FUNDAMENTALS OF DEVELOPING CONCEPTUAL COST ESTIMATION MODELS USING MACHINE LEARNING TECHNIQUES: SELECTION AND MEASUREMENT OF BUILDING ATTRIBUTES
DOI:
https://doi.org/10.21837/pm.v22i32.1505Keywords:
Conceptual cost estimation, machine learning, building attributesAbstract
Ensuring the identification of building attributes is the primary task in developing a machine learning cost estimation model. However, the existing research on building attributes has the following shortcomings: it struggles to categorize building characteristics according to various cost types, and the suggested sets of attributes do not clearly establish measurement standards for these qualities. To address these issues, this study aims to select a set of building attributes suitable for conceptual cost estimation and establishment of measurement standards. Through a two-round process of focused group discussions, this research ultimately identified 13 building attributes that can be collected before the completion of building design. These attributes serve as a basis for assessing completed building projects during the model development phase and for evaluating new projects during the model application phase. This study provides a foundational framework for the development of conceptual cost estimation models, ultimately enhancing the accuracy of machine learning cost estimation models.
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Abdel-Basset, M., Ali, M., & Atef, A. (2020). Resource levelling problem in construction projects under neutrosophic environment. The Journal of Supercomputing, 76, 964–988. https://doi.org/10.1007/s11227-019-03055-6 DOI: https://doi.org/10.1007/s11227-019-03055-6
Abed, Y. G., Hasan, T. M., & Zehawi, R. N. (2022). Machine learning algorithms for constructions cost prediction: A systematic review. International Journal of Nonlinear Analysis and Applications, 13(2), 2205–2218. https://doi.org/10.22075/ijnaa.2022.27673.3684
Brink, H., Richards, J., & Fetherolf, M. (2016). Real-world machine learning. Simon and Schuster. https://books.google.com.sg/books?hl=zh-CN&lr=&id=zTczEAAAQBAJ&oi=fnd&pg=PT13&dq=Real-world+machine+learning&ots=LTHAriHHpk&sig=x6FzEuO0i8uBDcDvFPXAMHVXVzA&redir_esc=y#v=onepage&q=Real-world%20machine%20learning&f=false
Car-Puši, D., & Mladen, M. (2020). Early Stage Construction Cost Prediction in Function of Project Sustainability. 631–638. Scopus. https://doi.org/10.23967/dbmc.2020.048 DOI: https://doi.org/10.23967/dbmc.2020.048
Cho, H.-G., Kim, K.-G., Kim, J.-Y., & Kim, G.-H. (2013). A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project. Journal of the Korea Institute of Building Construction, 13(1), 66–74. https://doi.org/10.5345/JKIBC.2013.13.1.066 DOI: https://doi.org/10.5345/JKIBC.2013.13.1.066
Elhag, T. M. S., & Boussabaine, A. H. (1998). An artificial neural system for cost estimation of construction projects. 14th Annual ARCOM Conference, 1, 219–226.
Elmousalami, H. H. (2020). Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. Journal of Construction Engineering and Management, 146(1), 03119008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678 DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678
Hashemi, S., Ebadati E., O. M., & Kaur, H. (2020). Cost estimation and prediction in construction projects: A systematic review on machine learning techniques. SN Applied Sciences, 2, 1–27. https://doi.org/10.1007/s42452-020-03497-1 DOI: https://doi.org/10.1007/s42452-020-03497-1
Jiang, Q. (2019). Estimation of construction project building cost by back-propagation neural network. Journal of Engineering, Design and Technology, 18(3), 601–609. https://doi.org/10.1108/JEDT-08-2019-0195 DOI: https://doi.org/10.1108/JEDT-08-2019-0195
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 DOI: https://doi.org/10.1016/j.proeng.2017.07.218
Juszczyk, M. (2020). Development of cost estimation models based on ANN ensembles and the SVM method. Civil and Environmental Engineering Reports, 30(3), 48–67. https://doi.org/10.2478/ceer-2020-0033 DOI: https://doi.org/10.2478/ceer-2020-0033
Ma, Z., Liu, Z., & Wei, Z. (2016). Formalized Representation of Specifications for Construction Cost Estimation by Using Ontology. Computer‐Aided Civil and Infrastructure Engineering, 31(1), 4–17. https://doi.org/10.1111/mice.12175 DOI: https://doi.org/10.1111/mice.12175
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 DOI: https://doi.org/10.1080/15623599.2019.1692400
Mir, M., Kabir, H. M. D., Nasirzadeh, F., & Khosravi, A. (2021). Neural network-based interval forecasting of construction material prices. Journal of Building Engineering, 39, 102288. https://doi.org/10.1016/j.jobe.2021.102288 DOI: https://doi.org/10.1016/j.jobe.2021.102288
Park, U., Kang, Y., Lee, H., & Yun, S. (2022). A stacking heterogeneous ensemble learning method for the prediction of building construction project costs. Applied Sciences, 12(19), 9729. https://doi.org/10.3390/app12199729 DOI: https://doi.org/10.3390/app12199729
Patil, M. P. A., & Salunkhe, M. A. (2020). Comparative analysis of construction cost estimation using artificial neural networks. Journal of Xidian University, 14, 1287–1305. https://doi.org/10.37896/jxu14.7/146 DOI: https://doi.org/10.37896/jxu14.7/146
Peleskei, C. A., Dorca, V., Munteanu, R. A., & Munteanu, R. (2015). Risk Consideration and Cost Estimation in Construction Projects Using Monte Carlo Simulation. Management, 10(2), 163–176.
Pike, J., & Grosse, S. D. (2018). Friction Cost Estimates of Productivity Costs in Cost-of-Illness Studies in Comparison with Human Capital Estimates: A Review. Applied Health Economics and Health Policy, 16(6), 765–778. https://doi.org/10.1007/s40258-018-0416-4 DOI: https://doi.org/10.1007/s40258-018-0416-4
Saeidlou, S., & Ghadiminia, N. (2023). A construction cost estimation framework using DNN and validation unit. Building Research & Information, 1–11. Scopus. https://doi.org/10.1080/09613218.2023.2196388 DOI: https://doi.org/10.1080/09613218.2023.2196388
Salleh, H., Wang, R., Affandi, N. Z. H., & Abdul-Samad, Z. (2023). SELECTING A STANDARD SET OF ATTRIBUTES FOR THE DEVELOPMENT OF MACHINE LEARNING MODELS OF BUILDING PROJECT COST ESTIMATION. PLANNING MALAYSIA, 21(5), 110–125. https://doi.org/10.21837/pm.v21i29.1359 DOI: https://doi.org/10.21837/pm.v21i29.1359
Shin, Y. (2015). Application of boosting regression trees to preliminary cost estimation in building construction projects. Computational Intelligence and Neuroscience, 2015, 1–1. https://doi.org/10.1155/2015/149702 DOI: https://doi.org/10.1155/2015/149702
Ugur, L. O. (2017). A neuro-adaptive learning (NAL) approach about costs of residential buildings. Acta Physica Polonica A, 132(3), 585–587. Scopus. https://doi.org/10.12693/APhysPolA.132.585 DOI: https://doi.org/10.12693/APhysPolA.132.585
Ugur, L. O., Kanit, R., Erdal, H., Namli, E., Erdal, H. I., Baykan, U. N., & Erdal, M. (2018). Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector. Computational Economics, 53(4), 1403–1419. https://doi.org/10.1007/s10614-018-9814-9 DOI: https://doi.org/10.1007/s10614-018-9814-9
Wang, B., Yuan, J., & Ghafoor, K. Z. (2021). Research on Construction Cost Estimation Based on Artificial Intelligence Technology. Scalable Computing: Practice and Experience, 22(2), 93–104. https://doi.org/10.12694/scpe.v22i2.1868 DOI: https://doi.org/10.12694/scpe.v22i2.1868
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. https://doi.org/10.1016/j.autcon.2021.104080 DOI: https://doi.org/10.1016/j.autcon.2021.104080
Wang, Y.-R., Yu, C.-Y., & Chan, H.-H. (2012). Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, 30(4), 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002 DOI: https://doi.org/10.1016/j.ijproman.2011.09.002
Xuan, L., & Li, J. (2022). Fusion of Computer Technology and Intelligent Logic Analysis Algorithm in Construction Engineering Cost Management. 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 1294–1297. Scopus. https://doi.org/10.1109/ICSCDS53736.2022.9760789 DOI: https://doi.org/10.1109/ICSCDS53736.2022.9760789
Zhou, C., Ding, L. Y., Skibniewski, M. J., Luo, H., & Zhang, H. T. (2018). Data based complex network modeling and analysis of shield tunneling performance in metro construction. Advanced Engineering Informatics, 38, 168–186. https://doi.org/10.1016/j.aei.2018.06.011 DOI: https://doi.org/10.1016/j.aei.2018.06.011
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