Center for Transportation Research and Education

About the research

Superheavy loading vehicles, commonly referred to as superloads, exhibit non-standardized loading configurations along with high gross vehicle weights and axle loadings, all of which may cause unexpected distresses on Iowa road infrastructure systems compared to those caused by conventional vehicle class types categorized by the Federal Highway Administration. Superloads encompass various types of vehicles, including implements of husbandry and superheavy loads, prevalent in the Midwestern region of the United States. The determination of critical load factors affecting road damage due to superloads is intricate due to their non-standardized loading configurations and high loading capacities.

This study developed methodologies to quantify superloads and evaluate their impact on Iowa’s road infrastructure, encompassing jointed plain concrete pavements, flexible pavements, and granular roads. It employed extensive mechanistic-based numerical analysis, life-cycle cost analysis, artificial intelligence (AI)-based predictive modeling, forensic investigations, field data analysis, and prototype tool development, with the research aimed at comprehensively evaluating superload impacts on various road types and structures.

Through extensive numerical analyses, incorporating both mechanistic and empirical methodologies, critical findings regarding the effects of different superload types on pavement and granular road distress, associated treatment cost, and service life reduction emerged. Moreover, the Road Infrastructure-Superload Analysis Tool (RISAT) developed in this study has the potential to provide a user-friendly platform for engineers and planners to evaluate structural damages and associated treatment costs induced by superload traffic. The integration of AI-based predictive models into the RISAT enables users to input pavement and superload properties to obtain highly accurate predictions of pavement damages, treatment costs, and service life reductions. Incorporating field data into the RISAT also enhanced its reliability and applicability to pavement management practices, providing engineers and planners with valuable insights for informed decision-making regarding pavement design, maintenance, and rehabilitation strategies.