Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

ABSTRACT

Due to the issues like operational difficulties, and extensive resource requirements at pavement design stage, dynamic modulus ($\left|E^{\ast }\right|$) is estimated using predictive models. However, these models suffer from issues like systematic bias, prediction accuracy, and extensive testing requirements. This study presents a novel hybrid Principal Component Analysis (PCA) – Gene Expression Programming (GEP) approach to predict the $\left|E^{\ast }\right|$ of asphalt concrete. The database developed during NCHRP 9-19 study was used for developing this methodology. The information of all properties (i.e. variables) was used as input. PCA helped in removing the redundancy at the input stage while reducing the dimensionality. The extracted principal components (PC's) were used to develop first set of $\left|E^{\ast }\right|$ predictive models. The second set of $\left|E^{\ast }\right|$ predictive models were developed using the parameters mostly contributing to the individual PC's. Comparison of these two sets indicated that predictive model obtained using variables as direct input resulted in improved accuracy. Comparison of this finalized model with the existing regression-based equations using goodness of fit indicators indicated that proposed hybrid model offers efficient and accurate alternative. The proposed model has flexibility to be used with any new database with recalibration.

KEYWORDS:

• Dynamic modulus
• Gene expression programming
• principal component analysis
• machine learning technique
• Witczak model
• Hirsch model
• al-Khateeb model
• complex modulus
• binder shear modulus

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data that support the findings of this study are available in Transportation Research Board repository at http://www.trb.org/Main/Public/Blurbs/157541.aspx, reference number NCHRP report Number 547. These data were derived from the following resources available in the public domain: https://trid.trb.org/view/759612.