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Application of soft computing techniques in tunnelling and underground excavations: state of the art and future prospects

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Abstract

This article aims to provide a concise review on the state-of-the-art application of soft computing techniques to predict various parameters in tunnelling and underground excavations. Various soft computing techniques involving data mining and machine learning have found their application in the tunnelling-related problems. This article explores the application of artificial neural networks (ANNs), radial basis functions (RBFs), decision trees (DTs), random forest (RF) method, support vector machines (SVMs), nonlinear regression methods such as multi-adaptive regression splines (MARS), and hybrid intelligent models in the prediction of engineering response of tunnels and underground excavations. They help in predicting crucial parameters that decide the serviceability of tunnels and associated structures lying above the tunnel cavity. The researchers working in this domain have utilized the real-time data available from the construction projects in creating various machine learning models. It is observed that there are no proper guidelines to obtain optimal network architecture in ANN for assessing the parameters of the stated problem. RBFs and wavelet neural networks, which evolved from ANN, showed improvement in prediction accuracy. SVM and MARS methods are ornamented with improved computational efficiency and robustness of the algorithm. DT and RF methods are interpretable and computationally less expensive compared to neural networks. Hybrid intelligent models provided globally optimal solutions for nonlinear complex problems than simple neural network models. The limitations of the adopted soft computing methods are also emphasized. Overall, this article provides an intricate insight on the various soft computing techniques used by researchers to improve the performance of the machine learning models.

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Shreyas, S.K., Dey, A. Application of soft computing techniques in tunnelling and underground excavations: state of the art and future prospects. Innov. Infrastruct. Solut. 4, 46 (2019). https://doi.org/10.1007/s41062-019-0234-z

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