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

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.

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

“…Accuracy of the proposed and applied models was validated by the coefficient of determination (R) and the root mean square error (RMSE) [27][28][29][30]. RMSE is the average squared difference between outputs and targets [31]. It is always positive and its units match the units of the response.…”

“…The R values measure the correlation between outputs and targets [34][35][36][37]. While R can be more easily interpreted, RMSE can be know the amount of predictions deviate, on average, from the actual values in the dataset [31]. The formulas were as follow [38][39][40][41]:…”

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

“…The R-value indicates the correlation between the predicted values, given by the ML models, and the actual values [78]. RMSE is the average squared difference between the predicted and actual values [19,79,80]. MAE measures the average of absolute difference between outputs (predicted values) and targets (actual values) [81][82][83].…”

“…In the past few decades, machine learning (ML) approaches have been the subject of extensive interest and expanding research in the field of materials science [15], especially in structural engineering [8,9,[16][17][18][19]. Currently, the rapid and constant development of computational software and hardware has facilitated the development of numerous alternative computer-aided data mining approaches.…”

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

“…where P p is accuracy and P exp are the expected agreements. RMSE is often used to evaluate the differences between the predicted and target values [57][58][59][60][61][62], it can be calculated using the following equation:…”