Clustering-Based Pricing of Inspection Services for Building Structures Affected by Water Leakage

In Taiwan, some cases charge high diagnostic fees based merely on manual visual inspection or other simple checks, which has severely undermined public trust and delayed judicial resolutions, forcing courts to repeatedly appoint alternative evaluators and prolonging dispute timelines. Based on convenient sampling under a 95% confidence level with a 10% margin of error and a 10–90% category proportion, this study analyzes 83 leakage identification cases collected through convenience sampling, covering diverse building types, leakage causes, and detection techniques such as infrared imaging, borescopes, and moisture meters. A clustering-based pricing framework was applied to classify cases by inspection methods and leakage causes and to link them with cost intervals. After rigorous filtering, cost categorization, one-hot encoding, and normalization, the model revealed three distinct cost groups and achieved an overall classification accuracy of 86.75%, with particularly high precision in the medium-cost range. The findings confirm that advanced methods (e.g., borescopes, high-pressure cleaning) correspond to higher fees, while simpler approaches (e.g., infrared imaging) remain in lower cost brackets. This framework supports transparent and standardized fee estimation, addresses long-standing pricing controversies, and enhances consumer trust in leakage diagnostics.