Support vector regression to predict the performance of stabilized aggregate bases subject to wet-dry cycles

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 36; no. 6; pp. 675 - 696
• Main Authors: Maalouf, Maher, Khoury, Naji, Laguros, Joakim G., Kumin, Hillel
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 25.04.2012; Wiley
• Subjects: Applied sciences; Buildings. Public works; cementitious stabilization; Computation methods. Tables. Charts; Exact sciences and technology; Geotechnics; pavement performance; resilient modulus; Road construction. Pavements. Maintenance; Stabilization. Consolidation; Structural analysis. Stresses; support vector regression; Transportation infrastructure; Performance evaluation; pavement performance; Pavement; Support vector machine; resilient modulus; Forecast model; Modeling; Stabilized soils; Resilience; support vector regression; Experimental result; cementitious stabilization; Cement; Observation; Aggregate(materials)
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.1023

SUMMARY Durability is a notion that is integrated with the performance of stabilized pavement materials. Also, because it can be quantified and measured, it carries significant influence on the design of pavements. This study focuses on using support vector machine, a machine learning algorithm, in assessing the performance of stabilized aggregate bases subject to wet–dry cycles. Support Vector Regression (SVR) is a statistical learning algorithm that is applied to regression problems and is gaining popularity in pavement and geotechnical engineering. In our study, SVR was shown to be superior to the least‐squares (LS) method. Results of this study show that SVR significantly reduces the mean‐squared error (MSE) and improves the coefficient of determination (R2) compared to the widely used LS method. Copyright © 2011 John Wiley & Sons, Ltd.