首页|基于改进PSO-RBF神经网络的三维边坡可靠度分析

基于改进PSO-RBF神经网络的三维边坡可靠度分析

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三维边坡模型能真实反映边坡空间效应,提升边坡可靠度计算精度,然而由于三维边坡模型计算量庞大且安全系数缺少显示表达,边坡可靠度分析主要以二维简化模型为主,针对三维边坡可靠度分析的研究仍存在不足.提出一种基于Spencer方法、自适应变异粒子群优化算法(PSO)和径向基函数神经网络(RBF)的三维边坡可靠度分析方法.通过对传统PSO算法引入变异算子,改善了其搜索精度较低、后期迭代效率不高等缺点.以三维Spencer方法为基础,结合改进PSO算法与RBF神经网络构建三维边坡安全系数的计算模型进行可靠度分析,实现三维边坡功能函数的显示化,通过标椎椭球滑体可靠度分析,验证了该方法相较于传统方法计算精度和效率的提升;进一步研究了卡基娃左岸边坡减载开挖过程稳定性及可靠度的变化规律,结果表明:削坡减载作用后有效提升了边坡的稳定性,边坡失效概率减小了近 2个数量级.
3D Slope Reliability Analysis Based on Improved PSO-RBF Neural Network
Three-dimensional slope model can truly reflect the spatial effect of slope and improve the accuracy of slope reliability calculation,however,due to the huge calculation volume of three-dimensional slope model and the lack of display expression of safety coefficient,the slope reliability analysis is mainly based on two-dimensional simplified model,and the research for three-dimensional slope reliability analysis is still insufficient.A three-dimensional slope reliability analysis method based on Spencer's method,adaptive variational particle swarm optimization algorithm and radial basis function neural network(RBF)is proposed.By introducing variational operators to the traditional PSO algorithm,the shortcomings of its low search accuracy and inefficient late iterations are improved.Based on the three-dimensional Spencer method,the calculation model of three-dimensional slope safety coefficient is constructed for reliability analysis by combining the improved PSO algorithm with RBF neural network to realize the display of three-dimensional slope function,and the improvement of the calculation accuracy and efficiency of the method compared with the traditional method is verified through the reliability analysis of the scalene vertebral ellipsoid slide;further research is conducted on the process of load-reducing excavation of the left bank slope of Kakiwa.The results show that the stability and reliability of the slope can be effectively improved after the effect of slope cutting and load reduction,and the probability of slope failure is reduced by nearly 2 orders of magnitude.

three-dimensional slope stabilizationreliabilityparticle swarm optimization algorithmneural networksgeotechnical engineeringengineering geology

彭宗桓、盛建龙、叶祖洋、袁乾峰

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武汉科技大学资源与环境工程学院,湖北武汉 430081

冶金矿产资源高效利用与造块湖北省重点实验室,湖北武汉 430081

三维边坡稳定 可靠度 粒子群优化算法 神经网络 岩土工程 工程地质

国家自然科学基金国家自然科学基金湖北省自然科学基金

42077243517092072018CFB631

2024

10.3799/dqkx.2022.341

地球科学
中国地质大学

地球科学

CSTPCD北大核心
影响因子：1.447
ISSN：1000-2383
年,卷(期)：2024.49(5)
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