Neural network based prediction of ground surface settlements due to tunnelling |
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| Institution: | 1. Underground Structures Group, Civil Engineering Division, Korea Institute of Construction Technology (KICT), 2311 Taehwa-Dong, Ilsan-Gu, Koyang, Kyonggi-Do, 411-712, South Korea;2. Department of Mineral and Petroleum Engineering, Hanyang University, 17 Haengdang-Dong, Sungdong-Gu, Seoul, 133-791, South Korea;3. Department of Civil Engineering, University of Wales Swansea, Singleton Park, Swansea, SA2 8PP, UK;1. School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, People’s Republic of China;2. The State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, People’s Republic of China;1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM, Skudai, Johor, Malaysia;2. AURECON Pty Ltd., Brisbane, Australia;3. Tokyo Electric Power Services Co., Ltd. (TEPSCO), Japan;4. Department of Geological Engineering, Engineering Faculty, Pamukkale University, 20020 Denizli, Turkey;1. State Key Laboratory of Safety and Health for Metal Mines, Sinosteel Maanshan Institute of Mining Research, Co., Ltd., Anhui, Maanshan 243000, China;2. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;3. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam;4. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam;5. Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam;6. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;7. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;8. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia;9. Department of Aerospace engineering, Indian Institute of Science, Bangalore 560012, India;10. Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India;1. College of Civil Engineering, Hunan University, Changsha 410082, China;2. Key Laboratory of Building Safety and Energy Efficiency (Hunan University), Ministry of Education, Changsha 410082, China;3. National Joint Research Center for Building Safety and Environment, Hunan University, Changsha 410082, China |
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| Abstract: | Ground surface settlement due to tunnel excavation varies in magnitude and trend depending on several factors such as tunnel geometry, ground conditions, etc. Although there are several empirical and semi-empirical formulae available for predicting ground surface settlement, most of these do not simultaneously take into consideration all the relevant factors, resulting in inaccurate predictions. In this study, an artificial neural network (ANN) is incorporated with '113' of monitored field results to predict surface settlement for a tunnel site with prescribed conditions. To achieve this, a standard format (a protocol) for a database of monitored field data is first proposed and then used for sorting out a variety of monitored data sets available in KICT. Using the capabilities of pattern recognition and memorization of the ANN, an attempt is made to capture the rich physical characteristics smeared in the database and at the same time filter inherent noise in the monitored data. Here, an optimal neural network model is suggested through preliminary parametric studies. It is shown that preliminary studies for generating an optimal ANN under given training data sets are necessary because no analytical method for this purpose is available to date. In addition, this study introduces a concept of relative strength of effects (RSE) Yang Y, Zhang Q. A heirarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering 1997; 30(4): 207–22] in sensitivity analysis for various major factors affecting the surface settlement in tunnelling. It is seen in some examples that the RSE rationally enables us to recognize the most significant factors of all the contributing factors. Two verification examples are undertaken with the trained ANN using the database created in this study. It is shown from the examples that the ANN has adequately recognized the characteristics of the monitored data sets retaining a generality for further prediction. It is believed that an ANN based hierarchical prediction procedure shown in this paper can be further employed in many kinds of geotechnical engineering problems with inherent uncertainties and imperfections. |
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