2026· Optimizing the Future: Management Innovation for a Sustainable World· 0 citations
TL;DR
It is confirmed that AI can move workplace safety from reactive to preventive management by enabling real-time monitoring, proactive alerts, and predictive forecasting and can significantly enhance worker protection, operational efficiency, and sustainability in the automotive repair sector.
Abstract
Auto repair workshops are high-risk environments where workers face frequent accidents caused by slips, trips, heavy lifting, exposure to hazardous substances, and malfunctioning equipment. Despite advances in occupational health and safety (OHS) regulations, accident rates in Romania and across the European Union remain above average, highlighting the need for new preventive strategies. This paper explores how artificial intelligence (AI) can be systematically applied to reduce operational and safety risks in auto repair shops. Accident statistics and a detailed risk assessment of slips, trips, and falls provide the foundation for analyzing AI-based solutions, including computer vision, augmented reality, smart floors, voice assistance, and predictive analytics. A six-month pilot project conducted in a Romanian auto repair shop demonstrated a 28% reduction in minor accidents, faster hazard detection, and measurable economic benefits, while also revealing challenges such as false positives, resistance to change, and maintenance requirements. The findings confirm that AI can move workplace safety from reactive to preventive management by enabling real-time monitoring, proactive alerts, and predictive forecasting. The study concludes that AI, when combined with training and organizational adaptation, can significantly enhance worker protection, operational efficiency, and sustainability in the automotive repair sector.
Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.
Musaed M. Al-Thubaiti, Saeed S. Al-Shahrani, Ryan A. Alsaihaty· World Journal of Advanced En...· 0 citations
The mining industry continues to face significant safety challenges, particularly with powered haulage equipment (PHE). PHE incidents account for a substantial percentage of mining-related fatalities, often resulting from vehicle collisions, equipment rollovers, operator errors, and blind-spot hazards. Despite the industry’s efforts to improve safety protocols, fatal accidents involving haulage trucks remain persistent. The mining industry has increasingly adopted automation to enhance operational efficiency and improve safety, particularly in surface mines where haulage truck accidents remain a critical concern. Automation has significantly reduced human exposure to hazardous tasks by removing operators from dangerous environments, thereby mitigating risks associated with human error and fatigue-related accidents. However, achieving zero fatalities in mining operations remains an ongoing challenge, necessitating a deeper evaluation of current technologies and safety interventions. This paper explores the review and integration of advanced safety technologies, such as real-time monitoring, machine learning-based predictive models, and enhanced automation frameworks to improve hazard detection and response time. A structured methodology is employed to review automated systems, accident data analysis, and an assessment of automation technologies in active mining operations. Specific findings highlight the impact of automation on reducing accident rates, the effectiveness of various intervention strategies, and challenges in full-scale implementation. The novelty of this paper lies in its roadmap to achieving zero fatalities through a review of structured integration of automation and predictive safety interventions. It outlines the broader benefits of Automated Haulage Systems, including productivity gains and operational cost reductions, contributing to the ongoing discourse on mining safety by providing a data-driven framework for the successful implementation of automated haulage trucks, ensuring a safer and more efficient mining environment.
Suggestions for ensuring safe person detection using AI in industrial environments are offered, including suggestions for ensuring safe person detection using AI in industrial environments.
Iwo Kurzidem, Andrea Matic-Flierl, Poulami Sinhamahapatra et al.· 0 citations
The problems addressed in this paper are platform architecture, multimodal data fusion and LLM grounding mechanism in the Nigerian industrial system in addition to evaluation metrics, security control, human-in-the-loop validation of safety alerts and the limitations to real-world deployment.
E. C. Ashinze· SPE Nigeria Annual Internati...· 0 citations
Ensuring reliable equipment operations are critical for production efficiency and safety compliance in manufacturing industries. Unexpected machine breakdowns not only disrupt operations but also increase maintenance costs and safety risk. Traditional approaches — whether reactive or preventive — often fail to incorporate real-time equipment data and often overlook early fault indicators. Predictive maintenance strategies based on artificial intelligence models, address these shortcomings by monitoring machine conditions in real time, detect anomalies and forecast failures. Thus, this study proposes a refined deep neural network to classify machine health and estimate failure probability, thereby emphasizing Remaining Useful Life (RUL) prediction as a strategy for optimizing maintenance scheduling. In this study, the AI4I 2020 Predictive Maintenance dataset, which contains 10,000 records of machine operating conditions, including air and process temperatures, rotational speed, torque, tool wear, product type, and failure status was used for the evaluation of the proposed model. To improve the proposed model's performance, detailed preprocessing steps were deployed on the dataset. These steps include some preliminary categorical encoding and feature standardization on the dataset while class weighting, SMOTE and focal loss were deployed for handling class imbalance issues. According to the results achieved, the proposed model performed better across all metrics considered in comparison with similar models including baseline models. By integrating data-driven AI techniques with predictive maintenance strategies, this research study demonstrates how manufacturing plants can reduce downtime, extend machine lifespan, and minimize unnecessary maintenance interventions.
O. A. Alimi, O.C. Tshidavhu, N. Khanyile et al.· 2026 6th International Confe...· 0 citations
Working at height is one of the leading causes of occupational fatalities and serious injuries across construction and industrial sectors. Despite the implementation of engineering controls, administrative procedures, work permit systems, safety training, and personal protective equipment (PPE), fall-related accidents continue to occur, primarily due to unsafe acts, human error, and non-compliance with safety harness anchoring requirements. Conventional safety monitoring methods rely heavily on manual supervision, making it difficult to ensure continuous compliance during high-risk operations. Therefore, there is a growing need for intelligent and technology-driven safety solutions that can provide real-time monitoring and early warning to prevent fall accidents.
This research presents the development of an AI and Internet of Things (IoT) enabled Smart Safety Harness System for enhancing working at height safety in industrial applications. The proposed system integrates sensor-based monitoring, AI communication, and real-time alert mechanisms to detect improper anchoring or unsafe usage of the safety harness. Whenever a deviation is identified, the system immediately generates an audio-visual warning to the worker while simultaneously transmitting the information to the supervisor for corrective action. In addition, all safety violations are automatically recorded in a centralized database for trend analysis, safety performance evaluation, and continuous improvement.
The study adopts a systematic methodology involving hazard identification, working at height survey, risk assessment, evaluation of existing engineering and administrative controls, validation of technological solutions, and implementation of an intelligent fall protection system. High-risk activities such as roof sheet installation, scaffold erection, maintenance work, pipeline installation, and structural construction are analyzed to identify critical areas where smart wearable technology can significantly improve safety performance. The proposed approach follows the hierarchy of risk control by strengthening engineering controls while minimizing dependence on manual observation and human intervention.
The outcome of this research is the development of a reliable, economical, and intelligent fall prevention system capable of reducing workplace injuries, improving PPE compliance, minimizing human error, and supporting proactive safety management. The integration of Artificial Intelligence, IoT, and smart wearable technology establishes a modern framework for industrial safety that enables real-time monitoring, predictive safety analytics, and data-driven decision-making. The proposed system has significant potential for application in construction, manufacturing, petrochemical, power, mining, and infrastructure industries, thereby contributing to the advancement of Industrial Safety Engineering and promoting a sustainable "Zero Harm" safety culture.
Dilip Patel, Mohsin Khan and Dr. Madhuri Asati· International Journal of Adv...· 0 citations