# Artificial Neural Networks Applied in Civil Engineering
## 2 Contributions
### Material Properties and Performance  
In the study by [PERSON] [12], a radial basis function (RBF) artificial neural network model for predicting the 28-day compressive strength of concrete was established. The database used in this study was the expansion to the one used in the author's previous work, with data added from other work. The stochastic gradient approach presented in the textbook was employed to determine the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs converged to the solutions with global minimum errors. The results of the verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author's previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. The objective of the study by [PERSON] et al. [13] was to compare conventional models used for estimating the load-carrying capacity of reinforced concrete members, i.e., current design codes (CDCs), with the method based on different assumptions, i.e., the compressive force path (CFP) method and an ANN-based solver. For this purpose, four different databases and the details of their critical parameters were developed based on the data from available experimental studies. These databases obtained from the published experimental studies helped us to estimate the member response at the ultimate limit-state (ULS). The results show that the predictions of the CFP and the ANNs often correlate closer to the experimental data than to the CDCs. The study by [PERSON] and [PERSON] [14] on concrete and cementitious materials focused on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attacks associated with reinforced concrete structures. This study examined the bond behavior of BFRP-reinforced SCGC specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted were 5, 10, and 15 times the bar diameter (db) and were denoted as 5 db, 10 db, and 15 db throughout the study. The result was then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-S806-02 guidelines. A prediction model for bond strength was proposed using ANN tools, which contributes to new knowledge on the use of Basalt FRP bars as internal reinforcement in ambient-cured self-compacting geopolymer concrete. Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. To obtain more accurate properties, a support vector machine (SVM), ANN, and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In the study by [PERSON] et al. [15], factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight, and curing regime were used as input parameters due to their significant effect on the compressive strength. An ANN model for predicting the compressive strength of concrete was established in the study by [PERSON] and [PERSON] [16]. A database of real concrete mix proportioning listed in earlier research by another author was used for training and testing the ANN. The results of the synaptic weights and the thresholds in the ANN are all listed. Therefore, the formulae expressed in this study can be used to predict the compressive strength of concrete according to the mix proportioning. The standard design concept for CRCP has been modified through several changes made in the design parameters to eliminate the cluster of closely spaced crack patterns, since these crack patterns lead to the development of spalling and punch-out distresses in CRCPs.