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Updated: May 27, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
Multistage assessment of construction delay factors using expert evaluation and real project data
Ahmed Eid1, Ayman Halabya2, Nabil M Nagy3
1Department of Civil Engineering, Military Technical College, Cairo, Egypt. Ahmedmoheb256@gmail.com.
Abstract:
Construction delays remain a major challenge, especially in developing countries where financial, administrative, and resource constraints intensify schedule disruptions. This study identifies and prioritizes construction delay factors through a three-phase research framework consisting of a literature review, an expert survey evaluation, and validation using real-life construction projects in Egypt. First, a comprehensive review of global literature led to the identification of 98 delay factors, which were classified into four main categories: owner-related factors (25 factors, 26%), contractor-related factors (34 factors, 35%), consultant/design-related factors (14 factors, 14%), and external factors (25 factors, 26%). Second, an expert survey was conducted to evaluate the previously identified delay factors. This phase employed a composite scoring approach that integrates both the frequency of occurrence and the impact of each factor to rank the most critical delay drivers. The findings indicate that owner-related factors constitute the most significant sources of delay, accounting for approximately 50% of the top 22 critical factors. Finally, the reliability of these findings was validated using the top 22 factors and a dataset of 141 real construction projects, showing strong alignment between the survey-based rankings and actual outcomes. Key factors, such as frequent change orders and design modifications, remained top-ranked, while some factors ranked higher or lower in practice, indicating minor variations in their relative importance under real project conditions. The study contributes a validated dataset of delay factors derived from both expert evaluation and real project evidence, providing a strong foundation for future predictive modeling applications using artificial intelligence and machine learning.
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