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基于PSO-LSSVM-BP模型的高边坡力学参数反分析及稳定性评价

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基于粒子群优化(PSO)算法和最小二乘支持向量机(LSSVM)算法构建非线性映射关系,结合反向传播(BP)神经网络对非线性映射关系生成的数据库进行机器学习,构建了 PSO-LSSVM-BP模型确定最优岩体力学参数.PSO-LSSVM-BP模型以高边坡监测位移数据作为输入信息,通过反分析获得高边坡岩体力学参数,将反分析参数用于FLAC3D位移数值计算,结果表明模拟结果与监测数据吻合较好,验证了该模型的可行性和有效性.基于PSO-LSSVM-BP模型,对不同蓄水位下两河口水电站进水口高边坡稳定性进行了评价,发现水位是影响边坡稳定性的主要因素,随着水位上升,边坡位移逐渐增大,其表面和断层处损伤程度加深,边坡局部点安全系数有所下降,但整体点安全系数均大于1.30,有一定安全裕度.
Inverse analysis of mechanical parameters of high slope based on PSO-LSSVM-BP model and stability evaluation
Based on the Particle Swarm Optimization(PSO)and Least Square Support Vector Machine(LSSVM),a nonlinear mapping relationship is constructed.Combined with the Back Propagation Neural Network(BP)for machine learning on the database generated by the nonlinear mapping relationship,the PSO-LSSVM-BP model is built to determine the optimal mechanical parameters of rock mass.The PSO-LSSVM-BP model takes the monitored displacement data of high slope as input information,obtains the mechanical parameters of high slope rock mass through the inverse analysis,and applies them in the numerical calculations of displacement with FLAC3D.The results show that the simulation results are in good agreement with the monitoring data,which verifies the feasibility and effectiveness of the model.Utilizing the PSO-LSSVM-BP model,a three-dimensional stability evaluation is established on the intake slope of the Lianghekou Hydropower Station at different reservoir water levels.The results indicate that water level is the primary factor affecting the slope stability.As the water level rises,slope displacement gradually increases,with surface and fault damage increasing.While safety coefficients of local point decrease,the overall point safety coefficients remain above 1.30,indicating the intake slope has a certain level of safety margin.

high slopeinverse analysis of mechanical parametersparticle swarm optimizationleast square support vector machineback propagation neural networkLianghekou Hydropower Station

徐卫亚、陈世壮、张贵科、胡明涛、黄威、许晓逸、张海龙、王如宾

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河海大学岩土工程科学研究所,江苏南京 210098

河海大学岩土力学与堤坝工程教育部重点实验室,江苏南京 210098

雅砻江流域水电开发有限公司,四川成都 610051

高边坡 力学参数反分析 粒子群优化 最小二乘向量机 反向传播神经网络 两河口水电站

国家自然科学基金重点项目雅砻江流域水电开发有限公司科技项目

51939004YLGZ-GZA-ZG2021113

2024

10.3876/j.issn.1000-1980.2024.05.007

河海大学学报(自然科学版)
河海大学

河海大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.803
ISSN：1000-1980
年,卷(期)：2024.52(5)