Probabilistic Design of Environmentally Sustainable Reinforced-Concrete Transportation Infrastructure Incorporating Maintenance Optimization

This paper presents a framework for probabilistic sustainability design of reinforced-concrete transportation infrastructure incorporating optimization of maintenance. The framework contains three components: (1)stochastic service life models predicting the deterioration of structural components and repair timing; (2)stochastic lifecycle impact assessment (LCIA) models that estimate the sustainability impacts from construction and maintenance activities; and (3)a lifecycle optimization model (LCO) automating the repair and limit-state selection process to minimize the sustainability impact. In this study, the LCO model is constructed by using a dynamic programming method along with Monte Carlo simulation. A case study is presented in which the authors compute the probability that a maintenance scheme allowing more degradation will meet the CO2 equivalent (CO2-eq) emission reduction target of the United Nations Intergovernmental Panel on Climate Change (IPCC) by 2050. By integrating lifecycle optimization with sustainability design, the proposed framework removes the arbitrary nature of repair and limit-state selection. It also provides a formal method to evaluate and compare different maintenance designs of reinforced-concrete transportation infrastructure toward a higher degree of sustainable development.