Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples

• Published in: Engineering with computers 2018-04, Vol.34 (2), p.307-317
• Main Authors: Khandelwal, Manoj, Marto, Aminaton, Fatemi, Seyed Alireza, Ghoroqi, Mahyar, Armaghani, Danial Jahed, Singh, T. N., Tabrizi, Omid
• Format: Article
• Language: English
• Subjects: Artificial neural networks; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Cohesion; Compressive strength; Computer Science; Computer-Aided Engineering (CAD; Control; Design engineering; Estimation; Genetic algorithms; Geotechnical engineering; Limestone; Math. Applications in Chemistry; Mathematical and Computational Engineering; Mathematical models; Neural networks; Original Article; Performance prediction; Shear strength; Stone; Systems Theory
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-017-0541-y

Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p -wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R 2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results ( R 2  = 0.976 and 0.967 for train and test) were better compared to ANN model results ( R 2  = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples.