Machine Learning-Based Worker-Safety Prediction in Construction Project Environments: A Systematic Literature Review and Risk-Control Framework
Construction safety has remained a major project risk because workers perform their activities in dynamic, temporary and equipment-intensive surroundings where the nature of hazards may change within a very short time. This paper reviews machine-learning-based worker-safety prediction in construction project environments and develops the reviewed findings into a practical risk-control framework. Using the verified coded Excel dataset as the authoritative evidence source, a PRISMA-informed systematic literature review was conducted. The final synthesis included 79 primary empirical or technical studies published during 2016–2026, while 10 background reviews and seven excluded or reclassified records remained outside the primary evidence base. The selected studies were coded according to bibliographic characteristics, AI/ML technique, specific algorithm, data source, modality, dataset, prediction or detection target, safety domain, outcome, availability of performance metrics, explainability method, major findings, safety implications, risk-control relevance and verification notes. The mapping findings show a clear concentration on computer vision and deep learning, particularly for PPE detection, monitoring of safety compliance, site-hazard detection, fall-related risk and worker–equipment interaction. However, traditional machine learning continues to be important for structured accident, injury and safety-indicator datasets. NLP, LLMs, wearable sensors, IoT-based analytics and multimodal approaches are also emerging for accident narratives, safety reports, physiological risk and contextual safety reasoning. The major limitations are related to data quality, class imbalance, small or customised datasets, weak cross-site validation, interpretability, privacy, workflow integration and incomplete extraction of full-text model-performance results. Since 73 included studies still need manual extraction of full-text metrics, this study is positioned as systematic mapping, thematic synthesis and framework development rather than a statistical meta-analysis. Therefore, the main contribution is a worker-safety prediction and risk-control framework that establishes a relationship among data acquisition, preprocessing, modelling, explainability, managerial decision-making, intervention and continuous learning.