Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

TLDR

Accurate estimation of the safety factor in civil engineering, especially for retaining walls, is crucial to reduce risk, yet these structures exhibit complex behavior under dynamic conditions. This study investigates AI methods to predict the safety factor of retaining walls. The authors developed ICA‑ANN and GA‑ANN hybrid models, using wall thickness, stone density, wall height, soil density, and internal friction angle as inputs, and compared their performance to select the best predictor. Both hybrid models predict safety factor accurately, but the ICA‑ANN model performs best for dynamic conditions, demonstrating that AI can help control and secure retaining walls.

Abstract

The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions.