AASHTO Journal - AASHTOWare Hosts Calibration Assistance Tool Webinar

A March 19 webinar will provide insights on how to use the Calibration Assistance Tool or CAT to verify and calibrate pavement design performance prediction models in the AASHTOWare Pavement ME Design or PMED software program. To register for this webinar, click here.

[Above image by AASHTO]

A division of the American Association of State Highway and Transportation Officials, AASHTOWare offers a suite of transportation software products delivered through a collaborative business model with state departments of transportation across the country.

AASHTOWare’s PMED program is the next generation of pavement design software, which builds upon the AASHTO mechanistic-empirical pavement design guide.

The software is a production-ready tool designed to support the day-to-day operations of public and private pavement engineers. As a comprehensive pavement design and analysis tool, it supports and insights to highway decision-makers, academia, and consultants.

This March 19 webinar will address a key element of implementation of the AASHTOWare PMED program: the local calibration of the pavement performance prediction models to match the performance of pavement sections within the geographic area of the transportation agency.

As a result, the CAT – created to assist PMED users with verifying and calibrating the design models – helps assess the magnitude and maturity of performance data; aggregates test sections into an experimental matrix; verifies model-fit statistics with global or local calibration coefficients; optimizes calibration coefficients for improved model-fit statistics; validates models with optimized coefficients; and calculates the standard deviation formulas for residual errors of predicted performance.

AASHTOWare noted that its March 19 webinar will explain actions users must take at various steps in the process of using the CAT within the PMED program.

Those actions include determining performance data suitability; evaluating the completeness of the experimental matrix; deciding if the existing models can be used without further optimization based on the verification results; choosing the calibration coefficients and incremental ranges for optimization; assessing the validation results; and selecting the performance data bin distribution for the standard deviation formula.

To sign up for this webinar, click here.