Accident Path Research Articles

Exploring hazardous chemical explosion accidents with association rules and Bayesian networks

Hazardous chemical accidents pose a severe threat to human and economic activities. To explore the causes and paths of hazardous chemical explosion accidents (HCEAs) in China, a qualitative and quantitative model was proposed in this study. Firstly, 129 accident cases were collected from the official website of the Ministry of Emergency Management of the People's Republic of China, and the accident factors were screened based on association rule (AR). Then, the fault tree (FT) structure of HCEA was developed by the AR, and the qualitative model was transformed into a quantitative Bayesian network (BN) model. Finally, the accident path, the importance size of the basic events, and the sensitivity of the direct cause of the accident were uncovered based on the BN model. The results show that improper temperature control due to operating procedures is the most likely route to accidents. Among the basic events, process management and operating procedures issues ranked the first in importance, with the most significant impact on HCEA. The sensitivity of improper temperature control far exceeds other direct causes and is most likely to induce accidents directly. According to the study results, some feasible measures are put forward to improve the safety management of hazardous chemical production.

An integrated TRIZ coupled safety function deployment and capital budgeting methodology for occupational safety improvement: A case of manufacturing industry

In this study, a novel scheme is proposed for occupational safety improvement by leveraging the concepts of Virtual Reality (VR), Safety Function Deployment (SFD), TRIZ (theory of inventive problem solving) and capital budgeting approach. This integrated approach helped in identifying safety interventions, which added a new dimension to the safety intervention design in the operational study at the workplace. By observing the effectiveness of the immersive safety training in identifying the accident path elements such as hazards and initiating mechanisms, a three-dimensional (3D) VR environment is created for safety training of Electric Overhead Travelling (EOT) crane operators of the studied manufacturing industry. This study is carried out in two phases based on accident path elements identified before and after safety training in the VR platform. Three House of Safety (HoSs) are used in this study to establish a relationship between tasks and hazards, hazards and initiating mechanism, and initiating mechanism and safety interventions. Priority weight of interventions in the last HoS is fed as input to the capital budgeting methodology for the selection of an optimal number of interventions for safety improvement. 0–1 multi-dimensional knapsack model is used in capital budgeting considering safety budget and cost of each intervention. We have introduced Z- number approach in capital budgeting methodology to characterize the reliability of experts’ opinion considered in this process. A 15% improvement in safety performance is observed after safety training. Further, it is observed that technology-based interventions (laser scanner, smart helmet, smart jacket, Radio Frequency Identification (RFID) to monitor Personal Protective Equipment (PPE), immersive safety training) are having more weightage than traditional safety interventions after safety training.

Accident causes data-driven coal and gas outburst accidents prevention: Application of data mining and machine learning in accident path mining and accident case-based deduction

Analyzing the causes of accidents, excavating accident paths, and applying accident prevention are important tasks in safety management. Focusing on coal and gas outburst accidents, this study examined the primary accident path and conducted applied research on the reasoning of the accident case. First, combined with the obtained accident causes, a coupling analysis of the causes of coal and gas outburst accidents was conducted. Second, using the method of data mining coupled with Apriori algorithm, the coupling relationship between each cause module of the coal and gas outburst accident was obtained, and consequently, a path map of the coal and gas outburst accident was drawn. Third, a Bayesian network model for the causes of coal and gas outburst accidents was established based on the accident path map and the probability of occurrence of each cause. Finally, considering the safety concept element (SC1) as an example, the Bayesian network model was used to conduct a sensitivity analysis of accident causes. Thereafter, considering the coal and gas outburst accident of the Sanjia Coal Mine in Guizhou Province as an example, probabilistic reasoning research on the cause of the accident was conducted. The application results showed that (1) under normal conditions, there are approximately 797,280 accident paths for coal and gas outbursts. Following data mining, 188 main accident paths were found. (2) Sensitivity analysis determined 19 factors that were sensitive to safety concept elements (SC1), of which the three most sensitive factors were (i) resource management system procedures (SM7), (ii) safety policy (SM1), and (iii) safety training system procedure (SM8). 13 paths exhibited a sensitivity ≥0.5%, of which 7 exhibited strong sensitivity. (3) The absolute accuracy rate of accident cause reasoning in the Sanjia Coal Mine in Guizhou Province was 71.43%, while the relative accuracy rate was close to 100%. Thus, it was concluded that: (1) the accident path mining method proposed in this paper is feasible for main accident path mining. (2) The Bayesian network model for the causes of coal and gas outburst accidents established in this study can be practically applied for the sensitivity analysis of accident causes and exhibits high reliability in the probabilistic reasoning of accident causes. The results of this study is expected to aid in the prevention of coal and gas outburst accidents, and provide reference and help for the path mining of other accident causes and the probabilistic reasoning of accident causes.

Causes of road accidents in northwestern iran in the period 2010–2018

Aims: Traffic accidents are one of the major causes of death and disability worldwide. The aim of this study was to determine the causes of road accidents in northwestern Iran in the period of 2010–2018. Materials and Methods: This cross-sectional study was performed on all road traffic accidents recorded by traffic police of West Azerbaijan Province during 2010–2018. Data were analyzed using descriptive statistics, Chi-square t-test, and time series by SPSS 16. Results: A total of 95,788 registered accidents were included in the study. Most of the accidents were in September with the frequency of 9960 cases (10.4%), in residential, office, and industrial regions 58,550 (56%), by cars and taxi 80,949 (66%), in collisions between a vehicle with a bicycle and a motorcycle 56,728 (58%), in front-to-rear and right-side crashes 49,714 (47%), in rural and main roads 59,855 (62%), in clean weather 73,887 (73%), and on Thursday 14891 (15%); the occurrence of traffic accidents showed a significant relationship with all of these variables (P = 0.001). Conclusions: Month of accident, type of accident, day of the week, location of accident, use of vehicle, type of collision, mode of collision, accident path, and weather were the effective factors contributing in the occurrence of the traffic accidents. It is suggested that, in addition to educating people regarding the prevention of traffic accidents, policymakers take steps to improve the safety and standardization of roads and increase the safety of vehicles.

Construction and analysis of a coal mine accident causation network based on text mining

It is important to systematically identify the contributing factors in coal mine accidents from a large-scale analysis of accident reports. However, previous scholars have mainly used human analysis methods to define accident-causing factors, leading to incomplete and biased cause checklists due to personal experience and knowledge. Furthermore, a data-driven method is needed to quantify the importance of each factor and clarify the mechanism of different types of accidents. Considering these, this study creatively combined text mining technology and a complex network to explore the coal mine accident-causing mechanism. Through the text mining of 307 accident reports, 52 main accident-causing factors were identified, and a coal mine accident causation network was constructed based on the strong association rules among factors. Second, eight core factors and their associated sets, as well as seven critical links for different accident types, were clarified through network centrality analysis and accident path analysis. This study shows that regulatory authority is the most influential level of accident causation, gas accidents are the most easily triggered accident type, a lack of effective mechanism for safety supervision→failure to arrange full-time safety inspectors to follow the shift→lack of serious and thorough on-site hidden danger investigations→inadequate anti-surge measures are the key links in gas accident causation. This study contributes new perspectives on identifying contributing factors and their complex interaction mechanisms from accident report data for practical applications in risk analysis and accident prevention.

Assessment of virtual reality based safety training simulator for electric overhead crane operations

In this paper, we have assessed the efficiency and effectiveness of a developed immersive VR-based safety training simulator that allowed procedural training to electric overhead crane (EOT) operators. The primary objective of the training simulator is to help the novices to understand the sequence of operations as well as manage the potential hazards while working. Two types of assessment are carried out: simulator effectiveness and safety training effectiveness. We have developed three experimental setups, namely practice environment, familiarization environment, and virtual workplace environment, for improving acquaintance, learning of navigation and control, and operational and safety training, respectively. 19 operators from an integrated steel plant have participated in the experiments; among them, 16 are young operators (average age: 32.68 years, average experience: 4 years) and 3 are experienced operators (average age: 49.66 years, average experience: 12.5 years). The participants have provided their responses on the effectiveness of the simulator using the presence, system usability, and simulation sickness questionnaires. Cronbach’s alpha (>0.7), concordance (>0.5), and convergent validity (>0.7) show that the responses are consistent and reliable. Further, the t-test shows that the simulator is equally effective for both young and experienced operators. For validation purposes, the proposed VR simulator’s performance is compared with the desktop simulator using t-test and signal to noise (S/N) ratio and the results show that the VR simulator is better than the desktop simulator. Finally, the safety training effectiveness is assessed based on the improvement of identifying accident path elements for five tasks. 18 hazardous elements (HEs) and 14 initiating mechanisms (IMs) were identified by the operators before VR-based safety training and additional 10 hazardous elements (HEs) and 13 initiating mechanisms (IMs) were identified after the training. Further, hazard-identification improvement index is proposed as a quantitative measure for safety training effectiveness. Then, task module prioritization analysis using analytic hierarchy process (AHP) was done and the results show that tasks 2 and 5 need more safety training compared to the other three tasks.-

An accident causation network for quantitative risk assessment of deepwater drilling

Quantitative risk assessment plays an important role in facilitating deepwater drilling safety. This paper proposes an accident causation network method for risk assessment during deepwater drilling. The method involves four basic steps: risk identification, failure scenarios definition, uncertainty evaluation, and accident path calculation. Risk factors regarding different sources such as the operator, process, equipment, and environment are firstly identified. Then a network topology based on graph theory models the underlying accident scenarios. Risk entropy is adopted to model the uncertain influence of multi-type risk factors. Dijkstra algorithm is finally used to calculate the shortest path from an initial event to a blowout accident. A deepwater drilling system is chosen as a case study to demonstrate the applicability of the proposed method. The result shows that changes of failure probabilities of risk factors lead to the variation of the shortest path both in probabilities and event sequences. With the assessment result, decision makers can take targeted measures to prevent accidents or reduce system risks.

A novel methodology to analyze accident path in deepwater drilling operation considering uncertain information

An initial failure in a vulnerable part of deepwater drilling system may escalate into major accidents such as blowout, fire, or explosion. Such accidents have characteristics of complexity, dynamics, and uncertainty, which traditional risk assessment methods fail to capture. This paper presents an integrated methodology for evaluating deepwater drilling risk by combining directed acyclic graph (DAG) and risk entropy. The methodology follows four basic steps: identifying risk factors, defining failure scenarios, determining failure probabilities and entropy values, and evaluating the most probable path of failure events. A network topology is established to develop the possible accident scenarios and paths. Risk entropy is then applied to handle both technical failures and human errors. Bayesian theory is used to describe the dynamics of random factors. The shortest path that represents the most probable failure path from an initial event to a blowout accident is further calculated using Dijkstra algorithm. The proposed approach is then applied in a case study about a managed pressure drilling (MPD) system. The result shows that changes of uncertainties of risk factors result in the variation of the shortest path both in probability values and event sequences. Hence the targeted measures can be implemented according to the assessment result.

A novel data mining approach for analysis of accident paths and performance assessment of risk control systems

The data mining researches to facilitate the process of safety management is fairly new, compared to other industrial management domains. The implementation of appropriate, effective, and safe risk control systems (RCSs) is vital to ensure zero-accident and zero-harm vision of industrial work-systems. In this work, we propose a data mining based tool to analyze accident paths from incident data and assess the performance of RCSs. Our work upgrades the existing pattern analysis methods through three new types of analyses (i) temporal frequent itemset generation (T-FIG) for studying the time effect on patterns, (ii) elevated severity itemset generation (ESIG) for examining the risk reduction due to RCSs, and (iii) High impact itemset generation (High_impact_IG) to identify accident paths with high risk. T-FIG and ESIG assist in performance assessment of preventive and mitigating RCSs, respectively. The results from each of the analyses are compared and eight types of inferences regarding the performance of RCSs are drawn. The proposed methodology is applied to 612 incident records reported during steel making process in a steel manufacturing plant. It was found that there are four accident paths which have ineffective preventive and mitigating RCSs, have high risk and are probable to recur in future. Two among four of these paths include hot metal/steel/slag as the hazardous element and three of them are due to damaged/degraded/poorly maintained equipment. Moreover, the case study also demonstrates that proposed data mining approach is an effective and easy to use tool for performance assessment of RCSs and accident path analysis.

In-vessel source term calculation using chemical equilibrium approach for a medium sized sodium cooled fast reactor

Quantifying the severe accident in-vessel source term for a fast reactor is a computationally challenging task owing to the complex phenomena and wide range of accident paths to be studied. A chemical equilibrium approach offers a relatively accident path independent way to quantify the source term. Here we follow a thermochemical equilibrium approach to the determination of in-vessel source term for a medium-size oxide-fuel sodium-cooled fast reactor. Assuming a uniformly mixed molten corium at a given temperature and volume, the quantity of radionuclides released to the cover gas volume in gaseous form is determined. The development of a modular code in Python for the calculation of equilibrium species in different phases from specified initial inventory at given thermodynamic conditions and its validation are discussed. From the calculations, it is observed that strontium, europium and other lanthanides form stable oxide species in the sodium as opposed to the elemental form in metal fuel reactors. As is the case with noble gases, almost complete inventory of alkali metals and antimony could be released to the cover gas in elemental form (Cs, Sb). The differences in release fractions between metal fuelled and oxide fuelled reactors are brought out in the study.

Chain of events model for safety management: Data analytics approach

In chain of events model, an accident path is defined by a series of sequential events from initiating event to accident realization. In this study, we define accident path as a 5-tuple set comprising hazardous element (HE), initiating event (IE), pivotal event (PV), accident scenario (AS), and consequence (C). We propose a new methodology to identify the path components (i.e., HE, IE, PV, AS and C) and quantify the path using data analytics approach. We have used both proactive (workplace observation (WO) and high risk control program (HRCP)) and reactive data (incident records (IR)) for this quantification. The HEs and their corresponding IEs are extracted from WO and HRCP, and then, IR is used to identify PVs, ASs and Cs. In the process, we have used K-modes categorical clustering and Expectation-Maximization (EM) algorithm based text clustering. We could identify 39 accident paths using proposed approach and after quantification, nine paths are found to be frequently recurring. Preventive strategies are identified for accident paths of high concern. The quantification of accident paths will assist safety management in decision making by prioritizing accident paths and assessing performance of preventive barriers. By the use of data mining techniques, experts will not have to read the huge safety data for accident path analysis. This analysis can be done by experts by identifying safety keywords from the clusters generated by clustering algorithms. This will reduce the labour of safety experts in accident analysis with respect to time and resource.

Human factors risk assessment and management: Process safety in engineering

Abstract Human factors are the primary factors leading to accidents. Therefore, managing human factors is an important way to prevent accidents. This paper aims to introduce a new method to assess and manage human factors. First, the accident causation model was improved based on Reason’s “Swiss-cheese” model, which was then combined with the Human Factor Analysis and Classification System (HFACS) to establish the human factors risk assessment model. The evaluation model includes 5 levels (organization influence, unsafe supervision, preconditions for unsafe acts, unsafe acts, and emergency influence) and 25 human factors. In the risk assessment process, the set pair analysis method was used to calculate the connection number and the partial connection number of each factor, level and whole system. The safety score and risk development interval were calculated by using the connection number, and the risk grade is determined. Thus, the dynamic quantitative evaluation of human risk is realized. By using the partial connection number, the risk development trend of each factor is predicted. Due to the lack of human managed enterprises, the safety status of people is approximately discrete. Therefore, this paper establishes the SPA–Markov chain risk prediction model to predict human risk. The verification results show that the prediction error is less than 2%. This indicates that the prediction model can be applied in practice. To reduce human risk, ABC analysis and the “S-O-R” model were used for human risk management. The application results show that this method has a significant effect on improving human safety factors. Finally, this paper summarizes 12 common unsafe factors and their effective safety “stimulus” measure, researching the accident path. According to the organizational level and individual level of human factors, different kinds of human factors management methods are suggested.

The synergic role of sociotechnical and personal characteristics on work injuries in mines

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.

Safety in the design of offshore platforms: Integrated safety versus safety as an add-on characteristic

The Norwegian offshore industry has established considerable experience and know-how in preventing accidents through design. This paper analysis the principles used by this industry during the different phases of design from two different perspectives. The first is human centred and the focus is here on the design of work places to allow the operators at the sharp end to function in an optimal way to minimise human errors and mitigate disturbances. The second “energy barrier” perspective aims at providing technical safety functions on the platform that intervene in the accident path to minimise loss. Accident and incident statistics show that the barrier perspective has been implemented in design to prevent fires and explosions with considerable achievements, whereas its application in occupational accident prevention is more arbitrary. Design of fire and explosion barriers fits well with the current engineering skills and work-processes in investment projects. The implementation of a systematic human-centred design approach is of a more recent date. It has proved to be more demanding and the merits have been more difficult to demonstrate. The implications for the organisation and management of the design process are discussed.