The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach

Choongwan Koo, Taehoon Hong, Changtaek Hyun

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects. The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.

Original languageEnglish
Pages (from-to)8597-8606
Number of pages10
JournalExpert Systems with Applications
Volume38
Issue number7
DOIs
Publication statusPublished - 2011 Jul 1

Fingerprint

Case based reasoning
Regression analysis
Neural networks
Costs
Genetic algorithms
Decision making

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

@article{5008681756ed4d55b22cf83e80553a46,
title = "The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach",
abstract = "Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects. The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.",
author = "Choongwan Koo and Taehoon Hong and Changtaek Hyun",
year = "2011",
month = "7",
day = "1",
doi = "10.1016/j.eswa.2011.01.063",
language = "English",
volume = "38",
pages = "8597--8606",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "7",

}

The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach. / Koo, Choongwan; Hong, Taehoon; Hyun, Changtaek.

In: Expert Systems with Applications, Vol. 38, No. 7, 01.07.2011, p. 8597-8606.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach

AU - Koo, Choongwan

AU - Hong, Taehoon

AU - Hyun, Changtaek

PY - 2011/7/1

Y1 - 2011/7/1

N2 - Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects. The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.

AB - Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects. The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.

UR - http://www.scopus.com/inward/record.url?scp=79952443537&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952443537&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2011.01.063

DO - 10.1016/j.eswa.2011.01.063

M3 - Article

VL - 38

SP - 8597

EP - 8606

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 7

ER -