- سلطانیان، بهنام، اشتهاردیان، احسان اله، عزیزی، مجتبی. (1402). استفاده از شبکه عصبی مصنوعی جهت تخمین هزینههای ساخت پروژههای مسکونی در فاز امکانسنجی. مهندسی سازه و ساخت، 10(6)، 20-33. https://doi.org/10.22065/jsce.2022.340013.2803
- فرزاد، عرفان، دهقان منشادی،هادی، دشتی رحمتآبادی، محمدعلی. (1402). پیشبینی مسائل مربوط به زمانبندی پروژههای عمرانی با استفاده از شبکه عصبی LSTM (حافظه طولانی کوتاهمدت). نشریه مهندسی عمران امیرکبیر، 55(9)، 1753-1764. https://doi.org/10.22060/ceej.2023.21383.7701
- نجفی زنگنه، سعید، شمس قارنه، ناصر، عزیزی، پرنیان و اشراق نیای جهرمی، عبدالحمید. (1399). بهینهسازی کیفیت پروژههای عمرانی از طریق تئوری پایایی سیستمها با استفاده از الگوریتم کلونی مورچگان کمینه بیشینه بهبودیافته. مهندسی سازه و ساخت، 7 (ویژهنامه 1 (پیاپی 30)). https://doi.org/10.22065/jsce.2018.126873.1536
- Abbasi F, Khalilzadeh M. (2021). The model of human resource management strategies for Iranian project-based construction organizations. Jordan Journal of Civil Engineering. 2021;15(2).
- Agostinelli, S., Cumo, F., Marzo, R., & Muzi, F. (2022, September). Digital construction strategy for project management optimization in a building renovation site: Machine learning and big data analysis. In International Conference on Trends on Construction in the Post-Digital Era, 20-35. https://doi.org/10.1007/978-3-031-20241-4_2
- Akinyokun, O. T., Onifade, M. K., & Adegoke, M. A. (2024, April). An Artificial Intelligence Framework for Project Planning and Control using Decision Tree Analysis and Artificial Neural Network. In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), 1-10. https://doi.org/10.1109/SEB4SDG60871.2024.10630126
- Al-Gahtani, K. S., Alsugair, A. M., Alsanabani, N. M., Alabduljabbar, A. A., & Almohsen, A. S. (2025). ANN prediction model of final construction cost at an early stage. Journal of Asian Architecture and Building Engineering, 24(2), 775–799. https://doi.org/10.1080/13467581.2023.2294883
- Amani, N., & Safarzadeh, K. (2022). Project risk management in Iranian small construction firms. Journal of Engineering and Applied Science, 69(1), 7. https://doi.org/10.1186/s44147-021-00050-8
- Awada, M., Srour, F. J., & Srour, I. M. (2021). Data-driven machine learning approach to integrate field submittals in project scheduling. Journal of Management in Engineering, 37(1), 1-13. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000873
- Ayhan, M., Dikmen, I., Birgonul, M. T. (2021). Predicting the occurrence of construction disputes using machine learning techniques. Journal of Construction Engineering and Management, 147 (4). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002027
- Azadeh, A., Ghaderi, S.F., Tarverdian, S., & Saberi, M. (2007). Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied Mathematics and Computation, 186, 1731–1741. https://doi.org/10.1016/j.amc.2006.08.093
- Chen, X., (2024). Optimization Control of Construction Engineering Management Projects Based on Deep Learning Algorithms. Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), New Delhi, India, 489-495. https://doi.org/10.1109/ICTEI60496.2023.00102
- Dan, S., (2024). An Artificial Neural Networks with Particle Swarm Optimization using ReliefF for Construction Cost Evaluation. International Conference on Integrated Intelligence and Communication Systems (ICIICS), Kalaburagi, India,1-5. https://doi.org/10.1109/ICIICS63763.2024.10860188
- Derakhshanalavijeh, R., & Teixeira, J. M. C. (2017). Cost overrun in construction projects in developing countries, Gas-Oil industry of Iran as a case study. Journal of Civil Engineering and Management, 23(1), 125-136. https://doi.org/10.3846/13923730.2014.992467
- Ding, S., Su, C., & Yu, J. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial intelligence review, 36(2), 153-162. https://doi.org/10.1007/s10462-011-9208-z
- Ding, S., Xu, X., Zhu, H., Wang, J., & Jin, F. (2011). Studies on optimization algorithms for some artificial neural networks based on genetic algorithm (GA). Journal of Computers, 6(5), 939-946. https://doi.org/10.4304/jcp.6.5.939-946
- Dybowski, R., Gant, V., Weller, P., & Chang, R. (1996). Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. The Lancet, 347(9009), 1146-1150. https://doi.org/10.1016/S0140-6736(96)90609-1
- Effat, A., (2025), Predicting the Indirect Cost of Construction Projects in Egypt: An Artificial Neural Network American University in Cairo, Master's Thesis. AUC Knowledge Fountain. https://fount.aucegypt.edu/etds/2402
- Fan, C., (2025). Predicting the Construction Quality of Projects by Using Hybrid Soft Computing Techniques. Computer Modeling in Engineering & Sciences (CMES), 142(2), 1995-2017. https://doi.org/10.32604/cmes.2025.059414
- Fathalizadeh, A., Hosseini, M. R., Silvius, A. G., Rahimian, A., Martek, I., & Edwards, D. J. (2021). Barriers impeding sustainable project management: A Social Network Analysis of the Iranian construction sector. Journal of Cleaner Production, 318, 128405. https://doi.org/10.1016/j.jclepro.2021.128405
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
- Haykin, S. (2009). Neural Networks and Learning Machines (3rd ed.). Pearson Education. https://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf
- Jayaram, M.A., Gowda, B., (2024). Machine Learning-Based Surrogate Models for Construction Project Duration Prediction. 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 544-548. https://doi.org/10.1109/Confluence60223.2024.10463458
- Joseph, X.F., Selvaraj, G., Babu, M.A., Banu, A.S., Bhuvanesh, A., Keerthanadevi. R., (2024). A Robust Evolutionary Gravity Neocognitron Neural Network Model for Construction Cash Flow Prediction in Complex Project Management. IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Chennai, India, 140-144. https://doi.org/10.1109/WIECON-ECE64149.2024.10915020
- Karki, S., & Hadikusumo, B. (2023). Machine learning for the identification of competent project managers for construction projects in Nepal. Construction Innovation, 23(1), 1-18. https://doi.org/10.1108/CI-08-2020-0139
- Kuo, Y. C., & Lu, S. T. (2013). Using fuzzy multiple criteria decision-making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), 602-614. https://doi.org/10.1016/j.ijproman.2012.10.003
- Leung, F.H.F., Lam, H.K., Ling, S.H., & Tam, P.K.S. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural networks, 14 (1). https://doi.org/10.1109/TNN.2002.804317
- Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55. https://psycnet.apa.org/record/1933-01885-001
- Liu, Q. (2025). Application of BP neural network model algorithm in safety risk identification of tunnel construction. International Journal of System Assurance Engineering and Management, 1-13. https://doi.org/10.1007/s13198-024-02701-4
- Omoush, M. M. (2020). Assessing and prioritizing the critical success factors and delays of project management implementation: Empirical evidence at construction projects in Jordan. International Journal of Business and Management,15(10), 117. DOI: 10.5539/ijbm.v15n10p117
- Ribeiro, A., Amaral, A., & Barros, T. (2021). Project Manager Competencies in the context of the Industry 4.0. Procedia computer science, 181, 803-810. https://doi.org/10.1016/j.procs.2021.01.233
- Sadatnya, A., Sadeghi, N., Sabzekar, S., Khanjani, M., Tak, A. N., & Taghaddos, H. (2023). Machine learning for construction crew productivity prediction using daily work reports. Automation in construction, 152, 104891. https://doi.org/10.1016/j.autcon.2023.104891
- Sharma, D. K., Hota, H. S., Brown, K., & Handa, R. (2022). Integration of genetic algorithm with artificial neural network for stock market forecasting. International Journal of System Assurance Engineering and Management,13(2), 828-841. https://doi.org/10.1007/s13198-021-01209-5
- Sullivan, G. M., & Artino Jr, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of graduate medical education, 5(4), 541. https://doi.org/10.4300/JGME-5-4-18
- Taniguchi, M., Haft, M., Hollmén, J., & Tresp, V. (1998). Fraud detection in communication networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'98, 2, 1241-1. https://doi.org/10.1109/ICASSP.1998.675496 244.
- Uddin, S., Ong, S., & Lu, H. (2022). Machine learning in project analytics: a data-driven framework and case study. Scientific reports, 12(1), 15252. https://doi.org/10.1038/s41598-022-19728-x
- Venkatesan, D., Kannan, K., & Saravanan, R. (2009). A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing and Applications, 18(2), 135-140. https://doi.org/10.1007/s00521-007-0166-y
|