Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies
Gang Niu, Xuzhen He, Haoding Xu, Shaoheng Dai
Abstract
Shallow tunnels in urban areas are close to adjacent buildings and municipal pipelines. Ground surface settlement (GSS) due to tunnelling can cause damage to those infrastructures surrounded. Many methods have been proposed for evaluating ground settlement induced by tunnelling, including empirical, analytical, numerical and artificial intelligence methods. This paper reviews the proposed methods in detail based on published 677 articles within past ten years. The principles, assumptions and application scope of those methods are summarized and the advantages and limitations of each method are discussed. Since artificial intelligence (AI) become popular in recent few years, the application of AI in the aspect of tunnelling-induced ground deformation is introduced emphatically. Finally, the challenges of ground displacement prediction by machine learning (ML) are clarified and future research directions are suggested.