Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

作     者：Gordan, Behrouz Koopialipoor, Mohammadreza Clementking, A. Tootoonchi, Hossein Mohamad, Edy Tonnizam 

作者机构：Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Don Bosco Coll PG & Res Dept Comp Sci Yelagiri Hills India Univ Teknol Malaysia Fac Civil Engn Ctr Trop Geoengn GEOTROPIK Johor Baharu 81310 Malaysia 

出 版 物：《ENGINEERING WITH COMPUTERS》 (计算机在工程中的应用)

年 卷 期：2019年第35卷第3期

页      面：945-954页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to express their sincere appreciation to the anonymous reviewers for their valuable and constructive suggestions 

主　　题：Retaining wall Safety factor ANN ABC Optimization algorithm 

摘      要：An important task of geotechnical engineering is a suitable design of safety factor (SF) of retaining wall under both static and dynamic conditions. This paper presents the advantages of both prediction and optimization of retaining wall SF through artificial neural network (ANN) and artificial bee colony (ABC), respectively. These techniques were selected because of their capability in predicting and optimizing science and engineering problems. To gain purpose of this research, a comprehensive database consisted of 2880 datasets of wall height, wall width, wall mass, soil mass and internal angle of friction as input parameters and SF of retaining wall as output was prepared. In fact, SF is considered as a function of the mentioned parameters. At the first step of modeling, several ANN models were constructed and the best one among them was selected. The coefficient of determination (R-2) value of 0.998 for both training and testing datasets was obtained for the best ANN model which indicates an excellent accuracy level in predicting SF values. In the next step of modeling, the results of selected ANN model were used as an input for the optimization technique of ABC. In general, 11 models of ABC optimization with different strategies were built. As a result, by decreasing wall height value from 10m to 8m and 5.628m and using almost constant values for the other input parameters, SF values were obtained as 2.142 and 5.628, respectively. Results of (8.003, 0.794, 0.667, 1800 and 2800) and (5.628, 0.763, 0.660, 1735 and 2679) were obtained for wall height, wall width, internal friction angle, soil mass and wall mass of the best models with 2.142 and 5.628 SF values, respectively.