流离失所(心理学)
打滑(空气动力学)
峰值地面加速度
强度(物理)
回归分析
回归
人工神经网络
地质学
震级(天文学)
加速度
边坡稳定性
线性回归
理论(学习稳定性)
预测建模
地震动
大地测量学
数学
岩土工程
地震学
统计
工程类
计算机科学
物理
人工智能
机器学习
心理学
经典力学
量子力学
天文
心理治疗师
航空航天工程
作者
Youngkyu Cho,Ellen M. Rathje
出处
期刊:Geo-Extreme 2021
日期:2021-11-04
标识
DOI:10.1061/9780784483695.045
摘要
Generic predictive models of earthquake-induced slope displacement are developed through classical regression analysis and artificial neural networks (ANNs). The maximum displacement on the slope surface at the end of shaking was computed by finite element simulations of 49 slope models subjected to 1,051 earthquake motions. Predictive models of seismic displacement are developed that characterizes the slope in terms of its yield acceleration (ky), the natural period of slope (Tslope), and the relative thickness of the slip surface to the height of the slope (Hratio), and characterizes ground shaking in terms of different ground-motion intensity measures. Across five intensity measures and 10 combinations of intensity measures, peak ground velocity (PGV) is found to be the most efficient and proficient parameter for the displacement prediction, leading to significantly small aleatory variability that is similar to the values derived from the use of multiple intensity measures. The models derived from ANN are similar to those developed from classical regression, although with slightly smaller variability and they did not require development of a complex functional form. These results indicate that ANN may be a viable alternative to classical regression for seismic slope stability models.
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