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Classification and regression tree theory application for assessment of building damage caused by surface deformation

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  • Agnieszka Malinowska

Abstract

A framework of applying the classification and regression tree theory (CART) for assessing the concrete building damage, caused by surface deformation, is proposed. The prognosis methods used for approximated building hazard estimation caused by continuous deformation are unsatisfactory. Variable local soil condition, changing intensity of the continuous deformation and variable resistance of the concrete buildings require the prognosis method adapted to the local condition. Terrains intensely induced by surface deformation are build-up with hundreds of building, so the method of their hazard estimation needs to be approximated and relatively fast. Therefore, promising might be addressing problems of reliable building damage risk assessment by application of classification and regression tree. The presented method based on the classification and regression tree theory enables to establish the most significant risk factors causing the building damage. Chosen risk factors underlie foundation for the concrete building damage prognosis method, which was caused by the surface continuous deformation. The established method enabled to assess the severity of building damage and was adapted to the local condition. High accuracy of shown approach is validated based on the independent data set of the buildings from the similar region. The research presented introduces the CART to determination of the risk of building damage with the emphasis on the grade of the building damage. Since presented method bases on the observations of the damages from the previous subsidence, the method might be applied to any local condition, where the previous subsidence is known. Copyright The Author(s) 2014

Suggested Citation

  • Agnieszka Malinowska, 2014. "Classification and regression tree theory application for assessment of building damage caused by surface deformation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 317-334, September.
    • Handle: RePEc:spr:nathaz:v:73:y:2014:i:2:p:317-334
    DOI: 10.1007/s11069-014-1070-2
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    References listed on IDEAS

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    1. Yang, Chun-Chieh & Prasher, Shiv O. & Enright, Peter & Madramootoo, Chandra & Burgess, Magdalena & Goel, Pradeep K. & Callum, Ian, 2003. "Application of decision tree technology for image classification using remote sensing data," Agricultural Systems, Elsevier, vol. 76(3), pages 1101-1117, June.
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    Cited by:

    1. Fatma Yerlikaya-Özkurt & Aysegul Askan, 2020. "Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3163-3180, September.
    2. Deeksha Tayal & Sourabh Paul, 2021. "Labour Force Participation Rate of Women in Urban India: An Age-Cohort-Wise Analysis," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 64(3), pages 565-593, September.
    3. Jan Blachowski, 2016. "Application of GIS spatial regression methods in assessment of land subsidence in complicated mining conditions: case study of the Walbrzych coal mine (SW Poland)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 997-1014, November.

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