Aug 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 183· 0 citations· 12 references
TL;DR
This paper assesses the value-add of AI in safety performance by deeply integrating advanced industrial safety engineering risk analysis methodologies, including Hazard Identification and Risk Assessment (HIRA), Fault Tree Analysis (FTA), and Failure Mode and Effects Analysis (FMEA).
Abstract
The rise of digital transformation (DT) across industries has catalyzed incremental progress through technological revolutions such as Artificial Intelligence (AI) and Digital Twins. This research explores the organizational digital transformation within complex workplaces—specifically construction sites and process plants—and evaluates how Digital Twins can enhance occupational health and safety (OSH). Current safety management procedures struggle to keep pace with dynamic industrial environments, leading to high accident rates. By digitizing physical environments and synchronizing real-time data from IoT sensors, cyber-physical systems, and historical logs, organizations can enable proactive risk assessment, hazard prediction, and effective safety training. This paper assesses the value-add of AI in safety performance by deeply integrating advanced industrial safety engineering risk analysis methodologies, including Hazard Identification and Risk Assessment (HIRA), Fault Tree Analysis (FTA), and Failure Mode and Effects Analysis (FMEA). The evolution of safety roles and shifting collaborative dynamics are examined in detail, ultimately providing a scalable framework for adopting predictive safety mechanisms up to the 2024-2025 technological horizon.
The mining industry is experiencing a profound transformation, moving from Automated Mines (Mining 3.0) to Intelligent Mining (Mining 4.0) and the emerging Cyber-Physical-Social Systems of Mining 5.0. While these technologies promise higher productivity, they fundamentally reshape Occupational Health and Safety (OHS) paradigms. This study examines emerging risks, both new hazards and familiar risks appearing in novel contexts, associated with complex human–technology interactions.A dual-phase approach was used. First, a systematic literature review following the PRISMA Statement synthesized 32 high-impact studies from Scopus, IEEEXplore, and PubMed to explore OHS implications and global technological trends, including Internet of Things (IoT), Artificial Intelligence (AI), and autonomous vehicles. Second, an empirical study employed a targeted questionnaire for managers and executives at digitally transforming mining sites, assessing smart technology adoption (e.g., collaborative robotics, big data) and perceived impacts on risk management.Findings reveal that while digital strategies are established, critical gaps remain in addressing “soft” OHS factors such as mental health and organizational risks. Traditional physical hazards are mitigated through teleoperation and Proximity Warning Systems, yet new risks are emerging, including cognitive overload, stress from constant monitoring, and cybersecurity vulnerabilities. These results emphasize the need for human-centered OHS frameworks that address psychosocial and cognitive demands in Mining 4.0 and 5.0. By linking technological trends with practical managerial insights, this study offers guidance for achieving a safer, sustainable digital transformation in the mining sector.
The rapid digitalization and decarbonization of electrical power systems have brought increased operational complexity and new occupational risk dynamics. This transition renders traditional compliance-based safety models inadequate for managing the emerging complexities of cyber–physical and socio-technical systems. This paper develops a conceptual socio-technical safety architecture for occupational risk management in electrical power systems, grounded in the concepts of systems innovation and socio-technical modeling. A structured narrative review of international standards, accident investigations, and emerging technologies is conducted to reinterpret hazards as interacting subsystems within a dynamic, adaptive framework. The proposed framework synthesizes technical safety controls, human reliability factors, and artificial intelligence-driven predictive maintenance within a single architecture, supported by dynamic feedback loops. The model addresses nonlinear risk propagation across smart grid applications, hydrogen systems, and battery energy storage systems. By transitioning from a reactive to a proactive, adaptive approach to safety governance, the architecture enhances the resilience of electrical power systems, reduces the potential for cascading failures, and aligns occupational safety with infrastructure modernization strategies for electrical power systems. The framework provides a conceptual basis for integrating technology innovation with occupational risk management across complex energy infrastructures undergoing digital transformation.
H. Smadi, S. Albatran, Yazan M. Alsmadi· Applied System Innovation· 0 citations
Overhead crane operations remain a high-risk activity in heavy manufacturing, yet safety management often relies on static assessments that fail to capture real-time operational dynamics. In developing economies such as Indonesia, a significant digital gap hinders the adoption of high-cost IoT solutions, leaving safety data fragmented and reactive. This study aims to bridge this gap by developing and validating a Business Intelligence (BI) Safety Dashboard that utilizes bridge technologies, defined as cost-effective digital solutions that leverage existing administrative and operational data instead of dedicated IoT infrastructure, to provide real-time predictive risk insights. Following a Design Science Research (DSR) framework, a three-year longitudinal study (2024–2026) was conducted at a metal fabrication facility in West Java. A Weighted Dynamic Risk Score (WDRS) was formulated using Data Analysis Expressions (DAX), integrating incident logs, maintenance records, and operator certification data into a unified star schema model. The results demonstrate a 95% reduction in data processing time and a 30% increase in near-miss reporting. The proposed artifact successfully identified critical risk outliers, such as Crane 08 (WDRS = 8.3), and generated spatiotemporal heatmaps that pinpointed specific risk hotspots within the facility. These findings confirm that the BI Dashboard is a feasible and highly practical solution for resource-constrained environments, providing a scalable blueprint for Indonesian SMEs to achieve Industry 4.0 safety standards by leveraging existing administrative data for predictive maintenance and proactive safety interventions.
Ridwan Kurniaji, Nur Azizah, Mohamad Rakhmansyah et al.· IAIC Transactions on Sustain...· 0 citations
The accelerating proliferation of digital technologies presents unprecedented opportunities to address the systemic implementation gaps that have long characterised occupational health and safety (OHS) management within Nigeria’s upstream oil and gas sector. Building on a foundational study that identified six critical barriers—including production-priority conflicts, resource inadequacy, regulatory enforcement deficits, and the invisibility of cumulative occupational exposures—this paper examines the gap-bridging potential of real-time risk platforms and artificial intelligence (AI)-assisted hazard identification systems in overcoming these entrenched challenges. Drawing on a critical, non-systematic review of recent empirical literature and industry deployment evidence, the study applies a Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) theoretical lens to evaluate how digital solutions interact with organisational, regulatory, and human factors in high-hazard offshore environments. The evidence synthesis indicates that AI-enabled predictive safeguards, IoT-integrated monitoring architectures, and data-driven decision-support platforms are theoretically and empirically positioned to address the production-priority override, resource inadequacy, and chronic exposure invisibility barriers—the three most pervasive implementation failures documented in the sector—while offering the potential to transform occupational health surveillance from reactive to continuous and predictive. However, significant contextual barriers—including legacy infrastructure constraints, cybersecurity vulnerabilities, workforce digital literacy deficits, ethical and privacy considerations, and cost-prohibitive adoption thresholds—continue to moderate uptake in the Nigerian context. The paper concludes with a strategic framework for staged digital integration and targeted policy recommendations for the Nigerian Upstream Petroleum Regulatory Commission (NUPRC) and industry operators seeking to operationalise technology-enabled safety governance.
K. Ekpo, E. Esitikot, Seye Tare Ganranwei et al.· International journal of re...· 0 citations
The development of an AI and Internet of Things (IoT) enabled Smart Safety Harness System for enhancing working at height safety in industrial applications with significant potential for application in construction, manufacturing, petrochemical, power, mining, and infrastructure industries.
Dilip Patel, Mohsin Khan and Dr. Madhuri Asati· International Journal of Adv...· 0 citations
Long-span bridges are strategic infrastructure assets because they connect cities, ports, logistics corridors, industrial zones and emergency routes. Their large scale, complex structural geometry, dynamic loading, wind exposure, corrosion risk and climate sensitivity require continuous and intelligent monitoring rather than periodic visual inspection alone. This strengthened review examines how Internet of Things (IoT) technologies support structural health monitoring (SHM) for long-span bridge safety and maintenance. A PRISMA-informed methodology was applied to screen studies published from 2020 to 2025 across Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink and other engineering databases. From 450 initially identified records, 30 studies were selected for detailed synthesis after duplicate removal, screening and eligibility assessment. The review evaluates sensing technologies, wireless communication protocols, edge and cloud platforms, artificial intelligence, digital twins, BIM integration and predictive maintenance. It also introduces a quantitative technology-occurrence analysis, a communication-protocol comparison matrix, three real bridge case studies and a Saudi Vision 2030 alignment section. The proposed framework integrates IoT sensors, LoRaWAN, edge computing, cloud analytics, AI, digital twins, BIM and a maintenance decision engine for Saudi smart bridges. Findings indicate that IoT-enabled SHM can improve damage detection, reduce unplanned closures, support maintenance prioritization and increase infrastructure resilience. However, technical barriers remain in sensor drift, false alarms, cybersecurity, data quality, energy supply, interoperability, capital cost and model explainability. The paper contributes a holistic review and conceptual framework linking bridge SHM to smart infrastructure, intelligent transportation systems and sustainable mega-project delivery in Saudi Arabia.