Prediction of manhole-cover skid-resistance performance using decision trees and regression analysis

This study conducts skid-resistance coefficient tests on 3,600 manhole covers and collects data on various influencing factors, such as surface conditions, pattern types, starting zones, turning zones, usage duration, wheel track locations, and surrounding traffic volume. A preliminary classification analysis of the data is performed using decision trees to identify key factors affecting manhole-cover skid resistance. Subsequently, a multivariate nonlinear regression model is applied to examine the impact of these factors on the target variables. By integrating decision trees and nonlinear regression in a hybrid modeling approach, the study aims to reveal complex patterns in the data and generate reliable predictive results. The decision-tree analysis using non-continuous variables reveals that manhole covers located at turns and starting zones, and those with worn surfaces are more likely to fail to meet safety standards for skid resistance. The multivariate nonlinear regression model, which compares the predicted values against the actual British Pendulum Number (BPN) test results, achieves an accuracy of 82.9%.