title: Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.
abstract: In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.
authors.affiliation: Shaanxi Provincial Major Laboratory for Highway Bridge & Tunnel, Chang'an University, Xi'an 710064, China; School of Highway, Chang'an University, Xi'an 710064, China.
authors.forename: Jinxing
authors.initials: J
authors.lastname: Lai
authors.affiliation: School of Highway, Chang'an University, Xi'an 710064, China.
authors.forename: Junling
authors.identifier: 0000-0002-7628-5431
authors.initials: J
authors.lastname: Qiu
authors.affiliation: School of Highway, Chang'an University, Xi'an 710064, China.
authors.forename: Zhihua
authors.identifier: 0000-0003-4168-4990
authors.initials: Z
authors.lastname: Feng
authors.affiliation: Shaanxi Provincial Major Laboratory for Highway Bridge & Tunnel, Chang'an University, Xi'an 710064, China; School of Highway, Chang'an University, Xi'an 710064, China.
authors.forename: Jianxun
authors.identifier: 0000-0002-8174-281X
authors.initials: J
authors.lastname: Chen
authors.affiliation: School of Highway, Chang'an University, Xi'an 710064, China.
authors.forename: Haobo
authors.identifier: 0000-0003-4022-0799
authors.initials: H
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D060389:Construction Industry; D004784:Environmental Monitoring; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012987:Soil
chemical_list: D012987:Soil
references.citation: Comput Intell Neurosci. 2014;2014:429657
references.pmid: 25587266
references.citation: Psychol Rev. 1958 Nov;65(6):386-408
references.pmid: 13602029