Author:
Rasheed Ruqayah H.,Rezouki Sedqi E.
Reference14 articles.
1. Petruseva, S., Zileska-Pancovska, V., Žujo, V., & Brkan-Vejzović, A. (2017). Construction costs forecasting: Comparison of the accuracy of linear regression and support vector machine models. Technical Gazette, 24(5), 1431–1438.
2. Cirilovic, J., Vajdic, N., Mladenovic, G., & Queiroz, C. (2014). Developing cost estimation models for road rehabilitation and reconstruction: Case study of projects in Europe and Central Asia. Journal of Construction Engineering and Management, 140(3), 04013065.
3. Zhang, Y., Minchin, R. E., Jr., & Agdas, D. (2017). Forecasting completed cost of highway construction projects using LASSO regularized regression. Journal of Construction Engineering and Management, 143(10), 04017071.
4. Si, X. S., Zhang, Z. X., & Hu, C. H. (2017). Data-driven remaining useful life prognosis techniques. National Defense Industry Press and Springer-Verlag GmbH.
5. Chou, J. S., Lin, C. W., Pham, A. D., & Shao, J. Y. (2015). Optimized artificial intelligence models for predicting project award price. Automation in construction, 54, 106–115.