## 2 Results and Discussion  
Pilot testing for plain concrete cubes of 150 mm size [5; 20; 22] and reinforced concrete beams were done in the laboratory to ensure that the nonlinearities or the presence of higher harmonics resulted from the material nonlinear response due to mechanical damage and not from the instrumentation used in the test. Shown in Figure 7 is the experimental result of WC60C specimen one where A3 and load is investigated. In addition, shown in Figure 8 is the result from WC60C specimen three. It showed that A2 and the applied load shows good agreement with each other. Experimental results show that the analysis of data using statistical method to model the behavior is very complex and ANN modeling is advantageous to classify sensitivity of parameters with load. Figure 6: Artificial neural network (ANN) model with 5 IN-3 HN-1 ON with layer 1 (L1) and layer 2 (L2) as transfer functions. Figure 7: WC60C Specim one: A3 versus load. The last parameter used for ANN modelling is NA. In determining the neutral axis location with respect to load, the recorded tensile and compressive strain is considered at the mid span of the beam. Cross-section of the sample strain diagram at a particular step load and its corresponding stress diagram is shown in Figure 9. The stress at the top fiber of the concrete is assumed to have a maximum value of 0.85 of ultimate confined compressive strength (fc') in MegaPascal for SI units. This location of neutral axis is dynamic due to the changes in elongation or contraction at the top and bottom of the beam during the step loads in the experiment. It is computed using Equation (2). Location of neutral axis \(y\) is based on the theory on elastic on elastic design of reinforced concrete. The distance NA is shown in Figure 9 based from the mid height of the cross section of the beam to the neutral axis for a particular load. See in Figure 10 is the behaviour of the neutral axis for WC40C Specim three with load. It shows that the relationship of NA with load is complex. \[y=\varepsilon_{c}\left(\mathrm{h}\right)/(\varepsilon_{t}-\varepsilon_{c}) \tag{2}\]  
where: \(\varepsilon_{c}\)--average strain at midpoint of the top surface in compression, \(\varepsilon_{t}\)--average strain at midpoint bottom surface in tension, h--height of the beam. From the 60 ANN models trained, validated, and tested, a model was chosen for each WC corresponding to each reinforcement design having the highest Pearson correlation coefficient (\(R\)) in testing, and the soundness of the behavior for the input parameters in relation to the damage in concrete in terms of load. Some of the models had its \(R=0.98\), but models may tend to overfit. The soundness of models was checked by having simulations attuned to theory to avoid overfitting. As an example,  
Figure 8: WC60C Specim three: A2 versus load. Figure 9: Neutral axis across (**A**) cross section of the beam; (**B**) strain diagram; (**C**) stress diagram. NA: neutral axis. decreasing PPA led to increasing compressive stress or increasing load. In this way, it prevented the trained ANN models to overfit even if it had the most desirable \(R\). Shown in Tables 2 and 3 are the ANN models \(R\), for the training, validating, and testing data. There were six optimum ANN models highlighted and taken from Tables 2 and 3. Selection of the optimum models were considered with two criteria, highest \(R\) in testing data and model that gave a sound simulation according to theories and literature. For WC40A, B and C, the ANN models