Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Abstract

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Keywords

• automated pavement damage detection technology
• K-means clustering analysis
• maintenance decision-making
• pavement management platform