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Open AccessFeature PaperArticle

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

1
School of Geological and Mining Engineering, Xinjiang University, Urumqi 830047, Xinjiang, China
2
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece
3
Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
4
Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor 81310, Malaysia
5
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1042; https://doi.org/10.3390/app9061042
Received: 13 February 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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. View Full-Text
Keywords: retaining wall; hybrid model; genetic algorithm-artificial neural network (GA-ANN); imperialist competitive algorithm-artificial neural network (ICA-ANN); dynamic conditions; safety factor retaining wall; hybrid model; genetic algorithm-artificial neural network (GA-ANN); imperialist competitive algorithm-artificial neural network (ICA-ANN); dynamic conditions; safety factor
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MDPI and ACS Style

Chen, H.; Asteris, P.G.; Jahed Armaghani, D.; Gordan, B.; Pham, B.T. Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Appl. Sci. 2019, 9, 1042.

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