Seismic performance prediction of slope reinforcement measures is an essential and significant problem in structure design and monitoring stage. Enlightened by the advancement of Recurrent Neural Network (RNN) in geotechnical engineering, this paper develops RNN models integrated with discrete wavelet transform and Dung Beetle Optimization (DBO) algorithms for predicting the dynamic response of slope-pile-anchor coupled reinforcement systems. Centrifuge shaking table tests and moving-steps strategy is used to create database for model training. Results shows that DBO-Bidirectional Long-Short Term Memory (BiLSTM) demonstrate superiority than DBO-RNN, DBO-LSTM and DBO-Gated Recurrent Unit (GRU) due to its unique bidirectional learning ability. Moreover, 20 cases involving artificial waves with an average PGA of 0.1 were predicted using the DBO-BiLSTM model and subsequently compared with measured data for validation. The results indicate that the proposed DBO-BiLSTM model is effective for time-series prediction of seismic responses in slope reinforcement structures. This model provides valuable solutions for the performance design and health monitoring of slope engineering structures, particularly in situations where monitoring data are limited.

中文翻译：

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使用循环神经网络预测边坡-桩-锚耦合加固系统的抗震性能


边坡加固措施的抗震性能预测是结构设计和监测阶段的一个重要且重要的问题。受循环神经网络（RNN）在岩土工程中的进步的启发，本文开发了结合离散小波变换和粪甲虫优化（DBO）算法的RNN模型，用于预测边坡-桩-锚耦合加固系统的动态响应。采用离心机振动台测试和移动步骤策略来创建模型训练数据库。结果表明，DBO-双向长短期记忆（BiLSTM）由于其独特的双向学习能力，比 DBO-RNN、DBO-LSTM 和 DBO-Gated Recurrent Unit（GRU）表现出优越性。此外，使用DBO-BiLSTM模型对20个平均PGA为0.1的人工波案例进行了预测，并随后与实测数据进行比较进行验证。结果表明，所提出的 DBO-BiLSTM 模型对于边坡加固结构地震响应的时间序列预测是有效的。该模型为边坡工程结构的性能设计和健康监测提供了有价值的解决方案，特别是在监测数据有限的情况下。