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Developing Two Hybrid Algorithms for Predicting the Elastic Modulus of Intact Rocks

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  • Yuzhen Wang

    (School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
    School of Civil Engineering, Henan Vocational College of Water Conservancy and Environment, Zhengzhou 450008, China)

  • Mohammad Rezaei

    (Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran)

  • Rini Asnida Abdullah

    (Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

  • Mahdi Hasanipanah

    (Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

Abstract

In the primary and final designs of projects related to rock mechanics and engineering geology, one of the key parameters that needs to be taken into account is the intact rock elastic modulus (E). To measure this parameter in a laboratory setting, core samples with high-quality and costly tools are required, which also makes for a time-consuming process. The aim of this study is to assess the effectiveness of two meta-heuristic-driven approaches to predicting E. The models proposed in this paper, which are based on integrated expert systems, hybridize the adaptive neuro-fuzzy inference system (ANFIS) with two optimization algorithms, i.e., the differential evolution (DE) and the firefly algorithm (FA). The performance quality of both ANFIS-DE and ANFIS-FA models was then evaluated by comparing them with ANFIS and neural network (NN) models. The ANFIS-DE and ANFIS-FA models were formed on the basis of the data collected from the Azad and Bakhtiari dam sites in Iran. After applying several statistical criteria, such as root mean square error (RMSE), the ANFIS-FA model was found superior to the ANFIS-DE, ANFIS, and NN models in terms of predicting the E value. Additionally, the sensitivity analysis results showed that the P-wave velocity further influenced E compared with the other independent variables.

Suggested Citation

  • Yuzhen Wang & Mohammad Rezaei & Rini Asnida Abdullah & Mahdi Hasanipanah, 2023. "Developing Two Hybrid Algorithms for Predicting the Elastic Modulus of Intact Rocks," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4230-:d:1081361
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    References listed on IDEAS

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    1. Niaz Muhammad Shahani & Xigui Zheng & Xiaowei Guo & Xin Wei, 2022. "Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
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