Neural Network Models for Actual Cost and Actual Duration Estimation in Construction Projects: Findings from Greece
Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.
[1] Al-Saadi, A.M., Zamiem, S.K. and Al-Jumaili, L.A. (2017) ‘Estimating the Optimum Duration of Road Projects using Neural Network Model’, International Journal of Engineering and Technology (IJET), Vol. 9, No.5, pp.3458-3469
[2] Al-Zubaidi, E.D.A., Yas, A.H. and Abbas, H.F. (2019) ‘Guess the time of implementation of residential construction projects using neural networks ANN’, Periodicals of Engineering and National Sciences, Vol. 7, No. 3, pp.1218-1227.
[3] Antoine, A.L., Alleman, D. and Molenaar, K.R. (2018) ‘Examination of project duration, project intensity, and timing of cost certainty in highway project delivery methods’, Journal of Management in Engineering, Vol.35 No. 1, p. 04018049.
[4] Arafa, M. and Alqedra, M. (2011) ‘Early Stage Cost Estimation of Buildings Construction Projects using Artificial Neural Networks’, Journal of Artificial Intelligence 4 (1):63-75.
[5] Aretoulis, G.N. (2019) ‘Neural network models for actual prediction in Greek public highway projects’, Int. J. Project Organisation and Management, Vol. 11, No. 1, pp.41-64.
[6] Aretoulis, G.N., Angelides, D.C., Kalfakou, G.P., Fotiadis, G.S. and Anastasiadis, K.I. (2006) ‘A Prototype System for the Prediction of Final Cost in Construction Projects’, Springer Operational Research, An International Journal, Special Issue, 6(3), pp. 423-432.
[7] Aretoulis, G.N., Kalfakou, G.P. and Seridou, A.A. (2015) ‘Project Managers’ Profile Influence on Design and Implementation of Cost Monitoring and Control Systems for Construction Projects’, IGI-Global IJITPM, 6(3), pp. 1-25
[8] Aziz, A.M.A. (2007). Performance Analysis and Forecasting for WSDOT Highway Projects, Washington State Transportation Commission, Agreement T2695 Task 92, Washington.
[9] Barros, L.B., Marcy, M. and Carvalho, M.T.M. (2018). Construction Cost Estimation of Brazilian Highways using Artificial Neural Networks. International Journal of Structural and Civil Engineering Research, vol 7, No 3, pp. 283-289.
[10] Chan D.W.M and Kumarswamy M.M. (1996). An evaluation of construction time performance in the building industry. Elsevier Build. Environ., 31(6), pp. 569-578.
[11] Chandanshive V.B. and Kambekar A.R. (2019) ‘Estimation of Building Construction Cost Using Artificial Neural Networks’, Journal of Soft Computing in Civil Engineering, 3-1, 91-107.
[12] Cheng, M.Y., Tsai, H.C. and Sudjono, E. (2010a) ‘Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry’, Expert Systems with Applications, Vol. 37, No. 6, pp.4224–4231.
[13] Cheng, M.Y., Tsai, H.C. and Sudjono, E. (2010b) ‘Evolutionary fuzzy hybrid neural network for project cash flow control’, Engineering Applications of Artificial Intelligence, Vol. 23, No. 4, pp.604–613.
[14] ElSawy, I., Hosny, H. and Razek, M.A. (2011) A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt, αrXiv preprint arXiv: 1106.1570.
[15] FANN Tool Users Guide (online) https://www.slideshare.net/bluekid/fann-tool-usersguide
[16] Field, A. (2009) Discovering Statistics Using SPSS, 3rd ed., Sage Publications, London.
[17] Gab-Allah, A.A., Ibrahim, A.H. and Hagras, O.A. (2015) ‘Predicting the construction duration of building projects using artificial neural networks’, Int. J. Applied Management Science, Vol.7, No.2.
[18] Glymis, E., Kanelakis, A., Aretoulis, G. and Mastoras, T. (2017) ‘Predicting highway projects’ actual duration using neural networks’, LC3 2017: Volume I – Proceedings of the Joint Conference on Computing in Construction (JC3), Heraklion, Greece, pp.691–697 (online) https://doi.org/10.24928/JC3-2017/0260
[19] Guerrero, M.A., Villacampa, Y. and Montoyo, A. (2014) ‘Modeling construction time in Spanish building projects’, International Journal of Project management, Vol. 32, No.5, pp.861-873.
[20] Hodgson, D., Paton, S. and Cicmil, S. (2011) ‘Great expectations and hard times: the paradoxical experience of the engineer as project manager’, International Journal of Project Management, Vol. 29, No. 4, pp.374-382
[21] Hosseinian, S. and Reinschmidt, K.F. (2015) ‘Finding best model to forecast construction duration of road tunnels with new Austrian tunneling method using Bayesian inference: case study of Niayesh highway tunnel in Iran’, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2522 No. 1, pp. 113-120.
[22] Irfan, M., Khurshid, B.M., Anastasopoulos, P., Labi, S. and Moavenzadeh, F. (2011) ‘Planning-stage estimation of highway project duration on the basis of anticipated project cost, project type and contract type’, International Journal of Project Management, Vol. 29, No. 1, pp.78–92.
[23] Jiang, Y. and Wu, H. (2007) ‘A method for highway agency to estimate highway construction durations and set contract times’, International Journal of Construction Education and Research, Vol. 3, No. 3, pp.199–216.
[24] Juszczyk, M. (2017) ‘The challenges of nonparametric cost estimation of construction works with the use of artificial intelligence tools’, Procedia Engineering, Vol. 196, pp.415–422.
[25] Kang, H.W. and Kim, Y.S. (2018) ‘A model for risk cost and bidding price prediction based on risk information in plant construction projects’, KSCE Journal of Civil Engineering, pp.1–15, Article in press, DOI: 10.1007/s12205-018-0587-4.
[26] Koo, C., Hong, T. and Hyun, C. (2011) ‘The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach’, Expert Systems with Applications, Vol. 38, No. 7, pp.8597–8606.
[27] Liu, W. (2011), “Duration estimation method for highway construction work. Management and service science (MASS)”, International Conference on, 2011, IEEE, pp. 1-3.
[28] Marzoughi, F., Arthanari, T. and Askarany, D. (2018), “A decision support framework for estimating project duration under the impact of weather”, Automation in Construction, Vol. 87, pp. 287-296.
[29] Migliaccio, G.C. and Shrestha, P.P. (2009), “Analysis of design-build procurement activities durations for highway projects”, Construction Research Congress 2009: Building a Sustainable Future, pp. 229-238.
[30] Petruseva, S., Zujo, V. and Zileska-Pankovska, V. (2012) ‘Neural Network Prediction Model for Construction Project Duration’, International Journal of Engineering Research and Technology, Vol.1(02), ISSN.2778-0181.
[31] Roxas, C.L. and Ongpeng, J.M. (2014) ‘An artificial neural network approach to structural cost estimation of building projects in the Philippines’, DLSU Research Congress, March 6-8.
[32] Sonmez, R. (2011) ‘Range estimation of construction costs using neural networks with bootstrap prediction intervals’, Expert Systems with Applications, Vol. 38, No. 8, pp.9913–9917.
[33] Stoy, C., Pollalis, S. and Dursun, O. (2012) ‘A concept for developing construction element cost models for German residential building projects’, International Journal of Project Organisation and Management, Vol. 4, No. 1, pp.38–53.
[34] Titirla, M. and Aretoulis, G.N. (2019) ‘Neural network models for actual duration of Greek highway projects’, Journal of Engineering Design and Technology, Vol. 17, No. 6, pp. 1323-1339.
[35] Wang, Y.R., Yu, C.Y. and Chan, H.H. (2012b) ‘Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models’, International Journal of Project Management, Vol. 30, No. 4, pp.470–478.
[36] WEKA (2018) (online) https://www.cs.waikato.ac.nz/ml/weka/
[37] Yadav, R., Vyas, M., Vyas, V., and Agrawal, S. (2016) ‘Cost Estimation Model for Residential Building using Artificial Neural Network’, International Journal of Engineering Research and Technology, Vol. 5, No. 2, pp. 312-314.
[1] Al-Saadi, A.M., Zamiem, S.K. and Al-Jumaili, L.A. (2017) ‘Estimating the Optimum Duration of Road Projects using Neural Network Model’, International Journal of Engineering and Technology (IJET), Vol. 9, No.5, pp.3458-3469
[2] Al-Zubaidi, E.D.A., Yas, A.H. and Abbas, H.F. (2019) ‘Guess the time of implementation of residential construction projects using neural networks ANN’, Periodicals of Engineering and National Sciences, Vol. 7, No. 3, pp.1218-1227.
[3] Antoine, A.L., Alleman, D. and Molenaar, K.R. (2018) ‘Examination of project duration, project intensity, and timing of cost certainty in highway project delivery methods’, Journal of Management in Engineering, Vol.35 No. 1, p. 04018049.
[4] Arafa, M. and Alqedra, M. (2011) ‘Early Stage Cost Estimation of Buildings Construction Projects using Artificial Neural Networks’, Journal of Artificial Intelligence 4 (1):63-75.
[5] Aretoulis, G.N. (2019) ‘Neural network models for actual prediction in Greek public highway projects’, Int. J. Project Organisation and Management, Vol. 11, No. 1, pp.41-64.
[6] Aretoulis, G.N., Angelides, D.C., Kalfakou, G.P., Fotiadis, G.S. and Anastasiadis, K.I. (2006) ‘A Prototype System for the Prediction of Final Cost in Construction Projects’, Springer Operational Research, An International Journal, Special Issue, 6(3), pp. 423-432.
[7] Aretoulis, G.N., Kalfakou, G.P. and Seridou, A.A. (2015) ‘Project Managers’ Profile Influence on Design and Implementation of Cost Monitoring and Control Systems for Construction Projects’, IGI-Global IJITPM, 6(3), pp. 1-25
[8] Aziz, A.M.A. (2007). Performance Analysis and Forecasting for WSDOT Highway Projects, Washington State Transportation Commission, Agreement T2695 Task 92, Washington.
[9] Barros, L.B., Marcy, M. and Carvalho, M.T.M. (2018). Construction Cost Estimation of Brazilian Highways using Artificial Neural Networks. International Journal of Structural and Civil Engineering Research, vol 7, No 3, pp. 283-289.
[10] Chan D.W.M and Kumarswamy M.M. (1996). An evaluation of construction time performance in the building industry. Elsevier Build. Environ., 31(6), pp. 569-578.
[11] Chandanshive V.B. and Kambekar A.R. (2019) ‘Estimation of Building Construction Cost Using Artificial Neural Networks’, Journal of Soft Computing in Civil Engineering, 3-1, 91-107.
[12] Cheng, M.Y., Tsai, H.C. and Sudjono, E. (2010a) ‘Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry’, Expert Systems with Applications, Vol. 37, No. 6, pp.4224–4231.
[13] Cheng, M.Y., Tsai, H.C. and Sudjono, E. (2010b) ‘Evolutionary fuzzy hybrid neural network for project cash flow control’, Engineering Applications of Artificial Intelligence, Vol. 23, No. 4, pp.604–613.
[14] ElSawy, I., Hosny, H. and Razek, M.A. (2011) A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt, αrXiv preprint arXiv: 1106.1570.
[15] FANN Tool Users Guide (online) https://www.slideshare.net/bluekid/fann-tool-usersguide
[16] Field, A. (2009) Discovering Statistics Using SPSS, 3rd ed., Sage Publications, London.
[17] Gab-Allah, A.A., Ibrahim, A.H. and Hagras, O.A. (2015) ‘Predicting the construction duration of building projects using artificial neural networks’, Int. J. Applied Management Science, Vol.7, No.2.
[18] Glymis, E., Kanelakis, A., Aretoulis, G. and Mastoras, T. (2017) ‘Predicting highway projects’ actual duration using neural networks’, LC3 2017: Volume I – Proceedings of the Joint Conference on Computing in Construction (JC3), Heraklion, Greece, pp.691–697 (online) https://doi.org/10.24928/JC3-2017/0260
[19] Guerrero, M.A., Villacampa, Y. and Montoyo, A. (2014) ‘Modeling construction time in Spanish building projects’, International Journal of Project management, Vol. 32, No.5, pp.861-873.
[20] Hodgson, D., Paton, S. and Cicmil, S. (2011) ‘Great expectations and hard times: the paradoxical experience of the engineer as project manager’, International Journal of Project Management, Vol. 29, No. 4, pp.374-382
[21] Hosseinian, S. and Reinschmidt, K.F. (2015) ‘Finding best model to forecast construction duration of road tunnels with new Austrian tunneling method using Bayesian inference: case study of Niayesh highway tunnel in Iran’, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2522 No. 1, pp. 113-120.
[22] Irfan, M., Khurshid, B.M., Anastasopoulos, P., Labi, S. and Moavenzadeh, F. (2011) ‘Planning-stage estimation of highway project duration on the basis of anticipated project cost, project type and contract type’, International Journal of Project Management, Vol. 29, No. 1, pp.78–92.
[23] Jiang, Y. and Wu, H. (2007) ‘A method for highway agency to estimate highway construction durations and set contract times’, International Journal of Construction Education and Research, Vol. 3, No. 3, pp.199–216.
[24] Juszczyk, M. (2017) ‘The challenges of nonparametric cost estimation of construction works with the use of artificial intelligence tools’, Procedia Engineering, Vol. 196, pp.415–422.
[25] Kang, H.W. and Kim, Y.S. (2018) ‘A model for risk cost and bidding price prediction based on risk information in plant construction projects’, KSCE Journal of Civil Engineering, pp.1–15, Article in press, DOI: 10.1007/s12205-018-0587-4.
[26] Koo, C., Hong, T. and Hyun, C. (2011) ‘The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach’, Expert Systems with Applications, Vol. 38, No. 7, pp.8597–8606.
[27] Liu, W. (2011), “Duration estimation method for highway construction work. Management and service science (MASS)”, International Conference on, 2011, IEEE, pp. 1-3.
[28] Marzoughi, F., Arthanari, T. and Askarany, D. (2018), “A decision support framework for estimating project duration under the impact of weather”, Automation in Construction, Vol. 87, pp. 287-296.
[29] Migliaccio, G.C. and Shrestha, P.P. (2009), “Analysis of design-build procurement activities durations for highway projects”, Construction Research Congress 2009: Building a Sustainable Future, pp. 229-238.
[30] Petruseva, S., Zujo, V. and Zileska-Pankovska, V. (2012) ‘Neural Network Prediction Model for Construction Project Duration’, International Journal of Engineering Research and Technology, Vol.1(02), ISSN.2778-0181.
[31] Roxas, C.L. and Ongpeng, J.M. (2014) ‘An artificial neural network approach to structural cost estimation of building projects in the Philippines’, DLSU Research Congress, March 6-8.
[32] Sonmez, R. (2011) ‘Range estimation of construction costs using neural networks with bootstrap prediction intervals’, Expert Systems with Applications, Vol. 38, No. 8, pp.9913–9917.
[33] Stoy, C., Pollalis, S. and Dursun, O. (2012) ‘A concept for developing construction element cost models for German residential building projects’, International Journal of Project Organisation and Management, Vol. 4, No. 1, pp.38–53.
[34] Titirla, M. and Aretoulis, G.N. (2019) ‘Neural network models for actual duration of Greek highway projects’, Journal of Engineering Design and Technology, Vol. 17, No. 6, pp. 1323-1339.
[35] Wang, Y.R., Yu, C.Y. and Chan, H.H. (2012b) ‘Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models’, International Journal of Project Management, Vol. 30, No. 4, pp.470–478.
[36] WEKA (2018) (online) https://www.cs.waikato.ac.nz/ml/weka/
[37] Yadav, R., Vyas, M., Vyas, V., and Agrawal, S. (2016) ‘Cost Estimation Model for Residential Building using Artificial Neural Network’, International Journal of Engineering Research and Technology, Vol. 5, No. 2, pp. 312-314.
@article{"International Journal of Architectural, Civil and Construction Sciences:80299", author = "Panagiotis Karadimos and Leonidas Anthopoulos", title = "Neural Network Models for Actual Cost and Actual Duration Estimation in Construction Projects: Findings from Greece ", abstract = "Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.
", keywords = "Actual cost and duration, attribute selection, bridge projects, neural networks, predicting models, FANN TOOL, WEKA. ", volume = "15", number = "5", pages = "130-12", }
