Surfer15.0在海绵城市下垫面地形可视化构建中的应用研究

针对海绵城市垫面地形可视化构建,用Surfer 15.0软件对比分析了普通克里金插值法中线性函数、高斯函数、球面函数和指数函数格网化有效性及其内插高程对海绵城市建设可视化的影响。结果表明:普通克里金插值法中线性函数内插精确度最高,插值与实际地形最为契合。通过该方法插值后,可以对下垫面地形进行面积计算和坡度、坡向及曲率分析并实现3D可视化地形构建。     

基于GIS的露天矿边坡位移敏感性分析

针对安太堡露天矿西北帮边坡远程监测区域,基于GIS空间分析模块,应用克里金插值法对边坡位移实测数据进行空间插值,对边坡位移影响因子进行栅格数据转化,统计分析各因子的敏感性.结果表明:边坡坡度、坡高对边坡位移的影响较大,随着坡度、高程的增大,边坡位移也会相应地增大;井工开采会导致坡体的变形,对边坡位移的发展起着重大的影响.分析结果与工程实际较为吻合,证实了该研究方法的科学性与合理性,能够用于指导边坡的防护与治理.     

BIM在上海地区岩土工程勘察中的应用

近年来,建筑信息模型(Building Information Modeling,以下简称“BIM”)技术逐渐成为建筑三维建模和可视化主要的信息技术,如何将其应用到勘察工程,实现勘察成果可视化,是当前岩土工程研究的热点。为了将BIM技术应用到实际勘察工程,结合上海地区岩土工程勘察实例,利用克里金插值法对采集数据进行插值拟合,然后将插值数据和原始数据进行整合。同时,对Revit子程序进行二次开发,基于Dynamo编写了一段生成三维地质体模型的程序,将整合后的数据导入程序,可生成符合真实地层情况的三维地质模型。该模型还将土层的非几何信息与模型挂接,为成果使用人员提供了土层走向和直观的土层物理力学性质展示。     

RBF插值参数对黄土高原古地形DEM模拟的影响

针对不同数据源来进行黄土高原古地形恢复重建对其土壤侵蚀研究有重要意义。本文基于ASTER GDEM数据、1∶450万黄河流域黄土厚度分布图等综合提取出黄土高原古地形高程点数据,采用径向基函数(Radial Basis Functions,RBF)进行空间插值,用交叉验证法和验证数据集方法评估插值精度。结果表明:(1)RBF插值过程中张力样条函数、0.01光滑因子、四方向搜索、搜索点数为24,为"最优"插值参数;(2)通过高程预测值与原始高程值的误差分析,绝对值不大于20m的误差点占总误差的95.18%,误差较大的点所占比例非常小。本文选择"最优"插值参数插值后预测高程值较接近原始高程值,说明在RBF插值中用此参数设定进行DEM构建能较好地模拟复原黄土高原古地形,可以为研究区现今水土保持提供科学的参考依据。     

高速公路桥梁永久桥面高程观测点优化布设

基于区域梯度理论,利用三次样条插值法,使插值拟合误差差和最小来配置桥面永久高程观测点,得到了优化的测点布置数量和位置,并在实桥上得以运用,结果实用可靠,为以后实际桥梁布设高程观测点提供参考依据。     

基于优化克里金插值方法的隧道主结构面建模

在围岩地质条件复杂的隧道施工过程中,对隧道开挖面前方地质体的描述是安全设计施工的关键。隧道主结构面的展布形态是围岩物理力学性质以及围岩稳定性的控制性因素之一。采用几何概率方法对隧道主结构面在三维空间内的产状和尺寸进行估计,并确定地质钻孔相关孔位参数,以准确获取用于插值的地质数据库。利用蚁群优化算法在离散数据中快速找到“巢穴”到“食物”之间最优路线的独特优势,将其应用于隧道主结构面建模中克里金插值方法的变异函数模型选择和参数优化,以克服其主观性和不确定性。通过结构面方差模型和结构面粗糙度系数的计算分析,验证优化克里金插值方法在隧道主结构面建模方面的有效性,实现较高的插值精度,可以应用于后续的三维复杂地质建模。     

改进粒子群优化BP神经网络的轮毂分类算法

针对轮毂识别系统前期图像特征提取误差较大时分类准确性降低的问题,提出了基于改进粒子群算法优化BP神经网络的轮毂识别模型。在标准粒子群中引入遗传算法的变异因子、惯性权重、时间因子、速度边界限制和反弹策略,以改进粒子群算法,从而提高寻找最优阈值与权值的性能。经过与不同算法的对比数据看出,采用改进粒子群优化BP神经网络算法的分类识别率比其他算法提高了9%左右,且收敛速度、收敛精度均有提高,证明了所提IPSO(improved particle swarm optimization)算法的有效性。     

一种差速器壳体形位误差评定及测量方法

为了满足差速器壳体的快速检测,对差速器壳体形位误差的在线测量方法进行了研究。在基本粒子群优化算法的基础上,引入了自适应惯性因数来改进粒子群优化的过程,并用于形位误差的计算。运用最小二乘法和最小区域法描述了圆柱度、同轴度等形位误差的评定算法。利用改进粒子群优化算法对误差目标函数进行求解。根据实际工程需要确定测量方案,搭建了测量平台。研究结果表明:该方法能够准确测得差速器壳体形位误差,并且减少了人为因素引入的误差。     

基于GIS的地形要素插值验证及三维建模研究

针对不同空间插值计算方法对于三维可视化建模精度差异问题,为进一步提高三维可视化效果,以重庆市某工程场区的高程数据作为样本点,基于普通克里格法、张力样条函数法、反距离加权法,根据地统计学理论与工程实际相结合,分别对高程数据进行空间插值.同时,预留部分样本点要素作为验证点进行插值精度对比,分析其插值结果及差异原因进而完成三维可视化建模与分析工作.结果表明:在本工程实例中,张力样条函数法在地表高程上插值精度最高、误差最小,其次是普通克里格法、反距离加权法.依据上述分析结果得出的最优插值方法,应用GIS(Geographic Information System)技术,在地形数据不充分的条件下完成了场区三维模型的构建,三维模型较好地反映了地表起伏和资源分布状态.     

用粒子群算法选择体育馆监测系统中传感器的最优布点

如何合理、有效地确定传感器的位置,是结构健康监测系统正常、高效运行的前提.基于模态能量和粒子群算法进行传感器优化布置研究.首先,讨论了粒子群算法的基本原理和优点;其次,根据结构模态应变能的大小挑选出结构的主要贡献模态,进而确定监测模态阶数;然后,针对不同的传感器类型,基于位移模态定义适用于加速度传感器的适应度函数,基于应变模态定义适用于应变传感器的适应度函数;最后,以西宁市体育馆钢结构为工程背景,采用粒子群算法对其监测系统中加速度传感器和应变传感器分别进行优化布置.结果表明,该方法优化结果稳定可靠且收敛迅速.     

地下工程并行优化反演分析及算例验证

 通过重构非线性惯性权重函数和引入“加速因子”,对传统粒子群优化算法的收敛性进行改进。同时基于消息传递平台对算法进行主从式并行改进,编程实现基于普通计算机机群系统分布式存储并行模式的大型地下工程并行优化反演分析。算例分析表明,改进的粒子群优化算法其收敛性能得到显著改善,并行改进策略可显著加快反演速度和提高计算效率。探讨围岩松动损伤劣化、监测数据可靠性、并行粒度和负载均衡等并行优化反演分析中所面临的主要问题及其对计算精度和效率的影响,并提出有效解决方案,为大型地下工程的参数反演和动态优化设计提供一种新思路。     

Particle swarm optimization model to predict scour depth around a bridge pier

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.     

基于v-SVR和MVPSO算法的边坡位移反分析方法及其应用

 针对传统粒子群算法存在搜索空间有限、容易陷入局部最优点的缺陷,通过引入迁徙算子和自适应变异算子,提出基于粒子迁徙和变异的粒子群优化(MVPSO)算法。基准测试函数结果表明,改进的MVPSO算法较传统的粒子群优化算法在收敛效率上有大幅度提高,在处理非线性、多峰值的复杂优化问题中能快速地搜索,得到全局最优解。应用改进的MVPSO算法搜索最佳的支持向量机(v-SVR)模型参数,建立岩体力学参数与岩体位移之间的非线性支持向量机模型,提高v-SVR的预测精度和推广泛化性。然后,利用v-SVR模型的外推预测替代耗时的FLAC正向计算,利用改进的MVPSO算法搜索岩体力学参数的最优组合,提出v-SVR和MVPSO相结合的边坡位移反分析方法(v-SVR-MVPSO算法),与传统的BP-GA算法和v-SVR-GA算法相比,该算法在反演精度和反演效率上均有较大幅度提高。最后,将本文发展的v-SVR-MVPSO算法应用到大岗山水电站右岸边坡岩体参数反演分析,并对边坡后续开挖位移和稳定性进行预测,取得较好的效果。     

Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine

The phenomenon of soil liquefaction is one of the most complex and interesting fields in geotechnical earthquakes that has drawn the attention of many researchers in recent years. The present study used hybrid particle swarm optimization and genetic algorithms with a fuzzy support vector machine (FSVM) as the classifier for the soil liquefaction prediction problem. Fuzzy logic is used to decrease the outlier sensitivity of the system by inferring the importance of each sample in the training phase to increase the ability of the classifier’s generalization. Using the appropriate combination of optimization algorithms, we can find the best parameters for the classifier during the training phase without the need for trial and error by the user due to the high accuracy of the classifier. The proposed approach was tested on 109 CPT-based field data from five major earthquakes between 1964 and 1983 recorded in Japan, China, the USA and Romania. Good results have been demonstrated for the proposed algorithm. Classification accuracy is 100% for the combination of the optimization algorithms with the FSVM classifier. The results show that the best kernel used in the training of the FSVM classifier is a radial basis function (RBF). According to the experimental results, the proposed algorithm can improve classification accuracy and that it is a feasible method for predicting soil liquefaction using the optimal parameters of the classifier with no user interface.

     

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating the standard Harris hawks optimization (HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm (MOA) used to solve continuous search problems. Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine (ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization, genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.     

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.     

基于粒子群算法的工程项目多目标优化问题研究

为了更有效地解决工程项目的工期—成本—质量优化问题,从施工单位的角度出发,基于工期、质量、成本间的对立统一关系,以双代号网络图中每项工作的持续时间为自变量,建立工期—成本—质量优化模型,采用标准粒子群算法来优化求解。为了消除量纲对评价标准的影响,对 3 个目标适应值采取了标准化的处理方法。利用 Matlab 软件,对一个工程案例进行多目标优化,通过与蒙特卡罗方法进行对比,分析了粒子群算法的计算效率,优化结果验证了粒子群算法求解工程项目多目标优化模型的可行性和适用性。     

混合粒子群算法搜索土坡危险滑动面

基本粒子群优化算法存在着对惯性因子敏感、计算量大等缺点,通过借鉴和声搜索算法产生新解的策略和不连续飞行假定,构成了混合粒子群算法。首先,当粒子飞行超越边界时,采用和声搜索算法产生新解;此外还引入了不连续飞行假定,即在每次迭代步中,随机选择一些个体更新速度、位置向量,以利于减少计算量。随机给定10组参数,分别利用基本粒子群优化算法和混合粒子群优化算法对某复杂土坡的最危险滑动面进行了搜索。比较发现,混合粒子群算法能在较短的计算时间内得到更好的结果。     

堆积碎石土斜坡浅表入渗的空间分布与变异性研究

为探究强降雨下堆积体斜坡入渗参数的空间分布与空间变异性,通过筛分试验及双环入渗试验分别对堆积体斜坡试验点不同粒径区间碎石质量分数、碎石土渗透系数、饱和渗透系数、不同深度碎石土含水率进行测量,对数据进行普通Kriging插值统计分析,运用交叉检验选定合理理论模型与参数,获得其空间分布与变异性特征。研究结果表明:测试区碎石土入渗符合Kostiakov模型;不同粒径区间碎石质量分数采用高斯半变异函数理论模型空间插值精度最高,碎石土渗透系数和不同深度碎石土含水率采用球状半变异函数理论模型空间插值精度最高;粒径大于10 mm碎石主要分布于坡顶与坡腰区域且使该区域碎石土渗透系数的空间变异性相对较大,细小碎石(粒径2~10 mm)则聚积在坡脚位置,碎石土渗透系数速率沿坡面由上至下呈逐渐减少的空间分布趋势;浅土层(0~15 cm)坡腰含水率比坡顶、坡脚要高,而深土层(15~25 cm)坡脚含水率比坡顶、坡腰要高,且随深度增加缓慢上升。研究结果可作为碎石土滑坡降雨致滑的理论参考依据。     

岩土工程参数反演的一种新方法

介绍了新近提出的一种模拟进化算法——粒子群算法,相对于其他进化算法,粒子群算法的优势在于简单、易实现且收敛快。把该算法引入岩土工程参数反演领域,同时为了克服粒子群算法后期搜索效率降低的缺陷,把单纯形法嵌入到粒子群算法中,提出岩土工程参数反演的一种新方法——基于粒子群算法和单纯形法的混合算法。算例表明,混合算法在参数反演计算中体现出良好的优化性能和很快的收敛速度,是一种新颖可行的参数反演方法。