A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational health (CSOH) and summarize the research paradigm of recent decades. We solicited 119 peer-reviewed journal articles and performed a bibliometric analysis as the foundation of the future directions, application bottlenecks, and research paradigm. (1) Based on the keyword cluster, future directions are divided into four layers: key directions, core themes, key problems, and important methods. (2) The network approaches are not independently applied in the CSOH research. It needs to rely on different theories or be combined with other methods and models. However, in terms of approach applications, there are still some common limitations that restrict its application and development. (3) The research paradigm of network analysis process can be divided into four stages: description, explanation, prediction, and control. When the same network method encounters different research objects, it focuses on different analysis processes and plays different roles.

Application of Bayesian Algorithm in Risk Quantification for Network Security

Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

Quality Risk Management Algorithm for Cold Storage Construction Based on Bayesian Networks

In the cold storage construction project, only by controlling the quality risk of the project can ensure that the cold storage can meet the expected use function and achieve the expected economic benefits after the completion of the cold storage. In order to effectively ensure the key pivot role of cold storage in cold chain logistics, a cold storage construction quality risk management system is constructed to identify and analyze quality risk factors from three dimensions: construction procedures, participating units, and work processes, construct a cold storage construction quality risk evaluation model based on Bayesian network, and through reverse reasoning analysis and sensitivity analysis, key quality risk factors are derived: inadequate quality assurance system, technical delivery not in place, mismatch of building materials and equipment, inadequate training of skilled workers, completion acceptance not careful or acceptance standards unreasonable, and duration not meeting the requirements. Finally, in view of the above quality risks, suggestions and measures are put forward from five aspects: man, material, machine, method, and environment.

Comparative Analysis of Degree of Risk between the Frequency Aspect and Probability Aspect Using Integrated Uncertainty Method Considering Work Type and Accident Type in Construction Industry

Fatal incidents in the construction business are higher than in other industries. Previous studies concentrated on the frequency of fatal incidents based on safety management, however, the probability of fatal incidents might be more important than the frequency of fatal incidents. For instance, certain work types have low fatal incident cases but show a high probability of fatal incidents, which means they are riskier than others. The purpose of this study is to analyze the level of risk by comparing the frequency of fatal incidents and probability of fatal incidents for 27 types of work and 18 types of accidents using an uncertainty analysis. This study is carried out in five stages from the collection of data to conducting the statistical analysis. The result of the research shows the estimated rank of frequency and probability for work and accident type, respectively. For instance, ‘reinforced concrete construction work’ (66.5 fatal incidents) showed the highest frequency work type, and ‘scaffold and demolition work’ (28.65‱) showed the highest fatality rate. This research addressed the uncertainty problem using an integrated time series and estimation method to compare the degree of risk from the viewpoint of frequency and probability aspects in the construction business.

Shaping Frontline Practices: A Scoping Review of Human Factors Implicated in Electrical Safety Incidents

Injuries sustained while performing electrical work are a significant threat to the health and safety of workers and occur frequently. In some jurisdictions, non-fatal serious incidents have increased in recent years. Although significant work has been carried out on electrical safety from a human factor perspective, reviews of this literature are sparse. Thus, the purpose of this review is to collate and summarize human factors implicated in electrical safety events. Articles were collected from three databases (Scopus, Web of Science, and Google Scholar), using the search terms: safety, electri*, human factors, and arc flash. Titles and abstracts were screened, full-text reviews were conducted, and 18 articles were included in the final review. Quality checks were undertaken using the Mixed Methods Appraisal Tool and the Critical Appraisal Skills Program. Environmental, individual, team, organizational, and macro factors were identified in the literature as factors which shape frontline electrical worker behavior, highlighting the complexity of injury prevention. The key contributions of this paper include: (1) a holistic and integrated summary of human factors implicated in electrical safety events, (2) the application of an established theoretical model to explain dynamic forces implicated in electrical safety incidents, and (3) several practical implications and recommendations to improve electrical safety. It is recommended that this framework is used to develop and test future interventions at the individual, team, organizational, and regulator level to mitigate risk and create meaningful and sustainable change in the electrical safety space.

Modeling sickness absence data: A scoping review

The identification of sick leave determinants could positively influence decision making to improve worker quality of life and to reduce consequently costs for society. Sick leave is a research topic of interest in economics, psychology, health and social behaviour. The question of choosing an appropriate statistical tool to analyse sick leave data can be challenging. In fact, sick leave data have a complex structure, characterized by two dimensions: frequency and duration, and involve numerous features related to individual and environmental factors. We conducted a scoping review to characterize statistical approaches to analyse individual sick leave data in order to synthesise key insights from the extensive literature, as well as to identify gaps in research. We followed the PRISMA methodology for scoping reviews and searched Medline, World of Science, Science Direct, Psycinfo and EconLit for publications using statistical modeling for explaining or predicting sick leave at the individual level. We selected 469 articles from the 5983 retrieved, dated from 1981 to 2019. In total, three types of model were identified: univariate outcome modeling using for the most part count models (438 articles), bivariate outcome modeling (14 articles), such as multistate models and structural equation modeling (22 articles). The review shows that there was a lack of evaluation of the models as predictive accuracy was only evaluated in 18 articles and the explanatory accuracy in 43 articles. Further research based on joint models could bring more insights on sick leave spells, considering both their frequency and duration.

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

Bayesian Network-Based Risk Assessment of Single-Phase Grounding Accidents of Power Transmission Lines

With the increasing demand for electricity transmission and distribution, single-phase grounding accidents, which cause great economic losses and casualties, have occurred frequently. In this study, a Bayesian network (BN)-based risk assessment model for representing single-phase grounding accidents is proposed to examine accident evolution from causes to potential consequences. The Bayesian network of single-phase grounding accidents includes 21 nodes that take into account the influential factors of environment, management, equipment and human error. The Bow-tie method was employed to build the accident evolution path and then converted to a BN. The BN conditional probability tables are determined with reference to historical accident data and expert opinion obtained by the Delphi method. The probability of a single-phase grounding accident and its potential consequences in normal conditions and three typical accident scenarios are analyzed. We found that “Storm” is the most critical hazard of single-phase grounding, followed by “Aging” and “Icing”. This study could quantitatively evaluate the single-phase grounding accident in multi-hazard coupling scenarios and provide technical support for occupational health and safety management of power transmission lines.