An Artificial Intelligence-Based Prediction Model for Optimum Design Variables of Reinforced Concrete Retaining Walls
Publication: International Journal of Geomechanics
Volume 21, Issue 12
Abstract
In this study, a hybrid method was developed to predict optimum dimensions of reinforced concrete (RC) cantilever-type retaining walls. The metaheuristic-based optimization process of RC retaining walls needs iterative analysis, including the consideration of two different design limit states. These limit states defined as design constraints are geotechnical and structural limit states. Since the optimization process is long, it is aimed to generate a prediction model for optimum dimensions of RC cantilever retaining walls. For this purpose, artificial intelligence and machine-learning methods can be combined with metaheuristic algorithms used in the optimization of the problem. The method uses artificial neural networks (ANNs) for prediction results without iterative process and flower pollination algorithm (FPA) to obtain training data for machine learning. The proposed hybrid model is called flower pollination algorithm-based artificial neural network (FPA-ANN). To verify the prediction model, mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) values are calculated for the optimum data set. Because this is the first proposed prediction model for a real structural engineering application related to RC cantilever-type retaining walls, the model was tested on different input values that are not included in the machine-learning process. Also, the relations between height, unit soil weight, internal friction angle, and surcharge load are investigated via the prediction model.
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Published In
International Journal of Geomechanics
Volume 21 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 15, 2021
Accepted: Aug 31, 2021
Published online: Oct 14, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 14, 2022
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