# Neural Networks--Deflection Prediction of Continuous Beams with GFRP Reinforcement
## 1 Introduction
In addition to prognostic costs, neural networks can also be used to predict the time required to complete construction projects, which has also been a topic addressed by some authors [13]. Predicting the quantity of material consumption for the construction of structures can, also, be conducting by applying neural networks. By applying neural networks, predictions of the behavior of structural elements in terms of boundary forces, deflections, fire resistance, etc., can also be made. [PERSON] and [PERSON] developed a prediction model of experimental research for slim reinforced concrete posts with the help of neural networks (2008) [14]. The results of experimental research on 22 models of reinforced concrete slender columns were used to make the model. A prediction model for the limit force and maximum deflections in the directions of the main central axes of inertia in relation to the varied six input parameters was developed. This model showed satisfactory results, and its control resulted in an error smaller than 6% for the results prediction. It was proven in this way that neural networks can model nonlinear material behavior. Artificial neural networks were also used to determine the fire resistance of reinforced concrete poles. The input data of this prediction model are: pole dimensions, concrete class, protective layer thickness, reinforcement percentage, load coefficient and aggregate \begin{table}
\begin{tabular}{c c} \hline
**Equation** & **Comment** \\ \hline \hline \(\Delta=\frac{7}{768}\cdot\frac{PL^{3}}{E_{c}I_{e}}\) & (1) & Equation derived from elastic analysis \\ \hline \(\Delta_{\max}=\frac{PL^{3}}{48E_{c}I_{e\tau}}\left(\frac{5}{16}-\frac{15}{8} \left(1-\frac{I_{e\tau}}{I_{g}}\right)\cdot\left(\frac{L_{g}}{L}\right)^{3}\right)\) & (2) & The deflection calculation according to CSA S806-12 [2] is based on the moment-curve ratio along the span \\ \hline \end{tabular}
\end{table} 
Table 1: Some of the equations for calculating the deflection of continuous beams loaded with concentrated forces in the middle of the span. type. The same data were calculated by the finite element method in combination with time integration using the FIRE program and neural networks. The obtained results, using both programs, for fire resistance are almost the same [15]. [PERSON] and [PERSON] defined a prediction model for predicting the strength of ready-mix concrete and high-strength concrete with chemical and mineral admixtures ([PERSON] 2011) [16]. The authors used two types of neural networks (backpropagation and cascade correlation) ([PERSON] 2009) to predict compressive strength for lightweight concrete mixtures of different ages. Prediction of the concrete admixture strength was conducted after three, seven, 14 and 28 days. The model has eight input variables: sand, water-cement ratio, lightweight aggregate, coarse aggregate, frozen smoke (aerogel) as an additive to cement, superplasticizer and a drying period. Compression force represents the output of the model. The research resulted in the conclusion that the model of cascade correlation gives more accurate results and learns very quickly compared to the backpropagation model [17]. [PERSON] and others, with the help of artificial neural networks, developed a prediction model for determining deflection in concrete beams. The defined model had 10 input values, two hidden layers and one output value. This model had high prediction accuracy and showed better results than the results obtained using linear regression. Model performance was measured via mean square error (MSE). The error value in the model of artificial neural networks is 40 times smaller than in the model of linear regression [18]. [PERSON] presented the applications of neural networks in concrete structures. The author chose backpropagation networks which were written using the software MATLAB. It was concluded that NN are comparatively effective.