Rock strength estimation: a PSO-based BP approach View Full Text


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Article Info

DATE

2016-12-21

AUTHORS

E. Tonnizam Mohamad, D. Jahed Armaghani, E. Momeni, A. H. Yazdavar, M. Ebrahimi

ABSTRACT

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. More... »

PAGES

1-12

References to SciGraph publications

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  • 2015-12-18. Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2010-08-27. Correlation between slake durability and rock properties for some carbonate rocks in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2012-07-03. Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks in ENVIRONMENTAL EARTH SCIENCES
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  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
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  • 2016-05-26. An optimized ANN model based on genetic algorithm for predicting ripping production in NEURAL COMPUTING AND APPLICATIONS
  • 2016-04-06. Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques in ENGINEERING WITH COMPUTERS
  • 2011-03-02. Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks in ENGINEERING WITH COMPUTERS
  • 2015-08-19. Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances in ENGINEERING WITH COMPUTERS
  • 2012-10-17. An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining in NEURAL COMPUTING AND APPLICATIONS
  • 2012-02-12. A neuro-fuzzy approach for prediction of longitudinal wave velocity in NEURAL COMPUTING AND APPLICATIONS
  • 2016-01-07. Rock strength assessment based on regression tree technique in ENGINEERING WITH COMPUTERS
  • 2016-08-08. Estimation of ground vibration produced by blasting operations through intelligent and empirical models in ENVIRONMENTAL EARTH SCIENCES
  • 2015-10-14. Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles in NEURAL COMPUTING AND APPLICATIONS
  • 1998. Parameter selection in particle swarm optimization in EVOLUTIONARY PROGRAMMING VII
  • 2015-12-14. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network in ARABIAN JOURNAL OF GEOSCIENCES
  • 2012-07-29. Correlating P-wave Velocity with the Physico-Mechanical Properties of Different Rocks in PURE AND APPLIED GEOPHYSICS
  • 2016-03-16. A study of soft computing models for prediction of longitudinal wave velocity in ARABIAN JOURNAL OF GEOSCIENCES
  • 2016-04-13. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study in JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
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