Abstract
Decision-making in the early stage of a project has a significant impact on the project. However, limited and uncertain information on the project and a complex correlation among various factors that affect the project's construction duration and cost, make it difficult to predict and manage the project. Therefore, this study developed a case-based reasoning (CBR)-based hybrid model with which to predict the construction duration and cost of a project in its early stage. One hundred and one cases among multi-family housing projects that were completed between 2000 and 2005 were used. The CBR-based hybrid model developed in this study is the result of integrating the advantages of (i) prediction methodolo-gies, such as case-based reasoning, multiple regression analysis, and artificial neural networks, (ii) the optimization process using a genetic algorithm, and (iii) the probability distribution and the analysis process of outlier using Monte-Carlo simu-lation. The results of this study are expected to support the owners and managers who are in charge of estimating budget and construction duration in both public and private sectors, in predicting accurately the construction duration and cost at the business planning or early stage of a project.
| Original language | English |
|---|---|
| Pages (from-to) | 739-752 |
| Number of pages | 14 |
| Journal | Canadian Journal of Civil Engineering |
| Volume | 37 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2010 May 1 |
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All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Environmental Science(all)
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A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects. / Koo, Choong Wan; Hong, Taehoon; Hyun, Chang Taek; Koo, Kyo Jin.
In: Canadian Journal of Civil Engineering, Vol. 37, No. 5, 01.05.2010, p. 739-752.Research output: Contribution to journal › Article
TY - JOUR
T1 - A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects
AU - Koo, Choong Wan
AU - Hong, Taehoon
AU - Hyun, Chang Taek
AU - Koo, Kyo Jin
PY - 2010/5/1
Y1 - 2010/5/1
N2 - Decision-making in the early stage of a project has a significant impact on the project. However, limited and uncertain information on the project and a complex correlation among various factors that affect the project's construction duration and cost, make it difficult to predict and manage the project. Therefore, this study developed a case-based reasoning (CBR)-based hybrid model with which to predict the construction duration and cost of a project in its early stage. One hundred and one cases among multi-family housing projects that were completed between 2000 and 2005 were used. The CBR-based hybrid model developed in this study is the result of integrating the advantages of (i) prediction methodolo-gies, such as case-based reasoning, multiple regression analysis, and artificial neural networks, (ii) the optimization process using a genetic algorithm, and (iii) the probability distribution and the analysis process of outlier using Monte-Carlo simu-lation. The results of this study are expected to support the owners and managers who are in charge of estimating budget and construction duration in both public and private sectors, in predicting accurately the construction duration and cost at the business planning or early stage of a project.
AB - Decision-making in the early stage of a project has a significant impact on the project. However, limited and uncertain information on the project and a complex correlation among various factors that affect the project's construction duration and cost, make it difficult to predict and manage the project. Therefore, this study developed a case-based reasoning (CBR)-based hybrid model with which to predict the construction duration and cost of a project in its early stage. One hundred and one cases among multi-family housing projects that were completed between 2000 and 2005 were used. The CBR-based hybrid model developed in this study is the result of integrating the advantages of (i) prediction methodolo-gies, such as case-based reasoning, multiple regression analysis, and artificial neural networks, (ii) the optimization process using a genetic algorithm, and (iii) the probability distribution and the analysis process of outlier using Monte-Carlo simu-lation. The results of this study are expected to support the owners and managers who are in charge of estimating budget and construction duration in both public and private sectors, in predicting accurately the construction duration and cost at the business planning or early stage of a project.
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UR - http://www.scopus.com/inward/citedby.url?scp=77953481735&partnerID=8YFLogxK
U2 - 10.1139/L10-007
DO - 10.1139/L10-007
M3 - Article
AN - SCOPUS:77953481735
VL - 37
SP - 739
EP - 752
JO - Canadian Journal of Civil Engineering
JF - Canadian Journal of Civil Engineering
SN - 0315-1468
IS - 5
ER -