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The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material

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Abstract

Reliable determination/evaluation of the rock deformation can be useful prior any structural design application. Young’s modulus (E) affords great insight into the characteristics of the rock. However, its direct determination in the laboratory is costly and time-consuming. Therefore, rock deformation prediction through indirect techniques is greatly suggested. This paper describes hybrid particle swarm optimization (PSO)–artificial neural network (ANN) and imperialism competitive algorithm (ICA)–ANN to solve shortcomings of ANN itself. In fact, the influence of PSO and ICA on ANN results in predicting E was studied in this research. By investigating the related studies, the most important parameters of PSO and ICA were identified and a series of parametric studies for their determination were conducted. All models were built using three inputs (Schmidt hammer rebound number, point load index and p-wave velocity) and one output which is E. To have a fair comparison and to show the capability of the hybrid models, a pre-developed ANN model was also constructed to estimate E. Evaluation of the obtained results demonstrated that a higher ability of E prediction is received developing a hybrid ICA–ANN model. Coefficient of determination (R2) values of (0.952, 0.943 and 0.753) and (0.955, 0.949 and 0.712) were obtained for training and testing of ICA–ANN, PSO–ANN and ANN models, respectively. In addition, VAF values near to 100 (95.182 and 95.143 for train and test) were achieved for a developed ICA–ANN hybrid model. The results indicated that the proposed ICA–ANN model can be implemented better in improving performance capacity of ANN model compared to another implemented hybrid model.

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Acknowledgements

This work was financially supported by the National Key Research and Development Plan (2016YFC0501103), Natural Science Foundation of Jiangsu Province (BK20160259), National Natural Science Foundation of China (51774271), National Natural Science Foundation of China (No. 51674245), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Fundamental Research Funds for the Central Universities (No. 2014XT01).

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Authors and Affiliations

  1. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Hua Tian

  2. Shenghua Xinjiang Energy Co., Ltd, Urumqi, 830000, China

    Hua Tian

  3. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Jisen Shu

  4. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Liu Han

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Correspondence to Jisen Shu.

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Tian, H., Shu, J. & Han, L. The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material. Engineering with Computers 35, 305–314 (2019). https://doi.org/10.1007/s00366-018-0600-z

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