Search

The traditional storage method of fire accident cases is mainly in the form of text, and it is difficult to effectively conduct comprehensive analysis due to the limited ability to display key information and fire knowledge. In this paper, a structured storage form of building fire cases was proposed based on knowledge graph, which can comprehensively describe and visualize the fire causes, the dynamic fire development process and evacuation process. It enables readers to get information and knowledge from building fire cases intuitively, and supports the comprehensive analysis for building fire prevention strategies. The knowledge graphs are constructed for two common building types (residential and public buildings), and have the capacity to reflect the dynamic development law of fires from ignition to spread in different buildings. Meanwhile, as the occupants' evacuation is the first concern when a fire occurs, the knowledge graphs also visualize the relationship among various conditions in the evacuation process. Different application scenarios are displayed in the paper, including case query, root-cause analysis and consequence forecasting, which shows the advantages and applicability of building fire knowledge graph. [ABSTRACT FROM AUTHOR]

Identifying the causes of accidents is essential for learning lessons and preventing similar accidents. Safety capability factors cannot be ignored when considering the causes of accidents. Therefore, safety capability is an important perspective to develop a cause analysis framework. This study focuses on a cause analysis framework based on safety capability in the 24Model. First, this study introduces the safety capability in the 24Model and elaborates on the five safety capability dimensions. Second, a 24Model‐based causal analysis framework from a safety capability perspective and its application process is proposed. Third, the Tianjiayi explosion accident is analyzed using the proposed framework. In this accident, fourteen safety capability factors in four categories: safety knowledge, safety awareness, safety habits, and safety psychology are identified and analyzed. The case study provides a critical process demonstration for analyzing safety capability factors and validates the usability of the proposed framework. Additionally, the strengths of the framework and the limitations of the study are discussed. In the future, this framework can facilitate the development of a safety capability factor database and data‐driven safety management approaches. [ABSTRACT FROM AUTHOR]

Coal is an important fossil fuel energy that occupies a high position in the energy use of China and even the world. However, gas explosions are still the deadliest coal mine accident in China, which has long plagued the safety of energy mining. Only through accident cause analysis can we know the exact cause of the accident so as to make targeted policies, safety trainings, etc. However, the lack of detailed accident cause classification in current coal mine gas explosion accidents affects the comprehensiveness and accuracy of energy mining safety strategies. Therefore, in this study, a classification method for coal mine gas explosion accident causes based on the sixth edition 24Model and the three-element classification of gas explosions was proposed. Then, the consistency and validity of the newly established classification system were evaluated based on the three indicators, i.e., observer consistency, content validity, and criterion validity, and the performance of the classification system was verified. The results showed that the classification method exhibits good consistency and validity, and, compared with other classification methods, it can significantly improve the comprehensiveness of accident cause analysis results so as to obtain a more scientific energy mining safety strategy. In addition, the process used in this study to construct the classification and evaluate the performance of the classification is transferable, and it can provide a reference for the construction and evaluation of accident cause classifications in other fields. [ABSTRACT FROM AUTHOR]

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform. [ABSTRACT FROM AUTHOR]