This meta-analysis has examined the past ten years’ studies concerning the causes of construction project delay. It aims to update the subject area and investigate critical causes of project delay in three different conditions of the external environment. The data from 50 studies have been analyzed and synthesized to determine the top ten critical causes of delay. The Relative Importance Index (RII) technique was applied to rank the critical causes; subsequently, the Spearman’s rank correlation coefficient was calculated to evaluate the critical causes. The review findings indicate substantial differences between the critical causes of project delay in defined situations. The top ten critical causes of delay in developed countries root in the project’s internal environment. The leading causes of delays in developing countries are from the project’s internal and task environment. While in countries with various constraints and high risk, the general environment has a critical impact alongside the project task and internal environment on time overrun of a project. Moreover, this review summarized and categorized the best available studies to propose a systematic approach in identifying critical causes of delay to bridge the existing knowledge gap.

Available literature shows no standard method or framework to identify critical causes of delay and assess its impact on the construction project. The use of ambiguous phrases and unstructured identification of causes has resulted in the ineffectiveness of the study findings. Therefore, this study has developed a framework to identify critical causes of delay through a systematic approach. This systematic approach helps to identify critical causes of delay in a standardized format. The framework categorically sequences causes of delay and connects them to the responsible party. From the obtained data, three hundred thirty-five critical causes of delay were extracted, categorized into 14 groups, and summarized into 48 major critical causes presented in tables. This study provides a comprehensive list and reference of the critical causes identified in the past 10 years. The study also quantitatively analysed the collected data presented in tables. The developed framework and approach minimize alternate and ambiguous phrasing, enhance study findings application in practice, and increases the research outcome’s consistency.

Purpose: This study aims to enhance construction scheduling through a computational methodology that enables structured input preparation, constraint formulation and combinatorial task analysis to improve schedule feasibility, automation and optimization Design/methodology/approach: The proposed methodology integrates building information modeling (BIM), enhanced planning and scheduling (EPS), a structured scheduling approach and constraint programming (CP) to enhance computational and combinatorial scheduling. BIM data is automatically extracted and enriched with material quantities, spatial breakdowns and task types, then structured using EPS into labor-hour–based units and spatial zones. These structured inputs feed into a CP model incorporating precedence logic, resource constraints and execution priorities to generate the construction schedule. Moreover, constraint modification and EPS-driven combinatorial analysis enable alternative scheduling and scenario evaluation. Findings: The methodology was applied to a multi-section residential project, resulting in a feasible and optimized construction schedule. The CP model optimized resource use and duration based on the objectives while maintaining logical sequencing, with automated BIM-EPS input reducing manual effort. The schedule was automatically generated based on the predefined constraints and consistency was confirmed using Kendall’s Tau-b correlation. Originality/value: This study presents a novel integration of BIM, EPS and CP to advance logic-based and computational scheduling. A key contribution of this study is the implementation of advanced scheduling by facilitating constraint formulation through the EPS methodology and enriched BIM data integration. This approach enables schedule feasibility analysis, improves consistency and addresses limitations of constraint- and logic-based methods through structured input and formalized constraints. By automating the extraction and structuring of data for CP, it reduces some manual effort in data preparation and supports optimization though initial setup and predefined constraints.

Construction scheduling still relies on fragmented manual preparation of activity, location, and labor data, even when BIM models are available. This paper addresses the challenge of systematically structuring and automatically extracting enriched BIM data to enable schedule‑ready inputs for advanced construction scheduling. The paper develops an Enhanced Planning and Scheduling (EPS) based framework and applies it through BIM enrichment, spatial decomposition, classification mapping, labor-hour calculation, and Dynamo-based data extraction in a building case. The framework generates a reusable dataset that links model elements to zones, floors, workspaces, activity categories, labor hours, and EPS priority IDs for downstream scheduling. The paper’s contribution lies in providing a structured EPS-based framework that transforms enriched BIM data into reusable schedule-ready inputs linking locations, activity categories, labor hours, and priority logic for downstream scheduling. Further research is needed to validate the framework across various project types and scheduling workflows, and to examine dynamic model–schedule updating and AI-assisted scheduling integration.

Construction planning and scheduling, including task sequences, constraints, and interdependencies, is poorly structured within digital models such as BIM and Digital Twin and lacks effective integration with planning documents to support scheduling analysis, logic-based reasoning, and automation. To address this gap, this paper develops an AI-enabled framework organized into three core dimensions: (1) enriching BIM and integrating reality data with activity, spatial, and resource attributes; (2) formalizing planning logic using a planning ontology to represent execution relationships; and (3) applying AI techniques to extract planning knowledge, validate constraints, and generate automated schedules. The framework supports logic-based planning, progress tracking, and coordination across construction processes.