Defect classification from text descriptions written by inspectors during the construction stage can be highly beneficial, offering advantages such as cost savings and improved reputation of apartment complexes by allowing early identification and resolution of issues. Combining automated methods with textual data can facilitate the rapid identification and diagnosis of faults. To develop such automated methods, this research constructed a dataset from real-world data collected from three apartment complexes. This study classifies fire door defects into eight categories: frame gap, door closer adjustment defect, contamination, dent, scratch, sealing components, mechanical operation components, and others. The level of detail in this classification ensures a comprehensive understanding of fire door issues. The main contributions of this dataset to the field are twofold. First, it represents a unique dataset based on real-world fire door defect descriptions, which is currently non-existent in this domain. Second, the dataset's expert labeling adds significant value by ensuring accurate fault classification. We hope this dataset will encourage the development of robust text classification techniques suitable for real-world applications by providing a reliable benchmark.
