Operational Tunnel Model Generation Using Reinforcement Learning

Reinforcement Learning (RL) has emerged as a promising approach to solve complex problems in many different domains, including the Architecture, Engineering, Construction and Operation (AECO) industry. RL is a type of machine learning that focuses on training an agent to interact with an environment in order to maximize a reward signal. In the AECO industry, RL has been used to optimize building design, construction planning and scheduling, and building energy management. This contribution presents an application of RL for the generation of an InfraBIM model of a tunnel built by mechanised excavation using a Tunnel Boring Machine (TBM). In particular, the present application was developed to minimise the distance between the theoretical layout and the operational one to be executed by the TBM, considering the geometry of the ring and the structural joints required for the solidity of the structure. RL has the potential to improve efficiency, sustainability, and safety in the AECO industry by enabling intelligent decision-making and optimization across different phases of the construction process.
QC 20231204