A Conceptual Framework for Construction Delay Management Integrating Project and Weather Factors

Construction project delays remain a major challenge in the construction industry, often leading to cost overruns, reduced productivity, contractual disputes, and poor project performance. Traditional project management approaches, which rely heavily on manual scheduling techniques and historical project records, are often insufficient to address the dynamic nature of construction environments, particularly when influenced by external factors such as weather conditions. Previous studies have primarily focused on identifying delay factors and developing predictive models based on historical project data, with limited integration of environmental variables and practical decision-support mechanisms. This study aims to identify the key factors influencing construction work delays and to develop a conceptual predictive framework that integrates project information with weather-related data to support more effective delay management. The proposed framework incorporates scheduling analysis through the Critical Path Method (CPM) to identify critical activities and evaluate potential delay risks that may affect project completion. In addition, the framework conceptually demonstrates how Artificial Intelligence (AI) and Machine Learning (ML) can be integrated with project and environmental data to enhance project monitoring, risk assessment, and planning processes. The framework was developed to illustrate the interaction between project scheduling information, weather-related variables, and predictive decision-making within a structured management system. Validation was conducted through semi-structured interviews involving three construction professionals with experience in project management and site operations. The evaluation focused on framework practicality, usability, and applicability in real construction environments. The experts generally agreed that the proposed framework is relevant and capable of supporting proactive delay management. The findings indicate that the integration of project and environmental data has the potential to improve planning accuracy, increase awareness of delay risks, and support proactive decision-making throughout the project lifecycle. The proposed framework contributes to the advancement of data-driven construction management practices and provides a foundation for future studies involving empirical validation using real project datasets.

Construction delay management, project scheduling

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