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    Enhancing Reliability of Construction Contingency Estimation at the Scoping Phase through Historical Cost Data Analysis

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 004::page 04024068-1
    Author:
    Taewoo Ko
    ,
    Chau Le
    ,
    H. David Jeong
    ,
    Kunhee Choi
    DOI: 10.1061/AJRUA6.RUENG-1260
    Publisher: American Society of Civil Engineers
    Abstract: Accurate estimates of construction contingency, a vital element that accounts for unforeseen additional expenses during building, are crucial for construction project agencies. In the initial planning phase, with project specifics still under development, the top-down method is a popular approach for estimating contingency. This method employs a straightforward approach to calculating contingency, using a fixed percentage or a sliding scale based on project variables. While such a method offers simplicity, it suffers from constraints, notably the absence of rigorous evaluation and the challenges of considering project-specific risks. Hence, this research conducts a comprehensive analysis of historical highway project cost performance data to refine the applicability of the sliding scale methodologies and strengthen current top-down approaches. Using various statistical methods, the study creates probability distribution curves that show the likelihood of different contingency costs occurring, along with their associated confidence levels. These probability distribution curves provide cost estimators and project managers with a reliable way to improve the accuracy of early cost estimates during critical planning phases. This study will benefit project agencies by providing a more accurate and informed approach to budgeting and contingency management, thereby improving project cost reliability.
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      Enhancing Reliability of Construction Contingency Estimation at the Scoping Phase through Historical Cost Data Analysis

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorTaewoo Ko
    contributor authorChau Le
    contributor authorH. David Jeong
    contributor authorKunhee Choi
    date accessioned2025-04-20T10:19:59Z
    date available2025-04-20T10:19:59Z
    date copyright9/30/2024 12:00:00 AM
    date issued2024
    identifier otherAJRUA6.RUENG-1260.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304490
    description abstractAccurate estimates of construction contingency, a vital element that accounts for unforeseen additional expenses during building, are crucial for construction project agencies. In the initial planning phase, with project specifics still under development, the top-down method is a popular approach for estimating contingency. This method employs a straightforward approach to calculating contingency, using a fixed percentage or a sliding scale based on project variables. While such a method offers simplicity, it suffers from constraints, notably the absence of rigorous evaluation and the challenges of considering project-specific risks. Hence, this research conducts a comprehensive analysis of historical highway project cost performance data to refine the applicability of the sliding scale methodologies and strengthen current top-down approaches. Using various statistical methods, the study creates probability distribution curves that show the likelihood of different contingency costs occurring, along with their associated confidence levels. These probability distribution curves provide cost estimators and project managers with a reliable way to improve the accuracy of early cost estimates during critical planning phases. This study will benefit project agencies by providing a more accurate and informed approach to budgeting and contingency management, thereby improving project cost reliability.
    publisherAmerican Society of Civil Engineers
    titleEnhancing Reliability of Construction Contingency Estimation at the Scoping Phase through Historical Cost Data Analysis
    typeJournal Article
    journal volume10
    journal issue4
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1260
    journal fristpage04024068-1
    journal lastpage04024068-12
    page12
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 004
    contenttypeFulltext
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