Scientific Online Resource System

Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series

Digital advantages for the construction industry

Plamen Yankov, Stefka Petrova, Svetlana Todorova

Abstract

Currently, the digital transformation is recognized as a source of many potential effects for the business organization from all sectors of the economy. The purpose of this study is to identify and highlight the possible advantages of the application of digital technologies in the construction sector. A scientometric analysis of the existing literature is performed. The scope of the study is given from three perspectives through three search criteria - digitalization, big data, and forecasting. A total of 2371 articles are abstracted. Then, the extracted data is visualized through Vosviewer software tool. The growth of publications increases significantly over the last decade. The results illustrate that the digitalization in the construction sector affect all aspects of construction projects, with the strongest impact on the architecture design and building information modelling. Big data in construction is associated with the data storage, data analytics and information management, during the whole life of the buildings. The third search criterion shows that construction companies most often forecast the total costs using regression analysis, machine learning algorithms, artificial neural networks, etc The research findings could support decision makers and practitioners with-depth understanding for the possible advantages of digital technologies in the construction industry. The current study is part of a larger project called "Digitalization of Economy in a Big Data Environment" BG05M2OP001-1.002-0002-C02.


Keywords

construction industry, big data, digitalization, forecast, scientometric analysis, Vosviewer

Full Text


References

Adamu, A. A. et al. (2020) ‘An integrated IoT system pathway for smart cities’, International Journal on Emerging Technologies, 11(1), pp. 1–9.

Agarwal, R. and Dhar, V. (2014) ‘Big data, data science, and analytics: The opportunity and challenge for IS research’, Information Systems Research, 25(3), pp. 443–448. doi: 10.1287/isre.2014.0546.

Alaloul, W. S. et al. (2019) ‘Industrial Revolution 4.0 in the construction industry: Challenges and opportunities for stakeholders’, Ain Shams Engineering Journal. THE AUTHORS, (xxxx). doi: 10.1016/j.asej.2019.08.010.

Alexandrova, Y. (2020) ‘ИНОВАТИВНА ЦИФРОВА ОБРАБОТКА НА НОВИ И СЪЩЕСТВУВАЩИ ДАННИ INNOVATIVE DIGITAL PROCESSING OF NEW AND

EXISTING DATA’, pp. 92–120.

Bilal, M. et al. (2016) ‘Big Data in the construction industry: A review of present status, opportunities, and future trends’, Advanced Engineering Informatics. Elsevier Ltd, 30(3), pp. 500–521. doi: 10.1016/j.aei.2016.07.001.

Calvetti, D. (2020) ‘Worker 4 . 0 : The Future of Sensored Construction Sites’, pp. 1–22.

Cox, M. and Ellsworth, D. (1997) ‘Application-controlled demand paging for out-of-core visualization’, Proceedings of the IEEE Visualization Conference, (July), pp. 235–244. doi: 10.1109/visual.1997.663888.

Dimitrov, G., Panayotova, G., Garvanov, I. et al. (2016). Performance analysis of the method for social search of information in university information systems.. 3rd International Conference on Artificial Intelligence and Pattern Recognition (AIPR), Lodz, Poland, IEEE, 2016, pp.149-153.

Doroshenko, A. (2020) ‘Applying Artificial Neural Networks in Construction’, E3S Web of Conferences, 143, pp. 2018–2021. doi: 10.1051/e3sconf/202014301029.

Van Eck, N. J. and Waltman, L. (2018) ‘VOSviewer Manual: Manual for VOSviewer version 1.6.7’, Univeristeit Leiden, (February), p. 51. Available at: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.8.pdf.

Garyaev, N. and Garyaeva, V. (2019) ‘Big data technology in construction’, E3S Web of Conferences, 97. doi: 10.1051/e3sconf/20199701032.

Hyder, F. and Bandi, S. (2021) ‘Associations Between Building Information Modelling ( BIM ) Data and Big Data Attributes’, pp. 1–11.

Khan, N. et al. (2019) ‘The 51 V’s of big data: Survey, technologies, characteristics, opportunities, issues and challenges’, ACM International Conference Proceeding Series, Part F1481, pp. 19–24. doi: 10.1145/3312614.3312623.

Kostadinova, I., Toshev, R., et al. (2016). Temporal Analysis of the Pedagogical Adoptions use and Application of the Augmented and Virtual Reality Technologies in Technical Subject Areas. 11th Annual International Conference of Education, Research and Innovation, ICERI2018 Proceedings, Seville, Spain : IATED, 2018, pp.4387-4393.

Ngo, J., Hwang, B.-G. and Zhang, C. (2020) ‘Factor-based big data and predictive analytics capability assessment tool for the construction industry’.

Panayotova, G., Dimitrov, G., et al. (2016). Modeling and data processing of information systems. 3rd International Conference on Artificial Intelligence and Pattern Recognition (AIPR), Lodz, Poland, IEEE, pp.154-158.

Petrivskyi, V., Dimitrov, G., Shevchenko, V., et al. (2020). Information Technology for Big Data Sensor Networks Stability Estimation. Information and Security, Sofia: Procon Ltd. 47(1), pp.141-154. https://doi.org/10.11610/isij.4710

Petrov, P., Nacheva, R. (2020). Информационни системи за социална бизнес аналитичност в реално време. Варна: Наука и икономика. Библ. Проф. Цани Калянджиев, Кн. 61.

Shahriari, B. et al. (2016) ‘Taking the human out of the loop: A review of Bayesian optimization’, Proceedings of the IEEE. IEEE, 104(1), pp. 148–175.

Stoyanova, M., Vasilev, J. and Cristescu, M. P. (2021) ‘Big data in property management’.

Sulova, S. (2020) ‘Юбилейна международна научна конференция ИКОНОМИЧЕСКА НАУКА, ОБРАЗОВАНИЕ И РЕАЛНА ИКОНОМИКА: РАЗВИТИЕ И ВЗАИМОДЕЙСТВИЯ В ДИГИТАЛНАТА ЕПОХА Jubilee International Scientific Conference ECONOMIC SCIENCE, EDUCATION AND THE REAL ECONOMY: DEVELOPMENT AND INTERA’, (August).

Yin, X. et al. (2019) ‘Building information modelling for off-site construction: Review and future directions’, Automation in Construction, 101(January), pp. 72–91. doi: 10.1016/j.autcon.2019.01.010.


Refbacks

Font Size


|