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

摘要

This article aims to provide a concise review on the state-of-the-art application of soft computing techniques to predict variousparameters in tunnelling and underground excavations. Various soft computing techniques involving data mining and machinelearning have found their application in the tunnelling-related problems. This article explores the application of artificialneural networks (ANNs), radial basis functions (RBFs), decision trees (DTs), random forest (RF) method, support vectormachines (SVMs), nonlinear regression methods such as multi-adaptive regression splines (MARS), and hybrid intelligentmodels in the prediction of engineering response of tunnels and underground excavations. They help in predicting crucialparameters that decide the serviceability of tunnels and associated structures lying above the tunnel cavity. The researchersworking in this domain have utilized the real-time data available from the construction projects in creating various machinelearning models. It is observed that there are no proper guidelines to obtain optimal network architecture in ANN for assessingthe parameters of the stated problem. RBFs and wavelet neural networks, which evolved from ANN, showed improvement inprediction accuracy. SVM and MARS methods are ornamented with improved computational efficiency and robustness of thealgorithm. DT and RF methods are interpretable and computationally less expensive compared to neural networks. Hybridintelligent 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 insighton the various soft computing techniques used by researchers to improve the performance of the machine learning models.