An artificial neural network-based model for material defects classification in oil and gas projects in Saudi Arabia
DOI: https://doi.org/10.3846/jcem.2026.26851Abstract
Maintaining the quality of materials supplied in construction projects is challenging, prompting government and businesses to dedicate substantial efforts and resources to ensure the highest quality standards. Any material defects can be a substantial threat to a project’s safety, schedule, cost, or scope. In addition, the literature review reveals a significant gap in the integration of data-driven models, such as Artificial Neural Networks (ANN), with construction material control processes. Furthermore, there is a lack of studies implementing these models specifically in the context of Saudi Arabia, which is currently experiencing an increase in construction projects. Consequently, this paper proposes a data-driven model based on a Feedforward Neural Network (FNN) to classify and predict construction material defects. The proposed model is designed to classify the material defects and propose a robust model that helps the stakeholders to avoid and minimize material defects in construction projects. The proposed model utilizes fully connected architecture with multiple hidden layers to learn complex relationships between input features that influence the quality of the supplied construction material. In addition, the proposed model is compared with support vector machine (SVM) model. These factors are identified based on previous studies and experts. Moreover, the relationship between the input factors and between the input and output is investigated. The proposed model is developed and trained using 494 real-world data points from Saudi construction projects based on sixteen input variables. The model classifies material defects, with 66.19% identified as minor and 33.81% as major, supporting quality control through data-driven insights. Furthermore, the proposed model is evaluated using 10-fold cross-validation. To the best of our knowledge, this is the first application of an ANN model specifically developed to classify construction material defects into major and minor categories using real-world industrial data. This also investigates the relative importance of each input factor in the classification process. The findings indicate that the proposed model achieves up to 90% F-score in classifying the case defects and outperforms the SVM. The developed model is extremely useful to decision-makers in predicting and classifying material defects in construction.
Keywords:
construction projects, Artificial Neural Networks, material defects, classificationHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170. https://doi.org/10.1016/j.jsv.2016.10.043
Alsugair, A. M., Al-Gahtani, K. S., Alsanabani, N. M., Alabduljabbar, A. A., & Almohsen, A. S. (2023). Artificial neural network model to predict final construction contract duration. Applied Sciences, 13(14), Article 8078. https://doi.org/10.3390/app13148078
Benitez, H., Ibarra-Castanedo, C., Loaiza, H., Caicedo, E., Bendada, A., & Maldague, X. (2007). Defect quantification with thermographic signal reconstruction and artificial neural networks. In K. M. Knettel, V. P. Vavilov, & J. J. Miles (Eds.), Proceedings of SPIE: Vol. 6541. Thermosense XXIX. SPIE – The International Society for Optical Engineering. https://doi.org/10.1117/12.718272
Benitez, H. D., Loaiza, H., Caicedo, E., Ibarra-Castaned, C., Bendada, A. H., & Maldague, X. (2009). Defect characterization in infrared nondestructive testing with learning machines. NDT & E International, 42(8), 630–643. https://doi.org/10.1016/j.ndteint.2009.05.004
Bezelga, A., & Brandon, P. S. (2006). Management, quality and economics in building. Routledge. https://doi.org/10.4324/9780203973486
Bhuvaneswari, S., & Sabarathinam, J. (2013). Defect analysis using artificial neural network. International Journal of Intelligent Systems and Applications, 5(5), 33–38. https://doi.org/10.5815/ijisa.2013.05.05
Boudaghi, E., & Saen, F. R. (2015). Developing a model for determining optimal η in DEA-discriminant analysis for predicting suppliers’ group membership in supply chain. OPSEARCH, 52(1), 134–155. https://doi.org/10.1007/s12597-014-0173-6
Boustani, F. A. (2021). A study on the impact of the relationship between risk management and the development of construction projects in the Kingdom of Saudi Arabia [Doctoral dissertation]. Asia Metropolitan University.
Chakraborty, S., Moore, M., & Parrillo-Chapman, L. (2022). Automatic defect detection for fabric printing using a deep convolutional neural network. International Journal of Fashion Design, Technology and Education, 15(2), 142–157. https://doi.org/10.1080/17543266.2021.1925355
Choi, T. M. (2018). A system of systems approach for global supply chain management in the big data era. IEEE Engineering Management Review, 46(1), 91–97. https://doi.org/10.1109/EMR.2018.2810069
Choi, W., Huh, H., Tama, B. A., Park, G., & Lee, S. (2019). A neural network model for material degradation detection and diagnosis using microscopic images. IEEE Access, 7, 92151–92160. https://doi.org/10.1109/ACCESS.2019.2927162
Dong, X., & Qiu, W. (2024). A method for managing scientific research project resource conflicts and predicting risks using BP neural networks. Scientific Reports, 14, Article 9238. https://doi.org/10.1038/s41598-024-59911-w
Dudzik, S. (2013). Characterization of material defects using active thermography and an artificial neural network. Metrology and Measurement Systems, 20, 491–500. https://doi.org/10.2478/mms-2013-0042
Dudzik, S. (2015). Two-stage neural algorithm for defect detection and characterization using active thermography. Infrared Physics & Technology, 71, 187–197. https://doi.org/10.1016/j.infrared.2015.03.003
Ekanayake, L. L., & Ofori, G. (2004). Building waste assessment score: Design-based tool. Building and Environment, 39(7), 851–861. https://doi.org/10.1016/j.buildenv.2004.01.007
Egwim, C. N., Alaka, H., Toriola-Coker, L. O., Balogun, H., & Sunmola, F. (2021). Applied artificial intelligence for predicting construction projects delay. Machine Learning with Applications, 6, Article 100166. https://doi.org/10.1016/j.mlwa.2021.100166
Garillos-Manliguez, C. A., & Chiang, J. Y. (2021). Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation. Sensors, 21(4), Article 1288. https://doi.org/10.3390/s21041288
Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978–6985. https://doi.org/10.1016/j.eswa.2008.08.074
Guo, L., Gao, H., Huang, H., He, X., & Li, S. (2016). Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring. Shock and Vibration, 2016, Article 8708750. https://doi.org/10.1155/2016/4632562
Gusikhin, O., & Klampfl, E. (2012). JEDI: Just-in-time execution and distribution information support system for automotive stamping operations decision policies for production networks. In D. Armbruster, & K. G. Kempf (Eds.), Decision policies for production networks (pp. 119–142). Springer. https://doi.org/10.1007/978-0-85729-644-3_6
Hamdi, F., Ghorbel, A., Masmoudi, F., & Dupont, L. (2018). Optimization of a supply portfolio in the context of supply chain risk management: Literature review. Journal of Intelligent Manufacturing, 29(4), 763–788. https://doi.org/10.1007/s10845-015-1128-3
Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15–16), 5119–5136. https://doi.org/10.1080/00207543.2018.1521025
Jamadar, I. M., & Vakharia, D. P. (2016). A novel approach integrating dimensional analysis and neural networks for the detection of localized faults in roller bearings. Measurement, 94, 177–185. https://doi.org/10.1016/j.measurement.2016.07.086
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., & Van Hoecke, S. (2016). Convolutional neural network-based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331–345. https://doi.org/10.1016/j.jsv.2016.05.027
Kazaz, A., Manisali, E., & Ulubeyli, S. (2008). Effect of basic motivational factors on construction workforce productivity in Turkey. Journal of Civil Engineering and Management, 14(2), 95–106. https://doi.org/10.3846/1392-3730.2008.14.4
Lin, C.-L., Fan, C.-L., & Chen, B.-K. (2022). Hybrid Analytic Hierarchy Process–Artificial Neural Network model for predicting the major risks and quality of Taiwanese construction projects. Applied Sciences, 12(15), Article 7790. https://doi.org/10.3390/app12157790
Love, P. E., & Li, H. (2000). Quantifying the causes and costs of rework in construction. Construction Management & Economics, 18(4), 479–490. https://doi.org/10.1080/01446190050024897
Maldague, X., Largouet, Y., & Couturier, J. P. (1998). A study of defect depth in pulse phase thermography using neural networks: Modelling, noise, experiments. Revue Générale de Thermique, 37(4), 704–717. https://doi.org/10.1016/S0035-3159(98)80048-2
Manavazhi, M. R., & Adhikari, D. K. (2002). Material and equipment procurement delays in highway projects in Nepal. International Journal of Project Management, 20(8), 627–632. https://doi.org/10.1016/S0263-7863(02)00027-3
Maya, R., Hassan, B., & Hassan, A. (2023). Develop an artificial neural network (ANN) model to predict construction projects performance in Syria. Journal of King Saud University – Engineering Sciences, 35, 366–371. https://doi.org/10.1016/j.jksues.2021.05.002
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, Article 102288. https://doi.org/10.1016/j.jobe.2021.102288
Nabawy, M., & Mohamed, A. G. (2024). Risks assessment in the construction of infrastructure projects using artificial neural networks. International Journal of Construction Management, 24(4), 361–373. https://doi.org/10.1080/15623599.2022.2156902
Navon, R., & Berkovich, O. (2006). An automated model for materials management and control. Construction Management and Economics, 24, 635–646. https://doi.org/10.1080/01446190500435671
Nwachukwu, C. C., & Emoh, F. I. (2010). A systems approach in analysing material constraining factors to construction project management success in Nigeria. Interdisciplinary Journal of Contemporary Research in Business, 2(5), 90–105.
Osmani, M., Glass, J., & Price, A. D. (2006). Architect and contractor attitudes to waste minimization. Proceedings of the Institution of Civil Engineers: Waste and Resource Management, 159, 65–72. https://doi.org/10.1680/warm.2006.159.2.65
Osmani, M., Glass, J., & Price, A. D. F. (2008). Architects’ perspectives on construction waste reduction by design. Waste Management, 28(7), 1147–1158. https://doi.org/10.1016/j.wasman.2007.05.011
Oukhellou, L., Debiolles, A., Denœux, T., & Aknin, P. (2010). Fault diagnosis in railway track circuits using Dempster–Shafer classifier fusion. Engineering Applications of Artificial Intelligence, 23(1), 117–128. https://doi.org/10.1016/j.engappai.2009.06.005
Pernkopf, F. (2004). Detection of surface defects on raw steel blocks using Bayesian network classifiers. Pattern Analysis & Applications, 7(4), 333–342. https://doi.org/10.1007/s10044-004-0232-3
Shavaki, H. F., & Ghahnavieh, E. A. (2023). Applications of deep learning into supply chain management: A systematic literature review and a framework for future research. Artificial Intelligence Review, 56(5), 4447–4489. https://doi.org/10.1007/s10462-022-10289-z
Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., & Ge, Y. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(4), 375–390. https://doi.org/10.1287/inte.2015.0804
Soares, A., Soltani, E., & Liao, Y. Y. (2017). The influence of supply chain quality management practices on quality performance: An empirical investigation. Supply Chain Management: An International Journal, 22(2), 122–144. https://doi.org/10.1108/SCM-08-2016-0286
Sueyoshi, T. (1999). DEA-discriminant analysis in the view of goal programming. European Journal of Operational Research, 115(3), 564–582. https://doi.org/10.1016/S0377-2217(98)00014-9
Tang, Q., Dai, J., Liu, J., Liu, C., Liu, Y., & Ren, C. (2016). Quantitative detection of defects based on Markov–PCA–BP algorithm using pulsed infrared thermography technology. Infrared Physics & Technology, 77, 144–148. https://doi.org/10.1016/j.infrared.2016.05.027
Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European Journal of Operational Research, 94(1), 1–15. https://doi.org/10.1016/0377-2217(96)00098-7
Toor, S. U. R., & Ogunlana, S. O. (2008). Problems causing delays in major construction projects in Thailand. Construction Management and Economics, 26(4), 395–408. https://doi.org/10.1080/01446190801905406
Trétout, H., David, D., Marin, J. Y., Dessendre, M., Couet, M., & Avenas-Payan, I. (1995). An evaluation of artificial neural networks applied to infrared thermography inspection of composite aerospace structures. In D. O. Thompson, & D. E. Chimenti (Eds.)., Review of progress in quantitative nondestructive evaluation (pp. 827–834). Springer. https://doi.org/10.1007/978-1-4615-1987-4_103
Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016). Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals, 65(1), 417–420. https://doi.org/10.1016/j.cirp.2016.04.072
Wetzstein, A., Hartmann, E., Benton, W. C., Jr., & Hohenstein, N. O. (2016). A systematic assessment of supplier selection literature–state-of-the-art and future scope. International Journal of Production Economics, 182, 304–323. https://doi.org/10.1016/j.ijpe.2016.06.022
Wong, A. (2000). Integrating supplier satisfaction with customer satisfaction. Total Quality Management, 11(4–6), 427–432. https://doi.org/10.1080/09544120050007733
Wu, P. Y., Sandels, C., Mjörnell, K., Mangold, M., & Johansson, T. (2022). Predicting the presence of hazardous materials in buildings using machine learning. Building and Environment, 213, Article 108894. https://doi.org/10.1016/j.buildenv.2022.108894
Zakeri, M., Olomolaiye, P. O., Holt, G. D., & Harris, F. C. (1996). A survey of constraints on Iranian construction operatives’ productivity. Construction Management and Economics, 14(5), 417–426. https://doi.org/10.1080/014461996373287
Zhang, W., Ye, C., Zheng, K., Zhong, J., Tang, Y., Fan, Y., Buehler, M. J., Ling, S., & Kaplan, D. L. (2018). Tensan silk-inspired hierarchical fibers for smart textile applications. ACS Nano, 12(7), 6968–6977. https://doi.org/10.1021/acsnano.8b02430
Zu, X., & Kaynak, H. (2012). An agency theory perspective on supply chain quality management. International Journal of Operations & Production Management, 32(4), 423–446. https://doi.org/10.1108/01443571211223086
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.