Jumas, Dwifitra and Mohd-Rahim, Faizul Azli and Zainon, Nurshuhada and Utama, Wayudi P. (2018) Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects. Built Environment Project and Asset Management, 8 (4). pp. 348-357. ISSN 2044-124X, DOI https://doi.org/10.1108/BEPAM-11-2017-0111.
Full text not available from this repository.Abstract
Purpose: The purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage. Design/methodology/approach: This paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model. Findings: The result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data. Research limitations/implications: Because the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied. Originality/value: The study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | ANFIS; Building project; Conceptual cost estimation; Cost model; Indonesia; Multiple regression |
| Subjects: | T Technology > TH Building construction |
| Divisions: | Faculty of the Built Environment |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 31 Jul 2019 04:16 |
| Last Modified: | 31 Jul 2019 04:16 |
| URI: | http://eprints.um.edu.my/id/eprint/21732 |
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