Hazard Identification Research Articles

Hazard, risk, reliability assessment and improvement of Darlington Tritium Removal Facility (DTRF) cryogenic refrigeration system

Tritium is a radioactive isotope of hydrogen, produced in heavy water, during CANDU reactor operations by neutron absorption. The tritium removal facility at Darlington (DTRF) is used to extract tritium from the heavy water on a continuous basis and concentrates it in preparation for immobilization and storage. There are several internal and external hazards associated with the operation of DTRF, such as fires, onsite transportation accidents, exposure to radioactive materials, explosion due to Hydrogen (H2) and the release of radioactive substances from the Tritium Immobilization System. This paper conducts a risk analysis for the DTRF Cryogenic Refrigeration System, associated with both Low and High Tritium Distillation columns. This study includes hazard identification, consequence analysis and risk estimation. Fault Tree and Event Tree Analysis conducted, system reliability, availability and human reliability was considered. Consequently, methods for risk reduction and integration with improvement to CRS design is discussed.

A Dualistic Perspective of Opportunity and Risk: The Impact of Head-Mounted Augmented Reality on Construction Onsite Hazard Identification of Workers

A Dualistic Perspective of Opportunity and Risk: The Impact of Head-Mounted Augmented Reality on Construction Onsite Hazard Identification of Workers

Risk Analysis of Railway Infrastructure Asset using Hazard Identification and FMEA Analysis on Track Asset

Risk Analysis of Railway Infrastructure Asset using Hazard Identification and FMEA Analysis on Track Asset

Knowledge Mapping of the Auxiliary Police: A Basis for Crafting a Capability Training Program

The auxiliary police force, particularly barangay tanods in the Philippines, faces significant challenges in effectively performing their roles due to limited formally training and capacity-building opportunities. This study was conducted to assess the level of knowledge among barangay tanods regarding their duties, responsibilities, roles as first responders, and patrolling and hazard identification and to develop a Capability Training Program tailored to address identified deficiencies. The need for this research arises from the observed gaps in advanced patrolling strategies and emergency response protocols, which hinder their effectiveness as community enforcers. Using a descriptive-quantitative research design, data were analyzed through regression analysis and descriptive statistics, including mean and standard deviation, using SPSS. The study employed adapted questionnaires targeting knowledge in key areas, such as duties and responsibilities, first responder actions, patrolling techniques, and hazard identification. Simple random sampling was applied, resulting in 140 barangay tanods participating as respondents. Findings revealed that although the overall knowledge of the respondents was high, specific gaps were evident in advanced emergency response and proactive patrolling strategies. These insights were instrumental in the development of a comprehensive Capability Training Program aimed at enhancing the effectiveness of police auxiliary services in critical operational areas.

A metamodel for sensitivity and uncertainty analysis in the modeling of loss of life due to dam failures

ABSTRACT Estimating the loss of life resulting from dam failures is required for devising emergency action plans and strategies for alert issuance and evacuation. However, current models for simulating fatalities are computationally expensive, forced by highly uncertain variables, and not readily interpretable, which may limit their use in engineering and research. For circumventing these problems, we utilize the polynomial chaos expansion technique (PCE) for approximating the loss of life as obtained from the agent-based model LifeSim and propagating uncertainty of inputs, namely, alerted population, mobilized population, alert issuance, and hazard identification, to the model responses. We also benefit from the PCE spectral representation for assessing the effects of each input in the loss of life associated with the dam failure in an urban area in Brazil, considering efficient and inefficient scenarios for alert and evacuation. The PCE error ranged from 10–3 to 10–2, and the mean squared error between the metamodel output and LifeSim was between two fatalities. In global sensitivity analysis, the variables alert issuance and hazard identification contributed the most to the number of fatalities. These findings provide objective guidelines for implementing more effective safety measures, potentially reducing loss of life resulting from a dam break in the study area.

Innovative geological–geotechnical zoning framework for urban planning: Wuhan’s experience

Traditional methods for understanding geological conditions, such as borehole drilling and on-site tests, are often costly and impractical for extensive urban areas like Wuhan’s Main Urban Area. This study introduces an innovative geological–geotechnical zoning framework that utilizes limited data to overcome these challenges, essential for urban planning, underground space development, and construction design. We began by dividing the study area into four primary geological units based on geomorphological characteristics, using a novel method validated at four metro stations. Our approach incorporated data from 1544 geological boreholes to compile a comprehensive database, from which six typical stratigraphic profiles for each unit were identified. These profiles facilitated the estimation of potential layers and their corresponding geotechnical parameters, and the identification of subsurface hazards. The study resulted in a detailed engineering geological zoning divided into nine secondary units. Our analysis not only demonstrates the effectiveness of the proposed criteria for geomorphological units in achieving more accurate and quantifiable outcomes but also shows the framework’s suitability for regions with complex sedimentary conditions. The resultant engineering geological zoning enhances understanding of geological risks, providing a foundation for safer and more sustainable urban development.

Systematic Literature Review on Knowledge-Driven Approaches for Construction Safety Analysis and Accident Prevention

Due to its inherent complexities in the process and the dynamic interactions with external environmental factors, the construction industry is widely considered one of the most hazardous industries worldwide. With advancements in artificial intelligence (AI), construction safety management practices have increasingly used knowledge-driven approaches. Such incorporation of knowledge-based methods has led to significant improvements in various elements of construction safety management systems, including hazard identification and risk assessment, selection of risk mitigation strategies, analysis of accident information, sharing of health and safety knowledge, access to regulations, and identification of applicable safety requirements. Against this background, this paper presents a systematic literature review to provide an overview of the current state of the art in the use of knowledge-driven approaches in construction safety management. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure, this study reviews how the knowledge-driven approach is utilized in the construction safety management field to automate different activities that come under it. Journal papers published from 2000 were considered for this review, and the analysis focused on the contributions of research, the evolution of knowledge-driven approaches, sources of incorporated knowledge, methods of system development, yearly publications, and publication by journals. The results provide a comparison of related studies over two decades and offer insights into trends and gaps in this research field. Notably, the trend analysis shows a dramatic increase in the number, as well as the depth, of research efforts utilizing AI techniques for analyzing unstructured data, such as construction images and texts from construction documents, and drawing data-based decisions for accident prevention.

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

Risk assessment for Na-Zn liquid metal batteries

Background Na-Zn liquid metal batteries, which operate at 600 °C, have recently been proposed as inexpensive stationary energy storage devices. As with any other electrochemical cell, their fabrication and operation involves certain risks, which need to be well understood in order to be minimised. Methods A risk assessment according to ISO 12100 is performed at the cell level for operating Na-Zn cells in the laboratory environment. Hazard identification and risk evaluation are systematically addressed, including a thorough literature review, theoretical calculations and selected experiments. Results Cell overpressure is found to be one of the main risks – and might be caused either by mistakes in battery production (humidity) or operation (over-charge/discharge). In terms of cell housing, the weakest component is clearly the feedthrough. Its failure might lead to the release of hazardous aerosols to the environment. In this context, the candidate electrolyte components LiCl and BaCl2 are especially dangerous, and should therefore be reduced or avoided if possible. Conclusions Overall, Na-Zn cells are expected to reach a very high safety level, similar to state-of-the-art ZEBRA technology, as they are not prone to thermal runaway. However, considering the still low TRL level and open questions concerning the durability of certain parts of their housing, the batteries should preferably be operated under a fume hood.

Hazard Identification of Well Test Operation in Drilling and Production Offshore Platform by HAZID

A significant portion of energy resources has been discovered in offshore sectors, leading to a continuous increase in the volume of activities and operations. Once a well is drilled and fluid extraction begins, all the reservoir parameters, start to change. well test operation is one of the most crucial tools for engineers to comprehend the behavior and parameters of hydrocarbon reservoirs. In this study, the hazards associated with the well test operations has been identified by using HAZID technique. A total of 189 risks were identified in the initial risk assessment, with 35 categorized as low risk, 88 as medium risk, and 66 as high risk and after implementing protection layers in the secondary risk assessment, the number of low risks incidents increased, while medium and high-risk incidents were reduced significantly. Most of the risks identified are associated with loading operations and sea transportation from the port to the drilling rig. Given that loading and unloading operations are among the most frequently repeated activities in well testing operations, it is determined that they play a critical role in the overall risk profile

Multi-hazard risk assessment analysis in La Palma: an approach for risk mitigation

BackgroundLa Palma, like many regions of the world, may be affected by various hazards, with different levels of susceptibility, which have a greater or lesser impact on society. In this context, a multi-risk approached model capable of estimating the total risk faced by the territory is required, considering the different hazards to which it is exposed (floods, earthquakes, volcanic eruptions and landslides) and the different conditions of vulnerability of both the population and the exposed assets.MethodsThe methodology employed in this study has been referred to as "multilayer single hazard", which consists of the aggregation or superposition of hazards located in the same space. As usual, the identification of the individual hazard of each one of them was carried out using heterogeneous methods, which is why an index is proposed to homogenize the hazards and make them comparable with each other. On the other hand, the model also integrates a prototype to evaluate vulnerability from a multiple perspective (vulnerability by hazard) and shows the aggregated information in maps at a medium representation scale (definition of 250 m).ResultsThe risk integration results show that 5% of the island is at high risk levels. Simultaneously, the south of the island is presented as the area that shows the highest risk by combining a high hazard, especially related to volcanic hazards, and a high exposure, as it is home to a significant volume of population.ConclusionsThe proposed methodology is also interesting because it can be replicated in other areas of study.

Harnessing the power of ChatGPT to promote Construction Hazard Prevention through Design (CHPtD)

PurposeConstruction Hazard Prevention through Design (CHPtD) is recognized as a pivotal strategy for enhancing workplace safety. Despite its theoretical potential, practical implementation faces challenges, particularly regarding designers' familiarity with field-level operations and hazard recognition. This study aims to investigate whether ChatGPT can address these challenges by assisting in hazard recognition during CHPtD sessions.Design/methodology/approachThe research utilized a randomized controlled experiment to assess ChatGPT as an intervention in supporting hazard recognition during CHPtD sessions. The study involves 162 civil and construction engineering student participants, representing future professionals. Participants engaged in hazard recognition during CHPtD sessions either with or without the assistance of ChatGPT.FindingsParticipants who utilized ChatGPT during CHPtD sessions demonstrated a significant improvement in hazard recognition, identifying approximately 40% more hazards compared to those who did not use ChatGPT. These findings underscore the efficacy of ChatGPT in supporting CHPtD efforts.Practical implicationsThe results highlight the practical utility of harnessing ChatGPT in CHPtD sessions to enhance hazard recognition and ultimately promote workplace safety. By leveraging ChatGPT, designers and engineers can better anticipate and mitigate potential hazards during the design phase, thus creating a safer working environment for field workers.Originality/valueThis research contributes to addressing the challenges associated with implementing CHPtD by introducing ChatGPT as a valuable tool to support hazard recognition. By demonstrating the effectiveness of ChatGPT in enhancing hazard identification during CHPtD sessions, this study offers a novel approach to promoting the adoption of CHPtD and advancing workplace safety initiatives.

The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding–decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by unmanned aerial vehicles (UAVs). The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) were obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers) proposed in this paper outperforms other methods. The main evaluation indicator, mIoU of HHSN-25, is higher than other methods, reaching 87.00%, and the Unet is 85.50%, and the Unet++ is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Four years of landslide observation with anthropogenic loading as an additional trigger - Analysis of seasonal and annual variability of physical parameters

Determining the structure and evolution of landslides is crucial for geophysical hazard assessment. In this study, we employed an approach integrating the methodologies of multi-channel analysis of surface waves (MASW) and electrical conductivity to image temporal and spatial changes within a landslide in southern Poland. The area, located in the Outer Carpathians, experiences significant climate fluctuations, compounded by anthropogenic activities such as recreational skiing requiring artificial snow.Our combined seismic and electrical methods techniques reveal the landslide's susceptibility to environmental factors on both annual and seasonal scales. Additional analysis, including data clustering and remote sensing, identifies three distinct landslide zones with varying vulnerability to natural and anthropogenic influences.While focusing on a specific area, our approach has global applicability to similar mass movements. This research addresses a gap in understanding time-dependent geophysical observations of moisture-driven landslides, providing valuable insights for hazard identification and mitigation strategies.

Mapping coastal resilience: a Gis-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

Rethinking Surgical Safety: Investigating the Impact of Gamified Training on Severe Flow Disruptions in Surgery

Objectives There is a need for effective and engaging training methods to enhance technical and nontechnical skills in robotic-assisted surgery (RAS), where deficiencies can compromise safety and efficiency. This study aims to evaluate the impact of a gamified team training intervention, the “RAS Olympics,” on the safety and efficiency of RAS procedures. Methods The study was conducted at a 958-bed tertiary care academic medical center in with a robust robotic surgery program. A total of 56 RAS procedures (general, urology, and gynecology) were included in the analysis, with a mix of procedure types representative of the surgical caseload at the medical center. A pretest posttest experimental design was employed, comparing the frequency and severity of flow disruptions (FD) between preintervention, postintervention without “RAS Olympics” participants, and postintervention with “RAS Olympics” participants. The “RAS Olympics” involved safety hazard identification, troubleshooting, workspace navigation, instrument retrieval, and turnover optimization. Results Postintervention cases with “RAS Olympics” participants exhibited significantly lower overall FDs compared to the postintervention control group. The reduction was particularly notable during phase 3 (surgeon on console) and in cases involving more severe FDs. Conclusions Gamified team training interventions may improve the safety and efficiency of RAS procedures. The positive outcomes underscore the potential of innovative and engaging training methods to address the evolving challenges in surgical practice, emphasizing the relevance of gamification in healthcare education. As the healthcare landscape continues to advance, incorporating such interventions may be crucial in ensuring the adaptability and competence of surgical teams.

Enhancing hyperspectral power transmission line defect and hazard identification with an improved YOLO-based model

Enhancing hyperspectral power transmission line defect and hazard identification with an improved YOLO-based model

Emerging signals of climate change from the equator to the poles: new insights into a warming world

The reality of human-induced climate change is unequivocal and exerts an ever-increasing global impact. Access to the latest scientific information on current climate change and projection of future trends is important for planning adaptation measures and for informing international efforts to reduce emissions of greenhouse gases (GHGs). Identification of hazards and risks may be used to assess vulnerability, determine limits to adaptation, and enhance resilience to climate change. This article highlights how recent research programs are continuing to elucidate current processes and advance projections across major climate systems and identifies remaining knowledge gaps. Key findings include projected future increases in monsoon rainfall, resulting from a changing balance between the rainfall-reducing effect of aerosols and rainfall-increasing GHGs; a strengthening of the storm track in the North Atlantic; an increase in the fraction of precipitation that falls as rain at both poles; an increase in the frequency and severity of El Niño Southern Oscillation (ENSO) events, along with changes in ENSO teleconnections to North America and Europe; and an increase in the frequency of hazardous hot-humid extremes. These changes have the potential to increase risks to both human and natural systems. Nevertheless, these risks may be reduced via urgent, science-led adaptation and resilience measures and by reductions in GHGs.

Risk Assessment for Inland Vessels with Various Levels of Automation: a Hazard Identification Study

Abstract The future viability of inland navigation is closely linked to the automation of inland vessels. This will make their use more attractive and efficient. Potential solutions for automation in the scope of this paper include remotely controlled inland vessels that do not require a boatmaster to be on board but are controlled from shore. The implementation of such new technologies involves unknown risks. It is therefore necessary to assess the safety and classify the risks associated with inland vessels of varying degrees of automation. To identify these risks, a Hazard Identification (HAZID) study has been conducted. The outcomes of the study may serve as a basis for adapting regulations and introducing novel technologies, which would facilitate increased automation of inland navigation

Stand-off hazardous materials identification based on near-infrared hyperspectral imaging combined with convolutional neural network

Near-infrared (NIR) hyperspectral imaging enables rapid, non-contact imaging of hazardous materials in a non-destructive manner, allowing for analysis based on spectral reflection information. However, using traditional methods, it is challenging to identify hazardous materials with less distinct spectral reflection features. This study utilizes a self-built NIR hyperspectral imaging system and proposes a new approach. Using a convolutional neural network (CNN), This allows for the rapid completion of high-throughput spectral screening, marking suspicious spectra at spatial points. we sophisticatedly classified six hazardous material types, generating impactful warning images. The optimized CNN demonstrated superior performance (accuracy 91.08 %, recall 91.15 %, specificity 91.62 %, precision 90.17 %, and 0.924 F1 score) compared to SVM and KNN methods. Our study included multitask validation tests, revealing a sensitive detection of 10 mg/cm2 for ammonium nitrate and trinitrotoluene, capable of identifying over 100 targets simultaneously. By simulating real-world scenarios, we successfully detected hazardous chemicals scattered on the ground and accurately identified these hazardous materials in glass and thin plastic products. Even in situations where clothing obstructed the view, we could still correctly identify hazardous chemicals and generate corresponding warning images. Our system demonstrated precise identification capabilities even amidst complex backgrounds. This method provides an accurate and rapid solution for identifying and locating hazardous chemicals, laying a strong foundation for the next steps in non-contact, long-distance quantitative determination of chemical concentrations. This study highlights the effective application of CNN in non-contact, long-distance classification, and recognition of hazardous materials, paving the way for further scientific and engineering applications.