The present work aims at developing a new neural network–based prediction model for Newmark’s sliding block displacements. The model is developed to predict slope displacement for given earthquake magnitude, focal mechanism, rupture distance, average shear wave velocity of the top 30 m of soil, and critical acceleration of the slope. The network architecture constitutes three layers (only one hidden layer) with nodes per layer 5-5-1. Thus, the network comprises of 36 unknown coefficients. The prediction model utilizes a total of 13,707 data points. Furthermore, inter- and intra-event residuals are evaluated using a mixed-effects algorithm and found to be unbiased, having respective standard deviation accounting to 0.837 and 1.645. The developed slope displacement prediction model is observed to capture the known displacement features, and the patterns are in agreement with the available relations in literature. The applicability of the new model in the estimation of slope displacements hazard is also demonstrated for a representative site in the Himalayan region.

目前的工作旨在为纽马克的滑块位移开发一种基于神经网络的新预测模型。该模型的开发是为了预测给定地震震级，震源机制，破裂距离，土层顶部30 m处的平均剪切波速度以及边坡的临界加速度的边坡位移。网络体系结构由三层组成（仅一个隐藏层），每层5-5-1具有节点。因此，网络包括36个未知系数。预测模型总共利用了13,707个数据点。此外，事件间和事件内残差使用混合效应算法进行评估，并且发现是无偏的，其标准偏差分别为0.837和1.645。观察到开发的边坡位移预测模型可以捕获已知的位移特征，并且这些模式与文献中可用的关系一致。在喜马拉雅地区的一个代表性地点，也证明了新模型在估计边坡位移危险中的适用性。