Accident Investigation Research Articles

Influential criteria in domino accident analysis: An evaluation using the logarithm methodology of additive weights

Domino incidents in process industries entail cascading failures, demanding thorough analysis to uncover root causes and enhance safety measures. Standard criteria are crucial for systematically evaluating incident investigation techniques, ensuring informed decision-making and consistency. Multi-Criteria Decision Making (MCDM) methods offer a structured approach to technique selection. This study addresses the need for systematic evaluation and prioritization of criteria influencing domino accident analysis technique selection. Through a comprehensive literature review, 16 main criteria and 42 sub-criteria were identified and weighted using the Logarithm Methodology of Additive Weights (LMAW) method, incorporating expert opinions. Results indicate the significance of criteria like applicability, accuracy, and comprehensiveness. Conversely, criteria such as relevance, cost-effectiveness, and emergency response team consideration had lower weights but remained significant. Findings regarding sub-criteria highlight the importance of consistent application of investigation methods and understanding sequential incident progression. This study advances domino accident investigation practices, promoting safety in process industries.

A qualitative study on apparent and latent contributing factors to driving errors in Iran

Road traffic crashes (RTCs) disproportionately impact low and middle-income countries (LMICs). Current interventions in LMICs primarily target road user behavior, neglecting systemic issues and casting drivers as mere contributors. Given Iran’s unique cultural, financial, and social intricacies, this study aims to explore the latent causes of RTCs, prioritizing drivers’ experiences and encompassing insights from various traffic system components. Applying a qualitative approach, data were collected through 46 semi-structured interviews with 38 participants, including drivers and experts from traffic-related organizations. Content analysis identified ten themes as contributing to driving errors. Direct factors included fatigue influenced by economic and occupational conditions, distraction from internal and external sources, reckless behaviors influenced by traffic culture and environment, and Inadequate driving skills due to training. Macro-scale challenges in road infrastructure, vehicle quality, education, and accident investigations were highlighted. The lack of a centralized traffic safety authority and a predominant focus on penalizing drivers overshadowed systemic issues. The study offers valuable insights into the complex interplay of factors contributing to driving errors in LMICs, advocating for a paradigm shift towards holistic, systemic interventions beyond individual driver behavior and challenging the conventional blame-centric view associated with driving errors.

An examination of high-speed aircraft – Part 2: Safety and reliability

This paper presents a detailed accident investigation into incidents involving high-speed vehicles, particularly supersonic and hypersonic platforms. Examining challenges related to engine performance, structural integrity, and economic aspects in military aerospace, the study emphasizes the importance of real-time health monitoring systems. A key highlight is the exploration of how these systems support the autonomy of hypersonic vehicle. While recognizing challenges related to sensitive technologies and data, the paper outlines research directions encompassing human factors, simulation and training programs, and policy advocacy for integrating high-speed aircraft into existing airspaces. In summary, this research contributes valuable insights to the understanding of high-speed aviation accidents, with implications for improving safety and efficiency in the future and ultimately shows that high-speed aviation safety cannot be treated in the same manner as its subsonic counterpart.

Analysis of risk influential factors of marine pilots during the embarkation and disembarkation

Due to the complex geographical conditions within the port waters, it is necessary to take pilotage operations. The embarkation and disembarkation (E&D) of marine pilots is the riskiest phase of pilotage work, with accidents occurring frequently. In order to examine the key Risk Influential Factors (RIFs) of E&D accident of marine pilots, in this study, a novel complex network model of RIFs of marine pilots during the E&D process is proposed. Firstly, based on the E&D accident investigation report of marine pilots, the causal chain of E&D accidents is extracted, and the RIFs leading to E&D accidents are identified. Secondly, a complex network model of RIFs of marine pilots during the E&D is constructed based on the complex networks theory, and the network reliability is verified. Finally, by comprehensive use of topology characteristics analysis, robustness analysis and module analysis, the key RIFs of pilot E&D accidents are identified. The research results show that pilot and management factors have important impact on pilot safety, ‘insufficient pilot ladder strength' is the most critical RIF. It should be focused on RIFs with high connectivity between modules and within modules, and the risk evolution can be blocked through the control of those RIFs. Abbreviations: BNs: Bayesian Networks; CDM: Critical Decision Method; DEMATEL: Decision-making Trial and Evaluation Laboratory; E&D: Embarkation and Disembarkation; FSA: Formal Safety Assessment; RIFs: Risk Influential Factors; RPD: Recognition-Primed Decision; SHERPA: Systematic Human Error Reduction and Prediction Approach; SOLAS: International Convention for the Safety of Life at Sea; VTS: Vessel Traffic Services; TECHR: Technique for Early Consideration of Human Reliability.

Analysis of Accident Investigation Techniques at The Location of The Ore Hauling Road Location (ICAM Analysis Method)

Analysis of Accident Investigation Techniques at The Location of The Ore Hauling Road Location (ICAM Analysis Method)

Arc erosion characteristics of cables with heatproof insulations in AC series circuits at low pressure

Abstract The droplet diameter, dripping frequency, and arc bead appearances vary significantly with pressure. This study investigated the arc erosion characteristics of copper-core cable with mica insulation (HC0) and aluminum-core cable with PI insulation (HA0) at different pressures. During arcing of HC0, the diameters of dripping droplets decrease as pressure decreases, but the dripping frequency exhibits an increasing trend. The primary arc beads (PABs) of HA0 at atmospheric pressure are mainly globular arc beads. HA0 PABs at 60 kPa are mostly coral arc beads, which is attributed to the slow oxidation rate and easier outflow of liquid aluminum through cracks at low pressure. HA0 PABs have the characteristics of coral arc beads and gradual necking, which broadens the previous theory in accident investigations that coral arc beads and gradual necking are considered as physical evidences of fire globules and short-circuit melted marks. Besides, the influence of pressure on arc duration of HC0 and HA0 is revealed. This study could provide support for fire accident investigation and arc accident prevention.

Comprehensive integral simulation of first 3 weeks of severe accident at Fukushima Daiichi Unit 1. Part II – Simulation of the in-vessel and ex-vessel phases

This paper is the second part of the summary presenting the results of IBRAE work over last 12 years on severe accident investigation at Unit 1 of Fukushima Daiichi with the SOCRAT code. The paper supplements the part I that was published previously, and is dedicated to the description of one-through integral simulation results of the 3 weeks of accident unfolding starting from the tsunami arrival. The integral simulation encompasses the different processes at in-vessel and ex-vessel phases of the accident including the processes in reactor building. The important issues discussed are the hypothesis on the induced leak from a steam line, the operation of isolation condenser, the possible early combustion of hydrogen in the reactor building room that houses the drywell head, as well as the time, conditions and regime of hydrogen combustion in the reactor hall, the flowrate of alternative water injection, and the depth of concrete ablation. The main criterion for checking the adequacy of assumptions and the simulation results is the agreement with factual information available including the measurement data and the results of inverse calculations of the source term. The shortcomings of current simulation with the SOCRAT code are briefly considered as a perspective for improving the realism of results in the next studies.

Investigation and CFD simulation of coal dust explosion accident in confined space: A case study of Gaohe Coal Mine Ventilation Air Methane oxidation power plant

The study investigates a coal dust explosion accident within a confined space in Shanxi. The accident was caused by coal dust coming into contact with a high-temperature heat source under the influence of the stack effect. Based on the investigation of the coal dust explosion accident, the study uses computational fluid dynamics (CFD) simulation to reconstruct the accident scenario. The simulation results provide a detailed depiction of coal dust movement within subsurface semi-enclosed confined spaces during the ventilation diffusion stage, influenced by the stack effect, and confirm the formation of a coal dust layer at the heat source due to coal dust deposition. The simulation results of the two explosion processes indicate that excessively high coal dust concentrations within confined spaces have a negative correlation with the development of explosion flames and the peak overpressure. It resulted in a secondary explosion with a relatively low concentration of coal dust, where the peak overpressure was five times that of the primary explosion. Comparative analysis between simulation results and actual investigations the accuracy of the CFD simulation in capturing the accident process and its characteristics, providing valuable insights for preventing and controlling coal dust explosion accidents.

Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

Ship accident re-enactment based on AIS data and virtual reality: a case study of “Xiang Xiang Yin Huo 0410” ship accident

Abstract The recurrence of ship accidents was an effective method of maritime investigation. To analyze the cause of the sinking of “Xiang Xiang Yin Huo 0410”, a vessel accident recurrence simulation system was constructed by integrating an automatic identification system and virtual reality technology to replicate the navigation and accident process of the accident vessel. The voyaging process of the accident vessel was reduplicated in the simulation system according to the AIS information, such as speed, course, and turning speed. The mathematical model of accident vessel maneuvering was established by using MMG to reproduce the ship maneuvering scheme based on the maneuvering data before the accident obtained through on-the-spot investigation. The navigation and sinking process of the accident vessel were reproduced visually, and the cause of the accident was judged and analyzed intuitively by constructing a three-dimensional model of the accident vessel and a mathematical model of ocean waves. The actual application indicates that the ship’s navigation and accident process under external force, combined with the ship’s maneuvering data, automatic identification data, and hydrometeorological data, was consistent with the actual accident process of the accident. The method proposed in this paper provided a new idea for the investigation and analysis of maritime accidents.

A hybrid approach integrating case mining (CM) and the Copula Bayesian Network (CBN) for accident causation probabilistic reasoning of building construction collapses

Construction collapses represent a prevalent type of structural failure in the building industry, involving multiple causal factors. Most current studies on these causal factors and their relationships are derived from expert knowledge and rely primarily on singular probabilistic estimates derived from expert input to predict the likelihood of accidents using Bayesian networks. However, fluctuations in expert knowledge can compromise the stability of Bayesian network structures, thus failing to accurately capture the uncertainty associated with construction collapses. To fill these gaps, a hybrid approach, integrating case mining (CM) and a Copula Bayesian Network (CBN), is proposed to infer the causation probability of construction collapse. To construct a stable Bayesian network structure, the CM method is used to extract the causal factors and their relationships from accident investigation reports. The Gaussian copula function and different marginal distribution functions are introduced to describe the probability distribution of the occurrence of causal factors. Correlation analysis, forward analysis and backward analysis are used to quantify the risk propagation relationships among the causal factors. The findings indicate that (1) construction collapses predominantly occur between 9:00-10:00 and 14:00-15:00 and (2) illegal construction, lack of administrative supervision and non-implementation of construction schemes are critical factors contributing to these incidents.

Safety evaluation of human-caused errors in civil aviation based on analytic hierarchy process and fuzzy comprehensive evaluation method

PurposeAt present, the statistics of human error events in domestic civil aviation are limited, and the analysis indicators are difficult to quantify. The purpose of this study is to reduce the incidence of human error events and improve the safety of civil aviation.Design/methodology/approachIn this paper, a safety prevention evaluation method combining analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) is proposed. The risk factors of civil aviation safety are identified through questionnaire survey and calculated by MATLAB software.FindingsThe results of the study are as follows: a safety risk evaluation index system including 4 first-level indicators and 16 second-level indicators is constructed; the AHP is used to calculate the weight of the influencing factors of human error and sort them; and the FCE method is used to quantitatively evaluate the safety prevention of civil aviation human error and put forward the countermeasures.Research limitations/implicationsThis study also has some limitations. While it provides an overall quantitative identification of civil aviation safety risk factors, the research methods chosen, such as the questionnaire survey method and the AHP, involve individual subjectivity. Consequently, the research results may have errors. In the preliminary preparation of the follow-up study, we should analyze a large number of civil aviation accident investigation reports, more accurately clarify the human error factors and completely adopt the quantitative analysis method in the research method.Practical implicationsThis study identifies the risk factors of civil aviation safety and conducts a reasonable analysis of human error factors. In the daily training of civil aviation, the training can be focused on previous man-made accidents; in view of the “important” influencing factors, the aviation management system is formulated to effectively improve the reliability of aviation staff; according to the evaluation criteria of human error in civil aviation, measures to prevent and control accidents can be better formulated.Social implicationsIn view of these four kinds of influencing factors, the corresponding countermeasures and preventive measures are taken according to the discussion, so as to provide the basis for the prevention of aviation human error analysis, management and decision-making, prevent the risk from brewing into safety accidents and improve the safety of aviation management.Originality/valueBased on the questionnaire survey, this study creatively applies the safety prevention evaluation method combining AHP and FCE to the study of civil aviation human error, integrates the advantages of qualitative and quantitative methods, flexibly designs qualitative problems, objectively quantifies research results and reduces subjective variables. Then, by discussing civil aviation safety management measures to avoid risk factors, reduce the incidence of human error events and improve the safety of civil aviation.

Over a decade of UAV incidents: A human factors analysis of causal factors

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations’ databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

A new risk level identification model for aviation safety

The aviation safety reporting system (ASRS) data, which are accident investigation reports issued by the national transportation safety board (NTSB), are often used for aviation risk level identification modeling. However, ASRS data suffer from sample imbalance and inaccurate encoding of textual information. To address these issues, this paper proposes a novel risk identification model. Initially, the pre-trained sentence bidirectional encoder representations from transformers (SBERT) model is fine-tuned using the textual information in the ASRS data, improving the accuracy of the text encoding. Subsequently, a generative adversarial network (GAN) is employed to generate samples for underrepresented categories in the ASRS data, thereby addressing the sample imbalance problem. Finally, a bayesian optimization algorithm is integrated to automatically search for the hyperparameters of the risk identification model, further enhancing the model's performance in risk level identification. Experimental results demonstrate that not only does the SBERT fine-tuning substantially improve the accuracy of text information encoding, but both data augmentation and automatic hyperparameter search also significantly contribute to the performance improvement of the risk level recognition model.

Investigation into the overcurrent failure and combustion characteristics of copper‐clad aluminum conductors

AbstractElectrical fires perennially rank first in fire occurrence types, with conductor overcurrent being one of the main inducements. This topic draws significant attention from scientific researchers and fire investigators. To understand the overcurrent fault and combustion characteristics of copper‐clad aluminum conductors, this paper examines 2.5 mm2 copper‐clad aluminum conductors that meet national standards, investigating morphological changes, temperature variations in the core and insulation layer, and flame propagation patterns under overcurrent conditions. Experiments using an electrical fault simulation device were conducted to study overcurrent failures of copper‐clad aluminum conductors under 52.5–105 A conditions. The results indicate that when the current exceeds 67.5 A, the conductor undergoes a series of changes during energization, including smoking, expanding, carbonizing, burning, and breaking; at 52.5 A, the insulation layer reaches thermal equilibrium at 150 s without combustion; for currents between 60–67.5 A, wire core temperature variations can be divided into three stages; at 75 A, the insulation layer reaches thermal equilibrium 10s before breaking; currents above 82.5 A see a sharp increase in temperature in both the core and insulation layer before the conductor breaks; above 97.5 A, the conductor first breaks and then burns. The research results have significant theoretical value in improving the scientific rigor of fire accident investigations and forensic evidence examinations.

Studies on in-flight aircraft rerouting

With the publication of this article, the authors continue the research on the development and testing of a methodology for in-flight aircraft rerouting which have begun in previously published articles in the Civil Aviation High Technologies of the Moscow State Technical University of Civil Aviation. This article presents the results of the study in terms of developing the potentials of the methodology from reconfiguring a route of a hypothetical aircraft and obstacles in the horizontal plane, which were previously demonstrated, prior to rerouting in both the horizontal and vertical planes for two different types of obstacles: 1) ground natural or artificial (mountain, power line support, etc.); 2) air (squall line, prohibited flight area, etc.) and their combinations using an example of a Mi-8 helicopter flight on route using a real digital map of the terrain. As mentioned above, a large amount of aviation accidents is associated with the loss of control in-flight, as well as the collision with terrain in a controlled flight (categories LOC-I, CFIT, LALT). As a result of the investigation of the aviation accidents, it was found that these accidents are often caused by the requirement to reroute quickly due to obstacles, for example, a squall line. When determining alternatives to avoid an obstacle, as well as while implementing the selected route for avoiding action, the crew makes errors due to the increased psychophysiological load and lack of time. The methodology and the algorithms, proposed by the authors, make it possible to assess the safety of an original route, estimate options for alternatives to avoid around obstacles detected in-flight, check them for feasibility, taking into account aircraft performance, flight envelope, and also select the optimal route from the view of some criterion, for example, based on minimizing the route length increase, reducing additional fuel consumption, the time required to implement a new route of flight, etc.

Undergraduates’ process safety knowledge development with virtual reality game

ABSTRACT Despite the movement to improve process safety, incidents in chemical-related industries are still occurring around the world. Personnel process safety lack of knowledge is a typical cause of these events. Process safety is an integral part of a chemical engineer’s work. Employees’ discontent with chemical engineering graduates’ process safety understanding revealed deficiencies in undergraduate courses. Inadequate accident investigation and prevention training has also hampered graduates’ knowledge and preparation for high-risk jobs. To address the gaps, an online game platform was suggested and tested with 450 undergraduate students at different levels and disciplines. Virtual reality process safety game proven to improve chemical engineering graduates’ process safety competency while delivering industry experience. The approach is also believed to improve students’ problem-solving and decision-making skills and motivate engagement to study process safety. The main features of the game are desired to be online web-based with serious role-play integrated into real-life simulation activities.

Technology roadmap of risk identification and collision avoidance decision-making in autonomous vehicles for domestic animals

At present, the multi-targets autonomous vehicles identify are mainly various road users such as vehicles and pedestrians in cities. However, in recent years, there are increasing vehicle-animal accidents in pastoral areas of China, and there is a lack of corresponding practical intelligent safety warning measurements. In order to realise the identification and collision avoidance of typical animals, this study obtains the basic characteristics of collisions and the animals’ behavioural characteristics from the pastoral accidents investigation. Firstly, an attempt to focus on the use of the algorithm of the binocular infra-red equipment is made, with the main targets of identification being domestic animals at night or under low brightness conditions. Secondly, the algorithm technology roadmap from the identification and the risk assessment to collision avoidance decision-making is designed. Further, based on the technical route, a feasible solution and overall technical program is presented, which outlines the five aspects of the proposed engineering-ready scheme’s architecture. The methodology used involves transfer learning and the PSMNet network model. The paper concludes that the system can predict the animal’s movement and make proper decisions by utilising binocular infra-red equipment, integrating its algorithms and the support of its behavioural risk assessment. The research results will contribute to the animal collision avoidance technology and the traffic safety in pastoral areas in the future.

Aircraft Accident and Crash Images Processing with Machine Learning

The aviation industry is in constant need of innovations in terms of safety and operational efficiency. In this context, low-light image enhancement technologies play an important role in a numerous areas of disciplines, from night flights to accident and collision investigations. Machine learning, deep learning methods and traditional methods not only provide the aviation industry with an effective image processing and improvement capacity in low light conditions, but also reveal important information by analysing the data of low-light images of crashed and destroyed aircraft. Within the scope of the study, traditional methods, deep learning method and machine learning are combined in order to enhance and process low-light ambient images of crashed and destroyed aircraft. By using Swish and Tanh activation functions together in the deep learning model, the performance of the neural networks used in the process of improving low-light environment images was improved and the image quality was increased. The enhanced images were evaluated and compared using PSNR and MSE as objective quality assessment measures. According to the PSNR and MSE criteria, the numerical results obtained from the image enhancement studies of the deep learning model were calculated as 29.85 and 100.44, respectively. The results introduce that the deep learning model provides better image enhancement than traditional methods. In conclusion, improvement of low-light image and processing is an important technological advancement in the aviation industry, enabling safer and more efficient operations. The successful of machine learning include deep learning and traditional methods shows that the aviation industry will achieve a safer and innovative structure in the future.

Safety Risk Assessment of Iranian Building Sites based on Construction Stage

To assist safety practitioners in the work of prioritizing preventative activities based on the stage of civil industry, it is necessary to identify the most common causes of accidents in the civil sector. Reports from official accident investigations are examined. For this aim, accidents are examined that were officially investigated by the Ministry of Cooperatives, Labour, and Social Welfare at the building sites of North of Iran (Mazandaran region) between 2013 and 2023. The percentage of causes in each of the several potential cause groups indicates a pattern of causation. There are notable correlations between the different cause categories and the phases of building and accident mechanisms. Depending on the accident's mechanism and the current building stage, there have been seen to be significant variances in accident causation. Prioritizing preventive measures to counter the most likely causes of each accident procedures and building stage should be done using these results.