ANN prediction model of final construction cost at an early stage
Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-03-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2023.2294883 |
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| author | Khalid S. Al-Gahtani Abdullah M. Alsugair Naif M. Alsanabani Abdulmajeed A. Alabduljabbar Abdulmohsen S. Almohsen |
| author_facet | Khalid S. Al-Gahtani Abdullah M. Alsugair Naif M. Alsanabani Abdulmajeed A. Alabduljabbar Abdulmohsen S. Almohsen |
| author_sort | Khalid S. Al-Gahtani |
| collection | DOAJ |
| description | Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network (ANN) model to predict the FCC based on contract cost (CC), contract duration, and project sector at an early stage of a project. The data collected and used for the ANN model was 135 Saudi Arabian construction projects. The Zavadskas and Turskis logarithmic approaches, and the Pasini method were utilized to overcome the limited data. Then, the ANN model was developed through two stages. The purpose of the first stage was to enhance the data by identifying the abnormal data using absolute percentage errors (APE). The enhanced data was used to develop the ANN in the second stage. The finding showed that the ANN model provided an average MAPE (mean absolute percentage error) of 18.7%. The MAPE of the ANN model is decreased to 8.7% on average by deleting data with an APE higher than 35%. The model allows stakeholders to evaluate the financial importance of potential risks and develop appropriate risk management strategies. |
| format | Article |
| id | doaj-art-9c2b4a99c36c4822b3c1a40bac265e8c |
| institution | Matheson Library |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-9c2b4a99c36c4822b3c1a40bac265e8c2025-07-16T14:34:08ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-03-0124277579910.1080/13467581.2023.22948832294883ANN prediction model of final construction cost at an early stageKhalid S. Al-Gahtani0Abdullah M. Alsugair1Naif M. Alsanabani2Abdulmajeed A. Alabduljabbar3Abdulmohsen S. Almohsen4Department of Civil Engineering, College of Engineering, King Saud UniversityDepartment of Civil Engineering, College of Engineering, King Saud UniversityDepartment of Civil Engineering, College of Engineering, King Saud UniversityDepartment of Civil Engineering, College of Engineering, King Saud UniversityDepartment of Civil Engineering, College of Engineering, King Saud UniversityPrevious studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network (ANN) model to predict the FCC based on contract cost (CC), contract duration, and project sector at an early stage of a project. The data collected and used for the ANN model was 135 Saudi Arabian construction projects. The Zavadskas and Turskis logarithmic approaches, and the Pasini method were utilized to overcome the limited data. Then, the ANN model was developed through two stages. The purpose of the first stage was to enhance the data by identifying the abnormal data using absolute percentage errors (APE). The enhanced data was used to develop the ANN in the second stage. The finding showed that the ANN model provided an average MAPE (mean absolute percentage error) of 18.7%. The MAPE of the ANN model is decreased to 8.7% on average by deleting data with an APE higher than 35%. The model allows stakeholders to evaluate the financial importance of potential risks and develop appropriate risk management strategies.http://dx.doi.org/10.1080/13467581.2023.2294883forecastcostneural networkmapesectordeterminationcontractduration |
| spellingShingle | Khalid S. Al-Gahtani Abdullah M. Alsugair Naif M. Alsanabani Abdulmajeed A. Alabduljabbar Abdulmohsen S. Almohsen ANN prediction model of final construction cost at an early stage Journal of Asian Architecture and Building Engineering forecast cost neural network mape sector determination contract duration |
| title | ANN prediction model of final construction cost at an early stage |
| title_full | ANN prediction model of final construction cost at an early stage |
| title_fullStr | ANN prediction model of final construction cost at an early stage |
| title_full_unstemmed | ANN prediction model of final construction cost at an early stage |
| title_short | ANN prediction model of final construction cost at an early stage |
| title_sort | ann prediction model of final construction cost at an early stage |
| topic | forecast cost neural network mape sector determination contract duration |
| url | http://dx.doi.org/10.1080/13467581.2023.2294883 |
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