Prediction of Interface Shear Stiffness Modulus of Asphalt Pavement using Bagging Ensemble-based Hybrid Machine Learning Model

• Published in: Arabian journal for science and engineering (2011) 2023-10, Vol.48 (10), p.13889-13900
• Main Authors: Bui, Quynh-Anh Thi, Nguyen, Duc Dam, Iqbal, Mudassir, Jalal, Fazal E., Prakash, Indra, Pham, Binh Thai
• Format: Article
• Language: English
• Subjects: Asphalt pavements; Bagging; Concrete aggregates; Concrete pavements; Correlation coefficients; Engineering; Error reduction; Humanities and Social Sciences; Interfacial shear strength; Interlayers; Machine learning; Mathematical models; Model testing; multidisciplinary; Multilayers; Neural networks; Parameters; Prediction models; Research Article-Civil Engineering; Science; Shear stiffness; Shear strength; Shear tests
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-023-08014-1

Interface shear stiffness modulus ( K ) is one of the important bonding properties of layers. It is also used to evaluate interface shear strength between asphalt layers of asphalt pavement. Direct determination of K parameter in field or laboratory requires time, cost and special equipment. In this article, K has been estimated based on three interlayer shear strength affecting factors namely maximum size of the asphalt concrete aggregate ( D max ), normal pressure and temperature using Machine Learning (ML) methods such as Multilayer Perception Neural Network, Bagging Random Forest (Bagging-RF), and Bagging Reduced Error Pruning Tree (Bagging-REPT). The ML models for the prediction of shear strength were built based on the laboratory shear tests results of 180 double-layer asphalt samples. The data was divided randomly into a ratio of 70/30 to train and test model, respectively. Standard statistical measures were used to evaluate and validate the models’ performance. All the developed models performed well in correctly predicting K value of AC, but performance of the Bagging-RF model is the best as it is giving Correlation Coefficient ( R ) value 0.88 between estimated value and determined value. The proposed ML predictive models will reduce the field and laboratory experimental efforts and increase the efficiency in estimating the K parameter for the safe designing, construction and maintenance of asphalt concrete pavements.