Svitlana Terenchuk, Bohdan Yeremenko, Serhii Kartavykh, Oleksandr Nasikovskyi


The aim of research is formalization of the expert experience, which is used in processing geometric parameters of building structure degradation, using fuzzy mathematics. Materials that are used to specify fuzzy models are contained in expert assessments and scientific and technical reports on the technical condition of buildings. The information contained in the reports and assessments is presented in text form and is accompanied by a large number of photographs and diagrams. Model specification methods, based on the analysis of such information on the technical state of structures with damages and defects of various types, primarily lead to difficulties associated with the presentation of knowledge and require the formalization of expert knowledge and experience in the form of fuzzy rules. Approbation and adaptation of the rules is carried out in the process of further research taking into account the influence of random loads and fields. The scientific novelty of the work is expanding of the knowledge base due to the geometric parameters of structural degradation, on the basis of which a fuzzy conclusion about their technical state in the systems of fuzzy product rules at different stages of the object's life cycle is realized. The results of the work are presented in the form of a formalized description of the geometric parameters of degradation. The knowledge presented in the work is intended for the development of technical documentation that is used at the pre-project stage of building reconstruction, but the gained experience is the source of information on the basis of which a constructive solution is selected in the design process of analogical objects. In addition, the knowledge gained from the analysis of expert assessments of the state of various designs is necessary for development of automated expert evaluation processing systems. The use of such evaluation systems will significantly reduce the risks of the human factor associated with the errors in the specification of models for predicting the processes of structural failure at various stages of ensuring the reliability and safety of buildings.


knowledge base; building structure; technical condition; expert assessment system

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Chen, G., Ma, Y.-S., Thimm, G., Tang, S.-H. (2004). Unified Feature Modeling Scheme for the Integration of CAD and CAx. Computer-Aided Design and Applications, 1 (1-4), 595–601. doi: 10.1080/16864360.2004.10738303

Haina, H. A. (2010). Kontseptsiia bahatomodelnoho pidkhodu do rozrobky intelektualnykh SPPR u mistobuduvanni. Upravlinnia rozvytkom skladnykh system, 1, 28–34.

Terentyev, O., Poltorak, O. (2016). Development of models and methods for determining the physical deterioration of items for the task of diagnostics of technical condition of buildings and structures. ScienceRise, 8 (2 (25)), 14–19. doi: 10.15587/2313-8416.2016.76318

Terenchuk, S. A., Yeremenko, B. M., Pashko, A. O. (2016). Otsiniuvannia tekhnichnoho stanu budivelnykh konstruktsii na osnovi nechitkoho vyvedennia. Budivelne vyrobnytstvo, 61, 23–31.

Terenchuk, S., Yeremenko, B., Sorotuyk, T. (2016). Implementation of intelligent information technology for the assessment of technical condition of building structures in the process of diagnosis. Eastern-European Journal of Enterprise Technologies, 5 (3 (83)), 30–39. doi: 10.15587/1729-4061.2016.80782

Yeremenko, B. M., Terenchuk, S. A., Kartavykh, S. M., Nasikovskyi, O. V. (2017). Zastosuvannia ekspertnykh znan dlia formuvannia bazy znan systemy otsiniuvannia tekhnichnoho stanu budivelnykh konstruktsii. Nauka ta budivnytstvo, 4, 63–69.

Cucakovic, A. (2010). Nacrtna geometrija. Beograd: Akademska misao.

George, A. (2011). Advansed in Biomemetrics. In Tech, Rijeka.

Cook, D. A., Ledbetter, S., Ring, S., Wenzel, F. (2000). Masonry crack damage: its origins, diagnosis, philosophy and a basis for repair. Proceedings of the Institution of Civil Engineers – Structures and Buildings, 140 (1), 39–50. doi: 10.1680/stbu.2000.140.1.39

Larose, D. T. (2005). Discovering Knowledge in Data: An Introducing to Data Mining. Wiley & Sons, Inc, 240. doi: 10.1002/9781118874059



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ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)