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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 基坑支护体系是临时结构，安全储备较小，具有较大风险。在实际工程中，每个基坑的平面尺寸、开挖深度、水文地质条件和周围环境都不一样，为了解决复杂的基坑工程问题，需对具体基坑支护结构进行设计。考虑工程周边环境及地质条件，选择适合的支护形式。应用朗肯土压力理论，用等值梁法计算支护结构内力，确定等设计参数，根据计算成果，绘制基坑支护设计施工图。本基坑设计遵循安全可靠、经济合理、方便施工的原则，完全能够满足基坑土方开挖过程中支护结构本身和周边环境安全保护的要求。 2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）： 1、工程概况：拟建建筑物地面以上9层，地下1层，建筑0.00相当于绝对标高23.4m，整平后地面标高为23.00m，其它标高均以此为准，地下室负二层底板顶标高为-3.2m，基坑开挖深度为8.20m。基坑东面为马路，下设通讯电缆、煤气管线等设施。2、设计资料岩土层分布（从上至下）及分布特征序号土层名称厚度(m)重度(kN/m3)内聚力(kPa)内摩擦角()极限侧阻力(kPa)1 填土1.5018.15.010.0602粘性土15.2019.518.012.8603粘性土6.1020.322.023.9604粉土2.0019.530.018.3605粘性土1.5020.125.016.7602、技术要求和工作要求：1.基坑支护设计资料收集：1）.场地岩土工程勘察报告，基坑支护设计参数。2）.建筑红线、施工红线的地形平面图及基础结构设计图；建筑场地及其附近的地下管线、地下埋设物的位置、深度、结构形式及埋设时间等。3）.基坑附近的地面堆载及大型车辆的动、静荷载情况。4）.临近的已有建筑物的位置、层数、高度、结构类型、完好程度。已建时间以及基础类型、埋设深度、主要尺寸、基础距基坑的净距离等。5）.基坑周围的地面排水情况，地面雨水与污水、上下水管排入和漏入基坑的可能性。6）.已有相似基坑支护的经验性资料。2.基坑支护方案和降水方案的选择，确定基坑支护围护结构布置，止水、降水技术方案，基坑开挖、监测方案。基坑支护计算断面的确定。3.按确定的计算断面分别进行基坑支护围护结构、支撑体系设计计算，降水方案计算，基坑稳定性验算，抗隆起验算。4.要求编写完整的基坑支护设计报告。5.按照工程设计和施工要求绘制基坑支护设计相关图纸。 毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： 一、文字部分：1、设计说明书、结构计算书；2、外文资料翻译（英-汉），设计摘要翻译（汉-英）。二、图纸部分基坑支护设计方案说明、平面布置图、支撑布置图1张；基坑支护设计结构剖面图、支撑大样图 4主要参考文献： 1 刘国彬. 基坑工程手册M. 北京：中国电力出版社，20092 刘起霞. 地基处理M. 中国建筑工业出版社，2013.3 曹云. 基础工程M. 北京：北京大学出版社，2012.4 陈忠汉. 深基坑工程M. 北京：机械工业出版社，1999.5 吴林高. 基坑工程降水案例M. 北京：人民交通出版社，20096 张维正. 深基坑开挖及支护工程理论与实践M. 北京：人民交通出版社，20147 曾宪明. 土钉支护设计与施工手册M. 北京：中国建筑工业出版社，20008 佴磊. 边坡工程M. 北京：科学出版社，20109 华南理工大学等. 基础工程M. 北京：中国建筑工业出版社，200810 郑刚. 软土地区基坑工程支护设计实例M. 北京：中国建筑工业出版社，201111 徐长节. 深基坑围护设计与实例解析M. 北京：机械工业出版社，201412 胡明亮. 基坑支护工程设计施工实例图集M. 北京：中国建筑工业出版社，200813 徐至钧. 深基坑支护新技术-加筋水泥土桩锚支护设计与工程应用M. 北京：水利水电出版社，201214 姚爱军. 复杂边坡稳定性评价方法与工程实践M. 北京：科学出版社，200815 赵晓彦. 花岗岩类土质边坡特性及其组合锚固设计M. 北京：人民交通出版社，201216 GB50010-2010，混凝土结构设计规范S.17 GB50007-2011，建筑地基基础设计规范S.18 GB50330-2013，建筑边坡工程技术规范S.19 GB 50086-2001，锚杆喷射混凝土支护技术规范S.20 JGJ120-2012，建筑基坑工程技术规范S.21 GB/T50104-2010，建筑制图标准S.22 GB/T50103-2010，总图制图标准S. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：起讫日期工作内容2015.12.222016.1.31学生熟悉任务书，收集资料，并撰写开题报告，进行参考英文翻译和设计（论文）大纲撰写2016.2.012016.2.28毕业设计完成方案确定、论文完成综述及技术路线2016.3.012016.3.31毕业设计完成手算部分2016.4.012016.4.18使用MIDAS GeoX进行电算2016.4.192016.4.26手算与电算分析对比2016.4.272016.5.03定稿并按学校规定整理设计成果所在专业审查意见：通过负责人： 2015 年 12 月21 日 译文题目：Co-evolution Optimization of Anchored Piles in Row for Deep Foundation Pit 原文：Co-evolution Optimization of Anchored Piles in Row for Deep Foundation PitAbstract: In this paper, the idea of co-evolution is applied to the optimization of retaining and protecting structure for deep excavation, and the system of optimization of anchored piles in row has been developed successfully. For the co-evolution algorithm provides an evolutionary mechanism to simulate ever-changing. Problem space, it is an optimization algorithm that is highly effective, especially to be applied to the optimization of complicated system of retaining and protecting for deep foundation pit. It is shown by engineering practice that the co-evolution algorithm has obvious optimization effect, so it can be an important method of optimization of retaining and protecting for deep foundation pit. The authors discuss the co-evolution model, object function, all kinds of constraint conditions and their disposal methods, and several key techniques of system realization.Key words: genetic algorithm; co-evolution; optimization; anchored row piles; deep foundation pit. 1. IntroductionIn all kinds of retaining and protecting techniques for deep foundation pit, anchored row piles have been extensively used because of their some features, such as good results, strong adaptability, easy construct ion and so on. Usually, the design procedures1 of anchored row piles are:(1)Select preliminarily the types of retaining and protecting piles and the layers of anchor, namely retaining and protecting scheme design;(2)Select preliminarily all members size and material parameters of retaining and protecting structure, namely detailing design;(3)Make calculation and analysis, which includes checking for embedded depth of piles and load capability of anchor, computing internal force of piles and bar arrangement and adjusting detailing design to meet various demands of the above checking computation;(4)Compare various schemes and find out the scheme whose cost is lowest as the ultimate design of retaining and protecting for deep foundation pit .Generally, designers need to adjust severally and check repeatedly the retaining and protecting scheme and detailing so as to make every computing procedure meet all design demands. However, the design gained from this is only a “feasible solution”, but not the optimum solution in all the feasible solutions. As we known, every retaining and protecting scheme has many relevant design parameters. Moreover, these design parameters all directly or indirectly affect the investment of engineering. Hence, how to find out a set of optimization parameters to ensure economy and safety is an important problem of the design of anchored row piles, also a complicated optimization problem. For this reason, the authors introduce the genetic algorithm to the optimization of retaining and protecting for deep foundation pit 2. Studies show that the introduction of genetic algorithm has opened a new path for the optimization of retaining and protecting for deep foundation pit 23. Here, the authors explore further a co-evolution algorithm that is suitable for the optimization of retaining and protecting for deep foundation pit 42. Model of Optimization of Anchored Row Piles 2. 1 Anchored row pile system and its optimization objectiveAs a system engineering, the optimization of anchored row piles is a subsystem of the optimization system for deep foundation pit. It can be plot out two hierarchies, scheme optimization and detailing optimization. Retaining and protecting scheme, composed of row pile sub-scheme and anchor sub-scheme, is a certain combination of the types of row piles ( bored pile, artificially-excavated pile, pre-cast pile etc.) and the anchor installing (without anchor, one-layer anchor, two-layer anchor and three-layer anchor) (shown in Fig.1) . Scheme optimization is to search a combination which meets all kinds of constraint conditions and has the lowest cost at the same time on the given conditions such as engineering information, engineering geological conditions, environmental conditions, const ruction conditions and so on. The optimization design of detailing structure is, pointing to a certain retaining and protecting scheme; to optimize the detailing structure of piles and anchors and make the detailing structure meet all constrain conditions and the engineering cost minimum. It contains the design of detailing sizes of piles and anchors, the style of bar arrangement, the parameters and quantity of materials, top ring beam, etc. Whether scheme optimization or detailing optimization, their optimization objectives are common, namely making the total engineering cost of anchored row pile system minimum. Its mathematical model is seeking: min f(X) = total engineering cost of anchored row piles, XUEns. t .(X) = 0( = 1, 2, ., p), (1)(X) 0( u = 1, 2, , m),where f (X) = object function, namely cost computation function, referring to Chinese flat rate of architectural engineering and architectural engineering unit estimation price list of each province or city ;X= a vector which is made up of design variables ，.，. There are only two design variables in scheme optimization, namely the type of row piles (Pt) and anchor installing (Np) . And in detailing optimization, there are the following design variables in two aspects: 1) Retaining and protecting pile: pile diameter(5) , perimeter-to-perimeter pile spacing (S) , embedded depth () , concrete grade (Pct) , bar arrangement sty le (Ms) , sort of reinforcing steel bar( Ps t) etc. 2) Anchor : anchor installing depth ( ha ) , horizontal anchor spacing (Par) , inclination angle of anchor (H) , free anchor length () , fixed anchor length () , diameter of the grouted mass (D) , sort of anchor bar ( Ast ) , grade of cement mortar(Cg) , etc.s. t . = const rain conditions which must be met, is const rain conditions of equations, p is the number of equations, is const rain conditions of inequalities, m is the number of inequalities.The above mathematical model of optimization of anchored row piles will have different forms in different design hierarchies or evolutionary spaces.2. 2 Mathematical model of scheme optimizationAssume that the population of retaining and protecting scheme is A, search the scheme individual a A to make corresponding total engineering cost minimum, namelymin (2)Where = the cost of the th sub-scheme of a certain scheme in a scheme space. The retaining and protecting scheme is formed by row pile and anchor sub-schemes, so i equals 1 or 2; feasible = seeking the feasible individual which meets the const rain conditions in scheme population. The objective of scheme optimization is to make the total cost of scheme minimum while it has met the const rain conditions.2. 3 Mathematical model of detailing optimizationThe detailing optimization of anchored row piles is done on the condition that each sub-scheme has been selected. And its mathematical model is the foundation of data structure design.Assume that a certain detailing population ( corresponding to a certain scheme) is B, seek the detailing individual b B to make the total cost of design corresponding to B minimum, namelyWhere = the cost of the th sub-scheme of a certain individual in the detailing space which corresponds to a certain scheme (the th sub-scheme chooses the th value available for selection). It is the function of vector ; = is a vector of design variables of the th sub-scheme and a piece of detailing chromosomes. is the th design variable, and l is the number of design variables; equals 1 or 2 ( the number of sub-scheme is 2) . These are linked tog ether to form a w hole chromosome of a detailing population, namely . the value range ( a discrete aggregate ) of design variable .We can know from the formulas (2) and (3) that evolution objectives of both scheme population and detailing population are to search the lowest cost. So a linking, which links two evolution spaces organically, is set up between scheme population and detailing population. Base on this, the fitness of both scheme population individuals and detailing population individuals can be obtained by making the suitable mathematical manipulation to the engineering cost of every individual. And then we w ill get a unified standard for evaluating individuals.3. Co-Evolution Model In the optimization design of anchored row piles, scheme design and detailing design are two different design problems that belong to different spaces and levels. The former is problem space, namely scheme space. It is an aggregate of all retaining and protecting schemes. The latter is solute ion (design) space, namely detailing space. It is an aggregate of all detailing schemes which correspond to each retaining and protecting scheme. They are not only mutual independence but also interrelation and interaction. The optimization design of anchored row piles is the process of alternately searching to scheme space and detailing space. To search scheme space w ill make retaining and protecting scheme change, which provides the new focus for searching detailing space. Thereby a new detailing space is created. To search detailing space will obtain a detailing solution that meets the demands of retaining and protecting scheme, which affect s further searching scheme space and makes the primary retaining and protecting scheme create a new change. To seek further a new detailing solution corresponding to the new retaining and protecting scheme, ,keep searching like this until finding a optimum retaining and protecting scheme and a detailing scheme which meet the design demands. The above-mentioned design thinking can be illustrated by problem-space and design-space co-evolution model as show n in Fig. 2, w here P = scheme space, and S= detailing space.(1) There are two distinct search spaces in all searching process, namely scheme space and detailing space.(2) Horizontal movement represents an evolutionary process, which is based on the sing le genetic algorithm (SGA) 56.Scheme space evolves from P ( t ) to P( t + 1) , P ( t + 2) , and so on; + Detailing space evolves from S( t ) to S( t + 1) , to S( t + 2) , and so on, w here t, t + 1, t + 2, are evolutionary generations.(3) Diagonal movement stands for a search process in which goals lead to the solution, namely “the scheme design leads to detailing design”( downward arrow ) and “t he detailing design refocuses scheme design”( upward arrow ) .The downward arrow: the process from problem to design solution. It is also the process of scheme design leading to detailing design in the optimization of anchored row piles. And it has two guide functions: one is that every individual of scheme population P (t) will generate a new detailing subspace, and provide an object (focus) for this detailing subspace evolution, the other is that every individual of scheme population P (t) will provide the basis of measuring fitness for the population of corresponding detailing subspace.The dashed upward arrow: the process of adjusting problem definition by design. It is also the process of detailing design affecting the scheme design in the optimization of anchored row piles. The effect is realized by sending the evolutionary results of detailing subspacethe best detailing solution to the relevant individual of scheme space and providing the basis of measuring fitness for the scheme individual.It is obvious that in the w hole searching process two state spaces interact, and that the evolution of each space is always guided by the most recent population in the other space.4. ConclusionsThe co-evolution algorithm is based on the SGA. As for some design problems in an ever-changing problem space, it provides an evolutionary mechanism to simulate the ever-changing problem space. Therefore, it is an optimization algorithm that has high performance, especially fits to the optimization of retaining and protecting for deep foundation pit. In this paper, a significant attempt has been done to the co-evolution optimization of anchored row piles for deep foundation pit. The engineering practice shows that it has obvious optimization effect and great engineering operation significance and is an important method of the optimization of complicated system of retaining and protecting for deep foundation pit. A future study about the following four aspects must be enhanced: (1) the features of retaining and protecting for deep foundation pit and the reasonable co-evolution algorithm; (2) the structure of fitness function of co-evolution algorithm and the appropriate method of measuring fitness; (3) the system structure of retaining and protecting for deep foundation pit, the hierarchies plotting-out of scheme and detailing; (4) all kinds of constraint s of retaining and protecting for deep foundation pit and their disposal methods. 中文译文：深基坑桩锚支护协同演化优化设计摘 要：将协同演化思想应用于基坑桩锚支护优化设计中，成功开发了深基坑桩锚支护优化设计系统。协同演化方法提供了模拟问题空间不断变化的演化机制，是一种高效的优化算法，适合于深基坑支护这一复杂系统的优化。工程实践表明，该方法具有明显的优化效果，可作为深基坑支护优化设计的一种重要手段。给出了协同演化模型、优化目标函数、全部约束条件及其处理方法以及系统实现的几项关键技术。关键词：遗传算法；协同演化；优化；桩锚支护；深基坑一、前言在各种深基坑围护技术中，桩锚支护结构以其效果好、适应性强和施工简便等特点，在我国得到了广泛应用。桩锚支护的一般设计步骤1为：（1）选择支护桩类型和锚杆层数，即支护方案设计；（2）初选支护结构各细部尺寸和材料参数，即细部结构设计；(3) 进行计算分析，包括桩的嵌固深度验算、锚杆承载力验算、桩身内力计算、配筋计算等，通过计算对各细部初选参数做出修改和调整，使之满足各种验算要求；(4) 对比多个方案，找出造价最低的方案作为基坑支护的最终设计。通常设计者需要对支护方案和细部结构进行多次调整、反复验算，才能使得各计算步骤均满足设计要求。但这样得到的设计往往只是一个“可行”解，而不一定是“最优”解。对于每一种支护方案，其细部设计参数有很多，它们都直接或间接地影响到工程投资。因此，如何寻找一组最佳设计参数，以达到既经济又安全，是桩锚支护设计的一个重要问题。这是一个复杂的优化设计问题，为此，笔者在文献2中把遗传算法引入深基坑支护优化设计中来，研究表明，遗传算法的引入为深基坑支护优化设计问题闯出了一条新的途径23。然而，由于深基坑支护优化设计一般包括方案优选与细部结构优化两个层次，最初提出的算法比较适用于单层次优化问题，为此，笔者探索了另一种适用于深基坑支护优化设计问题的协同演化算4。二、桩锚支护优化设计模型2.1 桩锚支护体系及其优化设计目标桩锚支护优化设计属于一项系统工程，是深基坑支护设计体系中的一个子系统，可以划分为方案优化设计和细部结构优化设计两个层次。支护方案由桩排与锚杆两个子方案构成,是排桩类型(钻孔灌注桩、人工挖孔桩和预制桩等)与锚杆设置(无锚杆即悬臂式、一层锚杆、二层锚杆和三层锚杆)的不同组合形式(图1)，方案优化设计就是在给定的工程信息、场地水文工程地质条件、环境条件、施工条件等已知条件下，寻找满足各种约束且造价最低的一种组合；细部结构优化设计则是针对某一确定的支护方案所进行的桩、锚细部结构优化，包括桩、锚的细部结构尺寸、配筋方式、材料参数及材料用量、顶部圈梁的设计等，使得该结构满足各种约束，同时造价最低。方案设计对细部结构设计具有指导作用，细部结构设计结果又可反馈回方案设计中以修改或调整方案设计。无论是方案优化还是细部结构优化，其优化目标是共同的，即桩锚支护体系的总造价最低。其数学模型为： （1）式中为目标函数，即工程造价计算函数，套用国家统一建筑工程基础定额及各省市建筑工程单位估价表。是由，.，组成的向量，是设计过程中要优选的量，即设计变量。在方案设计层次上，其设计变量仅两个，即排桩类型(Pt)与锚杆设置(Np)；在细部结构设计层次上，则包括以下两方面的设计变量：1）支护桩：桩径（）、桩边距（S）、嵌固深度（）、砼强度等级（Pct）、配筋方式（Ms ）、钢筋类别（Pst）等；2）锚杆：锚杆设置深度（ha）、水平间距（Par）、倾角（H）、自由段长度（）、锚固段长度（）、锚固段直径（D）、锚筋类别（Ast）、水泥砂浆强度等级（Cg）等。s.t.表示需要满足的约束条件，为等式约束条件，p为其数目，表示不等式约束条件，m表示其数目。图1 排桩-锚杆支护体系上述桩锚支护优化设计数学模型在不同设计层次或演化空间中又有不同的表现形式。2.2 方案优化数学模型设支护方案种群空间(染色体空间)为chrom，求方案个体chromchrom，使得chrom对应的方案总造价最小，即min （2）式中表示方案空间中某一方案个体第个子方案(取第个可选值) 的造价，由于总的支护方案仅由排桩与锚杆两个子方案构成，因此，的取值为1和2；feasible 表示在方案种群空间中求