Support Vector Machines Approach to HMA Stiffness Prediction

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E*|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E*| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E*| prediction models were developed using the latest comprehensive |E*| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E*| model as well as the artificial neural networks (ANN) based |E*| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.

[1]  Ekambaram Palaneeswaran,et al.  A support vector machine model for contractor prequalification , 2009 .

[2]  A. Goh,et al.  Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data , 2007 .

[3]  Halil Ceylan,et al.  Hot Mix Asphalt Dynamic Modulus Prediction Using Kernel Machines , 2009 .

[4]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[5]  J. C. Helton,et al.  A COMPARISON OF UNCERTAINTY AND SENSITIVITY ANALYSIS TECHNIQUES FOR COMPUTER MODELS , 1985 .

[6]  Halil Ceylan,et al.  Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models , 2009 .

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Pijush Samui,et al.  Support vector machine applied to settlement of shallow foundations on cohesionless soils , 2008 .

[9]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[10]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[11]  Matthew W Witczak,et al.  Development of a New Revised Version of the Witczak E* Predictive Model for Hot Mix Asphalt Mixtures (With Discussion) , 2006 .

[12]  Matthew W Witczak,et al.  Simple Performance Tests: Summary of Recommended Methods and Database , 2005 .

[13]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[14]  M. McKee,et al.  SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES 1 , 2006 .

[15]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[18]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[19]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[20]  Hun Hee Cho,et al.  Application of support vector machines in assessing conceptual cost estimates , 2007 .

[21]  L. Vanajakshi,et al.  A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[22]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[23]  Halil Ceylan,et al.  Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt , 2009 .

[24]  Hongbo Zhao.  Slope reliability analysis using a support vector machine , 2008 .

[25]  Carlos H. Caldas,et al.  Automating hierarchical document classification for construction management information systems , 2003 .

[26]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[27]  Ralf Herbrich,et al.  Learning Kernel Classifiers: Theory and Algorithms , 2001 .

[28]  Halil Ceylan,et al.  Hot Mix Asphalt Dynamic Modulus Prediction Models Using Neural Networks Approach , 2007 .

[29]  Halil Ceylan,et al.  Advanced approaches to hot-mix asphalt dynamic modulus prediction , 2008 .

[30]  Cihan H. Dagli,et al.  Intelligent Engineering Systems Through Artificial Neural Networks: Smart Systems Engineering Computational Intelligence in Architecting Complex Engineering , 2007 .