Predicting Excavation-Induced Tunnel Response by Process-Based Modelling
Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9048191 |
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| _version_ | 1849406329007898624 |
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| author | Linlong Mu Jianhong Lin Zhenhao Shi Xingyu Kang |
| author_facet | Linlong Mu Jianhong Lin Zhenhao Shi Xingyu Kang |
| author_sort | Linlong Mu |
| collection | DOAJ |
| description | Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries. |
| format | Article |
| id | doaj-art-4ad2b52a483a4a539dec3af7f25eeae6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-4ad2b52a483a4a539dec3af7f25eeae62025-08-20T03:36:26ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/90481919048191Predicting Excavation-Induced Tunnel Response by Process-Based ModellingLinlong Mu0Jianhong Lin1Zhenhao Shi2Xingyu Kang3Department of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaPotential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.http://dx.doi.org/10.1155/2020/9048191 |
| spellingShingle | Linlong Mu Jianhong Lin Zhenhao Shi Xingyu Kang Predicting Excavation-Induced Tunnel Response by Process-Based Modelling Complexity |
| title | Predicting Excavation-Induced Tunnel Response by Process-Based Modelling |
| title_full | Predicting Excavation-Induced Tunnel Response by Process-Based Modelling |
| title_fullStr | Predicting Excavation-Induced Tunnel Response by Process-Based Modelling |
| title_full_unstemmed | Predicting Excavation-Induced Tunnel Response by Process-Based Modelling |
| title_short | Predicting Excavation-Induced Tunnel Response by Process-Based Modelling |
| title_sort | predicting excavation induced tunnel response by process based modelling |
| url | http://dx.doi.org/10.1155/2020/9048191 |
| work_keys_str_mv | AT linlongmu predictingexcavationinducedtunnelresponsebyprocessbasedmodelling AT jianhonglin predictingexcavationinducedtunnelresponsebyprocessbasedmodelling AT zhenhaoshi predictingexcavationinducedtunnelresponsebyprocessbasedmodelling AT xingyukang predictingexcavationinducedtunnelresponsebyprocessbasedmodelling |