: Construction activities not only provide the necessary conditions for citizens to live, but also cause fatal accidents.
This study aimed to reveal the characteristics of fatal accidents in the ...construction industry in China based on statistical data. From 2010 to 2019, there were 6005 fatal accidents in China's construction industry causing 7275 deaths. The important features of these fatal accidents, such as the type, time of occurrence, site location, severity, and geographical region of the accident, were carefully analyzed.
There were 258 major and severe construction accidents causing 1037 deaths, accounting for 4.3% and 14.25% of the total number of construction accidents and deaths in this period, respectively. As an important finding, more deaths occurred in August and on Mondays. The greatest number of construction accidents took place along openings and edges, accounting for 22.9% of all fatal accidents. Taking into account their economic development level and number of employees, Qinghai and Hainan experienced a higher mortality rate than Jiangsu. Falls from a high place were the dominant type of construction accident, accounting for 51.66% of all accidents. However, collapses were the primary type of major and severe construction accident, accounting for 60.09% of such accidents. The predicted number of construction deaths in 2020 is 887 according to the GM(1,1) model. Corresponding safety measures should be adopted to improve the working environment of the construction industry.
The implications of these results with respect to the characteristics of construction accidents can be regarded as the foundation for accident prevention in practice.
Emergency response decision-making for maritime accidents needs to consider the possible consequences and scenarios of an accident to develop an effective emergency response strategy to reduce the ...severity of the accident. This paper proposes a novel machine learning-based methodology for predicting accident scenarios and analysing its factors to assist emergency response decision-making from an emergency rescue perspective. Specifically, the accident data used are collected from maritime accident investigation reports, and then two types of decision tree (DT) algorithms, classification and regression tree (CART) and random forest (RF), are used to develop scenario prediction models for three accident consequences including ship damage, casualty, and environmental damage. The hyper-parameters of these two DT algorithms are optimized using two state-of-the-art optimization algorithms, namely random search (RS) and Bayesian optimization (BO), respectively, aiming to obtain the prediction model with the highest accuracy. Experimental results reveal that BO-RF algorithm produces the best accuracy as compared to others. In addition, an analysis of feature importance shows that the number of people involved in an accident is the most important driving factor affecting the final accident scenario. Finally, decision rules are generated from the obtained optimal prediction model, which can provide decision support for emergency response decisions.
Summary
The link between sleepiness and the risk of motor vehicle accidents is well known, but little is understood regarding the risk of home, work and car accidents of subjects with insomnia. An ...international cross‐sectional survey was conducted across 10 countries in a population of subjects with sleep disturbances. Primary care physicians administered a questionnaire that included assessment of sociodemographic characteristics, sleep disturbance and accidents (motor vehicle, work and home) related to sleep problems to each subject. Insomnia was defined using the International Classification of Sleep Disorders (ICSD‐10) criteria. A total of 5293 subjects were included in the study, of whom 20.9% reported having had at least one home accident within the past 12 months, 10.1% at least one work accident, 9% reported having fallen asleep while driving at least once and 4.1% reported having had at least one car accident related to their sleepiness. All types of accident were reported more commonly by subjects living in urban compared to other residential areas. Car accidents were reported more commonly by employed subjects, whereas home injuries were reported more frequently by the unemployed. Car accidents were reported more frequently by males than by females, whereas home accidents were reported more commonly by females. Patients with insomnia have high rates of home accidents, car accidents and work accidents related to sleep disturbances independently of any adverse effects of hypnotic treatments. Reduced total sleep time may be one factor explaining the high risk of accidents in individuals who complain of insomnia.
Unsafe behavior among construction personnel poses significant risks in petroleum engineering construction projects. This study addresses this issue through the application of a multi-field coupled ...homogeneous analysis model. By conducting case analyses of petroleum engineering construction accidents and utilizing the WSR methodology, the influencing factors of unsafe behaviors among construction personnel are systematically categorized into organizational system factors, equipment management factors, and construction personnel factors. Subsequently, employing Risk coupling theory, the study delves into the analysis of these influencing factors, discussing their coupling mechanisms and classifications, and utilizing the N-K model to elucidate the coupling effect among them. Furthermore, a novel approach integrating coupling analysis and multi-agent modeling is employed to establish an evolutionary model of construction personnel's unsafe behavior. The findings reveal that a two-factor control method, concurrently reinforcing equipment and construction personnel management, significantly mitigates unsafe behavior. This study provides valuable insights into the evolution of unsafe behavior among construction personnel and offers a robust theoretical framework for targeted interventions. Significantly, it bears practical implications for guiding safety management practices within petroleum engineering construction enterprises. By effectively controlling unsafe behaviors and implementing targeted safety interventions, it contributes to fostering sustainable development within the petroleum engineering construction industry.
Providing a practical introduction to the basic theories and principals of accident prevention through diagnosis and feedback control, this book presents the various methods and tools of safety, ...health, and environment (SHE) practice where experience feedback is employed. These include methods of accident and near accident reporting and investigati
Human factors have been defined by the International Civil Aviation Organization (ICAO) as “about people in their living and working situations; about their relationship with machines, with ...procedures and with the environment about them; and about their relationships with other people (at work)”. Human factors contribute to approximately 75% of aircraft accidents and incidents. As such, understanding their influence is essential to improve safety in the aviation industry. This study examined the different human factors causations in a random sample of over 200 commercial air transport accidents and incidents from 2000 to 2016. The main objective of this study was to identify the principal human factor contributions to aviation accidents and incidents. An exploratory research design was utilised. The qualitative data were recorded in a database, and were coded into categories about the flights (including date, manufacturer, carrier, state of occurrence, etc). These categories were then analysed using Chi-Squared tests to determine which were statistically significant in terms of having an influence on the accidents/incidents. The most significant human factor was found to be situational awareness followed by non-adherence to procedures. In addition, charter operations proved to have a significantly higher rate of human factor related occurrence as compared to other type of operations. A significant finding was that Africa has a high rate of accidents/incidents relative to the amount of traffic and aircraft movements. These findings reflect some of the more noteworthy incidents that have received significant media attention, including Air Asia 8501 on the 28th of December 2014, TransAsia Airways 235 on the 4th of February 2015, and Air France 447 on the 1st of June 2009; these accidents resulted in a significant loss of lives where situational awareness and non-adherence to procedures were significant contributing factors.
The real-time monitoring on the risk status of the vehicle and its driver can provide the assistance for the early detection and blocking control of single-vehicle accidents. However, complex risk ...coupling relationship is one of the main features of single-vehicle accidents with high mortality rate. On the basis of investigating the coupling effect among multi-risk factors and establishing a safety management database throughout the life cycle of vehicles, single-vehicle driving risk network (SVDRN) with a three-level threshold was developed, and its topology features were analyzed to assessment the importance of nodes. To avoid the one-sidedness of single indicator, the multi-attribute comprehensive evaluation model was applied to measure the comprehensive effect of characteristic indicators for nodes importance. A algorithm for real-time monitoring of vehicle driving risk status was proposed to identify key risk chains. The result revealed that improper operation, speeding, loss of vehicle control and inefficient driver management were the sequence of top four risk factors in the comprehensive evaluation result of nodes importance (mean value = 0.185, SD = 0.119). There were minor differences of 0.017 in the node importance among environmental factors, among which non-standard road alignment had the larger value. The improper operation and non-standard road alignment were the highest combination correlation of factors affecting road safety, with the support of 51.81% and the confidence of 69.35%. This identification algorithm of key risk chains that combines node importance and its risk state threshold can effectively determine the high-frequency risk transmission paths and risk factors through multi-vehicle test, providing a basis for centralization management of transport enterprises.
To estimate the burden of road traffic injuries and deaths for all road users and among different road user groups in Africa.
We searched MEDLINE, EMBASE, Global Health, Google Scholar, websites of ...African road safety agencies and organizations for registry- and population-based studies and reports on road traffic injury and death estimates in Africa, published between 1980 and 2015. Available data for all road users and by road user group were extracted and analysed. We conducted a random-effects meta-analysis and estimated pooled rates of road traffic injuries and deaths.
We identified 39 studies from 15 African countries. The estimated pooled rate for road traffic injury was 65.2 per 100 000 population (95% confidence interval, CI: 60.8-69.5) and the death rate was 16.6 per 100 000 population (95% CI: 15.2-18.0). Road traffic injury rates increased from 40.7 per 100 000 population in the 1990s to 92.9 per 100 000 population between 2010 and 2015, while death rates decreased from 19.9 per 100 000 population in the 1990s to 9.3 per 100 000 population between 2010 and 2015. The highest road traffic death rate was among motorized four-wheeler occupants at 5.9 per 100 000 population (95% CI: 4.4-7.4), closely followed by pedestrians at 3.4 per 100 000 population (95% CI: 2.5-4.2).
The burden of road traffic injury and death is high in Africa. Since registry-based reports underestimate the burden, a systematic collation of road traffic injury and death data is needed to determine the true burden.
•The proposed method can classify and predict occupational accident types using a random forest (RF) model.•We presented key construction safety factors that affect the occupational accident types in ...Korea using feature importance.•The accuracy score of the RF model was obtained as 71.3%.•It will give a significant contribution to safety management of both practitioners and researchers in the construction industry.
Although industrial accident rates are gradually decreasing in Korea, the construction industry's accident rate is still higher compared with other industries. Human errors, mentally unstable workers, insufficient safety training, and safety policy affect the occurrence of construction accidents. Owing to the characteristics of this industry, occupational accident types, such as fall from height, collision with objects, rollover, and those due to falling objects, can be related to the weather data.
Therefore, to reduce and prevent occupational injury, it is necessary to classify and predict occupational accident types in detail. In this study, we built a model to classify and predict occupational accident types using a random forest (RF). We extracted important factors that affect the occupational accident types at construction sites using feature importance, and we analyzed the relationship between these factors and occupational accident types. The accuracy score of the RF model was obtained as 71.3%, and we presented key construction safety factors considering the feature importance. For future research, we will collect data and develop models to predict occupational accident types in real-time. Real-time construction accident prediction research will reduce accident at construction sites.
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