A Machine Learning And Ai-Integrated Decision Support System For Risk Prediction And Process Automation In Construction Engineering For Enhanced Infrastructure Safety And Efficiency

The construction engineering sector is increasingly characterized by high uncertainty, complex project dynamics, and elevated safety risks, necessitating advanced computational approaches for proactive decision-making. This research proposes a machine learning and artificial intelligence (AI)-integrated decision support system (DSS) for predictive risk analytics and process automation in construction engineering. The framework synthesizes multi-source data streams, including sensor-based IoT systems, computer vision, and historical project records, to enable real-time risk forecasting and operational optimization. Building upon advancements in deep learning, Bayesian inference, and hybrid optimization models (Chattapadhyay et al., 2021; Chen et al., 2021), the study develops a conceptual architecture that supports automated risk identification, predictive scheduling, and safety assurance mechanisms.

The system leverages intelligent sensing and wireless data transmission principles inspired by energy-efficient monitoring systems (Alshmeel et al., 2024), enabling continuous environmental and structural data acquisition. Machine learning models are applied for classification, regression, and anomaly detection, improving the accuracy of cost, delay, and safety risk predictions (Darko et al., 2023). The proposed DSS further integrates computer vision techniques for construction site monitoring and hazard detection (Fang et al., 2020). Results indicate that AI-driven decision systems significantly enhance infrastructure safety, reduce operational inefficiencies, and improve resource allocation.

The study contributes a unified framework bridging predictive analytics and automation, offering practical implications for smart construction ecosystems and Industry 4.0-enabled infrastructure management.

Artificial Intelligence, Machine Learning, Construction Risk Management, Decision Support System

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