Toolkit for description and management of event risks
DOI:
https://doi.org/10.31498/2225-6733.45.2022.276240Keywords:
risk, event, neural network, risk forecasting, motor transportAbstract
The work is based on the proposition that the probabilistic-statistical methods of risk assessment in modern engineering no longer meet the needs of users in various fields of human activity. The paper shows the capabilities of neural networks as a mechanism for predicting risks from undesirable events in various fields of application. A three-synapse model of a direct propagation perceptron has been developed to solve the problem of automobile transportation of flammable liquids, in particular, gasoline, which is very relevant for the current state of the issue in Ukraine. A software product has been created using the Python language and Keras applications that allow to operate with neural networks of this kind. The results of «training» of the three-synapse model for solving similar problems in the field of transport are shown. Preliminary weight coefficients for each input action are systematized. The example shows the results of training a neural network, in particular, the resulting values of the weight coefficients are calculated. The operation of such a network is shown. A mechanism for predicting the risks associated with accidents in some of the most relevant conditions of road transportation has been methodically developed. The result of the model operation is the idea that during the specified time period of gasoline transportation in a tank truck of a given configuration with a specific volume of transportation and a given state of roads, the main risk, oriented to a set of possible event factors, is associated with the accumulation of electrostatic charges as a result of system swaying on the way, followed by explosion, for example, when liquid is drained at the destination. Moreover, such a risk is unambiguous and does not depend on the state of other system parameters. The results of the analysis are confirmed by practical data, namely, the risks associated with the accumulation of static electricity have neutralization mechanisms even before their accumulation. The dependence of the volume of accumulation of electrostatic charges on the duration of liquid transportation is shown
References
Демин В.Ф. Научно-методические аспекты оценки риска / В.Ф. Демин // Атомная энергия. – Том 86, вып. 1. – 1999. – С. 46-63.
Пампуро В.И. Методологические ограничения метода дерева событий / В.И. Пампуро // Доповiдi Нацiональної академiї наук України. – 2008. – № 12. – С. 161-165.
Рудометкин С.В. Развитие механизмов риск-инжиниринга для укрепления экономиче-ской безопасности производственных предприятий: на материалах Ставропольского края : дис. ... канд. экон. наук : 08.00.05 / Рудометкин Сергей Владимирович. – Ставрополь, 2012. – 167 с.
Beck U. Risk Society: Toward a New Modernity / U. Beck. – Sage Pubns Ltd, 1992. – 142 р.
Луман Н. Общество, интеракция, социальная солидарность / Н. Луман // Человек. – 1996. – № 3. – С. 152-167.
Zhao D. Method of risk evaluation of information security based on neural network / D. Zhao, J. Liu, Z. Zhang // IEEE international Conference on Machine Learning and Cybernetics. – 2009. – Vol. 1, № 6. – Pp. 1127-1132. – Mode of access: https://doi.org/10.1109/ICMLC.2009.5212464.
Уоссермен Ф. Нейрокомпьютерная техника: Теория и практика. – 1992. – 184 с. – Режим доступу: http://www.immsp.kiev.ua/postgraduate/Biblioteka_trudy/NejpokomputernTechnikaUossermen1992.pdf.
Downloads
Published
How to Cite
Issue
Section
License
The journal «Reporter of the Priazovskyi State Technical University. Section: Technical sciences» is published under the CC BY license (Attribution License).
This license allows for the distribution, editing, modification, and use of the work as a basis for derivative works, even for commercial purposes, provided that proper attribution is given. It is the most flexible of all available licenses and is recommended for maximum dissemination and use of non-restricted materials.
Authors who publish in this journal agree to the following terms:
1. Authors retain the copyright of their work and grant the journal the right of first publication under the terms of the Creative Commons Attribution License (CC BY). This license allows others to freely distribute the published work, provided that proper attribution is given to the original authors and the first publication of the work in this journal is acknowledged.
2. Authors are allowed to enter into separate, additional agreements for non-exclusive distribution of the work in the same form as published in this journal (e.g., depositing it in an institutional repository or including it in a monograph), provided that a reference to the first publication in this journal is maintained.







