AI - Powered Predictive Risk Analysis in Construction Projects Using Hybrid Machine Learning and Simulation Models

Abstract

Highway construction projects are inherently complex and prone to a variety of risks that can lead to cost overruns, schedule delays, safety incidents, and quality deficiencies. Traditional risk assessment methods, relying on expert judgment and qualitative checklists, often fail to capture the dynamic and interdependent nature of construction hazards. Recent advances in machine learning offer powerful alternatives for proactive risk identification, quantification, and mitigation. This paper presents a comprehensive framework for risk assessment in Highway construction using data-driven models. Historical project datasets, environmental sensor readings, real-time UAV imagery, and BIM-derived parameters are integrated to train and validate several machine learning architectures—including deep neural networks, gradient boosting decision trees, LSTM networks, and graph neural networks. The proposed framework demonstrates significant improvements in predictive accuracy for cost and schedule overruns (up to 23% higher than statistical baselines), early detection of structural defects (87.3% accuracy), and real-time hazard identification via computer vision (45% faster inspection). A federated learning extension is also explored to address data privacy concerns and enable collaborative model development across stakeholders. Challenges related to data quality, model interpretability, and dynamic environmental conditions are discussed, along with future directions toward multimodal AI systems and explainable risk forecasts. Results indicate that machine learning–driven risk assessment can transform Highway construction management by shifting from reactive to proactive strategies, ultimately enhancing safety, reducing delays, and optimizing resource allocation.