Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R 2 . In comparison to statistical models (0.7182 for the value of R 2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling − i.e. the coefficient of determination R 2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 − not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.Keywords: optimal length of rock bolts, artificial neural networks, statistical methods, sensitivity analysis.
Ground Reaction Curve (GRC) is one of the most important elements of convergence-confinement method generally used to design tunnels. Realistic presentation of GRC is usually assessed based on the advanced rock strength criteria, also, rock mass behavior (including plasticity and softening treatments). Since taking these parameters into account is not simply possible for practitioners and needs complicated coupled theoretical-numerical solutions, this paper presents a simple novel approach based on Evolutionary Polynomial Regression to determine GRC of rock masses obeying both Mohr-Coulomb and Hoek-Brown criteria and strain softening behaviors. The proposed models accurately present support pressures based on radial displacement, rock mass strength and softening parameter (determination coefficient of 97.98% and 94.2% respectively for Mohr-Coulomb and Hoek-Brown strain softening materials). The accuracy of the proposed equations are approved through comparing the EPR developed GRCs with the ground reaction curves available in the literature. Besides, the sensitivity analysis is carried out and in-situ stress, residual Hoek-Brown’s m constant and residual dilation angle are introduced as parameters with the most influence on the support pressure in Hoek-Brown and peak and residual geological strength index are the most affective parameters on the support pressure of tunnels in the strain softening Mohr-Coulomb rock mass.
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