Analyzing surface settlement factors in single and twin tunnels : A review study

• Main Authors: Huat, Chia Yu, Danial Jahed, Armaghani, Lai, Sai Hin, Hossein, Motaghedi, Panagiotis G., Asteris, Pouyan, Fakharin
• Format: Article
• Language: English
• Published: Elsevier Ltd. 2024
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://ir.unimas.my/id/eprint/44867/2/Analyzing%20surface.pdf; http://ir.unimas.my/id/eprint/44867/; https://www.sciencedirect.com/science/article/pii/S2307187724001226; https://doi.org/10.1016/j.jer.2024.05.009

Surface settlement (SS) resulting from tunnel excavation operations is a critical concern in tunnel engineering due to its potential impact on adjacent structures. This review synthesizes current knowledge on factors influencing SS induced by tunneling activities, focusing on tunnel geometry, soil properties, and operational parameters. Empirical formulas, numerical analyses, and machine learning (ML) techniques are examined for the effectiveness in predicting SS, highlighting the limitations and potential. Key findings underscore the significant influence of tunnel geometry, soil properties and tunnel operational parameters on SS outcomes. However, limitations exist in current studies, including the lack of consideration for diverse soil types and operational parameters like jack force thrust and penetration rate. The study underscores the importance of proper management of tunneling operations, including optimizing face pressure, to mitigate SS risks. Practical implications for practicing engineers include thorough site investigations, risk assessments and comprehensive monitoring programs. Leveraging historical data and ML algorithms can enhance SS prediction accuracy and aid in proactive risk management. Ultimately, mitigating SS risks is crucial for safeguarding existing infrastructure in congested urban areas.