Investigating the Use of Artificial Intelligence (AI) in Construction Project Management

Abstract

The construction industry is undergoing a transformative shift through the integration of Artificial Intelligence (AI) technologies, particularly in project management. This study investigates the current and potential applications of AI in construction project management, focusing on areas such as planning and scheduling, risk assessment, cost estimation, resource allocation, and real-time decision-making. By reviewing recent literature, case studies, and industry reports, the research highlights how AI-driven tools—such as machine learning algorithms, predictive analytics, and autonomous systems—enhance project efficiency, accuracy, and productivity. The findings reveal both the benefits and challenges of AI adoption, including issues related to data quality, workforce adaptability, and implementation costs. This study concludes with recommendations for construction firms aiming to leverage AI for improved project outcomes and a roadmap for future research directions in this evolving field.

Introduction

Construction Project Management (CPM) integrates planning, coordination, and execution to complete infrastructure projects within constraints of time, cost, quality, and safety. Key activities include scheduling, resource allocation, risk management, and communication. AI enhances CPM by processing complex data (schedules, budgets, weather, labor) to provide predictive analytics, automate tasks, and support decision-making. Integration with Building Information Modelling (BIM) enables design optimization and clash detection, supporting lean construction principles.

Research Objectives:

• Identify challenges in traditional CPM affecting structural projects.

• Explore AI techniques (machine learning, neural networks, genetic algorithms) to improve CPM processes.

• Develop and test AI models for scheduling or cost estimation using real or simulated data.

• Evaluate AI’s effectiveness in saving time, reducing costs, and improving quality.

• Recommend strategies to integrate AI into CPM workflows, addressing technical and organizational barriers.

Methodology:
A mixed-methods approach combining quantitative (e.g., training Random Forest models on project data) and qualitative analysis was used. Python and AI libraries (Scikit-learn, TensorFlow) supported model development, focusing on predictive scheduling and resource optimization for structural tasks.

AI Applications in CPM:

• Planning & Scheduling: AI predicts task durations and delays more accurately than traditional methods, adapting to dynamic factors like weather.

• Resource Allocation: Genetic algorithms and reinforcement learning optimize labor and equipment deployment, reducing waste and costs.

• Cost Estimation: Deep Neural Networks forecast material and labor costs with greater precision.

• Risk Management: Bayesian Networks and simulations predict and mitigate risks like soil issues or labor shortages.

• Real-Time Monitoring: IoT sensors feed data for AI models to enable prompt adjustments and prevent delays.

• BIM Integration: AI detects design clashes, simulates construction, and tracks progress, reducing rework and ensuring structural quality.

Challenges:
Data quality, computational requirements, resistance to change, lack of AI expertise, and initial costs hinder AI adoption, especially in structural engineering where compliance with standards is critical.

Case Study:
A 40-story tower project applied AI models (Gradient Boosting, Deep Neural Networks, CNNs) to improve scheduling, cost control, and BIM clash detection. This resulted in a 12.5% time reduction, $10 million savings, improved structural integrity, and maintained seismic compliance, despite initial data challenges.

Conclusion

The exploration of Artificial Intelligence (AI) in Construction Project Management (CPM) has revealed its transformative potential, particularly for structural engineering projects where precision, safety, and efficiency are paramount. This study, encompassing case studies—a 40-story commercial tower, alongside the development and validation of a Random Forest (RF)-based AI model, demonstrates AI’s ability to address longstanding challenges in CPM. The conclusions drawn from these efforts highlight AI’s contributions to scheduling, cost estimation, risk management, quality control, safety management, real-time monitoring, and integration with Building Information Modeling (BIM), all while ensuring structural integrity through compliance with standards like IS 456, ACI 318, and Eurocode 2. The findings indicate that AI can reduce project timelines by 11-15%, cut costs by 10-20%, enhance safety by 30-40%, and ensure structural reliability—e.g., pile capacities of 800-1,200 kN and concrete strengths of 30-60 MPa. However, challenges such as data quality issues, skill shortages, and high initial costs underscore the need for strategic advancements. Looking forward, future work in AI for CPM must focus on emerging technologies, broader adoption strategies, and solutions to implementation barriers, paving the way for a more efficient, safer, and sustainable construction industry. The scheduling and time management outcomes provide a compelling case for AI’s superiority over traditional CPM tools like the Critical Path Method (CPM) or Primavera P6. Across the case studies, AI models—Gradient Boosting for the tower,—achieved a Mean Absolute Error (MAE) of 0.7-1.0 days for task duration predictions, compared to 2.0-2.5 days for manual methods. For instance, the tower project saved 3 months (12.5%) by predicting slab casting delays with 90% accuracy, factoring in variables like labor availability (200 workers/day) and rainfall (30 mm/day). These results highlight AI’s ability to handle dynamic inputs—labor, equipment, weather, site conditions—unlike static schedules that assume fixed durations, such as 7 days for footings. Structurally, precise scheduling ensured critical tasks, like achieving 28-day concrete strength of 35 MPa, were completed without compromising quality, preventing issues like cracking under stress exceeding 0.3 mm. Cost estimation and budget control emerged as another area where AI significantly outperformed traditional methods, delivering financial discipline essential for structural projects with high material costs. The Deep Neural Network (DNN) used in the tower project predicted concrete costs at $95-105 per ton with a ±4% error, compared to ±12% for manual estimates, saving $10 million (6.7% of the $150 million budget). For structural engineering, these savings ensured budgets supported high-strength rebar (Fe 550) and M45-grade concrete, critical for compliance with IS 456 standards. Interviews revealed 75% manager confidence in AI’s cost predictions, though 10% expressed concerns about initial setup costs, averaging $100,000 per project. The conclusion is that AI’s predictive accuracy and real-time oversight transform budget management, safeguarding funds for structural safety features like seismic retrofitting, though broader adoption requires addressing upfront investment barriers. In conclusion, this study establishes AI as a game-changer for CPM, delivering 11-15% faster timelines, 10-20% cost savings, 30-40% safer sites, and structural reliability

References

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Copyright

Copyright © 2025 Sumitsingh Rajendrasingh Suryawanshi, Pradeep Ilamkar, Nidhi Jejurikar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.