Decision making for road infrastructures in a network based on a policy gradient method

Author:

Sasai Kotaro1,Chouinard Luc E2,Power Gabriel J3,Conciatori David4,Zufferey Nicolas5

Affiliation:

1. Department of Civil Engineering, McGill University, Montreal, QC, Canada (corresponding author: )

2. Department of Civil Engineering, McGill University, Montreal, QC, Canada

3. Département de Finance, Assurance et Immobilier, Université Laval, Quebec, QC, Canada

4. Département de Génie Civil et de Génie des Eaux, Université Laval, Quebec, QC, Canada; Laboratoire ICube CNRS UMR 7357, Département Génie Civil, INSA de Strasbourg, Strasbourg, France

5. Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland

Abstract

Developing proper maintenance and rehabilitation investment plans is vital for prolonging the service life of road infrastructures while preserving the required service level under capital constraints. This paper proposes a reinforcement learning approach for determining an optimal policy of selecting maintenance, repair and rehabilitation alternatives for a network of road infrastructure facilities. The proposed approach is based on a policy gradient method and overcomes the computational complexity of optimisation problems due to a large number of possible combinations of network conditions and maintenance, repair and rehabilitation alternatives. The developed optimal management policy takes into consideration interdependencies among infrastructure facilities in a road network. Numerical studies on concrete bridge decks in road networks are performed to demonstrate the advantage, feasibility and capability of the proposed approach.

Publisher

Emerald

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