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Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization

Zhang, X and Nguyen, H and Bui, XN and Anh Le, H and Nguyen-Thoi, T and Moayedi, H and Mahesh, V (2020) Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization. In: Tunnelling and Underground Space Technology, 103 .

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Official URL: https://doi.org/10.1016/j.tust.2020.103517


In this study, a new technique for predicting roadways stability in tunneling and underground space was proposed based on a combination of particle swarm optimization (PSO) algorithm and artificial neural network (ANN), called ANN-PSO model. The dataset from five tunneling and underground mines in the 2006-2019 period was recorded monthly and used for this aim with 145 observations. Accordingly, the stability of roadways in tunneling and underground space was evaluated based on the geomechanical parameters. The uniaxial compressive strength, internal friction angle, rock mass rating, tensile strength, cohesion, density, Young's modulus, shear strength, and slake durability were used as the influence parameters for evaluating and predicting roadway stability. Five other intelligent methods were also developed and compared with the proposed ANN-PSO model in order to have a comprehensive assessment, including support vector machine (SVM), hybrid neural fuzzy inference system (HYFIS), multiple linear regression (MLR), classification and regression tree (CART), and conditional inference tree (CIT). Three model assessment indices, such as MAE, RMSE, and R2 were used to simulate the accuracy of the roadway stability predictive models. Besides, ranking and color intensity techniques were also applied for further assessment. The results showed that the stability of the roadway could be accurately assessed by the proposed ANN-PSO model with an RMSE of 9.708, R2 of 0.972, and MAE of 7.161. They also revealed that the proposed ANN-PSO model yielded the most outperformed over the other models. The sensitivity analysis resulting also indicated that the uniaxial compressive strength, shear strength, quench durability index, density, and rock mass rating were the most important parameters for predicting roadway stability. They should be used in predicting the stability of roadways in tunneling and underground space. © 2020 Elsevier Ltd

Item Type: Journal Article
Publication: Tunnelling and Underground Space Technology
Publisher: Elsevier Ltd
Additional Information: Copyright for this article belongs to Elsevier.
Keywords: Compressive strength; Durability; Elastic moduli; Forecasting; Forestry; Fuzzy inference; Linear regression; Neural networks; Predictive analytics; Rock mechanics; Rocks; Sensitivity analysis; Stability; Support vector machines; Support vector regression; Tensile strength, Classification and regression tree; Comprehensive assessment; Conditional inference; Internal friction angle; Multiple linear regressions; Neural fuzzy inference systems; Particle swarm optimization algorithm; Uniaxial compressive strength, Particle swarm optimization (PSO), artificial neural network; mine; optimization; prediction; road; stability analysis; tunneling
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 12 Nov 2021 18:01
Last Modified: 12 Nov 2021 18:01
URI: http://eprints.iisc.ac.in/id/eprint/66147

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