Bayesian network structure extraction algorithms from data in damage risk assessment of buildings in mining areas

openaccess, Vol. 603 (11) 2022 / poniedziałek, 28 listopada, 2022

(Open Access)

DOI: 10.15199/33.2022.11.18

Rusek Janusz, Chomacki Leszek, Słowik Leszek, Firek Karol. 2022. Bayesian network structure extraction algorithms from data in damage risk assessment of buildings in mining areas. Volume 603. Issue 11. Pages 66-69. Article in PDF file

Accepted for publication: 21.09.2022 r.

The article presents the results of research that was undertaken to create a model to assess the damage risk of buildings subjected to static and dynamic mining impacts. The justification of the adopted methodology on the basis of machine learning (ML) methods is given. The specificity of the problem was discussed and, on this basis, the main assumptions of the applied approach were presented, especially the methodology allowing for autonomous extraction of the Bayesian network structure from data (BSL – Bayesian Structure Learning). The results obtained in the research were presented in relation to multi-storey prefabricated and masonry buildings located in LGDC and USB mining terrain. The paper also indicates the possibility of universal application of the adopted methodology in the case of damage risk prediction and diagnosis of the causes of damage.
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dr hab. inż. Janusz Rusek, prof. uczelni, AGH Akademia Górniczo-Hutnicza, Wydział Geodezji Górniczej i Ochrony Środowiska ORCID: 0000-0003-0368-2580
dr inż. Leszek Chomacki, Instytut Techniki Budowlanej ORCID: 0000-0002-2291-3826
dr inż. Leszek Słowik, Instytut Techniki Budowlanej ORCID: 0000-0001-8770-1595
dr hab. inż. Karol Firek, prof. uczelni, AGH Akademia Górniczo-Hutnicza, Wydział Geodezji Górniczej i Ochrony Środowiska ORCID: 0000-0002-0985-6003

dr hab. inż. Janusz Rusek, prof. uczelni, AGH Akademia Górniczo-Hutnicza, Wydział Geodezji Górniczej i Ochrony Środowiska ORCID: 0000-0003-0368-2580

rusek@agh.edu.pl

Full paper:

DOI: 10.15199/33.2022.11.18

Article in PDF file