Construction Conceptual Cost Estimates Using Support Vector Machine

Conceptual cost estimate plays an essential role in project feasibility study. In practice, it is performed based on estimator’s experience. However, due to the inaccuracy of cost estimate, budgeting and cost control are planned and executed inefficiently. Support Vector Machines (SVMs), an Artificial Intelligent technique, is used to conduct the construction cost estimate. The algorithms of SVMs solve a convex optimization problem in a relative short time with satisfied accurate solution. Applying SVMs, the construction conceptual cost estimate model is developed for owners and planners to predict the construction cost of a project. The impact factors of cost estimate are identified through literature review and interview with experts. The cost data of 29 construction projects are used as training cases. Based on the training results, the average prediction error is less than 10% and the computation time is less than 5 minutes. The error is satisfied for the conceptual cost estimate of a project during the planning and conceptual design phase. Case studies show SVMs can efficiently and accurately assist planners to predict the construction cost.