PREVENTIVE MAINTENANCE MANAGEMENT USING PREDICTED PAVEMENT CRACK PROGRESSION FROM MMS POINT CLOUD DATA

Abstract

 To efficiently manage pavement maintenance, preventive maintenance approach that takes measures before damage becomes significant is required. This study proposes a method to evaluate the future damage state using the current damage state based on the crack rate and the crack progression predicted from MMS point cloud data. This method was applied to a national highway in the Tohoku region, classifying the crack rate into three diagnostic categories (I, II, III) according to the Pavement Inspection Manual, and the crack progression into two levels ("Low (L)" and "High (H)"). Combining these classifications, the pavement condition was categorized into five groups: I-L, I-H, II-L, II-H, and III. For repairs, areas with a crack rate of III were given the highest priority, followed by areas with a crack rate of II and a high crack progression (II-H). Additionally, it was revealed that there are critical areas (I-H) with a diagnostic category of I but a high crack progression.