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  11. Predicting Construction Cost Under Uncertainty Using Grey-fuzzy Earned Value Analysis

Predicting construction cost under uncertainty using grey-fuzzy earned value analysis

Endale Mamuye Desse1, Wubishet Jekale Mengesha2

  • 1Department of Civil Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.

Heliyon
|March 18, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces grey-fuzzy Earned Value Analysis (EVA) to predict construction costs accurately, even with uncertain data. This new method improves project cost control for better performance.

Area of Science:

  • Construction Management
  • Project Control
  • Uncertainty Quantification

Background:

  • Delayed and cost-overrun construction projects often stem from inadequate planning and control.
  • Existing project control techniques like Earned Value Analysis (EVA), fuzzy EVA, and grey EVA have limitations in handling uncertainty.
  • A gap exists in analytical models that integrate fuzzy and grey theories simultaneously with EVA.

Purpose of the Study:

  • To develop and validate a novel analytical model, grey-fuzzy EVA, for predicting construction costs under uncertainty.
  • To enhance project cost control performance by addressing imprecise data.
  • To provide a practical tool for continuous project cost performance improvement.

Main Methods:

  • Development of simple and valid grey-fuzzy EVA algorithms.
Keywords:
Cost controlEarned value analysisFuzzy theoryGrey theory

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  • Creation of an analysis result interpretation scheme.
  • Comparative analysis of grey-fuzzy EVA against fuzzy EVA and grey EVA.
  • Application demonstration via a case study of a road project in Addis Ababa, Ethiopia.

    • Main Results:

    • Grey-fuzzy EVA successfully predicts construction costs within lower, median, and upper limits, incorporating the degree of greyness.
    • The method simplifies cost analysis and requires minimal data points (BAC, PV, AC, Progress).
    • Grey-fuzzy EVA does not necessitate expert input for membership function creation, unlike fuzzy EVA.

    Conclusions:

    • Grey-fuzzy EVA offers a more comprehensible and effective approach to construction cost control compared to standalone fuzzy EVA or grey EVA.
    • The model enhances accuracy in cost prediction amidst data uncertainty.
    • This research provides a valuable, practical tool for construction practitioners to improve project cost management.
    Grey-fuzzy EVA
    Uncertainty