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首页> 外文会议>Hydropower 2006 International Conference >BACK ANALYSIS OF MECHANICAL PARAMETERS OFCONCRETE FACE ROCK-FILL DAMS BASED ON A MODIFIEDPARTICLE SWARM OPTIMIZATION
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BACK ANALYSIS OF MECHANICAL PARAMETERS OFCONCRETE FACE ROCK-FILL DAMS BASED ON A MODIFIEDPARTICLE SWARM OPTIMIZATION

机译：基于修正粒子群算法的混凝土面板堆石力学参数反演

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摘要

Earth-rock Dams safety evaluation is significant to maintain the normal operationrnof dams, where back analysis based on the observed data of prototype plays an important rolernin guaranteeing the precision of evaluation. Taking example for a Concrete Face Rock-fillrnDam(CFRD) in a real project, this paper attempts to introduce a novel intelligencernoptimization algorithm, one-population self-adaptive Particle Swarm Optimization(PSO), tornback analyze four considerably sensitive mechanical parameters of Duncan-Chang EBrnmodel(K, n, Kb, m) on the basis of measured displacements. PSO has been widely utilized inrnvarious fields due to its essential advantages like simple conception, easy implementation, fastrncalculation etc., while it is seldom applied to back analysis of mechanical parameters of dams.rnThe modified PSO, One-Population Self-Adaptive PSO, further improved the search speedrnand enhanced the ability of guaranteeing the convergence to the global optimization solution,rnwhich benefits the back analysis calculation more in the calculation affectivity and efficiencyrncompared with the standard PSO. Most of the former back analyses of earth-rock dams werernaccording to the displacements of different measuring points at a certain time or therndisplacements at one measuring point along the time-history, thus the back analyzedrnparameters can't reflect material properties very well. In this paper, back analysis is based onrnthe displacements at multiple measuring points along time-history during construction andrnoperation period. Through analyzing the rationality of parameter results, it is possible tornprovide a reliable reference for the safety evaluation of dams as well as design andrnconstruction of dams to be constructed. This optimization algorithm was also proved to berneffective in the back analysis of the mechanical parameters of CFRD based on displacements.
机译：土石坝安全性评价对维护正常大坝具有重要意义，其中基于原型观测数据的反分析在保证评价精度方面具有重要作用。以一个实际工程中的混凝土面板堆石坝（CFRD）为例，本文试图介绍一种新颖的智能优化算法，即单种群自适应粒子群优化（PSO），对Tuncan-在测得的位移的基础上，改变EBrnmodel（K，n，Kb，m）。 PSO由于具有构想简单，易于实现，计算快速等基本优点而被广泛应用于各个领域，而很少用于大坝的力学参数反分析。改进型PSO，单种群自适应PSO与标准PSO相比，提高了搜索速度，增强了对全局优化解决方案收敛性的保证能力，从而使反分析计算在计算有效性和效率上更加受益。以往大多数土石坝的反分析都是根据一定时间的不同测量点的位移或沿时间历史的一个测量点的位移，因此，反分析参数不能很好地反映材料的性能。本文的反向分析是基于在施工和运营期间沿时程的多个测量点的位移。通过分析参数结果的合理性，有可能为大坝的安全性评估以及拟建大坝的设计和施工提供可靠的参考。该优化算法在基于位移的CFRD力学参数的反分析中也被证明是有效的。

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