# Neural Networks--Deflection Prediction of Continuous Beams with GFRP Reinforcement
## 1 Introduction  
The most commonly used material for reinforcing reinforced concrete (RC) elements in the construction of building structures is steel. Steel reinforcement is subject to corrosion that causes damage to the concrete and ultimately endangers the functionality and usability of reinforced concrete structures. Stopping the corrosion process is quite difficult and, often, without a significant effect. In addition, corrosion prevention costs can be significant. This fact is particularly important in reinforced concrete structures that are exposed to the aggressive action of the environment (marine structures, reservoirs, culverts, garages, etc.). This is the reason why fiber-reinforced polymer (FRP) reinforcement has been increasingly used instead of steel reinforcement. FRP and steel reinforcement have different mechanical and deformation characteristics. FRP reinforcement, unlike steel reinforcement, has high tensile strength and a low modulus of elasticity. For these reasons, reinforced concrete elements made by using FRP reinforcement act differently compared to the elements with steel reinforcement. As a consequence of the low elasticity modulus in RC elements, wider and deeper cracks appear, as well as larger deflections. Thus, in contrast with steel RC elements, the serviceability limit state of usability is very often relevant for design elements with FRP reinforcement. The behavior of simple beams with FRP reinforcement under operating load was the subject of experimental research. The results of these studies have yielded numerous formulas that have been proposed for determining deflection. Continuous beams reinforced with FRP reinforcement have also been the subject of experimental research, but to a much lesser extent. In general, experimental studies have obtained values which indicate that current regulations [1,2] underestimate the deflection values of continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement under the action of load. Some of the equations for calculating the deflection of continuous beams loaded with concentrated forces in the middle of the span are given in Table 1.  
In expression (1), \(E_{c}I_{e}\) represents stiffness, and \(I_{e}\) represents the effective moment of inertia of the observed cross-section. In Equation (2), \(I_{g}\) represents the moment of inertia for the gross cross-section without fissures, and \(I_{e\tau}\) represents the moment of inertia of a cracked cross-section. Despite the existence of several equations for determining deflection, for continuous beams loaded with a concentrated load, it is not possible to determine this size absolutely precisely. Deflection prediction for continuous beams can be performed by applying one of the artificial intelligence methods, artificial neural networks. This method is suitable for making predictions based on a large database. This paper presents its application. The database was formed from available data from, in this case, experimental research on continuous beams reinforced with GFRP reinforcement [3,4]. The beginnings of neural networks in construction date back to 1989, when the journal _Microcomputers in Civil Engineering_ published a paper referring to the application of neural networks in this field. The authors of this paper are [PERSON] and [PERSON] [5]. The range of applications of artificial neural networks in construction is very wide. This method of artificial intelligence is used for the purpose of various types of predictions. The use of artificial neural networks in construction is becoming more common because of, in addition to the wide range of possibilities they have, the rapid development of software packages. Predictions through the application of artificial neural networks are performed in all areas of construction. One of the predictions is the one about the cost of different construction structures, which was also addressed by a number of authors in their works [6, 7, 8, 9, 10, 11, 12].