Maintenance is a key measure for improving the service condition of existing infrastructure. Various approaches based on mathematical modeling and simulation have been developed to scientifically determine maintenance programs. However, they typically assume that maintenance actions are perfect. This is inconsistent with the practical knowledge that existing neighborhoods cannot be restored to good-as-new condition. Therefore, imperfect maintenance is introduced into the preventive maintenance decision-making process for existing neighborhoods. The maintenance gain is introduced to quantify the degree of recovery from deterioration. The relationship between maintenance effects and frequency is also quantified. Meanwhile, the accelerated deterioration factor is used to describe the gradual increase in deterioration rate after maintenance. A condition-based imperfect preventive maintenance decision-making model for existing neighborhoods considering maintenance imperfections and cumulative impacts is ultimately proposed. The model is solved by Monte Carlo simulation. A maintenance program that achieves a synergistic balance between maintenance effects and economic benefits is determined. The results show that one or two imperfect maintenance actions increase the service period by 30%. Average annual maintenance cost outperforms total maintenance cost for evaluating economic benefits. Additionally, raising the maintenance technology level emerges as an optimal measure for improving the expected effects of maintenance programs. By considering maintenance imperfections and cumulative impacts, deterioration and recovery from it in a manner more aligned with actual maintenance practices are described. The outcome is a decision-making support tool for formulating precise and implementable maintenance programs that aims to promote sustainable development of existing neighborhoods.
Liu et al. (Mon,) studied this question.