Efficient Mechanistic-Empirical Analysis of Traffic Speed Deflectometer Device Data for Network-Level Pavement Evaluation

Abstract

Pavement Management Systems (PMS) are crucial for monitoring and maintaining road networks, traditionally relying on surface condition data to guide decision-making. The advent of traffic speed deflection devices (TSDDs) is revolutionizing pavement evaluation by efficiently collecting structural data at traffic speeds, bridging a critical gap in PMS capabilities. However, current TSDD data analysis approaches focus predominantly on basin geometry rather than mechanistic parameters, limiting its potential for comprehensive assessments. This paper examines the adaptation of TONN2010 software, initially developed for falling weight deflectometer (FWD) data, to analyze TSDD data for flexible pavements. TONN2010 offers an efficient framework for back-calculating layer moduli, assessing pavement damage, and evaluating load capacity with minimal user input and computational resources. Through a comprehensive review of the code, numerical simulations, and analysis of TSDD data from nine flexible pavement sections, this study demonstrates the software’s effectiveness in generating reliable mechanistic pavement responses and load-bearing capacities, which can be seamlessly integrated into PMS and pavement evaluation practices. The findings reveal significant correlations between deflection basin geometry parameters and pavement TONN damage indices, highlighting the potential for establishing mechanistic analysis-supported deflection thresholds. The study also identifies areas for improvement, providing a foundation for further enhancing the software’s capabilities.