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.
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
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.
A. Cană, Claudia Borda, Adrian Moise et al.· Optimizing the Future: Manag...· 0 citations
Underground coal mining remains one of the most hazardous industrial activities worldwide, particularly in emerging economies where complex geological conditions, methane emissions, roof instability, dust exposure, and equipment-related accidents continue to threaten worker safety. India, the world's second-largest coal producer, operates numerous underground mines under challenging geotechnical and environmental conditions. Despite significant advancements in mechanization and monitoring technologies, accident investigations indicate that a substantial proportion of mining incidents remain attributable to delayed hazard detection, fragmented monitoring systems, and limited predictive capabilities. Conventional safety management approaches are largely reactive, relying on threshold-based alarms and post-event analysis rather than proactive risk prediction. This study proposes a Mining 5.0-oriented intelligent safety framework that integrates Artificial Intelligence of Things (AIoT), Physics-Informed Neural Networks (PINNs), wearable sensing technologies, and Digital Twin models for real-time hazard prediction and decision support in underground coal mines. The proposed framework combines data from methane sensors, temperature sensors, air velocity monitors, geotechnical instruments, equipment health monitoring systems, and wearable devices measuring worker location, physiological status, and environmental exposure. These heterogeneous data streams are fused within a Digital Twin environment that continuously replicates underground mine conditions. PINNs are employed to incorporate ventilation physics, methane transport dynamics, and geomechanical principles into machine-learning models, thereby improving prediction accuracy and interpretability under sparse or uncertain data conditions. The study identifies critical research gaps in existing mine safety systems, including inadequate integration of physical laws with AI models, limited utilization of worker-centric sensing technologies, and the absence of comprehensive Digital Twin platforms for proactive safety management. To address these gaps, a socio-technical framework is developed that enables continuous risk assessment, predictive analytics, and human-AI collaborative decision-making. The proposed approach is expected to enhance situational awareness, reduce accident probability, improve emergency preparedness, and support sustainable Mining 5.0 transformation in India.
S. Kushwaha, R. Chaurasia· International Journal of Sci...· 0 citations
Off-highway machines, agricultural harvesters, construction excavators, and mining haul trucks operate under extreme load variability, harsh unstructured environments, and constrained sensor instrumentation, creating prognostic conditions fundamentally different from on-road vehicles. While AI-enabled predictive maintenance has matured for passenger vehicles and well-instrumented industrial assets, and off-highway telematics adoption is expanding rapidly, its translation to these software-defined field machines remains insufficiently addressed. This systematic review synthesizes AI-driven prognostic methods, data challenges, and deployment considerations specific to off-highway operation. Following a PRISMA 2020 protocol, the 2014–2025 literature is screened across seven databases, with the 51 studies retained for synthesis additionally quantified by method family, equipment sector, and publication year to expose the relative scarcity of off-highway-specific evidence, and a wide range of methodologies is synthesized, from foundational supervised learning (SVMs, Random Forests) and advanced deep learning (CNNs, LSTMs for RUL prediction) to unsupervised (Autoencoders), ensemble, and transfer-learning techniques. The review contrasts the primary prognostic frameworks—data-driven, physics-based, and hybrid—and the role of knowledge-based expert systems in delivering interpretable alerts. A significant focus is placed on the data pipeline, including sensor selection strategies, data quality, feature engineering, severe class imbalance, and labeling complexity. Implementation hurdles such as operating-condition variability, model validation, the computational constraints of edge devices, and Explainable AI (XAI) are further examined, with a critical analysis of where each method degrades under field variability. Finally, emerging directions are explored, including Digital Twins and Edge Computing, closing with reformulated, off-highway-specific research gaps for real-world deployment.
Yuvraj Patil, V. Bhojwani, Sachin S. Pawar et al.· Frontiers of Mechanical Engi...· 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
BackgroundCement companies supply construction sites, and the mining industry relies on heavy equipment such as cranes, excavators, and dump trucks. These vehicles pose risks of severe injuries due to heavy machinery operations, high-energy movements, and complex environments, leading to collisions, rollovers, mechanical failures, and human error.ObjectiveThis descriptive-analytical study examined the relationship between unsafe behaviors and workplace conditions among 40 mining machinery drivers in three Kerman cement factories.MethodsSafety behavior sampling (SBS) was used to measure unsafe acts through defining behaviors, training observers, and random observations. The sample size followed established formulas. Data were analyzed using SPSS 21, applying descriptive statistics, the Kolmogorov-Smirnov normality test, and correlation analyses to assess relationships between unsafe behaviors and factors including age, marital status, work experience, employment type, safety training, vehicle type, and environmental noise and vibration.ResultsFrom 421 observed activities, significant correlations emerged between unsafe behaviors and driver age (p = 0.001), work experience (p = 0.0001), education (p = 0.0495), employment status (p = 0.003), observation time (p = 0.0001), safety training (p = 0.02), car model (p = 0.0001), driving license (p = 0.0001), and vehicle inspection (p = 0.003). Additionally, unsafe behaviors were positively correlated with noise (p < 0.05) and vibration levels (p = 0.008).ConclusionsNoise levels strongly correlate with unsafe behaviors among mining vehicle drivers, emphasizing the importance of environmental factors. Integrated approaches involving driver training and noise reduction are essential to enhance occupational safety.
Maryam Abshahi, Ramezan Mirzaei, Z. Rezvani· Work· 0 citations