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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

Author

Listed:
  • Leszek Chomacki

    (Building Research Institute, 00611 Warsaw, Poland)

  • Janusz Rusek

    (Department of Engineering Surveying and Civil Engineering, AGH University of Science and Technology, 30059 Cracow, Poland)

  • Leszek Słowik

    (Building Research Institute, 00611 Warsaw, Poland)

Abstract

This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO 2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.

Suggested Citation

  • Leszek Chomacki & Janusz Rusek & Leszek Słowik, 2022. "Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines," Energies, MDPI, vol. 15(11), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3958-:d:825626
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    References listed on IDEAS

    as
    1. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    2. Artur Guzy & Wojciech T. Witkowski, 2021. "Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining," Energies, MDPI, vol. 14(15), pages 1-36, July.
    3. Indre Siksnelyte & Edmundas Kazimieras Zavadskas & Dalia Streimikiene & Deepak Sharma, 2018. "An Overview of Multi-Criteria Decision-Making Methods in Dealing with Sustainable Energy Development Issues," Energies, MDPI, vol. 11(10), pages 1-21, October.
    4. Huang, Lizhen & Krigsvoll, Guri & Johansen, Fred & Liu, Yongping & Zhang, Xiaoling, 2018. "Carbon emission of global construction sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1906-1916.
    5. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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    Cited by:

    1. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    2. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    3. Adrian Jędrzejczyk & Karol Firek & Janusz Rusek, 2022. "Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings," Energies, MDPI, vol. 15(13), pages 1-16, June.

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