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Construction Cost Estimation for Government Building Using Artificial Neural Network Technique |
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| รหัสดีโอไอ | |
| Creator | Sitthikorn Sitthikankun, Damrongsak Rinchumphu, Chinnapat Buachart, Eakasit Pacharawongsakda |
| Title | Construction Cost Estimation for Government Building Using Artificial Neural Network Technique |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2564 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 12 |
| Journal No. | 6 |
| Page no. | 12A6G: 1-12 |
| Keyword | Cost prediction factor, Building cost estimation, Artificial Neural Network (ANN), Machine Learning, Bidding process, Detailed estimation, Construction management. |
| URL Website | http://TuEngr.com/Vol12_6.html |
| Website title | ITJEMAST V12(6) 2021 @ TuEngr.com |
| ISSN | 2228-9860 |
| Abstract | The 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. |
