:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8
FEATURE PEER-REVIEWED ARTICLE
Vol.12(6) (2021) |
Construction Cost Estimation for Government Building Using Artificial Neural Network Technique
Sitthikorn Sitthikankun (Graduate Program in Construction Engineering and Management, Faculty of Engineering, Chiang Mai University, THAILAND and Department of Civil Engineering, Faculty of Engineering, Chiang Mai University, THAILAND),
Damrongsak Rinchumphu, Chinnapat Buachart (Department of Civil Engineering, Faculty of Engineering, Chiang Mai University, THAILAND),
Eakasit Pacharawongsakda (Big Data Engineering Program, College of Innovative Technology and Engineering, Dhurakij Pundit University, THAILAND).
Disciplinary: Civil & Construction Engineering and Management.
doi: 10.14456/ITJEMAST.2021.112
Keywords: Cost prediction factor; Building cost estimation; Artificial Neural Network (ANN); Machine Learning; Bidding process; Detailed estimation; Construction management.
AbstractThe construction bidding competition required effective precision to prevent losses in the bidding process, especially in the public sector. The bidders must have an estimate of the construction cost before the bidding. There are two widely used methods for construction cost estimation: 1) a rough estimation with an advantage of quick construction estimation cost and a disadvantage of a high price tolerance, and 2) a detailed estimation with an advantage of more accurate estimation construction costs, and disadvantages of the need for a complete construction plan and time-consuming. Considering these disadvantages, research on the government construction cost estimation model was conducted by using the Artificial Neural Network (ANN) technique of forecasting modeling. The study's results showed that the model consisted of two hidden layers which each layer has ten and eight nodes, respectively, with the best Root Mean Square Error (RMSE) value ± 0.331 million Baht. When the new data set was tested for validity, the R2 equal to 0.914 proving the accuracy of the forecasting model as an alternative for government bidding participants to reduce the tolerances and to spend less time to estimate construction costs more efficiently.Paper ID: 12A6G
Cite this article:
Sitthikankun, S., Rinchumphu, D., Buachart, C., Pacharawongsakda, E. (2021). Construction Cost Estimation for Government Building Using Artificial Neural Network Technique. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(6), 12A6G, 1-12. http://doi.org/10.14456/ITJEMAST.2021.112
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Other issues:
Vol.12(5)(2021)
Vol.12(4)(2021)
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Vol.12(2)(2021)
Vol.12(1)(2021)
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