THE USE OF NEURO-FUZZY MODELS IN EXPERT SUPPORT SYSTEMS FOR FORENSIC BUILDING-TECHNICAL EXPERTISE
The paper is focused on solving the problem of assessing the impact of repair-building works on the technical condition of objects near which these works were or are being carried out. Particular attention is paid to the analysis of the problems that accompany the creation of expert systems for supporting forensic building-technical expertise.
The main aim of the work: conceptual modeling of an expert system for supporting forensic building-technical expertise.
Object of research: the process of execution of forensic building-technical expertise and expert research.
Solved problem: automation of a system capable of functioning in conditions of fuzzy uncertainty caused by the non-uniformity of the logic of the process of performing forensic building-technical expertise and the ambiguity and inconsistency of the information provided for research.
Main scientific results: a model of a knowledge-based system is proposed and the use of neuro-fuzzy networks is justified to solve the problem of supporting the decision to assess the impact of repair-building works on the technical condition of the object, which has become the subject of expertise.
Field of practical use of research results: forensic activities in the framework of building-technical expertise to determine the possible causes of deterioration in the technical condition of structural elements of buildings and their individual premises.
Innovative technological product: a support system for forensic building-technical expertise based on knowledge and neuro-fuzzy models.
Scope of application of an innovative technological product: forensic and investigative practice in resolving issues requiring the use of special knowledge in assessing the impact of repair-building works on the technical condition of nearby facilities.
Psiarnetskyi, D., Asauliuk, S., Fishchuk, N., Pasko, R. (2006). Analiz vplyvu remontno-budivelnykh robit v prymishcheni na tekhnichnyi stan sumizhnykh prymishchen: zvit pro NDR (zakliuchnyi). KNDISE. Kyiv, 39.
Buratevych, O., Senyk, N., Kharchenko, V., Chaika, O., Pasko, R., Psiarnetskyi, D. et. al. (2015). Metodychni rekomendatsii z vyznachennia fizychnoho znosu nezhytlovykh budivel: zvit pro NDR (zakliuchnyi). 0114U000706. Kyiv, 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. doi: http://doi.org/10.35940/ijitee.d2050.029420
Snityuk, V. E., Rifat Mohammed Ali (2002). Modeli processa prinyatiya adaptivnyh reshenii kompozicionnoi struktury s determinirovannymi i veroyatnostnymi harakteristikami. Radioelektronika i informatika, 4, 123–127.
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 Enterprise Technologies, 3 (2 (93)), 47–53. doi: http://doi.org/10.15587/1729-4061.2018.132587
Bilchuk, V. M., Dzeverin, I. H., Vorobiov, O. V. (2012). Metodychnyi pidkhid opysu funktsionuvannia skladnoi systemy v nechitkomu stokhastychno nevyznachenomu seredovyshchi. Zbirnyk naukovykh prats KhUPS, 4 (33), 136–140.
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: http://doi.org/10.15587/1729-4061.2016.80782
Mettrey, W. (1987). An Assessment of Tools for Building Large Knowledge Based Systems. AI Magazine, 8 (4), 81–89.
Terenchuk, S. A., Yeremenko, B. M., Pashko, A. O. (2016) Otsiniuvannia tekhnichnoho stanu budivelnykh konstruktsii na osnovi nechitkoho vyvedennia. Budivelne vyrobnytstvo, 61 (1), 23–31.
Osowski, S. (2000). Sieci neuronowe do przetwarzania informacji. Warszawa, 342.
Tresp, V., Hollatz, J., Ahmad, S. (1993). Network structuring and training using rule-based knowledge. Advances in Neural Inform. San Mateo: Morgan Kaufmann, 977–984.
Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., Rosen, D. B. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3 (5), 698–713. doi: http://doi.org/10.1109/72.159059
Mamdani, E. H., Gaines, B. R. (1981). Fuzzy Reasonings and Its Applications. Academic Press, Inc, 381.
Domanetska, I., Khaddad, A., Krasovska, H., Yeremenko, B. (2019). Corporate System Users Identification by the Keyboard Handwriting based on Neural Networks. International Journal of Innovative Technology and Exploring Engineering, 9 (1), 4156–4161. doi: http://doi.org/10.35940/ijitee.a6101.119119
Ah-Hwee Tan (1997). Cascade ARTMAP: integrating neural computation and symbolic knowledge processing. IEEE Transactions on Neural Networks, 8 (2), 237–250. doi: http://doi.org/10.1109/72.557661
Khaddad, A., Riabchun, Y., Terenchuk, S., Yeremenko, B. (2019). Modeling of the Intelligent System of Searching Associative Images. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). Kyiv, 439–442. doi: http://doi.org/10.1109/picst47496.2019.9061398
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: http://doi.org/10.1680/stbu.2000.140.1.39
Markechová, D., Riečan, B. (2016). Logical Entropy of Fuzzy Dynamical Systems. Entropy, 18 (4), 157. doi: http://doi.org/10.3390/e18040157
Subbotyn, S. A. (2006). Syntez raspoznaiushchykh neiro-nechetkykh modelei s uchetom ynformatyvnosty pryznakov. Neirokompiuteri: razrabotka, prymenenye, 10, 50–56.
👁 148 ⬇ 95
Copyright (c) 2020 Roman Pasko, Svitlana Terenchuk
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.