Converting detailed estimates to primary estimates with data augmentation |
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| Affiliation: | 1. Laboratory for Artificial Intelligence in Design, Hong Kong, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China;3. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China;1. Engineering of Systems and Environment, University of Virginia, Charlottesville, VA 22903, United States;2. University of California Los Angeles, United States;1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, PR China;2. Beijing Institute of Electronic System Engineering, Beijing, PR China;3. School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, PR China;4. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, PR China;5. HKU-ZIRI Lab for Physical Internet, Dept. of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China |
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| Abstract: | In general, preliminary or primary cost estimates are used to select contractors from among bidders in Japan. The primary cost estimate must be accurate, otherwise the contractor selected from the bidding process will lose profit. A general contractor in the world does not have a super-skilled engineer who can achieve the accurate primary cost estimates. The conventional primary estimate has a high error range and low reliability. An automated system converting detailed estimates to primary estimates has been highly demanded in the world. This paper presents a prototype AI converter that can accurately and automatically convert detailed cost estimates into primary estimates. Converting detailed cost estimates to primary estimates lies in a regression problem. This paper proposes a feature-elimination based data augmentation method for regression problems. The empirical experiment shows that the proposed data augmentation method is quite effective with an Extra-Trees ensemble method. The proposed method was empirically examined by using Colorado Department of Transportation (CDOT) dataset for accurately predicting constructions costs with the Extra-Trees algorithm and random forest algorithm respectively. The CDOT dataset is one and only one of the largest datasets available in public for constructions costs quotation/estimation of roads, bridges and buildings. |
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| Keywords: | Data augmentation Primary estimate Detailed estimate Super-skilled engineers |
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