案,并将其与传统方法进行对比。调整方案模拟计算结果表明,神经网络更能够反映影响该地区管网运行状况的因素,其优化结果更适用于该地区管网。     

基于神经网络的结构优化设计

阐述了基于神经网络的结构优化优点，提出了基于神经网络的剪力墙结构中强连梁结构优化设计的模拟退火算法，用剪力墙结构中强连梁的ANSYS计算以及理论计算验证了所提方法的有效性与准确性，研究结果表明，该方法较之其他方法简单、有效。     

遗传算法BP神经网络在隧洞围岩分类中的应用

针对目前神经网络训练易陷入局部极小点问题，用遗传算法优化神经网络的连接权，并在遗传进化过程中采取保留最优个体的策略，建立了基于遗传算法的BP神经网络的模型，并应用于解决水工隧洞围岩分类这一非线性和不确定性较大的实际问题，证明了这种方法是科学可行的。     

一种基于神经网络的机动目标轨迹预测方法

提出了一种利用神经网络技术对机动目标的轨迹进行预测的方法,并给出了具体的实现算法和仿真实例.在BP神经网络和遗传算法的基础上作了改进,提出优劣基因重组的思想,克服了遗传算法中所存在的种群内过早收敛的缺点,极大地提高了预测算法的准确度.     

Optimal design of steel portal frames based on genetic algorithms

As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.     

基于ANN和SA的混凝土配合比优化设计

利用模拟退火方法探讨了混凝土配合比的优化设计，并用Visual C＋＋语言编制了实用系统，得出了只要数据充分，算法的精度可以得到保证的结论，以推广该设计方法的应用。