Citation :

Hicham Hassi, Mouna El Mkhalet, Nouzha Lamdouar, "Comparative Study of Neural Networks and Support Vector Regression for Final Project Duration Prediction using Simulated Scheduling Scenarios," International Journal of Civil Engineering, vol. 13, no. 10, pp. 104-132, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I10P108

Abstract

The prediction of the duration of a construction project is a highly complex task influenced by multiple factors. To tackle the heterogeneity and noise of historical datasets, this paper proposes the use of a highly controlled, simulation-generated dataset comprising 157 realistic schedules of school construction projects. The schedules were generated using six core planning variables. A distinctive contribution of this research is the systematic evaluation of the combinatorial performance between data normalization techniques (Min-Max and Z-score) and learning algorithms (Support Vector Regression and Artificial Neural Networks). The models were globally optimized using cross-validation to prevent data leakage. Comparison between the results of the SVR model with an RBF kernel under Min-Max normalization, and a compact ANN with one hidden layer using the Levenberg-Marquardt training function, demonstrated high theoretical accuracy within the simulated boundaries. The results indicate that SVR yields superior generalization accuracy across the analyzed scenarios. Furthermore, a conceptual reliability-aware framework, ScheduleRelia, is introduced. ScheduleRelia serves as a proof-of-concept application designed to estimate project duration and assess schedule health using critical path metrics. While broad generalization requires future external validation with real-world inputs, this study establishes a rigorous baseline for algorithm selection and data formatting in schedule forecasting.

Keywords

Construction management, Project duration prediction, Artificial Neural Networks (ANN), Support Vector Regression (SVR), ScheduleRelia, Project scheduling.

References

1. Yasser Elfahham, “Estimation and Prediction of Construction Cost Index Using Neural Networks, Time Series, and Regression,” Alexandria Engineering Journal, vol. 58, no. 2, pp. 499-506, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Fan Zhang, and Lauren J. O'Donnell, Support Vector Regression, Machine Learning, pp. 123-140, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Xiaojing Ding, and Qiulan Lu, “Construction Cost Management Strategy based on BIM Technology and Neural Network Model,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 40, no. 4, pp. 6669-6681, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Ying Hong, Hanyan Xie, and Loannis Brilakis, “Automate Construction Scheduling at the Pre-construction Stage – A Case Study,” Apollo - University of Cambridge, pp. 1-10, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Camilo I. Lagos et al., “Predicting Construction Schedule Performance with Last Planner System and Machine Learning,” Automation in Construction, vol. 167, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Xiaobing Yu, and Hengzhong Zuo, “Intelligent Construction Optimization Control of Construction Project Schedule Based on the Fuzzy Logic Neural Network Algorithm,” Mathematical Problems in Engineering, vol. 2022, no. 1, pp. 1-11, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Viren B. Chandanshive, and Ajaykumar R. Kambekar, “Prediction of Building Construction Project Cost Using Support Vector Machine,” Industrial Engineering and Strategic Management, vol. 1, no. 1, pp. 31-42, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Clyde L. Tamayo, and Jerome Jordan F. Famadico, “Predictive Models of Construction Project Success Rating Using Regression and Artificial Neural Network,” International Journal of Multidisciplinary: Applied Business and Education Research, vol. 5, no. 7, pp. 2733-2745, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Shen Zhang, and Xuechun Li, “A Comparative Study of Machine Learning Regression Models for Predicting Construction Duration,” Journal of Asian Architecture and Building Engineering, vol. 23, no. 6, pp. 1980-1996, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Mathieu Wauters, and Mario Vanhoucke, “Support Vector Machine Regression for Project Control Forecasting,” Automation in Construction, vol. 47, pp. 92-106, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Fangfang Yu et al., “An Active Construction Dynamic Schedule Management Model: Using the Fuzzy Earned Value Management and BP Neural Network,” KSCE Journal of Civil Engineering, vol. 25, no. 7, pp. 2335-2349, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Ayaz Ahmad et al., “Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA,” Buildings, vol. 11, no. 8, pp. 1-18, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Shihong Huang, Chengye Liang, and Jiao Liu, “Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model,” Advances in Civil Engineering, vol. 2024, no. 1, pp. 1-18, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Min-Yuan Cheng, Quoc-Tuan Vu, and Frederik Elly Gosal, “Hybrid Deep Learning Model for Accurate Cost and Schedule Estimation in Construction Projects Using Sequential and Non-Sequential Data,” Automation in Construction, vol. 170, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Mostafa Mir et al., “Neural Network-based Interval Forecasting of Construction Material Prices,” Journal of Building Engineering, vol. 39, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Erik Matel et al., “An Artificial Neural Network Approach for Cost Estimation of Engineering Services,” International Journal of Construction Management, vol. 22, no. 7, pp. 1274-1287, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Fatma Yaprakdal et al., “A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid,” Sustainability, vol. 12, no. 4, pp. 1-27, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
18. El Abidi Oumaima et al., “Performance Prediction of Moroccan Pavements Using Artificial Neural Networks,” International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp. 70-80, 2025.
    [CrossRef] [Publisher Link]
19. Putu Manik Sesa Arianti, Miftakhul Jannah, and Noviyanti Santoso, “A Metaheuristic-Based Approach to Inflation Prediction in Indonesia with Support Vector Regression (SVR),” Journal of Computer Engineering, Electronics and Information Technology, vol. 4, no. 1, pp. 1-10, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Kennedy Were et al., “A Comparative Assessment of Support Vector Regression, Artificial Neural Networks, and Random Forests for Predicting and Mapping Soil Organic Carbon Stocks Across an Afromontane Landscape,” Ecological Indicators, vol. 52, pp. 394-403, 2015.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Junrong Zhang et al., “Input-Parameter Optimization using a SVR Based Ensemble Model to Predict Landslide Displacements in a Reservoir Area – A Comparative Study,” Applied Soft Computing, vol. 150, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Yuexin Wang, Chao Wen, and Ping Huang, “Predicting the Effectiveness of Supplement Time on Delay Recoveries: A Support Vector Regression Approach,” International Journal of Rail Transportation, vol. 10, no. 3, pp. 375-392, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Wenhui Feng, and Yafeng Zou, “Construction Cost Prediction based on Adaptive Boosting and Artificial Neural Networks,” Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, vol. 178, no. 1, pp. 1-9, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Gökhan Aksu, Cem Oktay Güzeller, and Mehmet Taha Eser, “The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model,” International Journal of Assessment Tools in Education, vol. 6, no. 2, pp. 170-192, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Peshawa J. Muhammad Ali, “Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements,” ARO-The Scientific Journal of Koya University, vol. 10, no. 1, pp. 85-91, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
26. ThanhNgoc Tran et al., “Load Forecasting with Support Vector Regression: Influence of Data Normalization on Grid Search Algorithm,” International Journal of Electrical and Computer Engineering, vol. 12, no. 4, pp. 3410-3420, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Yasemin Onal, “Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction,”
    Computer Systems Science and Engineering, vol. 41, no. 1, pp. 141-156, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Rahmad Ramadhan Laska, and Anne Mudya Yolanda, “A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru,” Journal of Data Science, vol. 2024, no. 4, pp. 1-8, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Mohammed Z. Al-Faiz, Ali A. Ibrahim, and Sarmad M. Hadi, “The Effect of Z-Score Standardization (Normalization) on Binary Input Due the Speed of Learning in Back-Propagation Neural Network,” Iraqi Journal of Information & Communications Technology, vol. 1, no. 3, pp. 42-48, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Pei-Yu Wu et al., “Predictive Modeling and Estimation of Moisture Damages in Swedish Buildings: A Machine Learning Approach,” Sustainable Cities and Society, vol. 118, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Linlin Xie et al., “Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm,” Applied Sciences, vol. 11, no. 12, pp. 1-21, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Xiaobing Yu, “A RBF Fuzzy Logic Neural Network Algorithm for Construction Resource Scheduling,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 41, no. 4, pp. 4937-4945, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Arsalan Mahmoodzadeh et al., “Forecasting Tunnel Geology, Construction Time and Costs Using Machine Learning Methods,” Neural Computing and Applications, vol. 33, pp. 321-348, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Tiago Zonta et al., “A Predictive Maintenance Model for Optimizing Production Schedule Using Deep Neural Networks,” Journal of Manufacturing Systems, vol. 62, pp. 450-462, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Oleg A. Stepanov, and Oleg S. Amosov, “The Comparison of the Monte-Carlo Method and Neural Networks Algorithms in Nonlinear Estimation Problems,” IFAC Proceedings Volumes, vol. 40, no. 13, pp. 392-397, 2007.
    [CrossRef] [Google Scholar] [Publisher Link]
36. Molusiwa Stephan Ramabodu, “Harnessing Regression Models to Optimize Project Duration Estimation and Reduce Delays in South African Infrastructure Development,” Preprint, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Ban Ali Kamil, “An Empirical Evaluation of the Performance of Deep Neural Networks on Delay Risk Prediction in Urban Flexible Pavement Projects in Iraq,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 28343-28349, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
38. Sang-Jun Park et al., “Data Augmentation Approaches for Estimating Curtain Wall Construction Duration in High-Rise Buildings,” Buildings, vol. 15, no. 4, pp. 1-21, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
39. P. Velumani, N. V. N. Nampoothiri, and M. Urbański, “A Comparative Study of Models for the Construction Duration Prediction in Highway Road Projects of India,” Sustainability, vol. 13, no. 8, pp. 1-13, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
40. Farras Nadhif Danedrata Herman, and Mochamad Gaharu Dida Devedo, “Integrated Risk Analysis and Delay Prediction in Construction Projects using XGBoost, Monte Carlo Simulation, and Rule-Based Reasoning,” International Conference on Tropical Studies and Its Applications, vol. 5, no. 1, pp. 1-10, 2026.
    [Google Scholar] [Publisher Link]