PSO-based stability analysis of unreinforced and reinforced soil slopes using non-circular slip surface

Slope stability analysis is one of the most intricate problems of geotechnical engineering because it is mathematically difficult to search the critical slip surface of earth slopes with complex strata owing to the involved multimodal function optimization problem. At present, a minimum factor of safety for a non-circular slip surface in a uniform and unreinforced earth slope can be calculated using several methods; however, for a reinforced soil slope, it cannot be easily calculated because of the additional effect of the reinforcement. One efficient method to search the critical slip surface is particle swarm optimization (PSO). PSO can solve complex non-differentiable problems, and its increasing ease of use has facilitated its application to multimodal function optimization problems in a variety of fields. However, the recommended PSO parameters to calculate the safety factors of unreinforced and reinforced soil slopes, namely the inertia and local and global best solution weighting coefficients, have not been sufficiently investigated. Moreover, the computational efficiency of PSO for safety factor calculation, including computational accuracy and time, has not been clarified. To calculate the unreinforced and reinforced soil slope safety factors, this study considers force and moment equilibriums, including the tensile force of the reinforcement. Firstly, the computational efficiency of the calculation process by PSO was shown to increase the maximum number of slip surface nodes in the calculation of the safety factor. Then, an analysis was carried out to investigate the safety factor sensitivity to the PSO parameters. Based on this analysis, appropriate PSO parameters for the safety factor calculation of unreinforced and reinforced soil slopes were proposed.

     

大数据时代的岩土工程监测——转折与机遇

随着电子信息技术、科学研究与工程实践的快速发展,不断产生种类繁多、数量巨大的数据,大数据现象已经出现,在科学数据分析、电子商务和银行业等领域得到了越来越多的重视和应用,大数据即将深刻地影响着人类的生活、工作及思维。传统的数据存储方法、关系数据库、数据处理和数据分析方法逐渐不能满足大数据业务的需要。简要介绍了大数据的定义、特征、所涉及的方法和技术、带来的变革及简单的应用实例,回顾了岩土工程监测学科的历史、现状及面临的问题,指出大数据的概念与岩土工程监测具有天然的契合性,今后大规模、全方位、多维度及多场的岩土工程监测将得以实现。在大数据时代,以关联分析、成因分析及决策支持为核心的深入分析能力将成为监测工作的核心,现场监测将被提升到与试验、理论与数值模拟相同甚至是更为重要的地位。因此,岩土工程监测工作者应该重视大数据的研究,加强应用其他行业的大数据研究成果,抓住大数据对岩土工程监测带来的机遇,为未来岩土工程监测的跨越式发展奠定基础。     

Structural optimization in geotechnical engineering: basics and application

Structural optimization methods are used for a wide range of engineering problems. In geotechnical engineering however, only limited experience exists with these methods. The difficulties in applying such methods to geotechnical problems are discussed in this paper, and the adaption of the commonly known SIMP-method to geotechnical problems is introduced for a special case. An application example is used to present the potential of topology optimization methods, and the application to geotechnical engineering is evaluated.     

大型洞室群软岩置换方案优化的并行实现

并行计算己成为求解大规模岩土工程问题的一种强大趋势 。 以水布娅大型洞室群软岩置换方案优化为例 , 探讨了方案优化中的并行计算问题,分析了并行计算中的编程模式 、 任务划分 、负载平衡和编程方法等问题,在 W id n o w s 环境下的 PC 机群上成功实现了软岩置换方案优化的并行计算,并获得了近乎线性的加速比 , 从而大大提高了方案优化的计算速度和效率 , 为岩土工程计算并行化思路提供了重要参考 。     

位移反分析的粒子群优化-高斯过程协同优化方法

针对采用随机全局优化技术进行岩土工程位移反分析存在数值计算量大、效率低的问题,将粒子群优化算法与高斯过程机器学习技术相结合,提出了位移反分析的粒子群优化-高斯过程协同优化方法。该方法利用全局寻优性能优异的粒子群优化算法进行寻优的基础上,采用高斯过程机器学习模型不断地总结历史经验,预测包含全局最优解的最有前景区域,通过提高粒子群搜索效率并降低适应度评价次数,进而有效地降低位移反分析过程中的数值计算工作量。多种测试函数的数学验证和工程算例的研究结果表明该方法是可行的,与传统方法相比较,可显著地降低位移反分析的计算耗时。     

基于粒子群优化的岩土工程反分析研究

岩土工程优化反分析本质上看是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径,但由于优化反分析中多次调用正分析的特点使得整个算法的计算效率很低。为了提高优化反分析的计算效率,把一种计算效率更高的新型仿生算法--粒子群优化引入岩土工程反分析领域,提高反分析的计算效率。在此基础上,结合有限元数值分析技术,提出了一种新的岩土工程优化反分析算法--粒子群优化反分析。并通过一个简单算例验证了该法的有效性。     

PSO-LSSVM模型在位移反分析中的应用

提出了一种基于均匀设计原理、最小二乘支持向量机(LSSVM)和粒子群优化算法(PSO)的快速位移反分析方法。该方法利用均匀设计和有限差分法获得学习样本,再用粒子群算法搜索最优的最小二乘支持向量机模型参数。并用最小二乘支持向量机回归模型建立反演参数与监测点位移值之间的非线性映射关系,最后用粒子群算法从全局空间上搜索与实测位移最吻合的反演参数。该反演模型利用了粒子群算法高效简单、均匀设计构造高质量小样本以及最小二乘支持向量机的小样本、泛化性能好的特点。将该模型应用于龙滩水电站左岸地下厂房区岩体地应力场的反演分析中,计算结果与实测的位移值和地应力值均吻合较好,说明了该模型在岩土工程快速反演分析中具有良好的应用价值。     

地质和岩土工程光纤传感监测技术综述

光纤传感器与常规传感器相比具有很大的优越性,已经成为地质和岩土工程监测领域中的研究热点。本文介绍了光纤传感监测系统的种类、基本测量原理和目前国内外的研究发展状况,并对各种传感技术的性能和特点进行了比较,指出了光纤传感监测技术应用于地质和岩土工程监测尚需研究的相关课题。     

采用综合评判方法确定工程岩体力学参数研究

如何将室内岩块试验得到的力学参数合理转化为现场工程岩体的力学参数,一直是困扰岩土工程实践带有挑战性的难题。结合安太堡露天煤矿边坡工程,基于现场调查、钻孔取芯、室内岩块试验,分别应用RMR法和BQ法,对自地表至取芯深度的钻孔岩样进行评价和分级,综合评判确定了边坡工程岩体的力学参数,应用效果良好,其成果对类似工程设计和施工有重要指导意义。     

光纤和砂土界面耦合性能的分布式感测试验研究

地质与岩土工程领域中，变形监测一直是评价岩土体稳定性的一个重要指标，其时空演化规律可为工程设计与施工 技术参数优化、地质灾害预警阈值设定提供科学的依据。随着光电感测技术的不断发展，其在地质与岩土工程变形监测中 的作用越来越重要，然而分布式感测光纤与岩土体之间的耦合性对监测结果有显著影响，阐明两者间的作用机理成为该技 术应用的关键环节。文章在综合分析当前地质与岩土工程变形监测技术的基础上，开展了基于BOTDA感测技术的光纤—砂 土界面耦合性能拉拔试验，从试验结果和感测光纤特性两方面分析了感测光纤与砂土的耦合性及二者间的应变传递规律。 试验结果表明：除第一级拉拔外，数据拟合R2均在0.99以上，显示了光纤实测应变分布具有良好的规律性；由试验数据反 算得出的光纤弹性模量约为0.35 GPa，与给定值基本一致；通过感测光纤的F-S端和F-S尾关系，得出光纤—土界面作用过程 可分为全耦合、半耦合和相对滑动三个阶段。上述研究结果为分布式感测技术在各类地质与岩土工程变形监测中的应用提 供指导，同时为开展各类室内模型试验提供参考依据。     

基于ν-SVR和改进PSO算法的反分析方法及应用

岩土工程反分析面临着多元化、复杂化和精确化的挑战,反分析方法要求能在少而精的正分析基础上结合监测数据快速反馈出数值计算参数.充分发挥支持向量回归机ν-SVR和改进的变邻域PSO算法的优势,建立起岩土工程反分析的流程和方法.同时,将其应用于锦屏左岸边坡的反分析问题,选择稳定问题非常突出的II1-II1剖面作为分析对象,根据监测资料反馈设计关键岩体的力学参数,并与监测数据比对,反演成果合理准确,进一步验证了该方法的的正确性和有效性.     

光纤和砂土界面耦合性能的分布式感测试验研究

地质与岩土工程领域中,变形监测一直是评价岩土体稳定性的一个重要指标,其时空演化规律可为工程设计与施工 技术参数优化、地质灾害预警阈值设定提供科学的依据。随着光电感测技术的不断发展,其在地质与岩土工程变形监测中 的作用越来越重要,然而分布式感测光纤与岩土体之间的耦合性对监测结果有显著影响,阐明两者间的作用机理成为该技 术应用的关键环节。文章在综合分析当前地质与岩土工程变形监测技术的基础上,开展了基于BOTDA感测技术的光纤-砂 土界面耦合性能拉拔试验,从试验结果和感测光纤特性两方面分析了感测光纤与砂土的耦合性及二者间的应变传递规律。 试验结果表明：除第一级拉拔外,数据拟合R2均在0.99以上,显示了光纤实测应变分布具有良好的规律性;由试验数据反 算得出的光纤弹性模量约为0.35 GPa,与给定值基本一致;通过感测光纤的F-S端和F-S尾关系,得出光纤-土界面作用过程 可分为全耦合、半耦合和相对滑动三个阶段。上述研究结果为分布式感测技术在各类地质与岩土工程变形监测中的应用提 供指导,同时为开展各类室内模型试验提供参考依据。     

PSO-RBFNN模型及其在岩土工程非线性时间序列预测中的应用

岩土工程受力变形演化是一个典型的非线性问题,其演化的高度非线性和复杂性,很难用简单的力学、数学模型描述,但可用粒子群优化径向基神经网络对岩土工程应力、位移非线性时间序列进行动态实时预测。网络径向基层的单元数通过均值聚类法确定后,所有其它参数：中心位置、形状参数、网络权值,均通过粒子群优化算法在全局空间优化确定。工程实例应用表明,该模型预测结果准确、精度高,有良好的应用前景。     

http://www.sciencedirect.com/science/article/pii/S1674987114001327

Geotechnical engineering deals with materials(e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence(AI) is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.     

膨胀土地基工程地质分析专家系统(ESCES)

在中国南方地区膨胀土地基工程地质研究基础上,概括了国内外知名学者的研究成果,进行了土体结构的工程地质力学分析,归纳成近千条规则,设计并建立了膨胀土地基的专家系统。它包括膨胀土类型鉴别,胀缩特性判定,膨胀恶化预测,地基工程结构选定和基坑边坡稳定性评价等内容。本专家系统在河北、湖北、广西等四个不同地区的工程中得到验证,表明它是解决复杂的膨胀土地基问题及推动膨胀土工程地质研究的一种有效手段。     

高压旋喷加劲水泥土桩锚支护技术的应用与探讨

高压旋喷加劲水泥土桩锚是一种新型的、先进的岩土加固与支护技术。结合2个围护桩（SMW工法桩或组合排桩）+高压旋喷加劲水泥土桩锚支护体系在软土深基坑中应用的工程实例,阐明了高压旋喷加劲水泥土桩锚支护技术具有经济性好、施工方便、适应性强等优点,具有较好的综合效益,同时也指出在深厚软土中存在的问题。     

对岩土工程数值分析的几点思考

首先,介绍了笔者对我国岩土工程数值分析现状的调查结果及分析,然后,分析了采用连续介质力学分析岩土工程问题的关键,并讨论分析了岩土本构理论发展现状,提出对岩土本构理论发展方向的思考,最后对数值分析在岩土工程分析中的地位作了分析。分析表明,岩土工程数值分析结果是岩土工程师在岩土工程分析过程中进行综合判断的重要依据之一;采用连续介质力学模型求解岩土工程问题的关键是如何建立岩土的工程实用本构方程;建立多个工程实用本构方程结合积累大量工程经验才能促使数值方法在岩土工程中由用于定性分析转变到定量分析。     

计算机辅助岩土工程(CAGE)概论

本文简要论述了计算机辅助岩土工程(CAGE)的提出、发展、方法及主要内容, 最后给出了计算机辅助核电工程勘察实例。     

Three-dimensional topology optimization for geotechnical foundations in granular soil

This article presents three-dimensional structural optimization in geotechnical engineering for foundations in granular soil. The general design (topology) of a shallow foundation is optimized with respect to its deformational behaviour within the service limit state. The SIMP (solid isotropic material with penalization) method is applied to optimize the distribution of foundation material. The soil is modelled as a hypoplastic material with a constitutive model suitable for optimization using finite element analysis. Two load cases are examined. The optimized topology is validated against two-dimensional optimization and 1g-model test results. The present study proves the applicability and shows the potential of topology optimization in geotechnical engineering.     

Hybrid differential evolution and particle swarm optimization for optimal well placement

There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems.