GEMINI, a Novel Software System to Improve the Penetration Rate of a Tunnel Boring Machine

This technical paper presents GEMINI, a software system designed to optimize the performance of tunnel boring machines (TBMs) using machine learning-based techniques. The system key features include an advanced architecture for querying and processing data from the TBM database, big data analysis and visualization in a web browser, a ground-machine interaction model to predict the TBM advance rate and operation anomalies detection via machine learning algorithms to enhance the efficiency and reliability of tunnel excavation operations. In collaboration with ACCIONA, the system has been validated in the Mularroya (Spain) hydraulic tunnel (small diameter) in fractured rock mass with faulted and sheared zones, and implemneted in a larger diameter tunnel in Sao Paulo (Brazil) in weathered granite materials. In both cases, the ground-machine excavation model was trained with excavation data from each tunnel and the model was deployed to provide real-time predictions of the penetration rate during the TBM operation. 1. INTRODUCTION Generally., in tunnelling operations regardless the excavation method, penetration rate and advance rate are the most relevant indicators to deliver the project on time and within budget. In this context, an appropriate methodology to predict their values during operation is a challenge that ACCIONA as a contractor is willing to tackle for mechanized excavations with TBMs in collaboration with SAALG Geomechanics. Geotechnical back-analysis to characterize ground behaviour has received attention from both the academia and the industry since the 80s (Gioda and Maier, 1980) within the context of forensic geotechnics and the Observational Method. It consists in comparing a set of ground measurements that capture the actual ground response with a conceptual model that is able to predict such response by means of some ground-related parameters. The parameters that best represent the ground behaviour are those that minimize the difference between the measurements and model results, expressed as an objective function. SAALG Geomechanics develops and commercializes DAARWIN (Acosta et al. 2023), a cloud-based web application that implements the general workflow of geotechnical back-analysis at the same pace as construction progresses, also referred as real-time back-analysis – RTBA). Its inputs are: i) Finite Elements (FE) as a predictive model of ground behaviour, ii) data from sensors installed within the construction site and uses genetic algorithms as the optimization technique to minimize the objective function (de Santos, 2015).