Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 12
Abstract
In addition to materials, labor, equipment, and method, construction cost depends on many other factors such as the project locality, type, construction duration, scheduling, and the extent of use of recycled materials. Further, the fluctuation of economic variables and indexes (EV&Is), such as liquidity, wholesale price index, and building services index, causes variation in costs. These changes may increase or reduce the construction cost, are hard to predict, and are normally ignored in the traditional cost estimation computation. This paper presents an innovative construction cost estimation model using advanced machine-learning concepts and taking into account the EV&Is. A data structure is proposed that incorporates a set of physical and financial (P&F) variables of the real estate units as well as a set of EV&Is variables affecting the construction costs. The model includes an unsupervised deep Boltzmann machine (DBM) learning approach along with a softmax layer (DBM-SoftMax), and a three-layer back-propagation neural network (BPNN) or another regression model, support vector machine (SVM). The role of DBM-SoftMax is to extract relevant features from the input data. The role of the BPNN or SVM is to turn the trained unsupervised DBM into a supervised regression network. This combination improves the effectiveness and accuracy of both conventional BPNN and SVM. A sensitivity analysis was performed within the algorithm in order to achieve the best results taking into account the impact of the EV&I factors in different times (time lags). The model was verified using the construction cost data for 372 low- and midrise buildings in the range of three to nine stories. Cost estimation errors of the proposed model were much less than those of both the BPNN-only and SVM-only models, thus demonstrating the effectiveness of the strategies employed in this research and the superiority of the proposed model.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated by the authors or analyzed during the study are available at: https://archive.ics.uci.edu/ml/datasets/Residential+Building+Data+Set. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
References
Adeli, H., ed. 1994. Advances in design optimization. London: Chapman & Hall.
Adeli, H., and S. Hung. 1995. Machine learning, neural networks, genetic algorithms and fuzzy systems. New York: Wiley.
Adeli, H., and A. Karim. 1997. “Scheduling/cost optimization and neural dynamics model for construction.” J. Constr. Eng. Manage. 123 (4): 450–458. https://doi.org/10.1061/(ASCE)0733-9364(1997)123:4(450).
Adeli, H., and M. Wu. 1998. “Regularization neural network for construction cost estimation.” J. Constr. Eng. Manage. 124 (1): 18–24. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:1(18).
Alex, D. P., M. Al Hussein, A. Bouferguene, and S. Fernando. 2009. “Artificial neural network model for cost estimation: City of Edmonton’s water and sewer installation services.” J. Constr. Eng. Manage. 136 (7): 745–756. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000184.
Bhargava, A., S. Labi, S. Chen, T. U. Saeed, and K. C. Sinha. 2017. “Predicting cost escalation pathways and deviation severities of infrastructure projects using risk-based econometric models and Monte Carlo simulation.” Comput.-Aided Civ. Infrastruct. Eng. 32 (8): 620–640. https://doi.org/10.1111/mice.12279.
Bishop, C. M. 2006. Pattern recognition and machine learning. New York: Springer.
Cheng, M., H. Tsai, and E. Sudjono. 2010. “Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry.” Expert Syst. Appl. 37 (6): 4224–4231. https://doi.org/10.1016/j.eswa.2009.11.080.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (3): 273–297. https://doi.org/10.1007/BF00994018.
El Hajj, B., F. Schoefs, B. Castanier, and T. Yeung. 2017. “A condition-based deterioration model for the stochastic dependency of corrosion rate and crack propagation in corroded concrete structure.” Comput.-Aided Civ. Infrastruct. Eng. 32 (1): 18–33. https://doi.org/10.1111/mice.12208.
Fereshtehnejad, E., and A. Shafieezadeh. 2016. “Multiple hazard incidents lifecycle cost assessment of structural systems considering state-dependent repair times and fragility curves.” Earthquake Eng. Struct. Dyn. 45 (14): 2327–2347. https://doi.org/10.1002/eqe.2764.
Fernandez, A., C. J. Carmona, M. J. del Jesus, and F. Herrera. 2017. “A Pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets.” Int. J. Neural Syst. 27 (6): 1750028. https://doi.org/10.1142/S0129065717500289.
Florez, L. 2017. "Crew allocation system for the masonry industry." Comput. Aided Civ. Infrastruct. Eng. 32 (10): 874–889. https://doi.org/10.1111/mice.12301.
Gao, H., and X. Zhang. 2013. “A Markov-based road maintenance optimization model considering user costs.” Comput.-Aided Civ. Infrastruct. Eng. 28 (6): 451–464. https://doi.org/10.1111/mice.12009.
Ghosh-Dastidar, S., and H. Adeli. 2003. “Wavelet-clustering-neural network model for freeway incident detection.” Comput. Aided Civ. Infrastruct. Eng. 18 (5): 325–338. https://doi.org/10.1111/1467-8667.t01-1-00311.
Gribniak, V., H. A. Mang, R. Kupliauskas, G. Kaklauskas, and A. Juozapaitis. 2016. “Stochastic tension-stiffening approach for the solution of serviceability problems in reinforced concrete: Exploration of predictive capacity.” Comput.-Aided Civ. Infrastruct. Eng. 31 (6): 416–431. https://doi.org/10.1111/mice.12183.
Hinton, G. E., and R. R. Salakhutdinov. 2006. “Reducing the dimensionality of data with neural networks.” Science 313 (5786): 504–507. https://doi.org/10.1126/science.1127647.
Hsiao, F., S. Wang, W. Wang, C. Wen, and W. Yu. 2012. “Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction.” Comput.-Aided Civ. Infrastruct. Eng. 27 (10): 764–781. https://doi.org/10.1111/j.1467-8667.2012.00786.x.
Hung, S. L., and H. Adeli. 1993. “Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer.” Neurocomputing 5 (6): 287–302. https://doi.org/10.1016/0925-2312(93)90042-2.
Hung, S. L., and H. Adeli. 1994. “Object-oriented back propagation and its application to structural design.” Neurocomputing 6 (1): 45–55. https://doi.org/10.1016/0925-2312(94)90033-7.
Jafarzadeh, R., J. Ingham, S. Wilkinson, V. González, and A. Aghakouchak. 2013. “Application of artificial neural network methodology for predicting seismic retrofit construction costs.” J. Constr. Eng. Manage. 140 (2): 04013044. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000725.
Karim, A., and H. Adeli. 1999. “CONSCOM: An OO construction scheduling and change management system.” J. Constr. Eng. Manage. 125 (5): 368–376. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:5(368).
Karim, A., and H. Adeli. 2002. “Comparison of the fuzzy-wavelet RBFNN freeway incident detection model with the California algorithm.” J. Transp. Eng. 128 (1): 21–30. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:1(21).
Karim, A., and H. Adeli. 2003. “CBR model for freeway work zone traffic management.” J. Transp. Eng. 129 (2): 134–145. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:2(134).
Kim, G., J. Yoon, S. An, H. Cho, and K. Kang. 2004. “Neural network model incorporating a genetic algorithm in estimating construction costs.” Build. Environ. 39 (11): 1333–1340. https://doi.org/10.1016/j.buildenv.2004.03.009.
Kim, K. J., and K. Kim. 2010. “Preliminary cost estimation model using case-based reasoning and genetic algorithms.” J. Comput. Civ. Eng. 24 (6): 499–505. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000054.
Koziarski, M., and B. Cyganek. 2017. “Image recognition with deep neural networks in presence of noise—Dealing with and taking advantage of distortions.” Integr. Comput.-Aided Eng. 24 (4): 337–349. https://doi.org/10.3233/ICA-170551.
Kyriklidis, C., and G. Dounias. 2016. “Evolutionary computation for resource leveling optimization in project management.” Integr. Comput.-Aided Eng. 23 (2): 173–184. https://doi.org/10.3233/ICA-150508.
Li, H., H. Adeli, J. Sun, and J. Han. 2011. “Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction.” Comput. Oper. Res. 38 (2): 409–419. https://doi.org/10.1016/j.cor.2010.06.008.
Li, W., H. Pu, P. Schonfeld, J. Yang, H. Zhang, L. Wang, and J. Xiong. 2017. “Mountain railway alignment optimization with bidirectional distance transform and genetic algorithm.” Comput.-Aided Civ. Infrastruct. Eng. 32 (8): 691–709. https://doi.org/10.1111/mice.12280.
Ma, Z., Z. Liu, and Z. Wei. 2016. “Formalized representation of specifications for construction cost estimation by using ontology.” Comput.-Aided Civ. Infrastruct. Eng. 31 (1): 4–17. https://doi.org/10.1111/mice.12175.
Mandujano, M. G., C. Mourgues, L. F. Alarcón, and J. Kunz. 2017. “Modeling virtual design and construction implementation strategies considering lean management impacts.” Comput.-Aided Civ. Infrastruct. Eng. 32 (11): 930–951. https://doi.org/10.1111/mice.12253.
Mathwords. 2012. “Combination formula.” Accessed June 22, 2015. http://www.mathwords.com/c/combination_formula.htm.
Mencıa, R., M. R. Sierra, C. Mencıa, and R. Varela. 2016. “Genetic algorithms for the scheduling problem with arbitrary precedence relations and skilled operators.” Integr. Comput.-Aided Eng. 23 (3): 269–285. https://doi.org/10.3233/ICA-160519.
Morabito, F. C., et al. 2017. “Deep learning representation from electroencephalography of early-stage Creutzfeld-Jakob disease and features for differentiation from rapidly progressive dementia.” Int. J. Neural Syst. 27 (2): 1650039. https://doi.org/10.1142/S0129065716500398.
Myers, D. 2016. Construction economics: A new approach. New York: Taylor & Francis.
Ortega-Zamorano, F., J. M. Jerez, I. Gómez, and L. Franco. 2017. “Layer multiplexing FPGA implementation for deep back-propagation learning.” Integr. Comput.-Aided Eng. 24 (2): 171–185. https://doi.org/10.3233/ICA-170538.
Ortiz-Garcia, A., J. Munilla, J. M. Gorriz, and J. Ramirez. 2016. “Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease.” Int. J. Neural Syst. 26 (7): 1650025. https://doi.org/10.1142/S0129065716500258.
Ortiz-Rosario, A., H. Adeli, and J. A. Buford. 2017. “MUSIC—Expected maximization Gaussian mixture methodology for clustering and detection of task-related neuronal firing rates.” Behav. Brain Res. 317 (Jan): 226–236. https://doi.org/10.1016/j.bbr.2016.09.022.
Palomo, E. J., and E. Lopez-Rubio. 2016. “Learning topologies with the growing neural forest.” Int. J. Neural Syst. 26 (3): 1650019. https://doi.org/10.1142/S0129065716500192.
Peng, H., J. Wang, P. Shi, M. J. Perez-Jimenez, and A. Riscos-Nunez. 2016. “An extended membrane system with active membranes to solve automatic fuzzy clustering problems.” Int. J. Neural Syst. 26 (3): 1650004. https://doi.org/10.1142/S0129065716500040.
Pillon, P. E., E. C. Pedrino, V. O. Roda, and M. C. Nicoletti. 2016. “A hardware oriented ad-hoc computer-based method for binary structuring element decomposition based on genetic algorithm.” Integr. Comput.-Aided Eng. 23 (4): 369–383. https://doi.org/10.3233/ICA-160527.
Ponz-Tienda, J. L., A. Salcedo-Bernal, J. S. Rojas-Quintero, and E. Pellicer. 2017. “A parallel branch and bound algorithm for the resource levelling problem with minimal lags.” Comput.-Aided Civ. Infrastruct. Eng. 32 (6): 474–498. https://doi.org/10.1111/mice.12233.
Quintian, H., and E. Corchado. 2017. “Beta Hebbian learning as a new method for exploratory projection pursuit.” Int. J. Neural Syst. 27 (6): 1750024. https://doi.org/10.1142/S0129065717500241.
Rafiei, M. H., and H. Adeli. 2015. “A novel machine learning model for estimation of sale prices of real estate units.” J. Constr. Eng. Manage. 142 (2): 04015066. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001047.
Rafiei, M. H., and H. Adeli. 2016. “Sustainability in highrise building design and construction.” Struct. Des. Tall Spec. Build. 25 (13): 643–658. https://doi.org/10.1002/tal.1276.
Rafiei, M. H., and H. Adeli. 2017a. “A novel machine learning-based algorithm to detect damage in high-rise building structures.” Struct. Des. Tall Spec. Build. 26 (18): e1400. https://doi.org/10.1002/tal.1400.
Rafiei, M. H., and H. Adeli. 2017b. “NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization.” Soil Dyn. Earthquake Eng. 100 (Sep): 417–427. https://doi.org/10.1016/j.soildyn.2017.05.013.
Rafiei, M. H., and H. Adeli. 2018. “A novel unsupervised deep learning model for global and local health condition assessment of structures.” Eng. Struct. 156 (Feb): 598–607. https://doi.org/10.1016/j.engstruct.2017.10.070.
Rigos, A., G. E. Tsekouras, M. I. Vousdoukas, A. Chatzipavlis, and A. F. Velegrakis. 2016. “A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery.” Integr. Comput.-Aided Eng. 23 (2): 141–160. https://doi.org/10.3233/ICA-15050710.3233/ICA-150507.
Rostami, S., and F. Neri. 2016. “Covariance matrix adaptation Pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm.” Integr. Comput.-Aided Eng. 23 (4): 313–329. https://doi.org/10.3233/ICA-160529.
Sharif, M. M., M. Nahangi, C. T. Haas, and J. West. 2017. “Automated model-based finding of 3D objects in cluttered construction point cloud models.” Comput.-Aided Civ. Infrastruct. Eng. 32 (11): 893–908. https://doi.org/10.1111/mice.12306.
Siddique, N., and H. Adeli. 2015. “Nature inspired computing: An overview and some future directions.” Cognit. Comput. 7 (6): 706–714. https://doi.org/10.1007/s12559-015-9370-8.
Sirca, G. F., Jr., and H. Adeli. 2005. “Case-based reasoning for converting working stress design-based bridge ratings to load factor design-based ratings.” J. Bridge Eng. 10 (4): 450–459. https://doi.org/10.1061/(ASCE)1084-0702(2005)10:4(450).
Smith, G. S. 2002. Managerial accounting for libraries & other not-for-profit organizations. Chicago: American Library Association.
Smith, R., E. Ferrebee, Y. Ouyang, and J. Roesler. 2014. “Optimal staging area locations and material recycling strategies for sustainable highway reconstruction.” Comput.-Aided Civ. Infrastruct. Eng. 29 (8): 559–571. https://doi.org/10.1111/mice.12089.
Smolensky, P. 1986. “Information processing in dynamical systems: Foundations of harmony theory.” In Parallel distributed processing, 194–281. Cambridge, MA: MIT Press.
Umetani, T., K. Inoue, and T. Arai. 2011. “Pose estimation of construction materials using United Nations Statistics Division (2017).” Accessed December 26, 2017. http://data.un.org/Data.aspx?d=POP&f=tableCode:240.
US Department of Labor. 2015a. “Consumer price indexes: Bureau of Labor Statistics.” Accessed June 22, 2015. http://www.bls.gov/cpi/.
US Department of Labor. 2015b. “Producer price indexes: Bureau of Labor Statistics.” Accessed June 22, 2015. http://www.bls.gov/ppi/.
Waheed, A., and H. Adeli. 2005. “Case-based reasoning in steel bridge engineering.” Knowl.-Based Syst. 18 (1): 37–46. https://doi.org/10.1016/j.knosys.2004.06.001.
Wang, R., Y. Zhang, and L. Zhang. 2016. “An adaptive neural network approach for operator functional state prediction using psychophysiological data.” Integr. Comput.-Aided Eng. 23 (1): 81–97. https://doi.org/10.3233/ICA-150502.
Wang, Z., H. Hu, and W. Zhou. 2017. “RFID-enabled knowledge-based precast construction supply chain.” Comput.-Aided Civ. Infrastruct. Eng. 32 (6): 499–514. https://doi.org/10.1111/mice.12254.
Wilmot, C. G., and B. Mei. 2005. “Neural network modeling of highway construction costs.” J. Constr. Eng. Manage. 131 (7): 765–771. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(765).
Wright, J., and I. Jordanov. 2017. “Quantum inspired evolutionary algorithms with improved rotation gates for real-coded synthetic and real world optimization problems.” Integr. Comput.-Aided Eng. 24 (3): 203–223. https://doi.org/10.3233/ICA-170545.
Yi, W., and S. Wang. 2016. “Multi-objective mathematical programming approach to construction laborer assignment with equity consideration.” Comput.-Aided Civ. Infrastruct. Eng. 31 (12): 954–965. https://doi.org/10.1111/mice.12239.
Yi, W., and S. Wang. 2017. “Mixed-integer linear programming on work-rest schedule design for construction sites in hot weather.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 429–439. https://doi.org/10.1111/mice.12267.
Yu, J., D. Zhong, B. Ren, D. Tong, and K. Hong. 2017. “Probabilistic risk analysis of diversion tunnel construction simulation.” Comput.-Aided Civ. Infrastruct. Eng. 32 (9): 748–771. https://doi.org/10.1111/mice.12276.
Zeinalia, Y., and B. Story. 2017. “Competitive probabilistic neural network.” Integr. Comput.-Aided Eng. 24 (2): 105–118. https://doi.org/10.3233/ICA-170540.
Zhang, Y., Y. Wang, J. Jin, and X. Wang. 2017. “Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification.” Int. J. Neural Syst. 244 (2): 1650032. https://doi.org/10.1016/j.quaint.2010.11.008.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 12 • December 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Feb 20, 2018
Accepted: Jun 11, 2018
Published online: Sep 25, 2018
Published in print: Dec 1, 2018
Discussion open until: Feb 25, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.