ArticleScientific reports2026
Multistage assessment of construction delay factors using expert evaluation and real project data.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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