Rock strength estimation: a PSO-based BP approach

• Published in: Neural computing & applications Vol. 30; no. 5; pp. 1635 - 1646
• Main Authors: Mohamad, E. Tonnizam, Armaghani, D. Jahed, Momeni, E., Yazdavar, A. H., Ebrahimi, M.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-09-2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Back propagation networks; Compressive strength; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Correlation coefficients; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Laboratories; Laboratory tests; Mathematical models; Moisture content; Neural networks; Original Article; P waves; Particle swarm optimization; Performance prediction; Probability and Statistics in Computer Science; Sandstone; Artificial neural network; Particle swarm optimization; Uniaxial compressive strength; Sandstone; Predictive model
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2728-3

Application of back-propagation (BP) artificial neural network (ANN) as an accurate, practical and quick tool in indirect estimation of uniaxial compressive strength (UCS) of rocks has recently been highlighted in the literature. This is mainly due to difficulty in direct determination of UCS in laboratory as preparing the core samples for this test is troublesome and time-consuming. However, ANN technique has some limitations such as getting trapped in local minima. These limitations can be minimized by combining the ANNs with robust optimization algorithms like particle swarm optimization (PSO). This paper gives insight into development of a hybrid PSO–BP predictive model of UCS. For this reason, dataset comprising the results of 228 laboratory tests including dry density, moisture content,
P
wave velocity, point load index test, slake durability index and UCS was prepared. These tests were conducted on 38 sandstone samples which were taken from two excavation sites in Malaysia. Findings showed that PSO–BP model performs well in predicting UCS. Nevertheless, to compare the prediction performance of the PSO–BP model, the UCS is predicted using ANN-based PSO and BP models. The correlation coefficient,
R
, values equal to 0.988 and 0.999 for training and testing datasets, respectively, suggest that the PSO–BP model outperforms the other predictive models.