International Journal of Nonlinear Analysis and Applications
Machine learning algorithms for constructions cost prediction: A systematic review
Document Type : Research Paper
Authors
1 Department of Computer Science, College of Science, University of Diyala, Diyala, Iraq
2 Department of Highway and Airport Engineering, College of Engineering, University of Diyala, Diyala, Iraq
Abstract
Machine learning plays a vital role in construction estimation which could make improve the project's safety, and reliability. Many studies have been proposed to explore the potential opportunities to review this technology in the construction cost in structure and transport fields. However, no comprehensive study to review the global research trends on this area's advancement in construction cost. The goal is to taxonomy, review, and summarize the state-of-the-art knowledge body on this topic in a systematic manner based on machine learning (ML) and deep learning (DL) approaches. To achieve this, this paper considered many studies in construction management related to bibliographic records retrieved from the Scopus database by adopting a quantitative analysis approach. This paper found that from 2017 to 2021, there has been a considerable increase in the number of publications in this domain. We categorized and explained civil projects into structures and transport cost, ML/DL as supervised and unsupervised approaches, and the evaluation metrics proposed to evaluate the performance of ML-Cost estimations in the civil area. The findings will help both professionals and researchers to understand and evolve the recent trend research ML/DL methodologies and their role played in the construction management domain.
Keywords
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Proc. Int. Conf. IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017 (2017), 594–599.
[3] G.N. Aretoulis, Neural network models for actual cost prediction in Greek public highway projects, Int. J. Proj.
Organ. Manag. 11 (2019), no. 1, 41.[4] M.I.N.S.O.O. Baek and B.A.A.B.A.K. Ashuri, Spatial regression analysis for modeling the spatial variation in
highway construction costs, Resilient Structures and Sustainable Construction, Georgia Institute of Technology,
Atlanta, 2017.
[5] M. Barakchi, O. Torp and A.M. Belay, Cost estimation methods for transport infrastructure: a systematic literature
review, Proc. Eng. 196 (2017), 270–277.
[6] L.B. Barros, M. Marcy and M.T.M. Carvalho, Construction cost estimation of Brazilian highways using artificial
neural networks, Int. J. Struct. Civ. Eng. Res. 7 (2018), no. 3, 283–289.
[7] M. Bilal and L.O. Oyedele, Guidelines for applied machine learning in construction industry–a case of profit
margins estimation, Adv. Engin. Inf. 43 (2020), 101013.
[8] Bureau of Infrastructure, Transport and regional economics (BITRE), road construction cost and infrastructure
procurement benchmarking: 2017 update, BITRE, Canberra ACT, 2018.
[9] Cambridge Dictionary, Meaning of airport in English, https://dictionary.cambridge.org/dictionary/english/airport,
2022.
[10] Y. Cao, B. Ashuri and M. Baek, Prediction of unit price bids of resurfacing highway projects through ensemble
machine learning, J. Comput. Civ. Eng. 32 (2018), no. 5, 04018043.
[11] Y. Elfahham, Estimation and prediction of construction cost index using neural networks, time series, and regression, Alexandria Eng. J. 58 (2019), no. 2, 499–506.
[12] H.H. Elmousalami, Artificial intelligence and parametric construction cost estimate modeling: state-of-the-art
review, J. Constr. Eng. Manag. 146 (2020), no. 1, 03119008.
[13] H.H. Elmousalami, Comparison of artificial intelligence techniques for project conceptual cost prediction: a case
study and comparative analysis, IEEE Trans. Engin. Manag. 68 (2021), no. 1, 183–196.
[14] M. Flah, I. Nunez, W. Ben Chaabene and M.L. Nehdi, Machine learning algorithms in civil structural health
monitoring: a systematic review, Arch. Comput. Method in Engin. 28 (2021), no. 4, 2621–2643.
[15] D.M. Frangopol, Y. Dong and S. Sabatino, Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making, Struct. Infrastruct. Eng. 13 (2017), no. 10, 1239–1257.
[16] F. Ghodoosi, S. Abu-Samra, M. Zeynalian and T. Zayed, Maintenance cost optimization for bridge structures
using system reliability analysis and genetic algorithms, J. Constr. Eng. Manag. 144 (2018), no. 2, 04017116.
[17] A. Gondia, A. Siam, W. El-Dakhakhni and A.H. Nassar, Machine learning algorithms for construction projects
delay risk prediction, J. Constr. Eng. Manag. 146 (2020), no. 1, p. 04019085.
[18] W. Hakami and A. Hassan, Preliminary construction cost estimate in Yemen by artificial neural network, Balt.
J. Real Estate Econ. Constr. Manag. 7 (2019), no. 1, 110–122.
[19] G.E. Hinton, S. Osindero and Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18
(2006), no. 7, 1527–1554.
[20] M. Hu and M.J. Skibniewski, A review of building construction cost research: current status, gaps and green
buildings, Green Build. Constr. Econ. 2 (2021), no. 1, 1–17.
[21] A. Issa, R. Bdair and S. Abu-Eisheh, Assessment of compliance to planned cost and time for implemented municipal roads projects in Palestine, Ain Shams Eng. J. 13 (2022), no. 2, 101578.
[22] A. Jaafari, I. Pazhouhan and P. Bettinger, Machine learning modeling of forest road construction costs, Forests
146 (2021), no. 9, 1169.
[23] M. Juszczyk, The challenges of nonparametric cost estimation of construction works with the use of artificial
intelligence tools, Proc. Eng. 196 (2017), 415–422.
[24] M. Juszczyk, On the search of models for early cost estimates of bridges: an SVM-based approach, Build. 10
(2020), no. 1.
[25] M. Juszczyk, A. Le´sniak and K. Zima, ANN based approach for estimation of construction costs of sports fields,
Complexity 2018 (2018).[26] I. Karaca, D.D. Gransberg and H.D. Jeong, Improving the accuracy of early cost estimates on transportation
infrastructure projects, J. Manag. Eng. 36 (2020), no. 5, p. 04020063.
[27] A. Le´sniak and M. Juszczyk, Prediction of site overhead costs with the use of artificial neural network based model,
Arch. Civ. Mech. Eng. 18 (2018), no. 3, 973–982.
[28] G. Mahalakshmi and C. Rajasekaran, Early cost estimation of highway projects in India using artificial neural
network, Lecture Notes in Civil Engineering, 25 (2019), 659–672.
[29] A. Mahdavian, A. Shojaei, M. Salem, J.S. Yuan and A.A. Oloufa, Data-driven predictive modeling of highway
construction cost items, J. Constr. Eng. Manag. 147 (2021), no. 3, 04020180.
[30] A. Mahmoodzadeh, M. Mohammadi, A. Daraei, H. Farid Hama Ali, A. Ismail Abdullah and N. Kameran AlSalihi, Forecasting tunnel geology, construction time and costs using machine learning methods, Neural Comput.
Appl. 33 (2021), no. 1, 321–348.
[31] A. Mahmoodzadeh, M. Mohammadi, S. Nariman Abdulhamid, H. Hashim Ibrahim, H. Farid Hama Ali and S.
Ghafoor Salim, Dynamic reduction of time and cost uncertainties in tunneling projects, Tunnel. Underground
Space Technol. 109 (2021).
[32] Z.K. Maseer, R. Yusof, N. Bahaman, S.A. Mostafa and C.F.M. Foozy, Benchmarking of machine learning for
anomaly based intrusion detection systems in the CICIDS2017 dataset, IEEE Access 9 (2021), 22351–22370.
[33] J.D. McCaffrey, Restricted boltzmann machines are difficult to explain, Software Research, Development, Testing,
and Education, https://jamesmccaffrey.wordpress.com/, (2016).
[34] F. Ning, Y. Shi, M. Cai, W. Xu and X. Zhang, Manufacturing cost estimation based on a deep-learning method,
J. Manufactur. Syst. 54 (2020), 186–195.
[35] I. Peˇsko, V. Muˇcenski, M. Seˇslija, N. Radovi´c, A. Vujkov, D. Bibi´c and M. Krkljeˇs, ˇ Estimation of costs and
durations of construction of urban roads using ANN and SVM, Complexity 2017 (2017).
[36] C. Petheram and T.A. McMahon, Dams, dam costs and damnable cost overruns, J. Hydrol. X 3 (2019).
[37] F. Pour Rahimian, S. Seyedzadeh, S. Oliver, S. Rodriguez and N. Dawood, On-demand monitoring of construction
projects through a game-like hybrid application of BIM and machine learning, Autom. Constr. 110 (2020).
[38] M.H. Rafiei and H. Adeli, Novel machine-learning model for estimating construction costs considering economic
variables and indexes, J. Constr. Eng. Manag. 144 (2018), no. 12, p. 04018106.
[39] K. Riga, K. Jahr, C. Thielen and A. Borrmann, Mixed integer programming for dynamic tower crane and storage
area optimization on construction sites, Autom. Constr. 120 (2020).
[40] A. Sazali, B.H. Setiadji and B. Haryadi, Prediction of road handling cost using Markov chain method in regency
road network, Int. J. Integr. Eng. 13 (2021), no. 4, 275–283.
[41] A. Shehadeh, O. Alshboul, R.E. Al Mamlook and O. Hamedat, Machine learning models for predicting the
residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost
regression, Autom. Constr. 129 (2021).
[42] N. Suneja, J.P. Shah, Z.H. Shah and M.S. Holia, A neural network approach to design reality oriented cost estimate
model for infrastructure projects, Reliab. Theory Appl. 16 (2021), 254–263.
[43] A. Swalin, Choosing the right metric for evaluating machine learning models–part 2, Data Institute, 2018.
[44] O. Swei, J. Gregory and R. Kirchain, Construction cost estimation: a parametric approach for better estimates
of expected cost and variation, Transport. Res. Part B: Methodol. 101 (2017), 295–305.
[45] N. Tajziyehchi, M. Moshirpour, G. Jergeas and F. Sadeghpour, A predictive model of cost growth in construction
projects using feature selection, IEEE Third Int. Conf. Artificial Intell. Knowledge Engin. (AIKE), IEEE, 2020,
pp. 142–147.
[46] S. Tayefeh Hashemi, O.M. Ebadati and H. Kaur, Cost estimation and prediction in construction projects: a
systematic review on machine learning techniques, SN Appl. Sci. 2 (2020), no. 10.
[47] K. Tijani´c, D. Car-Puˇsi´c and M. Sperac, ˇ Cost estimation in road construction using artificial neural network,Neural Comput. Appl. 32 (2020), no. 13, 9343–9355.
[48] T.N. Van and T.N. Quoc, Research trends on machine learning in construction management: a scientometric
analysis, J. Appl. Sci. Tech. Trends 2 (2021), no. 3, 96–104.
[49] P. Velumani, N.V.N. Nampoothiri and U. Mariusz, A comparative study of models for the construction duration
prediction in highway road projects of India, Sustain. 13 (2021), no. 8, 4552.
[50] J.T. Von Brown, A planning methodology for railway construction cost estimation in North America, Iowa State
University, 2011.
[51] Y. Xu, Y. Zhou, P. Sekula and L. Ding, Machine learning in construction: from shallow to deep learning, Dev.
Built Envir. 6 (2021), 100045.
neural network model (highway projects as case study), Indian J. Sci. Technol. 10 (2017), no. 20, 1–12.
[2] S.S. Arage and N.V. Dharwadkar, Cost estimation of civil construction projects using machine learning paradigm,
Proc. Int. Conf. IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017 (2017), 594–599.
[3] G.N. Aretoulis, Neural network models for actual cost prediction in Greek public highway projects, Int. J. Proj.
Organ. Manag. 11 (2019), no. 1, 41.[4] M.I.N.S.O.O. Baek and B.A.A.B.A.K. Ashuri, Spatial regression analysis for modeling the spatial variation in
highway construction costs, Resilient Structures and Sustainable Construction, Georgia Institute of Technology,
Atlanta, 2017.
[5] M. Barakchi, O. Torp and A.M. Belay, Cost estimation methods for transport infrastructure: a systematic literature
review, Proc. Eng. 196 (2017), 270–277.
[6] L.B. Barros, M. Marcy and M.T.M. Carvalho, Construction cost estimation of Brazilian highways using artificial
neural networks, Int. J. Struct. Civ. Eng. Res. 7 (2018), no. 3, 283–289.
[7] M. Bilal and L.O. Oyedele, Guidelines for applied machine learning in construction industry–a case of profit
margins estimation, Adv. Engin. Inf. 43 (2020), 101013.
[8] Bureau of Infrastructure, Transport and regional economics (BITRE), road construction cost and infrastructure
procurement benchmarking: 2017 update, BITRE, Canberra ACT, 2018.
[9] Cambridge Dictionary, Meaning of airport in English, https://dictionary.cambridge.org/dictionary/english/airport,
2022.
[10] Y. Cao, B. Ashuri and M. Baek, Prediction of unit price bids of resurfacing highway projects through ensemble
machine learning, J. Comput. Civ. Eng. 32 (2018), no. 5, 04018043.
[11] Y. Elfahham, Estimation and prediction of construction cost index using neural networks, time series, and regression, Alexandria Eng. J. 58 (2019), no. 2, 499–506.
[12] H.H. Elmousalami, Artificial intelligence and parametric construction cost estimate modeling: state-of-the-art
review, J. Constr. Eng. Manag. 146 (2020), no. 1, 03119008.
[13] H.H. Elmousalami, Comparison of artificial intelligence techniques for project conceptual cost prediction: a case
study and comparative analysis, IEEE Trans. Engin. Manag. 68 (2021), no. 1, 183–196.
[14] M. Flah, I. Nunez, W. Ben Chaabene and M.L. Nehdi, Machine learning algorithms in civil structural health
monitoring: a systematic review, Arch. Comput. Method in Engin. 28 (2021), no. 4, 2621–2643.
[15] D.M. Frangopol, Y. Dong and S. Sabatino, Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making, Struct. Infrastruct. Eng. 13 (2017), no. 10, 1239–1257.
[16] F. Ghodoosi, S. Abu-Samra, M. Zeynalian and T. Zayed, Maintenance cost optimization for bridge structures
using system reliability analysis and genetic algorithms, J. Constr. Eng. Manag. 144 (2018), no. 2, 04017116.
[17] A. Gondia, A. Siam, W. El-Dakhakhni and A.H. Nassar, Machine learning algorithms for construction projects
delay risk prediction, J. Constr. Eng. Manag. 146 (2020), no. 1, p. 04019085.
[18] W. Hakami and A. Hassan, Preliminary construction cost estimate in Yemen by artificial neural network, Balt.
J. Real Estate Econ. Constr. Manag. 7 (2019), no. 1, 110–122.
[19] G.E. Hinton, S. Osindero and Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18
(2006), no. 7, 1527–1554.
[20] M. Hu and M.J. Skibniewski, A review of building construction cost research: current status, gaps and green
buildings, Green Build. Constr. Econ. 2 (2021), no. 1, 1–17.
[21] A. Issa, R. Bdair and S. Abu-Eisheh, Assessment of compliance to planned cost and time for implemented municipal roads projects in Palestine, Ain Shams Eng. J. 13 (2022), no. 2, 101578.
[22] A. Jaafari, I. Pazhouhan and P. Bettinger, Machine learning modeling of forest road construction costs, Forests
146 (2021), no. 9, 1169.
[23] M. Juszczyk, The challenges of nonparametric cost estimation of construction works with the use of artificial
intelligence tools, Proc. Eng. 196 (2017), 415–422.
[24] M. Juszczyk, On the search of models for early cost estimates of bridges: an SVM-based approach, Build. 10
(2020), no. 1.
[25] M. Juszczyk, A. Le´sniak and K. Zima, ANN based approach for estimation of construction costs of sports fields,
Complexity 2018 (2018).[26] I. Karaca, D.D. Gransberg and H.D. Jeong, Improving the accuracy of early cost estimates on transportation
infrastructure projects, J. Manag. Eng. 36 (2020), no. 5, p. 04020063.
[27] A. Le´sniak and M. Juszczyk, Prediction of site overhead costs with the use of artificial neural network based model,
Arch. Civ. Mech. Eng. 18 (2018), no. 3, 973–982.
[28] G. Mahalakshmi and C. Rajasekaran, Early cost estimation of highway projects in India using artificial neural
network, Lecture Notes in Civil Engineering, 25 (2019), 659–672.
[29] A. Mahdavian, A. Shojaei, M. Salem, J.S. Yuan and A.A. Oloufa, Data-driven predictive modeling of highway
construction cost items, J. Constr. Eng. Manag. 147 (2021), no. 3, 04020180.
[30] A. Mahmoodzadeh, M. Mohammadi, A. Daraei, H. Farid Hama Ali, A. Ismail Abdullah and N. Kameran AlSalihi, Forecasting tunnel geology, construction time and costs using machine learning methods, Neural Comput.
Appl. 33 (2021), no. 1, 321–348.
[31] A. Mahmoodzadeh, M. Mohammadi, S. Nariman Abdulhamid, H. Hashim Ibrahim, H. Farid Hama Ali and S.
Ghafoor Salim, Dynamic reduction of time and cost uncertainties in tunneling projects, Tunnel. Underground
Space Technol. 109 (2021).
[32] Z.K. Maseer, R. Yusof, N. Bahaman, S.A. Mostafa and C.F.M. Foozy, Benchmarking of machine learning for
anomaly based intrusion detection systems in the CICIDS2017 dataset, IEEE Access 9 (2021), 22351–22370.
[33] J.D. McCaffrey, Restricted boltzmann machines are difficult to explain, Software Research, Development, Testing,
and Education, https://jamesmccaffrey.wordpress.com/, (2016).
[34] F. Ning, Y. Shi, M. Cai, W. Xu and X. Zhang, Manufacturing cost estimation based on a deep-learning method,
J. Manufactur. Syst. 54 (2020), 186–195.
[35] I. Peˇsko, V. Muˇcenski, M. Seˇslija, N. Radovi´c, A. Vujkov, D. Bibi´c and M. Krkljeˇs, ˇ Estimation of costs and
durations of construction of urban roads using ANN and SVM, Complexity 2017 (2017).
[36] C. Petheram and T.A. McMahon, Dams, dam costs and damnable cost overruns, J. Hydrol. X 3 (2019).
[37] F. Pour Rahimian, S. Seyedzadeh, S. Oliver, S. Rodriguez and N. Dawood, On-demand monitoring of construction
projects through a game-like hybrid application of BIM and machine learning, Autom. Constr. 110 (2020).
[38] M.H. Rafiei and H. Adeli, Novel machine-learning model for estimating construction costs considering economic
variables and indexes, J. Constr. Eng. Manag. 144 (2018), no. 12, p. 04018106.
[39] K. Riga, K. Jahr, C. Thielen and A. Borrmann, Mixed integer programming for dynamic tower crane and storage
area optimization on construction sites, Autom. Constr. 120 (2020).
[40] A. Sazali, B.H. Setiadji and B. Haryadi, Prediction of road handling cost using Markov chain method in regency
road network, Int. J. Integr. Eng. 13 (2021), no. 4, 275–283.
[41] A. Shehadeh, O. Alshboul, R.E. Al Mamlook and O. Hamedat, Machine learning models for predicting the
residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost
regression, Autom. Constr. 129 (2021).
[42] N. Suneja, J.P. Shah, Z.H. Shah and M.S. Holia, A neural network approach to design reality oriented cost estimate
model for infrastructure projects, Reliab. Theory Appl. 16 (2021), 254–263.
[43] A. Swalin, Choosing the right metric for evaluating machine learning models–part 2, Data Institute, 2018.
[44] O. Swei, J. Gregory and R. Kirchain, Construction cost estimation: a parametric approach for better estimates
of expected cost and variation, Transport. Res. Part B: Methodol. 101 (2017), 295–305.
[45] N. Tajziyehchi, M. Moshirpour, G. Jergeas and F. Sadeghpour, A predictive model of cost growth in construction
projects using feature selection, IEEE Third Int. Conf. Artificial Intell. Knowledge Engin. (AIKE), IEEE, 2020,
pp. 142–147.
[46] S. Tayefeh Hashemi, O.M. Ebadati and H. Kaur, Cost estimation and prediction in construction projects: a
systematic review on machine learning techniques, SN Appl. Sci. 2 (2020), no. 10.
[47] K. Tijani´c, D. Car-Puˇsi´c and M. Sperac, ˇ Cost estimation in road construction using artificial neural network,Neural Comput. Appl. 32 (2020), no. 13, 9343–9355.
[48] T.N. Van and T.N. Quoc, Research trends on machine learning in construction management: a scientometric
analysis, J. Appl. Sci. Tech. Trends 2 (2021), no. 3, 96–104.
[49] P. Velumani, N.V.N. Nampoothiri and U. Mariusz, A comparative study of models for the construction duration
prediction in highway road projects of India, Sustain. 13 (2021), no. 8, 4552.
[50] J.T. Von Brown, A planning methodology for railway construction cost estimation in North America, Iowa State
University, 2011.
[51] Y. Xu, Y. Zhou, P. Sekula and L. Ding, Machine learning in construction: from shallow to deep learning, Dev.
Built Envir. 6 (2021), 100045.
)
Volume 13, Issue 2
July 2022Pages 2205-2218
- Receive Date: 03 April 2022
- Revise Date: 18 May 2022
- Accept Date: 30 June 2022