Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

To realize sustainable construction, planning for future maintenance costs is essential. In the case of multi-family housing, various maintenance issues can be expected to appear starting 10 years after completion. Therefore, preventive maintenance must be implemented in a systematic manner to cope with the problems caused by the natural aging of multi-family dwellings and to maintain a sustainable level of quality for the properties. In this study, maintenance costs were investigated for 224 multi-family housing units aged 20 years or older in Seoul, South Korea. Using Monte Carlo simulation in conjunction with expert interviews, a probabilistic maintenance cost analysis was conducted to analyze and estimate the variability in maintenance costs. The findings of the study propose that the use of probabilistic maintenance cost analysis can be developed into a useful planning tool for determining reasonable future maintenance costs in sustainable construction.