Prediction of tunneling-induced ground movement with the multi-layer perceptron
Introduction
In urban areas, it is essential to protect existing adjacent structures and underground facilities from damage due to tunneling. In order to minimize the risk, a tunnel engineer needs to be able to make reliable prediction of ground deformations induced by tunneling. Numerous investigations have been conducted in recent years to predict the settlement associated with tunneling; the selection of appropriate method depends on the complexity of the problems. Many environmental factors associated with tunneling have recently led to a considerable research effort being devoted to the study of settlements caused by tunneling through soft ground.
Progress has been made in recent years in the ability to predict ground movement, but the state-of-the-art is deficient in many ways. On the basis of detailed investigation, a viable approach for the prediction of the ground movement is necessary, and an artificial neural network (ANN) comes in handy to fulfill this approach.
In this paper, measurements of settlement and ground movements recorded in different tunnel projects have been reviewed and analyzed. The data from these case studies were used to train and test the developed neural network model to enable prediction of the magnitude of settlements and ground movements with the help of input variables that have direct physical significance.
Section snippets
Artificial neural network
Artificial neural networks (ANN) are networks of highly interconnected neural computing elements that have the ability to respond to input stimuli and to learn to adapt to the environment. ANN include two working phases, the phase of learning and that of recall. During the learning phase, known data sets are commonly used as a training signal in input and output layers. The recall phase is performed by one pass using the weight obtained in the learning phase. ANN is now a well-established tool
Theoretical background
Construction of a tunnel in soft ground brings about a change in the state of stress and corresponding strains and displacement around the tunnel opening. Therefore, some degree of soil deformation may always be induced. To make reliable forecast of the inevitable ground movements and settlements associated with every design or construction procedures, proper consideration as to type of soil, groundwater conditions geometry and depth of tunnel, etc. is necessary (Thongyot, 1995).
Parameters for surface settlement
Prediction of ground settlement above tunnels has long been a subject of research. A number of comprehensive works have been done based on empirical relationships; some of these relationships are presented in Table 1. Based on these existing models, a number of input parameters are identified namely D, Z, Vs, soil type (soil strength). Effect of groundwater level and construction method on soil behavior has also been incorporated. Although the hydraulic conductivity of clay is small, the
Prediction of maximum settlement
In order to define the relative strength of parameters used in the prediction of maximum tunnel settlement, a comprehensive parametric studies were conducted. At first three parameters namely tunnel depth (Z), diameter (D) and cohesion (Cu) were considered. The four layered neural network with three input parameters, five neurons in hidden layer 1, nine neurons in hidden layer 2 and single output neuron was trained and tested. The network trained with a learning rate of 0.5 conversed in 20,000
Conclusion
To minimize overall project costs and the risk of damage or accident as a result of ground movements due to tunneling, the engineer who designs the project needs to be able to make reliable predictions of the extent and amount of settlement that are likely to arise in various conditions. To fulfill the strategy, a BPNN was developed and used to predict the ground behavior around soft ground tunnels. The neural network demonstrated a promising result and predicted the settlement, width of
References (29)
- et al.
New nested adaptive neural networks (nann) for constitutive modeling
Computers and Geotechnics
(1998) - et al.
Prediction of pile bearing capacity using artificial neural networks
Computers and Geotechnics
(1996) - et al.
Prediction of ground settlements asscociated with shield tunneling
Soils and Foundations
(1978) - Agrawal, G., Frost, J.D., Chameau, C.L.A., 1994. Data analysis and modeling using artificial neural network. In:...
- et al.
Subsidence above shallow tunnels in soft ground
ASCE Journal of Geotechnical and Geoenvironmental Engineering
(1979) - et al.
Ground deformations resulting from shield tunneling in London Clay
Canadian Geotechnical Journal
(1974) - et al.
What size net gives valid generalization?
(1994) - et al.
Design and performance of excavations and tunnels in softclay
Soft Clay Engineering
(1981) Principles of Foundation Engineering
(1999)Fundamentals of Neural Networks: Architectures, Algorithms and Applications
(1994)
Estimation of lateral wall movements in braced excavations using neural networks
Canadian Geotechnical Journal
Neural Networks: A Comprehensive Foundation
Cpt-based liquefaction evaluation using artificial neural networks
Journal of Computer-aided Civil and Infrastructure Engineering
Cpt-based liquefaction analysis. Part i: Determination of limit state function
Geotechnique
Cited by (137)
Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground
2022, Underground Space (China)Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge
2022, Gondwana ResearchCitation Excerpt :To prevent or mitigate the disasters and ensure the geotechnical structures with sufficient safety (Ou et al., 2022), accurate predictions for occurrence of geohazards and reliability analysis for geotechnical structures are urgently needed. In recent years, with rapid development of AI, ML, DL, and OA techniques, these methods are gradually widely adopted to conduct the assessment for the occurrence probability of geohazards and the destroy or deformation of geotechnical structures with sufficient accuracy and efficiency (Darabi et al., 2012; Li et al., 2014; Liang et al., 2012; Luo et al., 2019; Mohammadi et al., 2015; Neaupane and Adhikari, 2006; Shi et al., 2019; Solomatine and Xue, 2004; Zhan et al., 2021). Although there have been some high-level overviews of ML for the prediction of some specific areas in geotechnical engineering such as slopes, tunnels, deep braced excavations (Sheil et al., 2020; Shreyas and Dey, 2019; Zhang et al., 2020a), yet a more comprehensive review of recent research progress and applications of ML, DL and OA in geotechnical engineering and geoscience is quite limited.
Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization
2022, Automation in ConstructionAuto machine learning-based modelling and prediction of excavation-induced tunnel displacement
2022, Journal of Rock Mechanics and Geotechnical EngineeringCitation Excerpt :As an emerging method, machine learning (ML) has been widely concerned in geotechnical engineering, due to the characteristic of high efficiency, first-class generalisation performance, and high-dimensional problem solving ability. Various kinds of ML algorithms have been utilised in specific fields, such as slope and landslides (Liu et al., 2014, 2020; Xu and Niu, 2018), pile settlement (Azizkandi et al., 2014; Armaghani et al., 2018, 2020), characterisation of soil properties (Kurnaz and Kaya, 2018; Ching and Phoon, 2019; Cheng et al., 2020b; Zhang et al., 2020b, 2021a), retaining wall deflection brought about by deep braced excavation (Kung et al., 2007; Ji et al., 2014; Goudjil and Arabet, 2021; Zhang et al., 2021b), ground surface settlement induced by tunnelling (Neaupane and Adhikari, 2006; Pourtaghi and Lotfollahi-Yaghin, 2012; Kohestani et al., 2017; Moeinossadat et al., 2018; Zhang et al., 2019a), and subsurface stratification from limited boreholes (Shi and Wang, 2021a, b). To be specific, Zhang et al. (2020b) developed a non-parametric ensemble artificial intelligence approach to calculate the compression modulus Es of soft clay based on a gradient-boosted regression tree algorithm.
- 1
Former Graduate Student, SIIT, Thammasat University.