Engineering Simulation of Market Value of Construction Materials

  • E. V. PUCHKOV Don State Technical University, Rostov-on-Don, Russian Federation
  • N. A. OSADCHAYA Don State Technical University, Rostov-on-Don, Russian Federation
  • A. D. MURZIN Southern Federal University, Rostov-on-Don, Russian Federation Don State Technical University, Rostov-on-Don, Russian Federation


Material resources are one of the main elements of the construction cost estimate. The nomenclature of material resources includes a large number of items. It is impossible to objectively estimate the construction cost, capital construction, and maintenance works without carrying out monitoring of the prices of constructional resources and accounting for these data in estimates. The purpose of the present study consists in developing an engineering approach to the simulation of pricing in the construction materials’ market. To achieve this purpose, we use mathematical statistics methods such as linear regression and autoregressive integrated moving average, as well as machine learning methods, namely gradient boosting and recurrent neural networks. In consequence of this work, we proposed a scheme to store statistical information based on the SpagoBI business intelligence platform, as well as designed hybrid intelligent predictive model, which allowed automating the engineering approach to the prediction of prices and objectifying advanced analytics of the construction materials’ market. The proposed engineering approach allows predicting the dynamics of the construction materials’ market segments when managing the construction cost at the level of enterprise and the region that will enable adequate decision- making in the course of investment projects development.


[1] Abdurakhmanov, A.M., Volodin, M.V., Zybin, E.Yu., and Ryabchenko, V.N. 2016. Metody prognozirovaniya ehlektropotrebleniya v raspredelitel'nyh setyah (obzor). [Electrical energy consumption forecasting methods in distribution networks (review)]. Electrical Engineering, scientific e-journal, 3(1), 3-23.
[2] Avdeev, A.S. 2010. Razrabotka adaptivnyh modelej i programmnogo kompleksa prognozirovaniya ehkonomicheskih vremennyh ryadov [Development of adaptive models and software package for economic time series forecasting]. Ph.D. thesis in Engineering, Polzunov Altai State Technical University, Barnaul.
[3] Babkin, A.V., Karlina, E.P., and Epifanova, N.S. 2015. Neural networks as a tool of forecasting of socioeconomic systems strategic development. Procedia-Social and Behavioral Sciences, 207, 274-279.
[4] Bassioni, H.A., Elmasry, M.I., Ragheb, A.M., and Youssef, A.A. 2012. Time series analysis for the prediction of RC material components prices in Egypt. In: Smith, S.D (Ed). Proceedings of the 28th Annual ARCOM Conference, Edinburg, UK, Association of Researchers in Construction Management, pp. 381-390.
[5] Belyavskiy, G., and Puchkov, E. 2017. Separate training of hybrid neural network. International Journal of Pure and Applied Mathematics, 115(4), 883-893
[6] Cazzin, G. 2012. Business intelligence with SpagoBI. Italy, Padua: SpagoBI Competency Cente
[7] Chuchueva, I.A. 2012. Model' prognozirovaniya vremennyh ryadov po vyborke maksimal'nogo podobiya [The model of forecasting time series by maximum similarity sample] [Text]. Ph.D. in Engineering, Bauman Moscow State Technical University, Moscow.
[8] Didkovskaya, O.V., Mamayeva, O.A., and Ilyina, M.V. 2016. Development of cost engineering system in construction. Procedia Engineering, 153, 131-135.
[9] Didkovskaya, O.V., Ramzaev, V.M., and Khaimovich, I.N. 2015. Problemy razrabotki metodologicheskih podhodov prognozirovaniya cenoobrazovaniya na rynke stroitel'nyh materialov na osnove nejrosetevogo modelirovaniya [Development of methodological approaches to pricing forecasting in the construction materials’ market based on neural network modeling]. Fundamental Research, 2-22, 4957-4962.
[10] Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., and Schmidhuber, J. 2017. LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222 - 2232
[11] Gubanova, E.S., and Kleshch, V.S. 2017. Methodological aspects in analyzing the level of non-uniformity of socio-economic development of regions. Economic and Social Changes: Facts, Trends, Forecast, 1, 58-75.
[12] Han, J., Pei, J., and Kamber, M. 2011. Data mining: Concepts and techniques. Elsevier.
[13] Hwang, S., Park, M., Lee, H.S., and Kim, H. 2012. Automated time-series cost forecasting system for construction materials. Journal of Construction Engineering and Management, 138(11), 1259-1269.
[14] Issa, R.R. 2000. Application of artificial neural networks to predicting construction material prices. Computing in Civil and Building Engineering, 2000, 1129-1132.
[15] Jain, M., and Pathak, K. 2014. Applications of artificial neural network in construction engineering and management: A review. International Journal of Engineering Technology, Management and Applied Sciences, 2(3), 134-142.
[16] Khaleel, T.A.M. 2015. Development of the artificial neural network model for prediction of Iraqi express ways construction cost. International Journal of Civil Engineering, 6(10), 62-76.
[17] Kitova, O.V., Kolmakov, I.B., Dyakonova, L.P., Grishina, O.A., Danko, T.P., and Sekerin, V.D. 2016. Hybrid intelligent system of forecasting the socio-economic development of the country. International Journal of Applied Business and Economic Research, 14(9), 5755-5766.
[18] Liu, Z., Gao, W., Wan, Y. H., and Muljadi, E. 2012. Wind power plant prediction by using neural networks. IEEE Energy Conversion Conference and Exposition, Raleigh, North Carolina September 15–20,
[19] Nizhegorodtsev, R., Piskun, E., and Kudrevich, V. 2017. The forecasting of regional social and economic development. Economy of Region, 1(1), 38-48.
[20] Osadchaya, N.A., Murzin, A.D., and Torgayan, E.E. 2017. Assessment of risks of investment and construction activities: Russian practice. Journal of Advanced Research in Law and Economics, 8(2), 529-544.
[21] Pisareva, O.M. 2007. Metody prognozirovaniya razvitiya social'no-ehkonomicheskih sistem [Forecasting methods of the socio-economic systems development]. Moscow: Higher School.
[22] Suveka, V., and Priya, T.S. 2016. A review on prediction of material prices in construction projects. International Research Journal of Engineering and Technology, 3(11), 757-760.
How to Cite
PUCHKOV, E. V.; OSADCHAYA, N. A.; MURZIN, A. D.. Engineering Simulation of Market Value of Construction Materials. Journal of Advanced Research in Law and Economics, [S.l.], v. 9, n. 2, p. 615-624, dec. 2018. ISSN 2068-696X. Available at: <>. Date accessed: 20 apr. 2024. doi: 2(32).25.