Naslov
Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

Autori
Hadzima-Nyarko, Marijana ; Trinh, Son Hoang

Izvornik
Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology (2734-9942) 1 (2022), 2; 9-21

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Machine learning ; ANN ; compressive strength ; Bayesian regularization ; K-fold cross-validation

Sažetak
Cement concrete is the most commonly used material today for constructing residential or commercial buildings, industrial parks, or particular components such as tunnel slabs where there is a high risk of fire. This structure requires concrete to be subjected to high temperatures generated by fires. However, concrete under the influence of high temperature has very complex behavior states with deformations, physical and chemical changes as the temperature rises dramatically. In this study, an artificial neural network-based Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition. The performance of the ANN model was evaluated using K-fold cross- validation and statistical criteria, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results show that the proposed ANN model is a reasonable, highly accurate, and useful prediction tool for saving time and minimizing costly experiments.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

POVEZANOST RADA