Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction
Abstract
:1. Introduction
2. Engineering Overview
2.1. Layout of Building Monitoring Points
2.2. Construction Parameters
2.3. Geotechnical Parameters
2.4. Spatial Parameters
3. Set Up Buildings’ Settlement Prediction Model
3.1. Determination of Datasets for Building Settlement Prediction
3.1.1. Determination of the Initial Dataset
3.1.2. Data Standardization and Anomaly Handling
3.2. Set Up LSTM Model
3.2.1. LSTM Model
3.2.2. Network Evaluation Metrics
3.2.3. Selection of Hyperparameters
3.3. Accuracy Validation of LSTM Neural Network Model
4. Accuracy Analysis of LSTM Network Models for Predicting Buildings’ Settlement
4.1. Analysis of the Effect of Construction Parameters on the Accuracy of Model
4.2. Analysis of the Effect of Engineering Geological Parameters on the Accuracy of Model
4.3. Analysis of the Effect of Spatial Parameters on the Accuracy of Model
4.4. Analysis of the Effect of Multi-Point Co-Prediction on the Accuracy of Model
5. Conclusions
- (1)
- Hyperparameters have a key role in the predictive ability of network models. This study explores the effects of batch size and training set ratio on model prediction accuracy and finds that when the batch size and training set ratio are taken as 16 and 60%, respectively, the average values of the network evaluation index RMSE are 0.59 and 0.50, respectively, and the RMSE is smaller, at which time the model established has the best prediction effect.
- (2)
- When the settlement of the buildings around the foundation pit constructed by the full reversal method is selected for prediction, the prediction accuracy of the network model reaches 14.88%, which is about 3% higher than that of the open excavation method, indicating that the prediction value of the long- and short-term memory neural network model is more accurate when the settlement of the buildings around the foundation pit constructed by the full reversal method is used.
- (3)
- The LSTM neural network model established in this paper for predicting the settlement of adjacent buildings caused by deep foundation pit construction has a model accuracy of more than 82%, and the model prediction accuracy is related to the construction parameters (construction conditions), engineering geological parameters (soil gravity, cohesion, friction angle, Poisson’s ratio, and pore space ratio), and the more carefully the parameters are considered, the more accurately the model prediction is formulated. The evaluation metrics MAE and R2 were improved by 10.96% percent and 30%, respectively, compared with the use of an Artificial Neural Network (ANN).
- (4)
- The accuracy of the network model for predicting single-point settlement using multi-point settlement of multiple buildings collaboratively is 88%. However, the accuracy of the network model predicting single-point settlement using single-point settlement is only 80%, which is about 8% lower than the accuracy of the collaborative prediction using multiple monitoring points.
- (5)
- This study investigates the feasibility of using a data-driven model (LSTM neural network) to predict the settlement of surrounding buildings caused by deep foundation pit excavation, and the results of the study can provide a certain basis for the construction of foundation pit projects. The LSTM neural network has the capability of predicting the settlement of surrounding buildings caused by the excavation of a foundation pit with high accuracy. The authors will subsequently try to develop physics-based machine learning to optimize the prediction results of the model.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Liu, L.; Li, Z.; Cai, G.; Liu, X.; Yan, S. Humidity field characteristics in road embankment constructed with recycled construction wastes. J. Clean. Prod. 2020, 259, 120977. [Google Scholar] [CrossRef]
- Qiao, S.; Tan, J.; Zhang, Y.; Wan, L.; Zhang, M.; Tang, J.; He, Q. Settlement prediction of foundation pit excavation based on the GWO-ELM model considering different states of influence. Adv. Civ. Eng. 2021, 2021, 8896210. [Google Scholar]
- Xu, C.; Yue, D.; Deng, C. Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. Eng. App. Art. Int. 2012, 25, 468–475. [Google Scholar] [CrossRef]
- Zhou, N.; Vermeer, P.; Lou, R.; Tang, Y.; Jiang, S. Numerical simulation of deep foundation pit dewatering and optimization of controlling land subsidence. Eng. Geo. 2010, 114, 251–260. [Google Scholar] [CrossRef]
- Gero, M.; Gayarre, F.; Fernandez, C.; Llardent, M. Forensic analysis of the failure of the foundations of a tunnel built to channel the course of a river. Eng. Fail. Anal. 2013, 32, 152–166. [Google Scholar] [CrossRef]
- Wang, G.; Chen, W.; Cao, L.; Li, Y.; Liu, S.; Yu, J.; Wang, B. Retaining technology for deep foundation pit excavation adjacent to high-speed railways based on deformation control. Front. Earth Sci. 2021, 9, 735315. [Google Scholar] [CrossRef]
- Wei, H. Influence of foundation pit excavation and precipitation on settlement of surrounding buildings. Adv. Civ. Eng. 2021, 2021, 6638868. [Google Scholar] [CrossRef]
- Lu, C.; Chen, B.; Hu, Y. Environmental impacts of the abnormal settlement of surrounding buildings caused by deep pit excavation in Shanghai. Ekoloji Derg. 2018, 27, 1337–1343. [Google Scholar]
- Tan, Y.; Huang, R.; Kang, Z.; Bin, W. Covered semi-top-down excavation of subway station surrounded by closely spaced buildings in downtown Shanghai: Building response. J. Perform. Constr. Facil. 2016, 30, 04016040. [Google Scholar] [CrossRef]
- Zhang, X.; Yang, J.; Zhang, Y.; Gao, Y. Cause investigation of damages in existing building adjacent to foundation pit in construction. Eng. Fail. Anal. 2018, 83, 117–124. [Google Scholar] [CrossRef]
- Mangushev, R.A.; Osokin, A.I.; Garnyk, L.V. Experience in preserving adjacent buildings during excavation of large foundation pits under conditions of dense development. Soil Mech. Found. 2016, 53, 291–297. [Google Scholar] [CrossRef]
- Zhu, M.; Li, S.; Wei, X.; Wang, P. Prediction and stability assessment of soft foundation settlement of the fishbone-shaped dike near the estuary of the Yangtze river using machine learning methods. Sustainability 2021, 13, 3744. [Google Scholar] [CrossRef]
- Ou, C.; Teng, F.; Li, C. A simplified estimation of excavation-induced ground movements for adjacent building damage potential assessment. Tunn. Undergr. Space Technol. 2020, 106, 103561. [Google Scholar] [CrossRef]
- Zhang, J.; Qin, Y.; Zhang, X.; Che, G.; Sun, X.; Duo, H. Application of non-equidistant GM (1, 1) model based on the fractional-order accumulation in building settlement monitoring. J. Intell. Fuzzy Syst. 2022, 42, 1559–1573. [Google Scholar] [CrossRef]
- Wei, J.; Jiang, H.; Diao, J. Application of non-equidistant gray model based on optimization of background value in settlement prediction. IOP Conf. Ser. Earth Environ. Sci. 2021, 636, 012004. [Google Scholar] [CrossRef]
- Zhang, J.; Dias, D.; An, L.; Li, C. Applying a novel slime mould algorithm-based artificial neural network to predict the settlement of a single footing on a soft soil reinforced by rigid inclusions. Mech. Adv. Mater Struct. 2022, 31, 422–437. [Google Scholar] [CrossRef]
- Shallal, H.; Aljanabi, Q. Prediction of gypseous soil settlement using artificial neural network (ANN). Diyala J. Eng. Sci. 2022, 15, 89–95. [Google Scholar] [CrossRef]
- Miao, J.; Wang, F. Inverse analysis of subgrade reaction coefficient of subway based on computer neural network. Geotech. Geol. Eng. 2023, 41, 1–13. [Google Scholar] [CrossRef]
- Moghaddasi, M.; Noorian-Bidgoli, M. ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling. Tunn. Undergr. Space Technol. 2018, 79, 197–209. [Google Scholar] [CrossRef]
- Su, Y.; Wang, X.; Fu, Y.; Zheng, X.; You, G. Research on surface settlement prediction based on the combination prediction model of s-shaped growth curves. Geo. Eng. 2018, 21, 236–241. [Google Scholar] [CrossRef]
- Zhang, K.; Lyu, H.; Shen, S.; Zhou, A.; Yin, Z. Evolutionary hybrid neural network approach to predict shield tunneling induced ground settlements. Tunn. Undergr. Space Technol. 2020, 106, 103594. [Google Scholar]
- Shi, J.; Ortigao, J.; Bai, J. Modular neural networks for predicting settlements during tunneling. J. Geo. Geo. Eng. 1998, 124, 389–395. [Google Scholar] [CrossRef]
- Zhang, N.; Zhou, A.; Pan, Y.; Shen, S. Measurement and prediction of tunneling-induced ground settlement in karst region by using expanding deep learning method. Measurement 2021, 183, 109700. [Google Scholar] [CrossRef]
- Fernández, S.; Graves, A.; Schmidhuber, J. Sequence labelling in structured domains with hierarchical recurrent neural networks. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6–12 January 2007; Morgan Kaufmann: San Mateo, CA, USA, 2007. [Google Scholar]
- Hsu, W.; Zhang, Y.; Lee, A.; Glass, J. Exploiting depth and highway connections in convolutional recurrent deep neural networks for speech recognition. Cell 2016, 50, 395–399. [Google Scholar]
- Palangi, H.; Deng, L.; Shen, Y.; Gao, J.; He, X.; Chen, J.; Ward, R. Deep sentence embedding using the long short-term memory network: Analysis and application to information retrieval. IEEE/ACM Trans. Audio Speech Lang. Process. 2015, 24, 694–707. [Google Scholar] [CrossRef]
- Mallinar, N.; Rosset, C. Deep canonically correlated LSTMs. arXiv 2018, arXiv:1801.05407. [Google Scholar]
- Mahmoodzadeh, A.; Mohammadi, M.; Salim, S.; Ali, H.; Ibrahim, H.; Abdulhamid, S.; Nejati, H.; Rashidi, S. Machine Learning Techniques to Predict Rock Strength Parameters. Rock Mech. Rock Eng. 2022, 55, 1721–1741. [Google Scholar] [CrossRef]
- Kumar, P.; Sihag, P.; Chaturvedi, P.; Uday, K.; Dutt, V. BS-LSTM: An ensemble recurrent approach to forecasting soil movements in the real world. Front. Earth. Sci. 2021, 9, 696792. [Google Scholar] [CrossRef]
- Li, B.; Li, R.; Sun, T.; Gong, A.; Tian, F.; Khan, M.; Ni, G. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau. J. Hydro. 2023, 620, 129401. [Google Scholar] [CrossRef]
- Wang, H.; Xu, Y.; Tang, S.; Wu, L.; Cao, W.; Huang, X. Well log prediction while drilling using seismic impedance with an improved LSTM artificial neural networks. Front. Earth Sci. 2023, 11, 1153619. [Google Scholar] [CrossRef]
- Chen, K.; Zhou, Y.; Dai, F. A LSTM-based method for stock returns prediction: A case study of China stock market. In Proceedings of the 2015 IEEE International Conference on Big Data, Santa Clara, CA, USA, 29 October–1 November 2015. [Google Scholar]
- Chen, S.; Ge, L. Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction. Qua. Financ. 2019, 19, 1507–1515. [Google Scholar] [CrossRef]
- Liu, S.; Zhang, C.; Ma, J. CNN-LSTM neural network model for quantitative strategy analysis in stock markets. In Proceedings of the Neural Information Processing, Guangzhou, China, 14–18 November 2017. [Google Scholar]
- Zhang, L.; Wu, X.; Ji, W.; AbouRizk, S.M. Intelligent approach to estimation of tunnel-induced ground settlement using wavelet packet and support vector machines. J. Comput. Civ. Eng. 2017, 31, 04016053. [Google Scholar] [CrossRef]
- Kim, T.; Cho, S. Predicting residential energy consumption using CNN-LSTM neural networks. Energy 2019, 182, 72–81. [Google Scholar] [CrossRef]
- Iervolino, I. Asymptotic behavior of seismic hazard curves. Struct. Saf. 2022, 99, 102264. [Google Scholar] [CrossRef]
- Lagomarsino, S.; Cattari, S.; Angiolilli, M.; Bracchi, S.; Rota, M.; Penna, A. Modelling and seismic response analysis of existing URM structures. Part 2: Archetypes of Italian historical buildings. J. Earthq. Eng. 2023, 27, 1849–1874. [Google Scholar] [CrossRef]
- Aloisio, A.; De Santis, Y.; Irti, F.; Pasca, D.P.; Scimia, L.; Fragiacomo, M. Machine learning predictions of code-based seismic vulnerability for reinforced concrete and masonry buildings: Insights from a 300-building database. Eng. Struct. 2024, 301, 117295. [Google Scholar] [CrossRef]
- Tang, L.; Na, S. Comparison of machine learning methods for ground settlement prediction with different tunneling datasets. J. Rock Mech. Geotech. Eng. 2021, 13, 1274–1289. [Google Scholar] [CrossRef]
- Vu, N.M.; Nguyen, L.V.; Dao, L.P. Methods for mitigating effects induced by tunnelling on nearby existing buildings in cities. J. Min. Earth Sci. 2020, 61, 57–65. [Google Scholar] [CrossRef]
- Mohammadi, S.D.; Naseri, F.; Alipoor, S. Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel. Tehran. Bull. Eng. Geol. Environ. 2015, 74, 827–843. [Google Scholar] [CrossRef]
- Tan, Y.; Wang, D. Structural behaviors of large underground earth-retaining systems in Shanghai. II: Multipropped rectangular diaphragm wall. J. Perform. Constr. Facil. 2015, 29, 04014059. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef] [PubMed]
- Li, G.; Zhao, X.; Fan, C.; Fang, X.; Li, F.; Wu, Y. Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions. J. Build. Eng. 2021, 43, 103182. [Google Scholar] [CrossRef]
- Kandel, I.; Castelli, M. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. Ict Express 2020, 6, 312–315. [Google Scholar] [CrossRef]
- Darabi, A.; Ahangari, K.; Noorzad, A.; Arab, A. Subsidence estimation utilizing various approaches–A case study: Tehran No. 3 subway line. Tunn. Undergr. Space Technol. 2012, 31, 117–127. [Google Scholar] [CrossRef]
Classification | Specific Methods |
---|---|
Traditional methods | Numerical modeling |
Theoretical equations | |
Empirical methods | Grey models |
Neural networks | |
Regression models | |
Curve-fitting methods |
Working Conditions | Open Cut | Total Reverse |
---|---|---|
1 | Ground Stress Equilibrium | Ground Stress Equilibrium |
2 | Construction of enclosing structure | Construction of enclosing structure |
3 | Construction of soil reinforcement at the base of the pit | Construction of soil reinforcement at the base of the pit |
4 | First layer of earth excavation and shoring | First layer excavation and shoring and pouring of the roof slab |
5 | Excavation layer by layer | Excavation layer by layer |
Soil Layer Number | Soil Layer Name | Thickness/m | Unit Weight/kN·m−3 | Cohesion/kPa | Internal Friction Angle/° | Void’s Ratio | Poisson’s Ratio |
---|---|---|---|---|---|---|---|
1 | fill | 1.9 | 17.5 | 8 | 10 | 1.06 | 0.31 |
2 | clay | 1.6 | 17.7 | 18 | 17 | 1.10 | 0.33 |
3 | silty chalky clay | 4.2 | 17.1 | 13 | 12 | 1.27 | 0.34 |
4 | silty clay | 11.3 | 16.5 | 11 | 13 | 1.46 | 0.36 |
5 | clay | 3.5 | 17.2 | 16 | 15 | 1.21 | 0.34 |
6 | clayey chalk with chalky clay | 4.5 | 17.9 | 21 | 12 | 1.11 | 0.32 |
7 | sandy chalk | 6.8 | 17.9 | 31 | 4 | 0.99 | 0.28 |
Hyperparameters | Value | RMSE (mm) |
---|---|---|
Batch size | 8 | 0.64 |
16 | 0.59 | |
32 | 0.61 | |
64 | 0.73 | |
128 | 0.71 | |
Training set ratio | 90% | 0.63 |
80% | 0.61 | |
70% | 0.67 | |
60% | 0.50 | |
50% | 0.55 |
Input Parameters | RMSE (mm) | MAE (mm) | R2 |
---|---|---|---|
Condition | 0.78 | 0.85 | 0.85 |
Geological | 0.68 | 0.73 | 0.88 |
Spatial | 0.57 | 0.65 | 0.91 |
Shallal | / | 0.73 | 0.70 |
Ou | / | / | 0.95 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xu, S.; Xu, J. Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction. Appl. Sci. 2024, 14, 2143. https://doi.org/10.3390/app14052143
Xu S, Xu J. Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction. Applied Sciences. 2024; 14(5):2143. https://doi.org/10.3390/app14052143
Chicago/Turabian StyleXu, Shuting, and Jinming Xu. 2024. "Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction" Applied Sciences 14, no. 5: 2143. https://doi.org/10.3390/app14052143
APA StyleXu, S., & Xu, J. (2024). Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction. Applied Sciences, 14(5), 2143. https://doi.org/10.3390/app14052143