PSI - Issue 70

Ch. Vihas et al. / Procedia Structural Integrity 70 (2025) 461–468

468

Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256 – 272. Nguyen-Sy, T., Vo, D. H., & Dao, T. V. (2023). Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using a novel regularized deep learning approach. Multiscale and Multidisciplinary Modeling, Experiments and Design, 6, 415 – 430. Phoeuk, P., & others. (2023). Accuracy prediction of compressive strength of concrete incorporating recycled aggregate using ensemble learning algorithms: Multinational dataset. Advances in Civil Engineering, 2023, 5076429 Rathakrishnan, R., et al. (2024). Machine learning prediction and optimization of compressive strength for blended concrete by applying ANN and genetic algorithm. Cogent Engineering, 11(1), 2376914. Sharma, U., Gupta, N., & Verma, M. (2023). Prediction of the compressive strength of Flyash and GGBS incorporated geopolymer concrete using artificial neural network. Asian Journal of Civil Engineering, 24, 2837 – 2850. Tran, N. T., Nguyen, D. H., Tran, Q. T., Le, H. V., & Nguyen, D.-L. (2024). Experimental and machine learning based study of compressive strength of geopolymer concrete. Magazine of Concrete Research. Zhang, Z. (2023). Estimating the concrete ultimate strength using a hybridized neural machine learning. Buildings, 13(7), 1852.

Made with FlippingBook - Online catalogs