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

• Published in: The international journal of pavement engineering Vol. 23; no. 6; pp. 2083 - 2098
• Main Authors: Eleyedath, Abhary, Swamy, Aravind Krishna
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 12.05.2022; Taylor & Francis LLC
• Subjects: al-Khateeb model; Asphalt; binder shear modulus; complex modulus; Dynamic modulus; Gene expression; Gene expression programming; Hirsch model; Machine learning; machine learning technique; Pavement design; Prediction models; Predictions; principal component analysis; Principal components analysis; Redundancy; Regression models; Witczak model
• ISSN: 1029-8436; 1477-268X
• DOI: 10.1080/10298436.2020.1841191

Due to the issues like operational difficulties, and extensive resource requirements at pavement design stage, dynamic modulus ( ) 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 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 predictive models. The second set of 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.