Estimating contract value using structural parameters: a machine learning approach with data preprocessing

Year 2024, , 755 - 765, 30.09.2024

Semi Emrah Aslay

https://doi.org/10.24012/dumf.1515160

Abstract

In this study, contract price in public construction tenders are predicted using structural project parameters. The variables applied in the study are created by adding the quantities of columns, shear walls, and beams to variables commonly used in the literature for cost estimations. Six different machine learning algorithms are employed as machine learning algorithms. Preprocessing methods and a series of parameter optimizations are applied to enhance the predictive capability on datasets. These processes and the applied algorithms are evaluated with five different performance metrics. The SVM algorithm produced the best results, achieving an value of 0.8966, MAPE of 23.70, NSE of 0.8956, MAE of 0.4849, and RMSE of 0.6989. This study contributes to the literature by developing machine learning models and data analysis processes for contract price approaches.

Keywords

SVM, machine learning, structural parameters, data preprocessing, feature importance.

Yapısal parametreler kullanarak sözleşme değerinin tahmin edilmesi: veri ön işleme ile bir makine öğrenimi yaklaşımı

Abstract

Bu çalışmada, kamu inşaat ihalelerinde sözleşme bedeli yapısal proje parametreleri kullanılarak tahmin edilmektedir. Çalışmada uygulanan değişkenler, literatürde maliyet tahminleri için yaygın olarak kullanılan değişkenlere kolon, perde duvar ve kiriş miktarlarının eklenmesiyle oluşturulmuştur. Makine öğrenmesi algoritmaları olarak altı farklı makine öğrenmesi algoritması kullanılmıştır. Veri kümeleri üzerinde tahmin kabiliyetini artırmak için ön işleme yöntemleri ve bir dizi parametre optimizasyonu uygulanmaktadır. Bu işlemler ve uygulanan algoritmalar beş farklı performans metriği ile değerlendirilmiştir. SVM algoritması 0,8966, MAPE 23,70, NSE 0,8956, MAE 0,4849 ve RMSE 0,6989 değerlerine ulaşarak en iyi sonuçları vermiştir. Bu çalışma sözleşme fiyatı yaklaşımları için makine öğrenimi modellerini ve veri analizi süreçlerini geliştirerek literatüre katkı sunmaktadır.

Keywords

SVM, makine öğrenmesi, yapısal parametreler, veri ön işleme, özellik önemi.

References

• [1] S.E. Aslay, T. Dede, 3D cost optimization of 3 story RC constructional building using Jaya algorithm, Structures 40 (2022) 803–811. https://doi.org/10.1016/j.istruc.2022.04.055.
• [2] B. Wang, J. Yuan, K.Z. Ghafoor, Research on Construction Cost Estimation Based on Artificial Intelligence Technology, Scalable Computing 22 (2021) 93–104. https://doi.org/10.12694:/scpe.v22i2.1868.
• [3] D. Chakraborty, H. Elhegazy, H. Elzarka, L. Gutierrez, A novel construction cost prediction model using hybrid natural and light gradient boosting, Advanced Engineering Informatics 46 (2020). https://doi.org/10.1016/j.aei.2020.101201.
• [4] J.A. Ujong, E.M. Mbadike, G.U. Alaneme, Prediction of cost and duration of building construction using artificial neural network, Asian Journal of Civil Engineering 23 (2022) 1117–1139. https://doi.org/10.1007/s42107-022-00474-4.
• [5] A.M. Alsugair, K.S. Al-Gahtani, N.M. Alsanabani, A.A. Alabduljabbar, A.S. Almohsen, Artificial Neural Network Model to Predict Final Construction Contract Duration, Applied Sciences (Switzerland) 13 (2023). https://doi.org/10.3390/app13148078.
• [6] S. Saeidlou, N. Ghadiminia, A construction cost estimation framework using DNN and validation unit, Building Research and Information (2023). https://doi.org/10.1080/09613218.2023.2196388.
• [7] D. Car-Pusic, S. Petruseva, V. Zileska Pancovska, Z. Zafirovski, Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System, Advances in Civil Engineering 2020 (2020). https://doi.org/10.1155/2020/8886170.
• [8] T.Q.D. Pham, T. Le-Hong, X. V. Tran, Efficient estimation and optimization of building costs using machine learning, International Journal of Construction Management 23 (2023) 909–921. https://doi.org/10.1080/15623599.2021.1943630.
• [9] Y. Zhang, S. Fang, RSVRs based on Feature Extraction: A Novel Method for Prediction of Construction Projects’ Costs, KSCE Journal of Civil Engineering 23 (2019) 1436–1441. https://doi.org/10.1007/s12205-019-0336-3.
• [10] M. Badawy, A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage, Asian Journal of Civil Engineering 21 (2020) 763–774. https://doi.org/10.1007/s42107-020-00237-z.
• [11] G.H. Coffie, C.O. Aigbavboa, W.D. Thwala, Modelling construction completion cost in Ghana public sector building projects, Asian Journal of Civil Engineering 20 (2019) 1063–1070. https://doi.org/10.1007/s42107-019-00165-7.
• [12] S. Hassim, R. Muniandy, A.H. Alias, P. Abdullah, Construction tender price estimation standardization (TPES) in Malaysia: Modeling using fuzzy neural network, Engineering, Construction and Architectural Management 25 (2018) 443–457. https://doi.org/10.1108/ECAM-09-2016-0215.
• [13] M. Sayed, M. Abdel-Hamid, K. El-Dash, Improving cost estimation in construction projects, International Journal of Construction Management 23 (2023) 135–143. https://doi.org/10.1080/15623599.2020.1853657.
• [14] N. Dang-Trinh, P. Duc-Thang, T. Nguyen-Ngoc Cuong, T. Duc-Hoc, Machine learning models for estimating preliminary factory construction cost: case study in Southern Vietnam, International Journal of Construction Management 23 (2023) 2879–2887. https://doi.org/10.1080/15623599.2022.2106043.
• [15] F. Uysal, R. Sonmez, Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation, Buildings 13 (2023). https://doi.org/10.3390/buildings13030651.
• [16] Z.H. Ali, A.M. Burhan, M. Kassim, Z. Al-Khafaji, Developing an Integrative Data Intelligence Model for Construction Cost Estimation, Complexity 2022 (2022). https://doi.org/10.1155/2022/4285328.
• [17] W. Alfaggi, S. Naimi, An Optimal Cost Estimation Practices of Fuzzy AHP for Building Construction Projects in Libya, Civil Engineering Journal (Iran) 8 (2022) 1194–1204. https://doi.org/10.28991/CEJ-2022-08-06-08.
• [18] R. Wang, V. Asghari, C.M. Cheung, S.C. Hsu, C.J. Lee, Assessing effects of economic factors on construction cost estimation using deep neural networks, Autom Constr 134 (2022). https://doi.org/10.1016/j.autcon.2021.104080.
• [19] Z.H. Ali, A.M. Burhan, Hybrid machine learning approach for construction cost estimation: an evaluation of extreme gradient boosting model, Asian Journal of Civil Engineering 24 (2023) 2427–2442. https://doi.org/10.1007/s42107-023-00651-z.
• [20] Y. Elfahham, Estimation and prediction of construction cost index using neural networks, time series, and regression, Alexandria Engineering Journal 58 (2019) 499–506. https://doi.org/10.1016/j.aej.2019.05.002.
• [21] F. Antoniou, G. Aretoulis, D. Giannoulakis, D. Konstantinidis, Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations, Buildings 13 (2023). https://doi.org/10.3390/buildings13020382.
• [22] B. Mohamed, O. Moselhi, Conceptual estimation of construction duration and cost of public highway project, Journal of Information Technology in Construction 27 (2022) 595–618. https://doi.org/10.36680/j.itcon.2022.029.
• [23] A. Mahmoodzadeh, H.R. Nejati, M. Mohammadi, Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects, Autom Constr 139 (2022). https://doi.org/10.1016/j.autcon.2022.104305.
• [24] M. Kovacevic, N. Ivaniševic, P. Petronijevic, V. Despotovic, Construction cost estimation of reinforced and prestressed concrete bridges using machine learning, Gradjevinar 73 (2021) 1–13. https://doi.org/10.14256/JCE.2738.2019.
• [25] K. Koc, A.P. Gurgun, Causal Relationships of Readability Risks in Construction Contracts, Teknik Dergi/Technical Journal of Turkish Chamber of Civil Engineers 33 (2022) 11823–11846. https://doi.org/10.18400/tekderg.962928.
• [26] S.E. Aslay, T. Dede, Reduce the construction cost of a 7-story RC public building with metaheuristic algorithms, Architectural Engineering and Design Management (2023). https://doi.org/10.1080/17452007.2023.2195612.
• [27] A. Asuncion, D. Newman, UCI machine learning repository, (2007).
• [28] A. Mohammed, B. Alshemosi, H. Saad, H. Alsaad, Cost Estimation Process for Construction Residential Projects by Using Multifactor Linear Regression Technique, International Journal of Science and Research 6 (2015) 2319–7064. https://doi.org/10.21275/ART20174128.
• [29] M.H. Rafiei, H. Adeli, Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes, J Constr Eng Manag 144 (2018). https://doi.org/10.1061/(asce)co.1943-7862.0001570.
• [30] Elektronik Kamu Alımları Platformu, https://ekap.kik.gov.tr/EKAP/Ortak/IhaleArama/index.html, (n.d.).
• [31] M.H. Rafiei, H. Adeli, Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes, J Constr Eng Manag 144 (2018). https://doi.org/10.1061/(asce)co.1943-7862.0001570.
• [32] S. Saeidlou, N. Ghadiminia, A construction cost estimation framework using DNN and validation unit, Building Research and Information 52 (2024) 38–48. https://doi.org/10.1080/09613218.2023.2196388.
• [33] V.N. Vapnik, The Nature of Statistical Learning Theory, Springer New York, New York, NY, 1995. https://doi.org/10.1007/978-1-4757-2440-0.
• [34] K. Koc, Ö. Ekmekcioğlu, A.P. Gurgun, Accident prediction in construction using hybrid wavelet-machine learning, Autom Constr 133 (2022). https://doi.org/10.1016/j.autcon.2021.103987.
• [35] O.M. Katipoğlu, Predicting hydrological droughts using ERA 5 reanalysis data and wavelet-based soft computing techniques, Environ Earth Sci 82 (2023). https://doi.org/10.1007/s12665-023-11280-9.
• [36] Ş. Emeç, D. Tekin, Housing Demand Forecasting with Machine Learning Methods, Erzincan University Journal of Science and Technology 15 (2022) 36–52. https://doi.org/10.18185/erzifbed.1199535.
• [37] L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification And Regression Trees, Routledge, 1984. https://doi.org/10.1201/9781315139470.
• [38] Emre Mumyakmaz, Prediction of reinforced concrete column capacitties by machine learning, Master Thesis, ESKİŞEHİR TECHNICAL UNIVERSITY, INSTUTE OF GRADUATE PROGRAMS, 2023.
• [39] L. Breiman, Random Forests, Mach Learn 45 (2001) 5–32. https://doi.org/10.1023/A:1010933404324.
• [40] R. Özdemir, M. Turanlı, Comparison of machine learning classification algorithms for purchasing forecast, Jouurnal of Life Economics 8 (2021) 59–68. https://doi.org/10.15637/jlecon.8.1.06.
• [41] T. Chen, C. Guestrin, XGBoost, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, 2016: pp. 785–794. https://doi.org/10.1145/2939672.2939785.
• [42] E.E. Başakın, Ö. Ekmekcioğlu, M. Özger, Developing a novel approach for missing data imputation of solar radiation: A hybrid differential evolution algorithm based eXtreme gradient boosting model, Energy Convers Manag 280 (2023). https://doi.org/10.1016/j.enconman.2023.116780.
• [43] Muhammet Emir Kılıç, Estimation and performance analysis of rough construction costs with machine learning methods at the pre-design stage of Induustrial buildings, Master Thesis, 2021.
• [44] Evelyn Fix, Joseph Lawson Hodges, Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties, Technical Report 4, USAF School of Aviation Medicine, Randolph Field. (1951).
• [45] Vehbi Hakan Sayan, Football Player Performance Analysis Using Machine Learning Techniques, Master Thesis, Burdur University, 2023.
• [46] W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull Math Biophys 5 (1943) 115–133. https://doi.org/10.1007/BF02478259.
• [47] E.E. Başakın, Ö. Ekmekcioğlu, H. Çıtakoğlu, M. Özger, A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment, Neural Comput Appl 34 (2022) 783–812. https://doi.org/10.1007/s00521-021-06424-6.