Analysis of the repair time of finishing works using a probabilistic approach for efficient residential buildings maintenance strategies

Abstract

In general, the long-term maintenance planning of residential buildings is performed based on uniform repair times. However, in fact, various factors, such as the quality and user patterns, affect the performance of residential building components in the Operation and Maintenance (O&M) phase. Hence, various residential building components are repaired at uncertain times, acting as a risk for the residential building maintenance plan. Therefore, an efficient maintenance plan should be established considering maintenance uncertainty. In this regard, this study aims to analyze the uncertainty of repair times for various finishing works in residential buildings based on a probabilistic methodology and outline the implications for the establishment of efficient maintenance strategies in these buildings. Hence, 47,344 repair data for 63 buildings in 12 public residential building complexes completed between 1991 and 2001 in the Republic of Korea were used for analysis. Before the analysis, a repair time matrix was constructed by classifying the finishing works in 25 types and setting service life times to 6–26 years. The repair time distribution for each finishing work was then derived. Results confirmed that basic repair time setting can be performed and various information for reasonable maintenance decision making regarding each finishing work can be provided through a probabilistic approach. The probabilistic approach can be used as a critical decision-making method because there is uncertainty associated with the repair time of each finishing work owing to the performance degradations of various finishing works due to complex causes. Although this study focused on repair time owing to data collection limitations, maintenance strategies with strategic flexibility can be established by developing probabilistic methods that simultaneously consider frequency and cost by securing additional high-quality cost data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.