| description abstract | This study investigates the effects of different floor surfaces on slip safety in public service buildings (PSBs) with heavy pedestrian traffic. The K-means clustering method is used to classify various floor types and slip safety risks. The dynamic friction coefficient (DCOF) for floor coverings, such as natural stone, ceramic, laminate, and PVC, was measured in both dry and wet conditions across 30 public institutions. These measurements were obtained using the GMG 200 and WESSEX S885 Pendulum testers, providing a comprehensive assessment of the slip resistance of these surfaces. The machine learning models employed in the study were XGBoost, K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC). The models were evaluated using fivefold cross-validation. The analysis revealed that the most significant parameter in DCOF predictions for the XGBoost model was environmental conditions (EC). Performance analysis showed that the SVC model achieved the highest F1 score (0.75 ± 0.01) and AUC value (0.83), outperforming the other models. Additionally, DCOF values from slip tests were grouped into five clusters using the K-means method, and a slip safety risk scale was developed. Statistically significant differences were observed in DCOF values based on usage areas, environmental conditions, test methods, and surface materials. For instance, hospital floors were found to be generally safe in dry conditions but posed a risk in wet conditions. Based on these findings, actionable safety measures were suggested, such as applying antislip coatings in high-risk areas, selecting flooring materials with higher DCOF values for moisture-prone environments, and implementing regular slip resistance testing to maintain safety standards. In conclusion, this study demonstrates that machine learning models can effectively assess the slip resistance of floor surfaces. The findings offer valuable guidance for construction industry professionals and researchers in improving safety measures and minimizing slip risks. Future research with larger datasets and diverse conditions could enhance the understanding of this issue and further improve model performance. | |
