PSI - Issue 70
Santhosh Kumar N V et al. / Procedia Structural Integrity 70 (2025) 440–446
446
Altman, N. S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175 – 185. Apostolopoulou, M., Armaghani, D. J., Bakolas, A., Douvika, M. G., Moropoulou, A., & Asteris, P. G. 2019. Compressive strength of natural hydraulic lime mortars using soft computing techniques. Procedia Structural Integrity, 17, 914 – 923. Altman, N. S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175 – 185. Breiman, L. 2001. Random forests. Machine Learning, 45(1), 5 – 32. Chai, T., & Draxler, R. R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE. Geoscientific Model Development Discussions, 7(1), 1525 – 1534. Chen, H., Li, X., Wu, Y., Zuo, L., Lu, M., & Zhou, Y. 2022. Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings, 12(3), 302. Chen, T., & Guestrin, C. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785 – 794). ACM. Chithra, S., Kumar, S. S., Chinnaraju, K., & Ashmita, F. A. 2016. A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks. Construction and Building Materials, 114, 528 – 535. Cohen, I., Huang, Y., Chen, J., & Benesty, J. 2009. Pearson correlation coefficient. In J. Benesty, M. M. Sondhi, & Y. Huang (Eds.), Noise reduction in speech processing (pp. 1 – 4). Springer. Costigan, A., Pavía, S., & Kinnane, O. 2015. An experimental evaluation of prediction models for the mechanical behavior of unreinforced, lime mortar masonry under compression. Journal of Building Engineering, 4, 283 – 294. Cover, T., & Hart, P. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21 – 27. Cowper, A. D. 2017. Lime and lime mortars. Routledge Cortes, C., & Vapnik, V. 1995. Support-vector networks. Machine Learning, 20(3), 273 – 297. Das, P., Kashem, A., Hasan, I., & Islam, M. 2024. A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis. Asian Journal of Civil Engineering, 25, 3301 – 3316. Draper, N. R., & Smith, H. 1998. Applied regression analysis (3rd ed.). Wiley. Drougkas, A., Roca, P., & Molins, C. 2016. Compressive strength and elasticity of pure lime mortar masonry. Materials and Structures, 49, 983 – 999. Elshaarawy, M. K., Alsaadawi, M. M., & Hamed, A. K. 2024. Machine learning and interactive GUI for concrete compressive strength prediction. Scientific Reports, 14(1), 16694. Fang, S. Q., Zhang, H., Zhang, B. J., & Zheng, Y. 2014. The identification of organic additives in traditional lime mortar. Journal of Cultural Heritage, 15(2), 144 – 150. Feng, D. C., Liu, Z. T., Wang, X. D., Chen, Y., Chang, J. Q., Wei, D. F., & Jiang, Z. M. 2020. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction and Building Materials, 230, 117000. Hanandeh, S., Al-Twaiqat, M., & Al-Mistarehi, B. W. 2022. Application of soft computing for estimation of pavement condition indicators and predictive modeling. Frontiers in Built Environment, 8, 12345. Kumar, A., et al. 2022. Compressive strength prediction of lightweight concrete: Machine learning models. Sustainability, 14(4), 2404. Kutner, M. H., Nachtsheim, C. J., & Neter, J. 2004. Applied linear regression models (4th ed.). McGraw-Hill Education. Lu, Y., Cohen, I., Zhou, X. S., & Tian, Q. 2007. Feature selection using principal feature analysis. In Proceedings of the 15th ACM International Conference on Multimedia (pp. 301 – 304). ACM. Miles, J. 2005. R-squared, adjusted R-squared. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science. Wiley. Moropoulou, A., Bakolas, A., Moundoulas, P., Aggelakopoulou, E., & Anagnostopoulou, S. 200). Strength development and lime reaction in mortars for repairing historic masonries. Cement and Concrete Composites, 27(2), 289 – 294. Moutassem, F., & Chidiac, S. E. 2016. Assessment of concrete compressive strength prediction models. KSCE Journal of Civil Engineering, 20(1), 343 – 358. Naderpour, H., Rafiean, A. H., & Fakharian, P. 2018. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, 213 – 219. Nakkeeran, G., & Krishnaraj, L. 2023. Prediction of cement mortar strength by replacement of hydrated lime using RSM and ANN. Asian Journal of Civil Engineering, 24(5), 1401 – 1410. Ni, H. G., & Wang, J. Z. 2000. Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 30(8), 1245 – 1250. Smola, A. J., & Schölkopf, B. 2004. A tutorial on support vector regression. Statistics and Computing, 14(3), 199 – 222. Tran, V. Q., Mai, H.-V. T., Nguyen, T.-A., & Ly, H.-B. 2022. Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete. Frontiers of Structural and Civil Engineering, 16(6), 928 – 945. Willmott, C. J., & Matsuura, K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79 – 82.
Made with FlippingBook - Online catalogs