Optimisation of Excavation Design Using Artificial Intelligence (AI)

Abstract

Reinforcement learning (RL), a branch of machine learning, incorporating human-level control, is attracting considerable interests in many fields. Currently, there is a transition from academia to real-world prototypes, with RL-examples like the optimization of a manufacturing process, for real-time steering of hydrocarbon drilling, in optimization of power grids and control systems in general, for UAVs, and in robotics and other autonomous vehicles becoming common. However, there is very little published application of RL to geotechnics in general. Therefore, in this study will present a novel RL-based framework for construction process optimization whereby displacements would be optimized by application of Hoeke-Brown criterion and GSI system to excavation design. Such models can act as decision support for the geotechnical engineer, engineering geologist, geotechnician etc. (design choices, progress-planning) and in the long run such models work towards full automation in underground construction. Hence, the model is one of the first attempt to automate decisions made by the geotechnician in excavation design for underground construction.