本文選題：改進(jìn)粒子群算法　切入點(diǎn)：邊坡穩(wěn)定性　出處：《大連理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：邊坡的變形和受力狀態(tài)分析的難題之一便是如何恰當(dāng)?shù)墓烙?jì)邊坡的力學(xué)參數(shù)和初始應(yīng)力場(chǎng),毫無(wú)疑問(wèn),實(shí)驗(yàn)室測(cè)試和現(xiàn)場(chǎng)試驗(yàn)是解決這一問(wèn)題的有效方法,但以上兩種方法各有其局限性,比如邊坡的非均勻特性,基于實(shí)驗(yàn)室內(nèi)小試樣的測(cè)試或局部的有限現(xiàn)場(chǎng)試驗(yàn)得到的邊坡力學(xué)參數(shù)存在較大的隨意性,并且實(shí)驗(yàn)結(jié)果代表性不強(qiáng),數(shù)據(jù)離散,使得與實(shí)際的邊坡力學(xué)參數(shù)有較大偏差,進(jìn)而導(dǎo)致在一定程度上按照這些參數(shù)計(jì)算的理論分析結(jié)果和現(xiàn)場(chǎng)實(shí)測(cè)結(jié)果有較大的誤差。反分析方法為合理確定邊坡力學(xué)參數(shù)提供了一條有效的途徑。伴隨著計(jì)算機(jī)技術(shù)的發(fā)展,正分析的理論和計(jì)算方法逐漸成熟,觀測(cè)儀器的精度也逐步提高,根據(jù)現(xiàn)場(chǎng)觀測(cè)數(shù)據(jù)進(jìn)行邊坡力學(xué)模型參數(shù)反演具有良好的應(yīng)用前景,本文主要研究成果如下：(1)分析了基本粒子群算法的代數(shù)和解析特性。(2)探討了一種基于自主學(xué)習(xí)的改善粒子群算法,通過(guò)賦予粒子一定的自主性來(lái)改善種群的全局廣度搜索與局部深度搜索能力,分析了該算法的計(jì)算效率,并通過(guò)實(shí)際測(cè)試函數(shù)驗(yàn)證了該算法比基本粒子群算法具有更好的尋優(yōu)能力和更快的收斂速度。將該算法應(yīng)用于邊坡工程力學(xué)反演參數(shù)運(yùn)算中,得到了比較滿意的反演參數(shù)。(3)在主動(dòng)學(xué)習(xí)的改進(jìn)粒子群算法的基礎(chǔ)上,將輪形結(jié)構(gòu)和非線性函數(shù)調(diào)整參數(shù)權(quán)值結(jié)合起來(lái),提出了一種全新的改進(jìn)粒子群優(yōu)化算法,并討論了其收斂性。通過(guò)測(cè)試函數(shù)對(duì)其進(jìn)行了函數(shù)優(yōu)化對(duì)比分析,并將改進(jìn)的粒子群算法應(yīng)用于構(gòu)建邊坡工程力學(xué)參數(shù)反演問(wèn)題中,結(jié)果表明該方法在邊坡反演參數(shù)中是行之有效的。
[Abstract]:One of the difficult problems in slope deformation and stress analysis is how to properly estimate the mechanical parameters and initial stress field of the slope. There is no doubt that laboratory tests and field tests are effective methods to solve this problem. However, each of the two methods has its own limitations, such as the non-uniform characteristics of the slope, the mechanical parameters of the slope obtained from the test of small samples in the laboratory or the local limited field test have greater randomness, and the experimental results are not representative. The data are discrete, which leads to a big deviation from the actual mechanical parameters of the slope. Therefore, the theoretical analysis results calculated according to these parameters to a certain extent and the field measured results have a large error. The inverse analysis method provides an effective way to reasonably determine the mechanical parameters of the slope. The development of computer technology, The theory and calculation method of forward analysis is maturing gradually, and the precision of observation instrument is improved gradually. The inversion of parameters of slope mechanics model based on field observation data has a good application prospect. The main research results of this paper are as follows: (1) the algebraic and analytical properties of the basic particle swarm optimization (PSO) are analyzed. (2) an improved PSO algorithm based on autonomous learning is discussed. By giving the particle some autonomy to improve the global breadth search and local depth search ability of the population, the computational efficiency of the algorithm is analyzed. The experimental results show that the proposed algorithm has better optimization ability and faster convergence speed than the basic particle swarm optimization algorithm. The proposed algorithm is applied to the inversion of slope engineering mechanics parameters. Based on the improved particle swarm optimization algorithm, a new improved particle swarm optimization algorithm is proposed, which combines the wheel structure with the parameter weights adjusted by nonlinear function. The convergence is discussed. The function optimization is compared and analyzed by the test function, and the improved particle swarm optimization algorithm is applied to the inversion problem of the mechanical parameters of slope engineering. The results show that the method is effective in the inversion of slope parameters.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TU43;TP18

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本文編號(hào)：1614351