Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study

Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study

Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networ...

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Main Authors: Min Hu, Wei Li, Ke Yan, Zhiwei Ji, Haigen Hu
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2019/7057612
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spelling doaj-c4dbc77eb5094478a9f72c59c90ad0a32020-11-24T23:59:51ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/70576127057612Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative StudyMin Hu0Wei Li1Ke Yan2Zhiwei Ji3Haigen Hu4SHU-UTS SILC Business School, Shanghai University, Shanghai 201800, ChinaCollege of Information Engineering, China Jiliang University, 258 Xueyuan Street, Hangzhou 310018, ChinaDepartment of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, SingaporeSchool of Information & Electronic Engineering, Zhejiang Gongshang University, 18 Xuezheng Road, Hangzhou 310018, ChinaComputer Science and Technology, Zhejiang University of Technology - Pingfeng Campus, 154477 Hangzhou, Zhejiang 310023, ChinaTunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms.http://dx.doi.org/10.1155/2019/7057612
collection DOAJ
language English
format Article
sources DOAJ
author Min Hu
Wei Li
Ke Yan
Zhiwei Ji
Haigen Hu
spellingShingle Min Hu
Wei Li
Ke Yan
Zhiwei Ji
Haigen Hu
Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
Mathematical Problems in Engineering
author_facet Min Hu
Wei Li
Ke Yan
Zhiwei Ji
Haigen Hu
author_sort Min Hu
title Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
title_short Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
title_full Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
title_fullStr Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
title_full_unstemmed Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study
title_sort modern machine learning techniques for univariate tunnel settlement forecasting: a comparative study
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2019-01-01
description Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms.
url http://dx.doi.org/10.1155/2019/7057612
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AT weili modernmachinelearningtechniquesforunivariatetunnelsettlementforecastingacomparativestudy
AT keyan modernmachinelearningtechniquesforunivariatetunnelsettlementforecastingacomparativestudy
AT zhiweiji modernmachinelearningtechniquesforunivariatetunnelsettlementforecastingacomparativestudy
AT haigenhu modernmachinelearningtechniquesforunivariatetunnelsettlementforecastingacomparativestudy
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