Assessing the impact of claims on construction project performance using machine learning techniques
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-024-01145-2.pdf
Reference29 articles.
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2. Ansari, R., Khalilzadeh, M., Taherkhani, R., Antucheviciene, J., Migilinskas, D., & Moradi, S. (2022). Performanceprediction of construction projects based on the causes of claims: A system dynamics approach. Sustainability, 14(7), 4138.
3. Alkhdour, A., Khazaleh, M. A., Mnaseer, R. A., Bisharah, M., Alkhadrawi, S., & Al-Bdour, H. (2023). Optimizing soil settlement/consolidation prediction in Finland clays: Machine learning regressions with Bayesian hyperparameter selection. Asian Journal of Civil Engineering, 24(8), 3209–3225.
4. almahameed, B. A., & Bisharah, M. (2024). Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management. Asian Journal of Civil Engineering, 25(2), 1281–1294.
5. Arabiat, A., Al-Bdour, H., & Bisharah, M. (2023). Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: A case study from Jordan. Asian Journal of Civil Engineering, 24(7), 2405–2414.
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