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 Optimized artificial intelligence models for predicting project bid award price
Tác giả hoặc Nhóm tác giả: Jui-Sheng Chou, Chih-Wei Lin, Anh-Duc Pham, Ji-Yao Shao
Nơi đăng: Automation in Construction (SCIE)
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; Số: 54;Từ->đến trang: 106-115;Năm: 2015
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Bridges are essential components of transportation systems. The bidding process is the main determinant of whether a contractor is commissioned to complete a construction project. Therefore, contractors must rapidly and precisely estimate construction costs and the bid award amount. This study involved optimizing artificial intelligence models to forecast bid award amounts for bridge construction projects. A genetic algorithm is used in several forecasting models, including models based on multiple regression analysis, artificial neural networks (ANNs), and case-based reasoning (CBR). Data for public bridge construction projects were collected from the Taiwan government e-procurement system. The cross-validation results show that the mathematical model for the ANNs provides more reliable simulations and has a superior fit compared with the regression methods, CBR, and the conventional approach. This study provides an optimization process for estimating project award prices that improves construction and evaluations of AI-based models as well as an auxiliary tool that contractors can use to make bidding decisions.
ABSTRACT
Bridges are essential components of transportation systems. The bidding process is the main determinant of whether a contractor is commissioned to complete a construction project. Therefore, contractors must rapidly and precisely estimate construction costs and the bid award amount. This study involved optimizing artificial intelligence models to forecast bid award amounts for bridge construction projects. A genetic algorithm is used in several forecasting models, including models based on multiple regression analysis, artificial neural networks (ANNs), and case-based reasoning (CBR). Data for public bridge construction projects were collected from the Taiwan government e-procurement system. The cross-validation results show that the mathematical model for the ANNs provides more reliable simulations and has a superior fit compared with the regression methods, CBR, and the conventional approach. This study provides an optimization process for estimating project award prices that improves construction and evaluations of AI-based models as well as an auxiliary tool that contractors can use to make bidding decisions.
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