MODELS, METHODS, AND MEANS OF REPRODUCTION  OF EXPERT KNOWLEDGE IN INTELLIGENT SUPPORT  SYSTEM BUILDING-TECHNICAL EXPERTISE

S. Terenchuk; R. Pasko; O. Panko; V. Zaprivoda

doi:10.37943/AITU.2021.43.51.007

MODELS, METHODS, AND MEANS OF REPRODUCTION OF EXPERT KNOWLEDGE IN INTELLIGENT SUPPORT SYSTEM BUILDING-TECHNICAL EXPERTISE

S. Terenchuk, R. Pasko, O. Panko, V. Zaprivoda

Аннотация

The paper is devoted to solving such a scientific and practical problem as the creation of computerized infocommunication systems for support building-technical expertise to determine the causes of destruction and deformation of buildings and structures. The analysis of the current state of expert activity within the framework of building-technical expertise is carried out. Perspective directions of the introduction of intelligent infocommunication systems in the course of performance of building-technical expertise and expert researches are outlined. The architecture of Intelligent Support System Building-Technical Expertise and the communication scheme of experts with the system are shown. To mapping expert knowledge formalized in the form of fuzzy associative rules to the memory card of the Cascade ARTMAP category fuzzy artificial neural network, it is proposed to use a fuzzy Mamdani-type inference system. The main input data, on the basis of which a fuzzy conclusion is realized to establish the degree of influence of various environmental factors on the technical condition of buildings and structures, are systematized and presented in a form acceptable for processing by computerized systems. At the same time, the main focus is on the study of facilities that are built and operated on subsidence loess soils. The process of formalization of heuristics, which is based on the formation of associations related to information on the position of signs of deterioration of the technical condition of the objects of expertise and the position of the changed soil, is described. Examples of interpretation and fuzzification of input information on soil properties, characteristics of the soil base of the object of building-technical expertise, and the surrounding area are given. The described approach provides an opportunity to reduce the risks of making wrong decisions by using the system as an intelligent database. The use of an artificial fuzzy neural network of the Cascade ARTMAP category gives the system the ability to form an expert conclusion on the degree of influence of various environmental factors on the technical condition of objects in the fuzzy conditions of a partially observed environment.

Ключевые слова

Associative rule, expert conclusion, fuzzy inference, subsidence loess soil.

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Литература

Ukraine, Z. (1994). About forensic examination. Information of the Verkhovna Rada of Ukraine (VVR),

(28), 4038-12. https://zakon.rada.gov.ua/laws/show/4038-12#Text.

DBN A.2.1-1-2014. Research, design and territorial activities. Engineering surveys for construction,

Ministry of Regional Development of Ukraine, 2014.

Buratevich, O.I., Senik, N.V., Kharchenko, V.V. (2015). Methodical recommendations for determining

the physical demolition of non-residential buildings. Report on research work IV.3.3-2014/2 (state

registration number 0114U000706), 177.

Kulikov, P., Pasko, R., Terenchuk, S., Ploskyi, V., & Yeremenko, B. (2020). Using of Artificial Neural

Networks in Support System of Forensic Building-Technical Expertise. International Journal of

Innovative Technology and Exploring Engineering, 9(4), 3162-3168.

Terenchuk, S., Pashko, A., Yeremenko, B., Kartavykh, S., & Ershovа, N. (2018). Modeling an intelligent

system for the estimation of technical state of construction structures. Eastern European Journal of

Advanced Technology, (3 (2)), 47-53.

Pasko, R., Terenchuk, S., Aghezzaf, A. (2020). Analysis of Deterioration Causes to the Technical

Condition of Buildings Constructed on Subsidence Loess Soils. Management of Development of

Complex Systems, 43, 116 – 122.

ISO, B. (2018). ISO 19650-2: 2018. Organization and Digitization of Information about Buildings and

Civil Engineering Works, Including Building Information Modeling (BIM)-Information Management using

Building Information Modeling. Part, 2. URL: https://www.iso.org/standard/68078.html.

Komandirov, O., Levchenko, O., & Kisil O. V. (2019). Prospects for the use of BIM-technology

(BUILDING INFORMATION MODELING) in construction and technical expertise. Forensics and

Forensics, 64, 633-638.

Domanetska, I., Honcharenko, T. Borodavka, Y., Dolya, E., & Fedusenko, O. (2020). Comprehensive

Information Support of Urban Planning on BIM-based Design. International Journal of Advanced

Trends in Computer Science and Engineering, 10, 9197-9203.

Razov, I.O., Bereznev, A.V., & Korkishko, O.A. (2018). Problems and prospects for the introduction

of BIM technologies in construction and design. In BIM modeling in construction and architecture

problems (pp. 27-31).

Katasyov, A.S. (2019). Neurofuzzy model of formation of fuzzy rules for estimation of a condition of

objects in the conditions of uncertainty. Computer Research and Modeling, 11 (3), 477-492.

Menshikov, P.V. (2021). Modeling methods in forensic engineering: Artificial modeling languages in

forensics. Technology and language, 2 (2), 77-85.

Pasko, R., & Terenchuk, S. (2020). The use of neuro-fuzzy models in expert support systems for

forensic building-technical expertise. ScienceRise, (2), 10-18.

Fu, L.M., & Fu, L.C. (1990). Mapping rule-based systems into neural architecture. Knowledge-Based Systems, 3(1), 48-56.

Tan, A.H. (1997). Cascade ARTMAP: Integrating neural computation and symbolic knowledge processing. IEEE Transactions on Neural Networks, 8(2), 237-250.

Jang, J.S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.

Atanassov, K. T. (2017). Type-1 fuzzy sets and intuitionistic fuzzy sets. Algorithms, 10(3), 106. https://doi.org/10.3390/a10030106.

Pasko, R., Aznaurian, I., Terenchuk, S., (2020). Adaptation of fuzzy inference system to the task of assessment impact of repair-building works on the technical condition of the construction object.

Management of Development of Complex Systems, 42, 113-118.

DOI: http://dx.doi.org/10.37943/AITU.2021.43.51.007

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