## 4 Results
### CAE ANN Multi-Parametric Model as an ANN Bridge Design Assistant
#### 4.4.2 Application of ANN BD Assistant  
The proposed application was tested on two completed projects that were not included in the existing (primary) database. Another engineering and design company created these two projects. The output parameters of the cross-section were predicted for both test cases based on the values of three selected input parameters (characteristic span, cross-section width, and cross-section height). Following that, different combinations of an increasing number of input parameters were used to predict the output parameters of the cross-section for one test case by running two simulations of the iterative process. The first test case was Viaduct 6-1. The input parameters 40 m, 11.52 m, and 2.8 m were selected for the construction span, cross-section width, and cross-section height, respectively. The differences between the predicted and actual parameters (calculated and specified in the project documentation) were found to be minimal (up to a maximum of 8%) in the use phase, while in the construction phase the differences were slightly larger (up to a maximum of 26%). In addition, Figure 9 shows a comparison between the actual (specified) cross-section and the predicted cross-section by the ANN BD assistant. The predicted cross-section is marked with a thick red line. In this case, it should be noted that the top slab of the actual cross-section is slightly inclined (due to drainage and the horizontal level of the roadway in the radius), but this generally has a negligible effect on the main characteristics of the cross-section. It is critical to emphasize that the variations fall within the range of differences associated with technical guidelines, as well as differences made by various established engineers and designers in solving the same problem. The second test case was Viaduct 6-8, and the values of the input parameters were chosen as 50 m, 13.34 m, and 3.75 m for the construction span, width of the cross-section, and height of the cross-section, respectively. Again, it was found that the differences between the predicted and actual parameters (calculated and specified in the project documentation) were minimal in the use phase (up to a maximum of 6%), while in the construction phase, the differences were slightly larger (up to a maximum of 13%). Figure 10 shows a comparison between the actual (specified) cross-section and the cross-section predicted by the ANN BD assistant. The predicted cross-section is marked with a thick red line. Also, it can be observed that the variations in prediction fall within the range of differences associated with technical guidelines, as well as the differences made by various established engineers and designers in solving the same problem. Additional simulations were run for the test case of Viaduct 6-1 using various options provided by the ANN BD assistant in order to demonstrate the advanced design process (see Appendix A). Initial values for the two input parameters were chosen, and then updating and changing the input parameter values was considered. With each prediction, the values of the initial parameters are obtained, which (can) become the new input parameters for the prediction of new initial parameters in the next step. When updating and changing the input parameters, the database is changed (generally increased, but also decreased) in each step. For example, in the first step, one has \(D1\) input parameters and \(E1\) output parameters; in the next step, one typically has (\(D1+E1\)) input parameters and \(E2\) output parameters, etc. It should be noted that the maximum number of input and output parameters can be equal to the number of all parameters in the database. Based on the results presented, it can be assumed that CAE ANN successfully stores, models, and applies the knowledge required for effective design of incrementally launched prestressed concrete bridges.