Classifying apartment defect repair tasks in South Korea: a machine learning approach

Abstract

Managing building defects in the residential environment is an important social issue in South Korea. Therefore, most South Korean construction companies devote a large amount of human resources and economic costs in managing such defects. This paper proposes a machine learning approach for investigating whether a specific defect can be autonomously categorized into one of the categories of repair tasks. To this end, we employed a dataset of 310,044 defect cases (from 656,266 validated cases of 717,550 total collected cases). Three machine learning classifiers (support vector machine, random forest, and logistic regression) with three word embedding methods (bag-of-words, term frequency-inverse document frequency, and Word2Vec) were employed for the classification tasks. The highest yielded results showed more than 99% accuracy, precision, recall, and F1-scores for the random forest classifier with the Word2Vec embedding. Finally, based on these findings, the implications and limitations of this study are discussed. Representatively, the findings of this research can improve the defect management effectiveness of the apartment construction industry in South Korea. Moreover, to contribute to future research, we have made the dataset publicly available.