A construction cost estimation framework using DNN and validation unit

Accurate construction cost estimation is crucial to completing projects within the planned timeframe and expenditure. The estimation process depends on multiple variables maintaining complex relationships between themselves and the target cost. As a result, an in-depth analysis from an experienced construction consultant is required to estimate construction costs accurately. Machine learning (ML) technology can learn from previous data, which is equivalent to human experience. Many project-specific ML models estimate the construction cost, which misses the generalizability. This paper addresses the gap and designs, develops, implements, and analyzes a deep learning (DL) based novel framework that maps 94.67% of the independent variables with a mean average percentage error (MAPE) of 11.60%. The proposed framework is not limited to any specific project. It estimates the construction cost of similar projects, further validated by an innovative estimator validation unit.

Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. [Google Scholar]
Akintoye, A., & Fitzgerald, E. (2000). A survey of current cost estimating practices in the UK. Construction Management and Economics, 18(2), 161–172. https://doi.org/10.1080/014461900370799 [Taylor & Francis Online], [Google Scholar]
Al-Momani, A. H. (1996). Construction cost prediction for public school buildings in Jordan. Construction Management and Economics, 14(4), 311–317. https://doi.org/10.1080/014461996373386 [Taylor & Francis Online], [Google Scholar]
Al-Nassafi, N. M. (2022). The effect of cash flow variation on project performance: An empirical study from Kuwait. The Journal of Asian Finance Economics and Business, 9(3), 53–63. https://doi.org/10.13106/jafeb.2022.vol9.no3.0053 [Google Scholar]
Alex, D. P., Al Hussein, M., Bouferguene, A., & Fernando, S. (2010). Artificial neural network model for cost estimation: City of Edmonton's water and sewer installation services. Journal of Construction Engineering and Management, 136(7), 745–756. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000184 [Crossref], [Web of Science ®], [Google Scholar]
Alshboul, O., Shehadeh, A., Al-Kasasbeh, M., Al Mamlook, R. E., Halalsheh, N., & Alkasasbeh, M. (2022a). Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: A management decision support model. Engineering, Construction and Architectural Management, 29(10), 4153–4176. https://doi.org/10.1108/ECAM-08-2020-0614 [Crossref], [Google Scholar]
Alshboul, O., Shehadeh, A., Almasabha, G., & Almuflih, A. S. (2022b). Extreme gradient boosting-based machine learning approach for green building cost prediction. Sustainability, 14(11), 6651. https://doi.org/10.3390/su14116651 [Crossref], [Google Scholar]
Alshemosi, A. M. B., & Alsaad, H. S. H. (2017). Cost estimation process for construction residential projects by using multifactor linear regression technique. Criterion, 6(6), 7. https://doi.org/10.21275/ART20174128 [Google Scholar]
Amoore, L. (2022). Machine learning political orders. Review of International Studies, 49(1), 1–17. https://doi.org/10.1017/S0260210522000031 [Google Scholar]
Asuncion, A., & Newman, D. (2007). UCI machine learning repository. [Google Scholar]
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440 [Crossref], [Google Scholar]
Banks-Grasedyck, D., Lippke, E., Oelfin, H., Schwaiger, R., & Seemann, V. (2022). The underestimated success factor: People, in: Successfully managing S/4HANA projects (pp. 125–176). Springer. [Crossref], [Google Scholar]
Bernagros, J. T., Pankani, D., Struck, S. D., & Deerhake, M. E. (2021). Estimating regionalized planning costs of green infrastructure and low-impact development stormwater management practices: Updates to the US environmental protection agency's national stormwater calculator. Journal of Sustainable Water in the Built Environment, 7(2), 2. https://doi.org/10.1061/JSWBAY.0000934 [Crossref], [Google Scholar]
Bird, J. J., Ekárt, A., Buckingham, C. D., & Faria, D. R. (2019, July). Evolutionary optimisation of fully connected artificial neural network topology. In Intelligent Computing-Proceedings of the Computing Conference (pp. 751–762). Springer. [Crossref], [Google Scholar]
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014 [Crossref], [Web of Science ®], [Google Scholar]
Chakraborty, D., Elhegazy, H., Elzarka, H., & Gutierrez, L. (2020). A novel construction cost prediction model using hybrid natural and light gradient boosting. Advanced Engineering Informatics, 46, 101201. https://doi.org/10.1016/j.aei.2020.101201 [Crossref], [Google Scholar]
Chan, S. L., & Park, M. (2005). Project cost estimation using principal component regression. Construction Management and Economics, 23(3), 295–304. https://doi.org/10.1080/01446190500039812 [Taylor & Francis Online], [Google Scholar]
Cheng, M. Y., & Hoang, N. D. (2018). Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine. Neural Computing and Applications, 30(8), 2489–2497. https://doi.org/10.1007/s00521-017-2840-z [Crossref], [Google Scholar]
Choudhry, R. M. (2016). Appointing the design consultant as supervision consultant on construction projects. Journal of Legal Affaires and Dispute Resolution in Engineering Construction, 8(4), 04516005. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000195 [Crossref], [Google Scholar]
Dang-Trinh, N., Duc-Thang, P., Nguyen-Ngoc Cuong, T., & Duc-Hoc, T. (2022). Machine learning models for estimating preliminary factory construction cost: Case study in southern Vietnam. International Journal of Construction Management, 1–9. https://doi.org/10.1080/15623599.2022.2106043 [Taylor & Francis Online], [Google Scholar]
Datta, L. (2020). A survey on activation functions and their relation with xavier and he normal initialization. arXiv preprint arXiv:2004.06632. [Google Scholar]
Doloi, H. (2013). Cost overruns and failure in project management: Understanding the roles of key stakeholders in construction projects. Journal of Construction Engineering and Management, 139(3), 267–279. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000621 [Crossref], [Web of Science ®], [Google Scholar]
Elhag, T. M. S., Boussabaine, A. H., & Ballal, T. M. A. (2005). Critical determinants of construction tendering costs: Quantity surveyors’ standpoint. International Journal of Project Management, 23(7), 538–545. https://doi.org/10.1016/j.ijproman.2005.04.002 [Crossref], [Google Scholar]
Elhegazy, H., Chakraborty, D., Elzarka, H., Ebid, A. M., Mahdi, I. M., Aboul Haggag, S. Y., & Abdel Rashid, I. (2022). Artificial intelligence for developing accurate preliminary cost estimates for composite flooring systems of multi-storey buildings. Journal of Asian Architecture and Building Engineering, 21(1), 120–132. https://doi.org/10.1080/13467581.2020.1838288 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
Erdis, E. (2013). The effect of current public procurement law on duration and cost of construction projects in Turkey. Journal of Civil Engineering and Management, 19(1), 121–135. https://doi.org/10.3846/13923730.2012.746238 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
Faruqui, N., Yousuf, M. A., Whaiduzzaman, M., Azad, A. K. M., Barros, A., & Moni, M. A. (2021). Lungnet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Computers in Biology and Medicine, 139, 104961. https://doi.org/10.1016/j.compbiomed.2021.104961 [Crossref], [PubMed], [Google Scholar]
Goodwin, P., & Lawton, R. (1999). On the asymmetry of the symmetric MAPE. International Journal of Forecasting, 15(4), 405–408. https://doi.org/10.1016/S0169-2070(99)00007-2 [Crossref], [Web of Science ®], [Google Scholar]
Hashemi, S. T., Ebadati E, O. M., & Kaur, H. (2019). A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143–2154. https://doi.org/10.1007/s00521-017-3175-5 [Crossref], [Google Scholar]
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13(4), 18–28. https://doi.org/10.1109/5254.708428 [Crossref], [Web of Science ®], [Google Scholar]
Hitsanu, M. S. (2022). Conceptual Cost Estimation of Highway Earthwork Construction in Iowa Using Spatial Statistical Modeling. [Doctoral dissertation, North Dakota State University]. [Google Scholar]
Hu, Q., Che, X., Zhang, L., & Yu, D. (2010). Feature evaluation and selection based on neighborhood soft margin. Neurocomputing, 73(10-12), 2114–2124. https://doi.org/10.1016/j.neucom.2010.02.007 [Crossref], [Google Scholar]
Huang, C. H., & Hsieh, S. H. (2020). Predicting BIM labor cost with random forest and simple linear regression. Automation in Construction, 118, 103280. https://doi.org/10.1016/j.autcon.2020.103280 [Crossref], [Web of Science ®], [Google Scholar]
Hui, F., Wei, C., ShangGuan, W., Ando, R., & Fang, S. (2022). Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model. Physica A: Statistical Mechanics and its Applications, 593, 126869. https://doi.org/10.1016/j.physa.2022.126869 [Crossref], [Google Scholar]
Juszczyk, M. (2017). 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 [Crossref], [Google Scholar]
K'akumu, O. A. (2007). Construction statistics review for Kenya. Construction Management and Economics, 25(3), 315–326. https://doi.org/10.1080/01446190601139883 [Taylor & Francis Online], [Google Scholar]
Kahloot, K. M., & Ekler, P. (2021). Algorithmic splitting: A method for dataset preparation. IEEE Access, 9, 125229–125237. https://doi.org/10.1109/ACCESS.2021.3110745 [Crossref], [Google Scholar]
Kim, J., & Cha, H. S. (2022). Expediting the cost estimation process for aged-housing renovation projects using a probabilistic deep learning approach. Sustainability, 14(1), 564. https://doi.org/10.3390/su14010564 [Crossref], [Google Scholar]
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [Google Scholar]
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. https://doi.org/10.1016/j.eswa.2022.116659 [Crossref], [Google Scholar]
Lee, H., Chung, S. H., & Choi, E. J. (2016). A case study on machine learning applications and performance improvement in learning algorithm. Journal of Digital Convergence, 14(2), 245–258. https://doi.org/10.14400/JDC.2016.14.2.245 [Crossref], [Google Scholar]
Li, Q., Guo, L., & Zhou, H. (2022). Construction quality evaluation of large-scale concrete canal lining based on statistical analysis. FAHM, and Cloud Model. Sustainability, 14(13), 7663. https://doi.org/10.3390/su14137663 [Google Scholar]
Liao, S. W., Hsu, C. H., Lin, J. W., Wu, Y. T., & Leu, F. Y. (2022). A deep learning-based Chinese semantic parser for the almond virtual assistant. Sensors, 22(5), 1891. https://doi.org/10.3390/s22051891 [Crossref], [PubMed], [Google Scholar]
Lowe, D. J., Emsley, M. W., & Harding, A. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750–758. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750) [Crossref], [Web of Science ®], [Google Scholar]
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Markiz, N., & Jrade, A. (2022). Integrating an expert system with BrIMS, cost estimation, and linear scheduling at conceptual design stage of bridge projects. International Journal of Construction Management, 22(5), 913–928. https://doi.org/10.1080/15623599.2019.1661572 [Taylor & Francis Online], [Google Scholar]
Matel, E., Vahdatikhaki, F., Hosseinyalamdary, S., Evers, T., & Voordijk, H. (2022). An artificial neural network approach for cost estimation of engineering services. International Journal of Construction Management, 22(7), 1274–1287. https://doi.org/10.1080/15623599.2019.1692400 [Taylor & Francis Online], [Google Scholar]
Mianjy, P., Arora, R., & Vidal, R. (2018, July). On the implicit bias of dropout. In Jennifer Dy & Andreas Krause (Eds.), International conference on machine learning (pp. 3540–3548). PMLR. [Google Scholar]
Nguyen, H. T., Cheah, C. C., & Toh, K. A. (2022). An analytic layer-wise deep learning framework with applications to robotics. Automatica, 135, 110007. https://doi.org/10.1016/j.automatica.2021.110007 [Crossref], [Google Scholar]
Okonkwo, C., Evans, U. F., & Ekung, S. (2022). Unearthing direct and indirect material waste-related factors underpinning cost overruns in construction projects. International Journal of Construction Management, 1–7. https://doi.org/10.1080/15623599.2022.2052431 [Taylor & Francis Online], [Google Scholar]
Ozer, D. J. (1985). Correlation and the coefficient of determination. Psychological Bulletin, 97(2), 307. https://doi.org/10.1037/0033-2909.97.2.307 [Crossref], [Web of Science ®], [Google Scholar]
Presnell, K. V., & Alper, H. S. (2019). Systems metabolic engineering meets machine learning: A new era for data-driven metabolic engineering. Biotechnology Journal, 14(9), 1800416. https://doi.org/10.1002/biot.201800416 [Crossref], [Web of Science ®], [Google Scholar]
Rafiei, M. H., & Adeli, H. (2018). Novel machine-learning model for estimating construction costs considering economic variables and indexes. Journal of Construction Engineering and Management, 144(12), 04018106. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001570 [Crossref], [Google Scholar]
Saranya, C., & Manikandan, G. (2013). A study on normalization techniques for privacy preserving data mining. International Journal of Engineering Technology (IJET), 5(3), 2701–2704. [Google Scholar]
Scheres, S. H. (2016). Processing of structurally heterogeneous cryo-EM data in RELION. Methods in Enzymology, 579, 125–157. https://doi.org/10.1016/bs.mie.2016.04.012 [Crossref], [PubMed], [Google Scholar]
Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for svm. Mathematical Programming, 127(1), 3–30. https://doi.org/10.1007/s10107-010-0420-4 [Crossref], [Web of Science ®], [Google Scholar]
Shoar, S., Chileshe, N., & Edwards, J. D. (2022). Machine learning-aided engineering services’ cost overruns prediction in high-rise residential building projects: Application of random forest regression. Journal of Building Engineering, 50, 104102. https://doi.org/10.1016/j.jobe.2022.104102 [Crossref], [Google Scholar]
Shutian, F., Tianyi, Z., & Ying, Z. (2017). Prediction of construction projects’ costs based on fusion method. Engineering Computations, 34(7), 2396–2408. https://doi.org/10.1108/EC-02-2017-0065 [Crossref], [Google Scholar]
Strömbäck, A., & Tärnell, E. (2022). Evaluation and Learning about Social Sustainability in the Real Estate Industry: A Qualitative and Quantitative Study of how Real Estate Companies can Contribute to Society and Profitability. [Google Scholar]
Sun, C., Adamopoulos, P., Ghose, A., & Luo, X. (2022). Predicting stages in omnichannel path to purchase: A deep learning model. Information Systems Research, 33(2), 429–445. https://doi.org/10.1287/isre.2021.1071 [Crossref], [Google Scholar]
Tas, E., & Yaman, H. (2005). A building cost estimation model based on cost significant work packages. Engineering Construction and Architechtural Management, 12(3), 251–263. https://doi.org/10.1108/09699980510600116 [Crossref], [Google Scholar]
Tayefeh Hashemi, S., Ebadati, O. M., & Kaur, H. (2020). Cost estimation and prediction in construction projects: A systematic review on machine learning techniques. SN Applied Sciences, 2(10), 1–27. https://doi.org/10.1007/s42452-020-03497-1 [Crossref], [Google Scholar]
Wang, R., Asghari, V., Cheung, C. M., Hsu, S. C., & Lee, C. J. (2022). Assessing effects of economic factors on construction cost estimation using deep neural networks. Automation in Construction, 134, 104080. https://doi.org/10.1016/j.autcon.2021.104080 [Crossref], [Google Scholar]
Weisberg, S. (2005). Applied linear regression (Vol. 528). John Wiley & Sons. [Crossref], [Google Scholar]
Yanik, E., Intes, X., Kruger, U., Yan, P., Diller, D., Van Voorst, B., … De, S. (2022). Deep neural networks for the assessment of surgical skills: A systematic review. The Journal of Defense Modeling and Simulation, 19(2), 159–171. https://doi.org/10.1177/15485129211034586 [Crossref], [Google Scholar]
Zabin, A., González, V. A., Zou, Y., & Amor, R. (2022). Applications of machine learning to BIM: A systematic literature review. Advanced Engineering Informatics, 51, 101474. https://doi.org/10.1016/j.aei.2021.101474 [Crossref], [Google Scholar]
Zhang, S., Bogus, S. M., Lippitt, C. D., & Migliaccio, G. C. (2017). Estimating location-adjustment factors for conceptual cost estimating based on nighttime light satellite imagery. Journal of Construction Engineering and Management, 143(1), 04016087. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001216 [Crossref], [Google Scholar]