Application of support vector machines in assessing conceptual cost estimates

Total conceptual cost estimates and the assessment of the quality of these estimates are critical in the early stages of a building construction project. In this study, the support vector machine (SVM) model for assessing the quality of conceptual cost estimates is proposed, and the application of SVM in construction areas is investigated. The results show that the SVM model assessed the quality of conceptual cost estimates slightly more accurately than the discriminant analysis model. This shows that using the SVM has potential in construction areas. In addition, the SVM model can assist clients in their evaluation of the quality of the estimated cost and the probability of exceeding the target cost, and in their decision on whether or not it is necessary to seek a more accurate estimate in the early stages of a project.

[1]  Nello Cristianini,et al.  Advances in Kernel Methods - Support Vector Learning , 1999 .

[2]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[3]  Sung Hoon An,et al.  Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning , 2004 .

[4]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[5]  Srinath Perera,et al.  Cost Studies of Buildings , 2004 .

[6]  Nie-Jia Yau,et al.  Applying case-based reasoning technique to retaining wall selection , 1998 .

[7]  Shigeo Abe Analysis of Multiclass Support Vector Machines , 2002 .

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Richard L. Tucker,et al.  Effective Practice Utilization Using Performance Prediction Software , 2004 .

[10]  Robert P. Parker,et al.  Reliability and Accuracy of the Quarterly Estimates of , 1995 .

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

[12]  Nie-Jia Yau,et al.  Case‐Based Reasoning in Construction Management , 1998 .

[13]  Garold D. Oberlender,et al.  Predicting Accuracy of Early Cost Estimates using Factor Analysis and Multivariate Regression , 2003 .

[14]  Garold D. Oberlender,et al.  Predicting accuracy of early cost estimates based on estimate quality , 2001 .

[15]  Weng Tat Chan,et al.  Case-based reasoning approach in bid decision making , 2001 .

[16]  S. T Ng,et al.  Client and consultant perspectives of prequalification criteria , 1999 .

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

[18]  Akintola Akintoye,et al.  A survey of current cost estimating practices in the UK , 2000 .

[19]  Martin Skitmore Early stage construction price forecasting: a review of performance (RICS Occasional Paper) , 1991 .

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

[21]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

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

[23]  Alfredo Serpell,et al.  Towards a knowledge-based assessment of conceptual cost estimates , 2004 .

[24]  J. Weston,et al.  Support Vector Machines for Multi-class Pattern Recognition 1. K-class Pattern Recognition 2. Solving K-class Problems with Binary Svms , 1999 .