ORIGINAL ARTICLE
Application of the Bayesian Networks in Construction Engineering
1
Department of Civil Engineering, Cracow University of Technology, Cracow, Poland
Online publication date: 2020-08-20
Publication date: 2020-06-01
Civil and Environmental Engineering Reports 2020;30(2):221-233
KEYWORDS
ABSTRACT
Currently, significant development of methods supporting decision making under uncertainty conditions is observed. One of such methods includes Bayesian networks used in many fields of economy and science. The paper presents the use of the Bayesian network method in civil engineering problems with particular emphasis on construction engineering projects. In addition to the existing examples of the use of the method cited, the authors’ method for the risk estimation of additional works is presented.
REFERENCES (27)
1.
Apollo, M 2017. Prognostic and diagnostic capabilities of OOBN in assessing investment risk of complex construction projects. Procedia engineering, 196, 236-243.
2.
Apollo, M, Grzyl, B and Miszewska-Urbańska, E 2017. Application of BN in risk diagnostics arising from the degree of urban regeneration area degradation. In 2017 Baltic Geodetic Congress (BGC Geomatics), 83-88. IEEE.
3.
Bayes T, An Essay towards solving a Problem in the Doctrine of Changes. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, M. A., and F. R. S., Philosophical Transactions of the Royal Society of London, 53 (1763), 370-418.
4.
Chickering, D M 1996. Learning Bayesian networks is NP-complete. In Learning from data (pp. 121-130). Springer, New York, NY,121-130.
5.
Ghahramani, Z and Beal, MJ 2001. Propagation algorithms for variational Bayesian learning. In Advances in neural information processing systems, 507-513.
6.
Guo, S, Luo, H and Yong, L 2015. A big data-based workers behavior observation in China metro construction. Procedia Engineering, 123, 190-197.
7.
Grzyl, B, Migda, W and Apollo, M 2019. Building Information Modeling in Small and Middle Sized Buildings–Case Study. In IOP Conference Series: Materials Science and Engineering, 603(3), 032077. IOP Publishing.
8.
Heckerman, D and Wellman, MP 1995. Bayesian networks. Communications of the ACM, 38(3), 27-31.
9.
Hoła, B and Nowobilski, T 2019. Analysis of the influence of socioeconomic factors on occupational safety in the construction industry. Sustainability, 11(16), 4469.
13.
Juszczyk, M 2019. Early Cost Estimates of Bridge Structures Aided by Artificial Neural Networks. In International Scientific Siberian Transport Forum (10-20). Springer, Cham.
14.
Kjaerulff, UB and Madsen, AL 2008. Bayesian networks and influence diagrams. Springer Science+ Business Media, 200, 114.
15.
Kowacka, M, Skorupka, D, Duchaczek, A and Zagrodnik, P 2019. Identification of geodetic risk factors occurring at the construction project preparation stage. Open Engineering, 9(1), 14-17.
16.
Leśniak, A and Janowiec, F 2019. Risk Assessment of Additional Works in Railway Construction Investments Using the Bayes Network. Sustainability, 11(19), 5388.
17.
Leśniak, A, Górka, M and Wieczorek, D 2019. Identification of factors shaping tender prices for lightweight. Scientific Review – Engineering and Environmental Sciences, 28 (2), 171–182.
18.
Mrówczyńska, M, Skiba, M, Bazan-Krzywoszańska, A and Sztubecka, M 2020. Household standards and socio-economic aspects as a factor determining energy consumption in the city. Applied Energy, 264, 114680.
19.
Mrówczyńska, M and Gibowski, S 2016. Indicating vertical deviation of historical buildings using geodetic methods-case study of brick and wood tower in Nowe Miasteczko. Civil and Environmental Engineering Reports, 22(3), 127-136.
20.
Neapolitan, RE 2004. Learning bayesian networks (Vol. 38). Upper Saddle River, NJ: Pearson Prentice Hall.
21.
Nowogońska, B and Korentz, J 2020. Value of Technical Wear and Costs of Restoring Performance Characteristics to Residential Buildings. Buildings, 10(1), 9.
22.
Odimabo, OO, Oduoza, C and Suresh, S 2017. Methodology for project risk assessment of building construction projects using Bayesian belief networks. International Journal of Construction Engineering and Management, 6(6), 221-234.
23.
Siemaszko, A, Grzyl, B and Kristowski, A 2018. Evidence-Based Risk Management for Civil Engineering Projects Using Bayesian Belief Networks (BBN). In 2018 Baltic Geodetic Congress (BGC Geomatics) 191-195, IEEE.
24.
Švajlenka, J, Kozlovská, M and Spišáková, M 2017. The benefits of modern method of construction based on wood in the context of sustainability. International Journal of Environmental Science and Technology, 14(8), 1591-1602.
25.
Wieczorek, D, Plebankiewicz, E and Zima, K 2019. Model estimation of the whole life cost of a building with respect to risk factors. Technological and Economic Development of Economy, 25(1), 20-38.
26.
Yuen, KV 2010. Recent developments of Bayesian model class selection and applications in civil engineering. Structural Safety, 32(5), 338-346.
27.
Zima, K, Plebankiewicz, E and Wieczorek, D 2020. A SWOT Analysis of the Use of BIM Technology in the Polish Construction Industry. Buildings, 10(1), 16.
| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
