Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics

Abstract

Application of Artificial Intelligence (AI) in tunnel construction has the potential to transform the industry by improving efficiency, safety, and cost-effectiveness. This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction. A total of 554 articles published between 2011 and 2023 were collected from the Web of Science (WOS) core collection database and analyzed using CiteSpace software. The analysis identified three main study areas: Tunnel Boring Machine (TBM) performance, construction optimization, and rock and soil mechanics. The review highlights the advancements made in each area, focusing on design and operation, performance prediction models, and fault detection in TBM performance; computer vision and image processing, neural network algorithms, and optimization and decision-making in construction optimization; and geo-properties and behaviours, tunnel stability and excavation, and risk assessment and safety management in rock and soil mechanics. The paper concludes by discussing future research directions, emphasizing the integration of AI with other advanced technologies, real-time decision-making systems, and the management of environmental impacts in tunnel construction. This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolving field.