Application of adaboost to the retaining wall method selection in construction

The appropriate selection of construction methods is a critical factor in the successful completion of any construction project. Artificial intelligence techniques are widely used to assist in the selection of a construction method. This paper proposes the use of the adaptive boosting (AdaBoost) model to select an appropriate retaining wall method suitable for particular construction site conditions, in order to examine the applicability of AdaBoost in construction method selection. To verify its applicability, the proposed model was compared with a support vector machine (SVM) model, which have been attracting attention for their high performance in various classification problems. The AdaBoost model showed a slightly more accurate result than the SVM model in the selection of retaining wall methods, demonstrating that AdaBoost has advantages (e.g., robustness against defective data with missing values) in application to decision support systems. Moreover, the AdaBoost model can be used in future projects to assist engineers in determining the appropriate construction method, such as a retaining wall method, at an early stage of the project.

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

[2]  S. Mallat,et al.  Adaptive covariance estimation of locally stationary processes , 1998 .

[3]  박은주.  A)Comparison of SVM and boosting methods and their application for credit scoring , 2005 .

[4]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[5]  Robert Weibel,et al.  An Approach for the Classification of Urban Building Structures Based on Discriminant Analysis Techniques , 2008, Trans. GIS.

[6]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[7]  Myung Jin Chung,et al.  Efficient rectangle feature extraction for real-time facial expression recognition based on AdaBoost , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[9]  Kim Mee-Kyung,et al.  An Empirical Comparison of Bagging, Boosting and Support Vector Machine Classifiers in Data Mining , 2005 .

[10]  David Arditi,et al.  Predicting the Outcome of Construction Litigation Using Boosted Decision Trees , 2005 .

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[13]  Nie-Jia Yau,et al.  Inducing rules for selecting retaining wall systems , 1999 .

[14]  U-Yeol Park,et al.  A Study on the Selection Model of Retaining Wall Methods Using Support Vector Machines , 2006 .

[15]  Jian-Hua Yin,et al.  An expert system for selection of retaining walls and groundwater controls in deep excavation , 2003 .

[16]  Jyh-Bin Yang,et al.  A rule induction-based knowledge system for retaining wall selection , 2002, Expert Syst. Appl..

[17]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  David A. Elizondo,et al.  Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks , 2008, Decis. Support Syst..

[20]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.