The use of new intelligent techniques in designing retaining walls

The stability of retaining walls against overturning is analyzed in this study using artificial intelligence methods. Five input parameters including wall height, wall thickness, soil friction angle, soil density, and stone cement mixture density were varied and 2000 cases were considered in develop...

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Bibliographic Details
Published in:Engineering with computers 2020, Vol.36 (1), p.283-294
Main Authors: Koopialipoor, Mohammadreza, Murlidhar, Bhatawdekar Ramesh, Hedayat, Ahmadreza, Armaghani, Danial Jahed, Gordan, Behrouz, Mohamad, Edy Tonnizam
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Artificial intelligence
Artificial neural networks
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Density
Math. Applications in Chemistry
Mathematical and Computational Engineering
Mathematical models
Modelling
Optimization
Original Article
Parameters
Prediction models
Retaining walls
Safety factors
Soils
Stability analysis
Systems Theory
Wall thickness
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Summary:The stability of retaining walls against overturning is analyzed in this study using artificial intelligence methods. Five input parameters including wall height, wall thickness, soil friction angle, soil density, and stone cement mixture density were varied and 2000 cases were considered in developing the predictive models. Using the artificial neural network (ANN) method, eight prediction models were developed and evaluated based on the coefficient of determination ( R 2 ) and the root mean square error. R 2 values of 0.9740 and 0.9824 for training and testing datasets, respectively (for the best model), indicate the level of ANN capability in predicting safety factor (SF) of retaining walls. After developing the ANN model, the ant colony optimization (ACO) algorithm was used to maximize the safety factor of the wall by varying the input parameters. In fact, the best ANN model was selected to be used as a modeling function in ACO algorithm. The SF result from optimization section was obtained as 3.057 which show a significant difference from the mean SF values used in the modeling. It can be concluded that ACO may be used as a powerful optimization algorithm in optimizing SF results of retaining walls.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-018-00700-1