What question did this study set out to answer?

The aim is to evaluate AI-driven predictive analysis for enhancing construction project scheduling efficiency.

April 27, 2026

Use of Artificial Intelligence Predictive Analysis for Project Scheduling in Construction

Key Points

The aim is to evaluate AI-driven predictive analysis for enhancing construction project scheduling efficiency.
Systematic review of literature on AI applications in construction scheduling
Analysis of case studies demonstrating the use of AI models
Evaluation of traditional methods like PERT and CPM against AI methodologies.
AI models showed a significant enhancement in scheduling accuracy, reducing project delays by 20% (p<0.05).
Deep learning techniques improved prediction of potential delays with an overall accuracy of 85%.
Genetic algorithms optimized resource allocation, reducing costs by 15%.

Abstract

Project scheduling is a vital constituent of construction management, meaningfully persuading project performance, and deliverables. Program Evaluation and Review Technique (PERT) and the Critical Path Method (CPM) are popular conventional methods that have ruled scheduling all this time, yet are insufficient for today’s composite, dynamic projects. From 2025, construction environments will involve multiple interdependent variables that exceed the projecting capability of these traditional methods. Artificial intelligence (AI) proposes potential by improving analytical accurateness, allowing automated decision-making, and sustaining real-time alterations. Some of them include potential deep learning models to forecast potential delays before they occur, while genetic algorithms improve resource deployment and crew allocation, digital twins further strengthen project control by continuously comparing planned and actual progress. By methodically analyzing current literature and combining various case studies of applications, this study assesses the relative performance of AI models across project types. The objective of this study is to use the findings of this research in advancing the expansion of adaptable and strong scheduling frameworks, delivering practical visions for the integration of AI-driven tools in construction project management.

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