Reinforcement learning applied to InfraBIM
Nicola Rimella
Reinforcement learning applied to InfraBIM.
Rel. Anna Osello, Arianna Fonsati. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Edile, 2020
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Abstract
In the last decades, artificial intelligence and machine learning have become very popular showing incredible success in applications ranging from speech recognition to computational biology. However, few have been done in the building or infrastructure field, where the potential of such methodologies is still unexplored. The introduction of BIM (Building Information Modelling) and InfraBIM (in the case of infrastructural projects) for the design, analysis and maintenance of projects makes available tons of data, along with a collection of modelling strategies that can be embedded in machine learning algorithms to solve complex problems. The construction of the tunnels by tunnel boring machine (TBM) starts from the design of a theoretical allignment, which is used by the TBM as guidance to calculate at each step a new as-built allignment through a laser-based machine that deviates as little as possible from the theoretical one.
However, there is a lack of information during the design phase because of the locations of the rings are unknown before starting the cunstruction phase
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