高拱坝施工仿真多目标优化与多属性决策研究

发布时间：2018-12-16 21:27

【摘要】：混凝土高拱坝所在区域往往河谷深切、地形陡峻、地质条件复杂,空间资源有限,不利于施工场地的总体布置及交通运输。根据混凝土高拱坝的地形地质特点、施工规模及施工强度等的不同,可能有多种施工方案可供选择,而不同的施工方案在保证施工进度、施工质量和施工组织难度等方面差异较大。对于同一种施工方案,由于坝体通常被划分成许多坝块进行浇筑,坝块跳仓排序规则会影响和制约工程整体的施工进度以及施工的均衡性。因此如何综合考虑影响高拱坝施工的各项因素,对大坝仓面排序规则进行优化,并对多个高拱坝施工方案进行优化和比选,获得多目标综合最优的施工方案和仓面排序规则是高拱坝设计及施工阶段面临的重要问题。目前的仓面排序方法大都是根据已有的经验或基于对坝块属性值的数学分析来制定跳仓排序规则,无法有效地解决仓面排序的多目标优化问题。此外,现有的施工方案比选研究中,所采用的评价方法无法全面反映各方案与理想方案的贴近关系,各方案的评价参数只有一个且不一定是各方案所能获得的最优施工参数,影响评价结果的准确性。本文针对高拱坝施工仿真多目标优化与多属性决策进行了相关研究。通过详细分析高拱坝浇筑块选择原理,综合考虑影响混凝土浇筑的各项因素,建立了高拱坝施工仓面排序多目标优化模型,该模型以高拱坝跳仓排序规则为变量,以优化施工工期、月浇筑强度为目标,应用粒子群算法对该多目标优化问题进行求解,获得多目标综合优化的仓面排序方案。对于多方案多属性决策问题,鉴于逼近理想解法及灰色关联分析方法在解决多属性决策问题中各自的优缺点,对二者进行有效集成,构建了基于改进逼近理想解法的高拱坝多方案多属性决策方法。采用上述仓面排序多目标优化方法为每个方案获得多个跳仓排序规则,并对各排序规则进行仿真计算获得各自的工期、施工强度、机械利用率等施工参数,而后采用改进逼近理想解法对多个方案的多个跳仓排序规则进行多属性决策,获得决策解,提高了决策结果的全面性和准确性。通过工程实例计算,结果表明:采用粒子群算法实现了对仓面排序方案的优化调整,计算得到的Pareto解集能够为决策者提供多个方案的多个跳仓排序规则以便更好地决策,利用改进逼近理想解法优选的方案能够获得更短的工期和更均衡的浇筑过程,对于指导现场施工具有重要意义。
[Abstract]:The area where the high arch dam is located often has deep valley steep topography complicated geological conditions and limited space resources which is not conducive to the overall layout of the construction site and transportation. According to the topographic and geological characteristics, construction scale and construction intensity of high arch dam, there may be a variety of construction schemes to be selected, and different construction schemes can ensure the construction progress. The construction quality and the construction organization difficulty and so on aspect difference is big. For the same construction scheme, because the dam body is usually divided into many dam blocks for pouring, the sorting rules of dam block jump silo will affect and restrict the construction schedule and the balance of construction as a whole. Therefore, how to comprehensively consider the factors that affect the construction of high arch dam, optimize the sorting rules of dam silo surface, and optimize and compare many construction schemes of high arch dam. It is an important problem in the design and construction stage of high arch dam to obtain the comprehensive optimal construction scheme of multi-objective and the sorting rules of silo surface. Most of the current sorting methods are based on the existing experience or based on the mathematical analysis of the dam block property value to make the jump sorting rules, which can not effectively solve the multi-objective optimization problem of silo surface scheduling. In addition, the evaluation methods used in the existing research on the comparison and selection of construction schemes can not fully reflect the close relationship between each scheme and the ideal scheme. The evaluation parameters of each scheme are only one and not necessarily the optimal construction parameters that can be obtained by each scheme. Accuracy of impact evaluation results. This paper studies the multi-objective optimization and multi-attribute decision-making of high arch dam construction simulation. Based on the detailed analysis of the selection principle of high arch dam construction blocks and the comprehensive consideration of various factors affecting concrete pouring, a multi-objective optimization model of warehouse surface sequencing for high arch dam construction is established. The model takes the order rule of high arch dam jump silo as a variable. In order to optimize the construction period and the monthly pouring strength, the particle swarm optimization algorithm is applied to solve the multi-objective optimization problem. In view of the advantages and disadvantages of the approximate ideal method and the grey relational analysis method in solving the multi-attribute decision making problem, the two methods are effectively integrated. A multi-scheme and multi-attribute decision-making method for high arch dam based on the improved approximation ideal method is proposed. The multi-objective optimization method is used to obtain multiple hopping sorting rules for each scheme, and the construction parameters, such as duration, construction intensity and mechanical utilization ratio, are obtained by simulation calculation of each sorting rule. Then, the improved approximation ideal method is used to make multi-attribute decision making for multiple hopping sorting rules of multiple schemes, and the decision solution is obtained, which improves the comprehensiveness and accuracy of the decision results. The results of engineering examples show that the particle swarm optimization algorithm is used to optimize and adjust the sorting scheme of warehouse surface, and the calculated Pareto solution set can provide multiple hopping sorting rules for decision makers for better decision making. The improved approximate ideal solution can obtain shorter construction period and more balanced pouring process, which is of great significance for guiding the field construction.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TV642.4

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