AI-Based TBM Performance Models to Predict the Rate of Penetration: An Overview and Perspective

Going underground is one of the inevitable elements of modern transportation infrastructure in sustainable development. Despite the economical and safety benefits of a tunnel boring machine (TBM) when using them to construct tunnels in a broad range of geological conditions, the prediction of TBM performance, especially the rate of penetration (ROP), remains a challenging task. Over the past few decades, this has attracted the attention of numerous researchers to different develop methods ranging from empirical, physical, statistical, and artificially intelligent (AI) techniques. The chapter discusses the existing hard rock TBM performance prediction models developed on the basis on AI algorithms, from the several perspectives. It also highlights a few issues related to the TBM operational data, rock mass, and intact rock properties that affect the TBMs’ performance and, finally, the tunneling conditions needed to achieve optimal performances are examined.