基于人工神经网络和差分演化的边坡稳定性评价

IF 1.1 Q3 ENGINEERING, CIVIL
V. T. Vu
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引用次数: 0

摘要

摘要本研究的目的有两个:首先,使用差分进化方法结合极限平衡方法,找出各种不同边坡配置和土壤参数的安全系数。对路堤的两种模式进行了评估,一种是单层土壤模式,有540种情况,另一种是双层土壤模式,共有24300种情况。其次,利用这些数据对用于预测边坡安全系数的人工神经网络进行训练和测试。实验数据和人工神经网络预测的值具有良好的相关性,线性相关系数约为0.99。在给定足够大的训练数据的情况下,所提出的方法在快速评估边坡稳定性方面显示了其可靠性,而无需长时间搜索临界滑动面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Slope Stability with the Assistance of Artificial Neural Network and Differential Evolution
Abstract This study aims for two purposes: firstly, using the Differential Evolution method combined with limit equilibrium methods to find the factor of safety of a variety of different configurations of slopes and soil parameters. Two patterns of the embankments are assessed, a one-layer soil pattern with 540 cases and a two-layer soil pattern with 24300 cases. Secondly, using these data to train and test an artificial neural network for predicting the factor of safety of slopes. The experimental data and values predicted by the artificial neural network correlate well with a linear coefficient of correlation of around 0.99. Given large enough training data, the proposed approach shows its reliability in quick evaluation of the slope stability without a long process of searching for a critical slip surface.
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来源期刊
CiteScore
2.00
自引率
58.30%
发文量
69
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