Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Abstract

Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

Strength Characteristics of Cement-Rice Husk Ash Stabilised Sand-Clay Mixture Reinforced with Polypropylene Fibers

“…Artificial intelligence techniques have large applicability in the prediction of civil engineering's complicated functions (Ahangar-Asr, Johari, & Javadi, 2012; Baziar & Ghorbani, 2005; Nikoo, Torabian Moghadam, & Sadowski, 2015;Rezania, Faramarzi, & Javadi, 2011;Sadowski & Hola, 2015;Ghorbani, Hasanzadehshooiili, & Sadowski 2018). Among them, EPR, which is a new hybrid regression technique, combines the best features of conventional numerical and the genetic programming symbolic regression methods.…”

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

“…An ANN can be applied to establish a model to depict the complicated relationship between the stable status of tunnel support and rock mechanics and construction parameters. BPNN, MLP, RBFNN are the primary neural networks for predicting the interaction of underground structure stability, tunnel support pressure, and groundsupport during deep rock excavation [7,[53][54][55][56].…”

“…According to the review results, the comparison results between the ANNs and other methods are listed in Table Ⅶ. It illustrates that when comparing the ANN models with the optimized ANN models, most of the optimized ANN models outperform the original ANN models [11,56]. Moreover, the advantage of optimized ANNs is not apparent when the optimization algorithm varies.…”

“…Moreover, the advantage of optimized ANNs is not apparent when the optimization algorithm varies. For example, in literature [30,56], the performance of ANN optimized by ICA is better than ANN optimized by PSO. However, in literature [39], an opposite conclusion is drawn: the performance of PSO-ANN is better than that of ICA-ANN.…”

Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model

“…Rezaei Rad and Banazadeh [48] presented a comprehensive application of probabilistic soft computing technique in damage determination of steel structure. Ghorbani et al [49] used the MLP and radial basis function ANNs to predict the support pressure, and develop the ground motion curve in an underground structure (circular tunnel). An elastoplastic, strain-softening rock mass was considered including both single-and double-layer hidden neurons.…”