Forecasting of sports fields construction costs aided by ensembles of neural networks
Abstract
The paper presents an original approach to construction cost analysis and development of predictive models based on ensembles of artificial neural networks. The research was focused on the application of two alternative approaches of ensemble averaging that allow for combining a number of multilayer perceptron neural networks and developing effective models for cost predictions. The models have been developed for the purpose of forecasting construction costs of sports fields as a specific type of construction objects. The research included simulation and selection of numerous neural networks that became the members of the ensembles. The ensembles included either the networks of different types in terms of their structure and activation functions or the networks of the same type. The research also included practical implementation of the developed models for cost analysis based on a sports field BIM model. This case study examined and confirmed all of the four models’ predictive capabilities and superiority over models based on single networks for the particular problem. Verification including testing and the case study enabled selection of the best ensemble-based model that combined ten networks of different types. The proposed approach is prospective for fast cost analyses and conceptual estimates in construction projects.
Keyword : construction cost management, conceptual estimates, neural networks, ensembles, artificial intelligence, building information modelling, sport fields
How to Cite
Juszczyk, M., Zima, K., & Lelek, W. (2019). Forecasting of sports fields construction costs aided by ensembles of neural networks. Journal of Civil Engineering and Management, 25(7), 715-729. https://doi.org/10.3846/jcem.2019.10534
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Jovanović, R., Jovanović, R. Ž., & Sretenović, A. A. (2017). Ensemble of radial basis neural networks with K-means clustering for heating energy consumption prediction. FME Transactions, 45, 51-57.
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Juszczyk, M., Leśniak, A., & Zima, K. (2018). ANN based approach for estimation of construction costs of sports fields. Complexity, Article ID 7952434. https://doi.org/10.1155/2018/7952434
Juszczyk, M., & Zima, K. (2018). Clustering of sports fields as specific construction objects aided by Kohonen’s neural networks. AIP Conference Proceedings, 1978, 240009. https://doi.org/10.1063/1.5043870
Kalibatas, D., Kalibatienė, D., & Kapliński, O. (2018). A systematic review of information modelling of individual residential Buildings. Engineering Structures and Technologies, 10(2), 58-71. https://doi.org/10.3846/est.2018.6479
Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3), 973-982. https://doi.org/10.1016/j.acme.2018.01.014
Leśniak, A., & Zima, K. (2018). Cost calculation of construction projects including sustainability factors using the Case Based Reasoning (CBR) method. Sustainability, 10(5), 1608. https://doi.org/10.3390/su10051608
Liu, M., & Ling, Y. Y. (2005). Modeling a contractor’s markup estimation. Journal of Construction Engineering and Management, 131(4), 391-399. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:4(391)
Mrówczyńska, M. (2011). Neural networks and neuro-fuzzy systems applied to the analysis of selected problems of geodesy. Computer Assisted Mechanics and Engineering Sciences, 18(3), 161-173.
Naik, M. G., & Kumar, D. R. (2015). Construction project cost and duration optimization using artificial neural network. In AEI 2015 (pp. 433-444). Reston, VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784479070.038
Osowski, S. (1997). Sieci neuronowe w ujęciu algorytmicznym. Wydawnictwa Naukowo-Techniczne.
Park, T., Kang, T., Lee, Y., & Seo, K. (2014). Project cost estimation of national road in preliminary feasibility stage using BIM/GIS platform. In Fifth International Conference ”Computing in Civil and Building Engineering” (V-ICCCBE) (pp. 423-430). Reston, VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784413616.053
Pavlovskis, M., Antucheviciene, J., & Migilinskas, D. (2017). Assessment of buildings redevelopment possibilities using MCDM and BIM techniques. Procedia Engineering, 172, 846850. https://doi.org/10.1016/J.PROENG.2017.02.083
Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), 679-687. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000479
Rashidi, A., Jazebi, F., & Brilakis, I. (2011). Neurofuzzy genetic system for selection of construction project managers. Journal of Construction Engineering and Management, 137(1), 17-29. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000200
Reizgevičius, M., Ustinovičius, L., Cibulskienė, D., Kutut, V., & Nazarko, L. (2018). Promoting sustainability through investment in building information modeling (BIM) technologies: A design company perspective. Sustainability, 10(3), 600. https://doi.org/10.3390/su10030600
Schabowicz, K., & Hoła, B. (2007). Mathematical-neural model for assessing productivity of earthmoving machinery. Journal of Civil Engineering and Management, 13(1), 47-54. https://doi.org/10.1080/13923730.2007.9636418
Sharkey, A. J. C. (1999). Combining artificial neural nets: ensemble and modular multi-net systems. Springer. https://doi.org/10.1007/978-1-4471-0793-4
Skiba, M., Mrówczyńska, M., & Bazan-Krzywoszańska, A. (2016). Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: The town of Zielona Góra. Applied Energy, 188, 356-366. https://doi.org/10.1016/j.apenergy.2016.12.006
Tadeusiewicz, R. (1993). Sieci neuronowe. Akademicka Oficyna Wydawnicza.
Urząd Zamówień Publicznych. (2019). Retrieved from https://www.uzp.gov.pl/urzad-zamowien-publicznych
Ustinovichius, L., Popov, V., Cepurnaite, J., Vilutienė, T., Samofalov, M., & Miedziałowski, C. (2018). BIM-based process management model for building design and refurbishment. Archives of Civil and Mechanical Engineering, 18(4), 1136-1149. https://doi.org/10.1016/J.ACME.2018.02.004
Yazdani-Chamzini, A., Zavadskas, E., Antucheviciene, J., & Bausys, R. (2017). A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks. Symmetry, 9(12), 298. https://doi.org/10.3390/sym9120298
Yip, H., Fan, H., & Chiang, Y. (2014). Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models. Automation in Construction, 38, 30-38. https://doi.org/10.1016/J.AUTCON.2013.10.024
Zhao, Z., Gong, Q., Zhang, Y., & Zhao, J. (2007). Prediction model of tunnel boring machine performance by ensemble neural networks. Geomechanics and Geoengineering, 2(2), 123-128. https://doi.org/10.1080/17486020701377140
Zima, K. (2015). The case-based reasoning model of cost estimation at the preliminary stage of a construction project. Procedia Engineering, 122, 57-64. https://doi.org/10.1016/j.proeng.2015.10.007
Zima, K. (2017). Impact of information included in the BIM on preparation of bill of quantities. Procedia Engineering, 208, 203-210. https://doi.org/10.1016/j.proeng.2017.11.039
Bishop, C. M. (1995). Neural networks for pattern recognition. Clarendon Press. https://doi.org/10.1201/9781420050646.ptb6
Bryde, D., Broquetas, M., & Volm, J. M. (2013). The project benefits of Building Information Modelling (BIM). International Journal of Project Management, 31(7), 971-980. https://doi.org/10.1016/J.IJPROMAN.2012.12.001
Cheng, M.-Y., Tsai, H.-C., & Sudjono, E. (2012). Evolutionary fuzzy hybrid neural network for dynamic project success assessment in construction industry. Automation in Construction, 21, 46-51. https://doi.org/10.1016/J.AUTCON.2011.05.011
Cheung, F. K. T., Rihan, J., Tah, J., Duce, D., & Kurul, E. (2012). Early stage multi-level cost estimation for schematic BIM models. Automation in Construction, 27, 67-77. https://doi.org/10.1016/J.AUTCON.2012.05.008
Dikmen, S. U., & Sonmez, M. (2011). An artificial neural networks model for the estimation of formwork labour. Journal of Civil Engineering and Management, 17(3), 340-347. https://doi.org/10.3846/13923730.2011.594154
Eastman, C. M., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook : a guide to building information modeling for owners, managers, designers, engineers and contractors. New York: John Wiley & Sons.
El-Sawalhi, N. I., & Shehatto, O. (2014). A neural network model for building construction projects cost estimating. Journal of Construction Engineering and Project Management, 4(4), 9-16. https://doi.org/10.6106/JCEPM.2014.4.4.009
Forgues, D., Iordanova, I., Valdivesio, F., & Staub-French, S. (2012). Rethinking the cost estimating process through 5D BIM: A case study. In Construction Research Congress 2012 (pp. 778-786). Reston, VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784412329.079
Grzyl, B., Miszewska-Urbańska, E., & Apollo, M. (2017). Building information modelling as an opportunity and risk for stakeholders involved in construction investment process. Procedia Engineering, 196, 1026-1033. https://doi.org/10.1016/J.PROENG.2017.08.045
Guo, Z., Chen, Q., Wu, G., Xu, Y., Shibasaki, R., & Shao, X. (2017). Village building identification based on ensemble convolutional neural networks. Sensors, 17(11), 2487. https://doi.org/10.3390/s17112487
Hakim, S., Razak, H. A., & Ravanfar, S. (2016). Ensemble neural networks for structural damage identification using modal data. International Journal of Damage Mechanics, 25(3), 400430. https://doi.org/10.1177/1056789515598639
Hardin, B., & McCool, D. (2015). BIM and construction management : proven tools, methods, and workflows. New York: John Wiley & Sons.
Hashem, S., & Schmeiser, B. (1995). Improving model accuracy using optimal linear combinations of trained neural networks. IEEE Transactions on Neural Networks, 6(3), 792-794. https://doi.org/10.1109/72.377990
Haykin, S. S. (1999). Neural networks: a comprehensive foundation. Prentice Hall.
Jovanović, R., Jovanović, R. Ž., & Sretenović, A. A. (2017). Ensemble of radial basis neural networks with K-means clustering for heating energy consumption prediction. FME Transactions, 45, 51-57.
Juszczyk, M. (2017a). Studies on the ANN implementation in the macro BIM cost analyzes. Scientific Review Engineering and Environmental Sciences, 26(2), 183-192. https://doi.org/10.22630/PNIKS.2017.26.2.16
Juszczyk, M. (2017b). The challenges of nonparametric cost estimation of construction works with the use of artificial intelligence tools. Procedia Engineering, 196, 415-422. https://doi.org/10.1016/j.proeng.2017.07.218
Juszczyk, M., Leśniak, A., & Zima, K. (2018). ANN based approach for estimation of construction costs of sports fields. Complexity, Article ID 7952434. https://doi.org/10.1155/2018/7952434
Juszczyk, M., & Zima, K. (2018). Clustering of sports fields as specific construction objects aided by Kohonen’s neural networks. AIP Conference Proceedings, 1978, 240009. https://doi.org/10.1063/1.5043870
Kalibatas, D., Kalibatienė, D., & Kapliński, O. (2018). A systematic review of information modelling of individual residential Buildings. Engineering Structures and Technologies, 10(2), 58-71. https://doi.org/10.3846/est.2018.6479
Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil and Mechanical Engineering, 18(3), 973-982. https://doi.org/10.1016/j.acme.2018.01.014
Leśniak, A., & Zima, K. (2018). Cost calculation of construction projects including sustainability factors using the Case Based Reasoning (CBR) method. Sustainability, 10(5), 1608. https://doi.org/10.3390/su10051608
Liu, M., & Ling, Y. Y. (2005). Modeling a contractor’s markup estimation. Journal of Construction Engineering and Management, 131(4), 391-399. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:4(391)
Mrówczyńska, M. (2011). Neural networks and neuro-fuzzy systems applied to the analysis of selected problems of geodesy. Computer Assisted Mechanics and Engineering Sciences, 18(3), 161-173.
Naik, M. G., & Kumar, D. R. (2015). Construction project cost and duration optimization using artificial neural network. In AEI 2015 (pp. 433-444). Reston, VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784479070.038
Osowski, S. (1997). Sieci neuronowe w ujęciu algorytmicznym. Wydawnictwa Naukowo-Techniczne.
Park, T., Kang, T., Lee, Y., & Seo, K. (2014). Project cost estimation of national road in preliminary feasibility stage using BIM/GIS platform. In Fifth International Conference ”Computing in Civil and Building Engineering” (V-ICCCBE) (pp. 423-430). Reston, VA: American Society of Civil Engineers. https://doi.org/10.1061/9780784413616.053
Pavlovskis, M., Antucheviciene, J., & Migilinskas, D. (2017). Assessment of buildings redevelopment possibilities using MCDM and BIM techniques. Procedia Engineering, 172, 846850. https://doi.org/10.1016/J.PROENG.2017.02.083
Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), 679-687. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000479
Rashidi, A., Jazebi, F., & Brilakis, I. (2011). Neurofuzzy genetic system for selection of construction project managers. Journal of Construction Engineering and Management, 137(1), 17-29. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000200
Reizgevičius, M., Ustinovičius, L., Cibulskienė, D., Kutut, V., & Nazarko, L. (2018). Promoting sustainability through investment in building information modeling (BIM) technologies: A design company perspective. Sustainability, 10(3), 600. https://doi.org/10.3390/su10030600
Schabowicz, K., & Hoła, B. (2007). Mathematical-neural model for assessing productivity of earthmoving machinery. Journal of Civil Engineering and Management, 13(1), 47-54. https://doi.org/10.1080/13923730.2007.9636418
Sharkey, A. J. C. (1999). Combining artificial neural nets: ensemble and modular multi-net systems. Springer. https://doi.org/10.1007/978-1-4471-0793-4
Skiba, M., Mrówczyńska, M., & Bazan-Krzywoszańska, A. (2016). Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: The town of Zielona Góra. Applied Energy, 188, 356-366. https://doi.org/10.1016/j.apenergy.2016.12.006
Tadeusiewicz, R. (1993). Sieci neuronowe. Akademicka Oficyna Wydawnicza.
Urząd Zamówień Publicznych. (2019). Retrieved from https://www.uzp.gov.pl/urzad-zamowien-publicznych
Ustinovichius, L., Popov, V., Cepurnaite, J., Vilutienė, T., Samofalov, M., & Miedziałowski, C. (2018). BIM-based process management model for building design and refurbishment. Archives of Civil and Mechanical Engineering, 18(4), 1136-1149. https://doi.org/10.1016/J.ACME.2018.02.004
Yazdani-Chamzini, A., Zavadskas, E., Antucheviciene, J., & Bausys, R. (2017). A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks. Symmetry, 9(12), 298. https://doi.org/10.3390/sym9120298
Yip, H., Fan, H., & Chiang, Y. (2014). Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models. Automation in Construction, 38, 30-38. https://doi.org/10.1016/J.AUTCON.2013.10.024
Zhao, Z., Gong, Q., Zhang, Y., & Zhao, J. (2007). Prediction model of tunnel boring machine performance by ensemble neural networks. Geomechanics and Geoengineering, 2(2), 123-128. https://doi.org/10.1080/17486020701377140
Zima, K. (2015). The case-based reasoning model of cost estimation at the preliminary stage of a construction project. Procedia Engineering, 122, 57-64. https://doi.org/10.1016/j.proeng.2015.10.007
Zima, K. (2017). Impact of information included in the BIM on preparation of bill of quantities. Procedia Engineering, 208, 203-210. https://doi.org/10.1016/j.proeng.2017.11.039