Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data
Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-06-01
|
| Series: | Underground Space |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967423001381 |
| _version_ | 1827317831111278592 |
|---|---|
| author | Zilong Zhang Tingting Zhang Xiaozhou Li Daniel Dias |
| author_facet | Zilong Zhang Tingting Zhang Xiaozhou Li Daniel Dias |
| author_sort | Zilong Zhang |
| collection | DOAJ |
| description | Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations. |
| first_indexed | 2024-03-08T14:20:59Z |
| format | Article |
| id | doaj.art-4ceb0bc4398042629b0b6d5a9af3992d |
| institution | Directory Open Access Journal |
| issn | 2467-9674 |
| language | English |
| last_indexed | 2024-04-24T23:47:31Z |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj.art-4ceb0bc4398042629b0b6d5a9af3992d2024-03-15T04:44:08ZengKeAi Communications Co., Ltd.Underground Space2467-96742024-06-01167993Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring dataZilong Zhang0Tingting Zhang1Xiaozhou Li2Daniel Dias3School of Civil Engineering, Central South University, Changsha, Hunan 410075, China; Laboratory 3SR, Grenoble Alpes University, CNRS UMR 5521, Grenoble 38000, FranceLaboratory 3SR, Grenoble Alpes University, CNRS UMR 5521, Grenoble 38000, France; Corresponding author.School of Civil Engineering, Central South University, Changsha, Hunan 410075, ChinaLaboratory 3SR, Grenoble Alpes University, CNRS UMR 5521, Grenoble 38000, FranceNumerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations.http://www.sciencedirect.com/science/article/pii/S2467967423001381Tunnelling-induced ground deformationsSparse dataModel uncertaintiesBayesian model averaging |
| spellingShingle | Zilong Zhang Tingting Zhang Xiaozhou Li Daniel Dias Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data Underground Space Tunnelling-induced ground deformations Sparse data Model uncertainties Bayesian model averaging |
| title | Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| title_full | Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| title_fullStr | Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| title_full_unstemmed | Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| title_short | Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| title_sort | bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data |
| topic | Tunnelling-induced ground deformations Sparse data Model uncertainties Bayesian model averaging |
| url | http://www.sciencedirect.com/science/article/pii/S2467967423001381 |
| work_keys_str_mv | AT zilongzhang bayesianensemblemethodsforpredictinggrounddeformationduetotunnellingwithsparsemonitoringdata AT tingtingzhang bayesianensemblemethodsforpredictinggrounddeformationduetotunnellingwithsparsemonitoringdata AT xiaozhouli bayesianensemblemethodsforpredictinggrounddeformationduetotunnellingwithsparsemonitoringdata AT danieldias bayesianensemblemethodsforpredictinggrounddeformationduetotunnellingwithsparsemonitoringdata |