Accident Reports Research Articles

Modification of HFACS model for path identification of causal factors of collapse accidents in the construction industry

PurposeThis study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.Design/methodology/approachThe main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.FindingsEight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.Originality/valueThis paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

Risk assessment of unmanned aerial vehicle accidents based on data-driven Bayesian networks

With the popularity of unmanned aerial vehicles (UAVs) in different application scenarios, a series of potential risks and challenges have emerged. Few quantitative studies in the existing literature analyze the risk factors that affect and cause UAV accidents. Therefore, this study aims to reveal the dependency relationships among the risk factors associated with UAV accidents by using a data-driven tree-augmented naïve Bayesian network (TAN-BN) method. Firstly, UAV accident reports from transportation departments of various countries are collected and analyzed to establish a dataset on the risk factors inducing different UAV accidents. Then, a data-driven UAV risk analysis model is constructed through data learning and training approaches to reveal the interdependency of risk factors and their comprehensive impacts on the severity of UAV accidents. Sensitivity analysis is comprehensively conducted to show the rationality of the proposed TAN-BN. The results reveal that the risk factors “other aircraft”, “observer errors”, “non-powerplant”, “preconditions for unsafe acts”, “powerplant”, and “adverse weather” are the key factors resulting in UAV accidents. In addition, “observer errors”, “other aircraft”, “non-powerplant” and “adverse weather” are more likely to incur severe “accidents”. Some potential proactive plans and measures are proposed to help prevent the occurrence of UAV accidents.

Estimated Prevalence of Resident-to-Resident Aggression in Assisted Living

Resident-to-resident aggression in assisted living facilities can result in physical and psychological harm, but its prevalence is unknown. To estimate the prevalence of resident-to-resident aggression, including physical, verbal, and sexual, among residents in assisted living facilities. This study used cross-sectional, observational data from a clinical trial, in which residents of assisted living facilities were monitored for events over a 1-month period. All residents of 14 large facilities randomly selected from 2 geographic locations (N = 1067), except those receiving hospice care (n = 11), were invited to participate; 93 died or moved prior to enrollment. There were 33 family and resident refusals; 930 residents were enrolled. Data were collected between May 30, 2018, and August 11, 2022. The data are from a clinical trial testing the effectiveness of an intervention to reduce resident-to-resident aggression. In addition, the study was designed to assess prevalence using the Time 1 (baseline) data, using a probability sample of facilities to allow for this analysis. Resident-to-resident aggression was identified using a mixed-method, case-finding strategy involving 6 sources: (1) cognitively capable resident reports regarding 22 possible events, (2) direct care staff report, (3) staff member reports collected from event-reporting forms, (4) research assistant observation of events in real time, (5) facility accident or incident reports, and (6) resident records. The prevalence of resident-to-resident aggression among the 930 participants (mean [SD] age, 88.0 [7.2] years; 738 women [79.4%]) during the past month was estimated to be 15.2% (141 of 930 residents; 95% CI, 12.1%-18.8%). The most common forms of aggression included verbal (11.2% [104 of 930 residents; 95% CI, 8.8%-14.2%]), physical (41 of 930 residents; 4.4% [95% CI, 3.1%-6.3%]), sexual (0.8% [7 of 930 residents; 95% CI, 0.4%-1.6%]), and other (70 of 930 residents; 7.5% [95% CI, 5.5%-10.2%]). These categories are not mutually exclusive as residents could be involved with more than 1 type of aggressive behavior. In this cross-sectional, observational prevalence study, resident-to-resident aggression in assisted living facilities was highly prevalent. Verbal aggression was the most common form, and physical aggression also occurred frequently. The effects of resident-to-resident aggression can be both morbid and mortal; therefore, intervention research is needed to prevent it and to treat it when it occurs.

Experimental study on the characteristics of buoyant gas flow produced by vertical gas leakage in tunnels under natural ventilation

In recent years, there have been reports of gas leakage accidents during the transportation of gas tankers in tunnels, potentially leading to destructive explosions. However, the overall distribution of gas concentration in the entire tunnel has not been experimentally investigated. This study presents a series of brine-water experiments to explore the characteristics of buoyant gas flow in tunnel leakage accidents. The gas distribution is categorized into a stably stratified longitudinal current and a source region. Gas concentration in the tunnel is correlated with the dimensionless reduced gravity, which increases with the source buoyancy flux but decreases with the source release velocity. A model is proposed to estimate the maximum gas concentration of the longitudinal current. The spatial range of the explosive region is influenced by the source buoyancy flux and the leakage velocity. However, the propagation velocity of the longitudinal current is primarily controlled by the source buoyancy flux, with the source momentum having a negligible effect. The thickness of the longitudinal gas flow, δ, linearly decreases from the source region to the tunnel ends. A dimensionless parameter, λ, is introduced to characterize δ. When λ < 1.25, δ rapidly increases with λ and then remains approximately constant when λ exceeds 1.25. This study contributes to a better understanding of buoyant gas flow in tunnel leakage accidents.

Investigating the influence of human errors in master-pilot information exchange on maritime accident risk during pilotage

Pilotage-related errors have led to too many accidents in recent years, resulting in significant economic losses and disruptions in global logistics. Despite its importance, Master-Pilot Exchange (MPX) can be overlooked due to several factors. This study focuses on MPX, a standard protocol for information exchange between shipmasters and maritime pilots to ensure safe ship handling. Deficiencies in MPX procedures have been highlighted in various accident reports, emphasizing the need for improvement. This research aims to identify an optimal MPX process, evaluate potential shortcomings, assess their likelihood and severity, and analyse the human factors contributing to these deficiencies. The study utilizes a fuzzy Bayesian Network (BN) to model the MPX process and identify factors that may lead to its failure. It also employs the Success Likelihood Index Method (SLIM) approach to calculate the probability of human errors in the MPX process and identify the performance-shaping factors triggering them. The finding of the Bayesian method shows that the machinery problem and the navigational equipment problem are the two most significant root causes of MPX process failure. According to the SLIM findings, providing details of "the ship's contingency plan", and "navigational information" are the two most significant tasks for this process. Among the identified performance-shaping factors, the degree of teamwork and harmony, experience and knowledge appear to be the three most significant contributors with the highest human error probability (HEP) in mentioned tasks. This study represents a significant contribution to research on master-pilot interactions, addressing a critical aspect of maritime safety.

Prevalence of Accidents in Smaller Amish Settlements: 2015–2022

Many Amish people engage in manual labor tasks at work, on the farm, and at home. As such, the daily risk for injury is high. The primary mode of transportation, horses and buggies, also creates opportunities for accidents and injuries. Much of the research on injuries among the Amish focuses on hospital reports, capturing only the most severe injuries. Research is also limited by a focus on injuries to children. Many safety programs are tailored toward people living in large Amish settlements, as more people can be reached. We use accident reports submitted to The Diary, a monthly newspaper, to determine the type and prevalence of accidents in small Amish communities. Accidents related to falls, manual labor, and buggy and/or vehicle crashes are the most prevalent forms of injury across age groups. Most accidents do not involve serious injuries and are likely to escape the purview of mainstream medicine. The prevalence of accidents at the house or on the property involving young children suggests that culturally appropriate prevention measures tailored to daily activities would be helpful to lower the incidence of injury.

Fire accident report verification using fire dynamics simulator: a case study from Chiayi City, Taiwan

Fire accident report verification using fire dynamics simulator: a case study from Chiayi City, Taiwan

COLREGs Misunderstood or Ignored?

As various scholarly articles and accident investigation reports indicate, the risk of collisions at sea is still high. The objective of this research is to identify if this is due to misunderstanding of the International Regulations for Preventing Collisions at Sea 1972, as amended (COLREGs), by watchkeeping officers working onboard cargo ships. A total of 76 collisions while navigating officers (OOW) were at the con that occurred during a period of 10 years were analysed.80% of the collisions could have been avoided since one vessel was aware of the presence of the other. The highest number of collisions had taken place in cases of crossing situations. Furthermore, it is also important to note that 52% of the OOWs involved in collisions were sufficiently experienced.No evidence was found to conclude that these accidents occurred due to misinterpretation or misunderstanding of the COLREG’s by the OOW’s onboard the cargo ships, but as incorrect or insufficient action had been taken by experienced OOW’s, this could have been due to complacency and/or overconfidence on their part.Among the measures to be taken to minimize collisions at sea, overconfidence and complacency are two factors that should also require attention by all interested parties.

Evolutionary model and risk analysis of ship collision accidents based on complex networks and DEMATEL

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships.

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

Cause Analysis of Elevator Accidents Based on Complex Network

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

Modeling the causal mechanism in process safety management (PSM) systems from historical accidents

Process safety management (PSM) underpins reducing major accidents in the process industries. For decades, PSM systems have been implemented globally. However, few studies have modeled the complexities within the PSM system, leading to some underlying chronic problems within organizations that go unnoticed. Here, we investigate causal mechanisms among PSM elements from historical accidents to deliver a practical tool for safety intervention formulation. Specifically, based on the Center for Chemical Process Safety's (CCPS) PSM framework, 100 chemical accident reports derived from the U.S. Chemical Safety and Hazard Investigation Board (CSB) were analyzed and converted into structured information stored in 22 PSM elements. Then, employing two causal structure learning algorithms, the causal structures among 22 elements were learned from the processed accident information. Finally, the causal effects among PSM elements were estimated by extending Rubin's counterfactual causal framework to an approach applicable to multi-treatment scenarios. We demonstrate that the causal structures and effects learned from accident reports reveal the causal mechanism within the PSM system. A total of 20 significant causal relationships among single elements, nine significant causal relationships among element combinations, and several interesting PSM failure paths were identified. According to these causal patterns, the PSM effort will shift from the traditional "what to do" to "how to do it."

Dental injuries in Swiss children - an analysis of Swiss national health insurance data from the year 2019.

This study, the first to analyze accident data from a major compulsory Swiss health insurer (Concordia), reviewed 5,063 dental accident reports of 122,370 children under the age of 16. The predominant cause of injury was a "fall," with "playing" being the foremost activity mentioned and "ground" identified as the primary object of impact. The analysis of the involved objects showed that dental injuries occur most frequently with scooters, bicycles, and stairs. In 2019, 8.14% of children aged one and insured by Concordia suffered a dental injury. By age 16, 58.8% of all children had experienced a dental injury. 0.72% had suffered a primary dentition crown fracture with pulp involvement. Regarding their permanent teeth, 0.21% suffered an avulsion, 0.84% another luxation injury, 0.65% a crown fracture with pulp involvement, and 0.16% a root fracture. A significant increase in injuries per day was observed after the summer holidays. On weekends, there were 28% fewer injuries per day on average than on weekdays. Despite differences among the cantons, the dataset can be considered to be representative for Switzerland. Accident descriptions were often too brief for detailed prevention strategies. Detailed accident information is essential for effective structural measures, which are more effective than promoting behavioural changes. A detailed recording could also be used to draw up a list of the objects frequently involved in accidents. An updated insurance form with an improved nomenclature, the option of digital submission, photo uploads and AI-supported data recording could greatly improve the quality and interpretability of injury data.

Highlighting Traffic Accidents on Roundabouts Using MRSS-AHP Expert System

The frequency and severity of traffic accidents are causing growing concern. This study aims to develop a tool to improve the traffic safety level on roundabouts and identify the influence of traffic operations, geometric parameters, weather, and time of day on improving roundabout traffic safety. It is the first study to evaluate the performance of the integrated Median Ranked Set Sample (MRSS) and Analytic Hierarchy Process (AHP) with statistical analysis. A hierarchy tree of accident causes has been developed using data gathered from accident reports and relevant authorities. Then, the selected stakeholders’ professionals prioritized the traffic accident causes using a MRSS and AHP. Moreover, traffic microsimulation software VISSIM was also used to extract traffic operation parameters for the analysis. Afterwards, Analysis of Variance (ANOVA) was used to validate the causes of traffic accidents. The results show that geometric design accounts for 36% of accidents at roundabouts, followed by traffic operation (22%). However, conflicting and queuing lengths are responsible for about 20% of traffic accidents. The tools developed, and the causes of accidents determined in this study will help geometric designers and city planners to take the necessary measures to minimize accidents and enhance traffic safety levels in urban areas. Doi: 10.28991/CEJ-2024-010-04-013 Full Text: PDF

A socio-technical systems approach of the accident analysis in Indonesian multiple train accident cases: An application of AcciMap methodology.

The number of cases of train accidents in Indonesia continues to be large in the past few years. This paper analyzes train accident cases in Indonesia using a socio-technical system approach accident model called AcciMap, developed by Jens Rasmussen. The source of data comes from train accident investigation reports from 2015 to 2021 published by the National Transportation Safety Committee. This study uses a qualitative approach with thematic analysis to identify contributing factors and their interactions within the reports. Based on the socio-technical system approach, there are roles and contributions from the five levels of the socio-technical railway transportation system in Indonesia that interact with each other in the occurrence of accidents, including the railway regulatory bodies; other organizations; management of railway service company; processes and physical actions by staff; and condition of equipment and environment. This study has shown that the AcciMap methodology can provide a comprehensive view of the problems and their interactions across the socio-technical levels that influence each other in the occurrence of train accidents in Indonesia. The results also suggest that the socio-technical system approach is expected to be applied in the accident investigation process for the related industry to get more comprehensive insights into the accident.

Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022

Major chemical accidents (MCAs) usually cause multiple casualties, property losses, and environmental damage. MCAs in hazardous chemical production enterprises (HCPEs) in China have been frequent in recent years. Understanding past accidents is essential for accident prevention and risk mitigation. However, no accurate statistical analysis and systematic human cause investigation of MCAs in China is available. Hence, this study systematically identified the main characteristics and human factors of MCAs in China during 2011–2022 using official accident investigation reports. First, the 160 MCAs were investigated by mathematical and statistical methods involving eight main characteristics: evolution trend, spatial locations, accident types, domino effects involved, occurrence links, time volatility, hazardous chemicals involved, and enterprise scales. Second, based on the characteristics of MCAs and existing research on chemical accidents, the Human Factors Analysis and Classification System (HFACS) for Chemical Accidents (HFACS-CA), which consists of 26 cause categories and is suitable for the analysis of chemical accidents, was proposed on the basis of HFACS and other extended HFACS. Then, the selected 146 representative MCAs were analyzed using the HFACS-CA framework; the human factors (comprising human and organizational factors (HOFs) and outside factors) of MCAs in China were systematically explored. Finally, according to the main characteristics and human factors of MCAs, comprehensive recommendations for the prevention of MCAs involving enterprises, governments, and interested parties were proposed. This work can provide a useful human factor analysis tool for investigating chemical accidents and valuable information for preventing MCAs in China and other countries.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Analisis Profil Kecelakaan Konstruksi pada Proyek Bangunan Gedung di Indonesia

The construction sector is the highest contributor to construction accidents, which is 31.9% of the total construction accidents. One of the leading causes of accidents is the need for more awareness from workers and companies of the importance of implementing OHS at work. This situation arises due to the need for maximum planning and implementation of the Construction Safety Management System through the Minister of Public Works and Public Housing, and the Indonesian government has issued Regulation Number 10 of 2021 concerning construction safety guidelines. Data collection in this study is secondary data, namely construction accident reports by the Construction Safety Committee. The analysis used is archival analysis, where the report is mapped to produce statistics on construction accidents based on the type of project, time of occurrence, day of occurrence, the impact of the accident, and the cause of the accident. The results showed that the building became the first rank of construction project types with a percentage of 35% where the occurrence was in the morning before noon, namely 08.00-12.00, and on Tuesday became a day that often occurred construction accidents based on the construction safety committee. It was found that the cause of the accident was dominated by human factors and work equipment, which would impact the damage to the building itself with a percentage of 56%. The results of this study will be helpful for implementers/construction accident experts as guidelines for the future

Dynamic risk analysis of allision in port areas using DBN based on HFACS-PV

The increased number, speed, and size of ships leads to an increased risk of accidents in ports with restricted navigational areas. A comprehensive risk assessment is required to understand the causal mechanisms involved in accidents. This study investigated the factors causing allision accidents that occurred during ship manoeuvres in the port areas. Nonconformities obtained from 112 allision accident reports were classified qualitatively with the Human Factors Analysis and Classification System for Passenger Vessel Accidents (HFACS-PV) method. Then, temporal and causal relationships between nonconformities were evaluated quantitatively with the DBN method. As a result, the probability of an allision accident and the risk identifying factors (RIFs) affecting allision during port manoeuvres were calculated. Familiarity with the vessel, using the bridge navigation equipment and features of structures in the port area are the riskiest factors leading to allision accidents. The study is thought to contribute at a practical and strategic level to ship owners, ship personnel, pilotage services, port authorities and all relevant stakeholders in understanding the risk of allision accidents occurring in ports and developing preventive regulations.

A Direct Functional Measure of Text Quality: Did the Reader Understand?

Assessing text quality as an indication of underlying skills still remains challenging; irrespective of the approach, many studies struggle with reliability or validity problems. If writing is considered problem-solving, a report must make the reader understand the described situation and call for its mental reconstruction. Therefore, text quality may not only comprise linguistic aspects but also the cognitive-functional power of a text. The presented study aims at exploring the functionality of students’ reporting texts in relation to general text-quality measures, using a corpus of accident reports written by German fifth- and ninth-graders (n = 277) prompted by a pictorial stimulus of a bike accident scenario. An online tool was developed in which 277 university students graphically reenacted the situation from one respective text according to the existence, position, and color of the involved elements. Thereafter, the match of the resulting spatial reconstructions with the original situation was assessed by two raters. While most subscales showed sufficiently high interrater reliabilities, the aggregated functionality score (α = .74) had medium-high correlations with other text-quality ratings and was comparably dependent on grade, education level, and linguistic family background. However, the correlational pattern, regression analysis, and factor analysis showed that the functionality score also contributed unique portions of variance to the assessment of writing skill that were not represented by rating measures. Moreover, the direct indication of whether a text allows for the reader’s adequate cognitive representation is evident. Altogether, the approach of indicating text functionality through practical understanding offers a sound, though empirically laborious, alternative for text-quality measurement. Results are discussed with regard to the didactical strategy according to which students can improve their writing when they observe whether others can make use of their texts.