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• W3129556078 abstract "The article describes the results of using the application of Bayesian approach, to create a decision support system to evaluate the risk of the damages to prefabricated reinforced concrete (RC) buildings, that have been exposed to the impacts from the industrial environment of mines in the form of spatial subsidence and tremors. This problem is important from the point of view of both structural safety and utility aspects. It concerns many buildings, in which the damage is often caused by the excessive consumption of thermal energy used for heating interiors or dehumidifying damp walls. The above issues make the subject extremely important from a technical, socio-economical, and energy point of view, therefore it can be categorised as sustainable development problem. The Bayesian belief network (BBN) is used to build a risk model that encompasses all the issues and variables that describe the damage process associated with the entire scope. The use of BBNs is an innovative approach in this interdisciplinary area, that encompasses civil engineering and mining environmental protection. There are two types of score-based methods for learning the structure of a BBN from data which will be applied and compared. The first is an iterative method for searching the space of potential solutions, based on the Tabu-search algorithm. The second approach used a method of stochastic searching for a space of structures (node orders) based on the global optimisation algorithm Simulated Annealing (SA). The analyses were performed in R, using the catnet and bnlearn packages. The basis for the study was a database relating to 129 prefabricated reinforced concrete buildings with a wall bearing structure, located in a copper mining area in Poland. Finally, there were eight damage intensity indexes (wui) obtained from using the above models when analysing the risk of damage to structural and finishing elements. The most effective method turned out to be using the Tabu-search algorithm with the assumed K2 criterion. As a result, the following damage indexes were obtained – 91.43%–97.14% correctly classified patterns for the training set, while for the test set – 79.17%–100%. The conducted research is interdisciplinary in the field of civil engineering and mining and environmental engineering, and the very issue of damage is also important from the socio-economical and energy point of view, especially for a large number of buildings." @default.
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• W3129556078 date "2021-05-01" @default.
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• W3129556078 title "Score-based Bayesian belief network structure learning in damage risk modelling of mining areas building development" @default.
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• W3129556078 doi "https://doi.org/10.1016/j.jclepro.2021.126528" @default.
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