The UTC Graduate School is pleased to announce that Maxine Otieno will present Master’s research titled, VALIDATING THE AASHTOWARE PAVEMENT MECHANISTIC-EMPIRICAL DESIGN USING TENNESSEE PAVEMENT PERFORMANCE DATA FROM THE LONG TERM PAVEMENT PERFORMANCE DATABASE on 03/20/2024 at Between 1pm – 2pm in ECS Building, Maytag Conference Room, #426. Everyone is invited to attend.
Engineering
Chair: Dr. Mbakisya Onyango
Co-Chair:
Abstract:
The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) represents a recent alternative approach to pavement design, developed by the National Cooperative Highway Research Program (NCHRP). Empirical pavement distress prediction models integrated into the associated design software, Pavement Mechanistic-Empirical Design (PMED), were developed and calibrated using national data. Validation of these performance prediction models is therefore crucial before implementing the software at a local scale. Additionally, each revised version of the PMED presents a possibility for re-evaluation in order to account for improvements in the prediction models. This study validates the performance of the current iteration of the PMED (version 2.6.2.2), using pavement performance data of select pavement sections in Tennessee, from the Long Term Pavement Performance (LTPP) Database. Predicted roughness and distress values generated by the software were compared against the corresponding actual values recorded in the LTPP database. Statistical analyses show that the PMED adequately predicts International Roughness Index (IRI) for both Jointed Plain Concrete Pavements (JPCP) and Asphalt Concrete (AC) pavements but notably underestimates percent slab cracking for JPCP, and alligator cracking and total rutting for AC pavements. Additionally, the initial IRI is found to be an important input for the PMED software. These results emphasize the need for local calibration for concrete pavement prediction models and recalibration for flexible pavement prediction models.