Abstract. The danger of injuries and accidents in various industries such as transportation and construction urges the government to control the occupational health and safety more strictly. However, in order to do so with the minimal costs modern risk management tools, have to be implemented. Risk-based approach is an essential tool for competent risk-assessment and used in a great variety of other countries, demonstrating great results in providing of safe working environment. The article describes the problems that the implementation of the method faces in Russia and suggests certain ways to resolve them.
This article discusses the application of mathematical simulation for planning the timing of repair of building structures according to their degree of physical deterioration. For the current study, 296 apartment buildings were selected from the housing stock in Archangelsk city, using neural network Self-Organizing Maps (SOMs). Optimal clustering of selected housing was achieved using special Deductor platforms to define 16 housing clusters with maximum difference but high internal similarity. For more detailed scheduling of building overhaul and running repairs, selected clusters were divided into 4 model-groups that needed the same repairs in similar time frames. Integration of the particular characteristics of each group created a framework for calculating the capital investments for each model group, with specific payments identified for each cluster.
The algorithm for clustering based on neural network modeling using T. Kohonen's self-organizing maps for the analysis of the housing stock is considered. This analysis of housing stock is required for the planning of complex reproduction of housing and major repairs regional programs development. The mechanism of self-organization is submitted. The representative sample clustering of the housing stock is produced. Its result is 16 groups of objects with a high level of internal similarity. The basic advantages of this approach for monitoring and analysis of the city housing stock are described.
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