# Neural Networks--Deflection Prediction of Continuous Beams with GFRP Reinforcement
## 4 Conclusions  
This paper presented the application of artificial neural networks with the purpose of estimating deflection values on continuous beams with GFRP reinforcement by applying the concentrated force in the midpoints of the spans.  
Based on the results presented in the research, it is concluded that the model with the highest accuracy of deflection estimation in continuous beams with GFRP reinforcement and loaded with concentrated force in the midpoints of the spans is a model of an artificial neural network whose architecture is presented with one input, three hidden layers and one output layer of neurons. There are three neurons in the input layer of neurons, seven neurons in the first hidden layer of neurons, 15 in the second and three in the third and one neuron in the output layer of neurons. The activation function of the hidden layers of neurons is _Swish_, while the activation function of the output layer is linear (_Identity_). The number of epochs conducted on the model is 1000. The measure of model accuracy is represented by the mean absolute error (MAPE). Its value for models with the best performance is MAPE = 9.00%.  
The accuracy of the prediction model can be improved by increasing the number of data in the database. In addition to this, the prediction model would have the possibility of wider application if the database were expanded with certain material characteristics or continuous beam characteristics. Some of the characteristics could be: cross-section of a beam, type of FRP reinforcement, the percentage of reinforcement in the span, the percentage of reinforcement above the support, ratio of percentage of reinforcement in the span and above the support, etc. Potential parameters which could be used for expanding the database would, actually, be the input parameters of the prediction model.  
The high reliability of the prediction model confirms the possibility of using artificial neural networks for the purpose of deflection prediction. This statement is important in the fact that the deflection calculation by using conventional computing techniques is quite complex. The advantage of neural networks is that they can solve new problems, based on previously learned solved problems. In addition to this, they have an advantage which is reflected in their adaptability. The reliability of prediction models made by neural networks depends on the quality and quantity of data. Experimental research provides a large amount of high-quality data. The data used to define the prediction model for deflection prediction are those from an experimental study conducted on eleven continuous beams with GFRP reinforcement. The beams were loaded with concentrated forces in the midpoints of the spans and the deflection was measured in the midpoints of the spans.  
Figure 7: An overview of the impacts of variables on deflection.  
Based on all of the above, it can be concluded that neural networks represent a reliable method for deflection estimation on GFRP-reinforced continuous beams under load with concentrated forces in the midpoints of the spans.  
Conceptualization, Z.B. and N.B.; methodology, Z.B.; software, Z.B.; validation, Z.B. and N.B.; formal analysis, Z.B. and N.B.; investigation, Z.B. and N.B.; resources, Z.B. and N.B.; data curation, Z.B. and N.B.; writing--original draft preparation, Z.B.; writing--review and editing, Z.B. and N.B.; visualization, Z.B.; supervision, Z.B. and N.B.; project administration, Z.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that there is no conflict of interest regarding the publication of this paper.