## 5 Conclusions  
An experimental study of 24 continuous RC beams and 12 continuous RC frames with various design parameters was performed to investigate the whole process of moment redistribution. Moreover, ANN and SVM models were established using MATLAB software (MathWorks, Neddick, MA, USA) for predicting the coefficient of the moment redistribution based on a reliable database with a total of 111 datasets, which was achieved using the experimental results in this study and the test data gathered from the published literature. The following conclusions can be drawn from the present study:  
(1) In statically indeterminate RC structures, moment redistribution occurs during the whole loading process, beginning from the cracking of the concrete and increasing sharply after the yielding of the reinforcement at the critical section. Due to the complex process, the influential factors related to the material, structure and loading should be fully considered in the prediction models. (2) Through a process of trial and error, a BP neural network model trained by the Levenberg-Marqardt (LM) algorithm, with 8 neurons in the input layer and 13 neurons in the hidden layer, was selected as being optimal for predicting the coefficient of the moment redistribution. The \(R^{2}\) and MSE values for both the training and testing data were 0.95 and 0.0009, respectively, denoting that the proposed ANN models have high prediction accuracy. (3) Through a grid search algorithm, the values of the parameters involved in the SVR model were determined as \(\varepsilon\) = 0.01, \(C\) = 20, and \(g\) = 0.03. With RBF as the kernel function, the proposed SVR model exhibited good predictive performance. The values of \(R^{2}\) for the training and testing data were 0.99 and 0.95, and the MSE values were 0.001 and 0.008, respectively. Moreover, the comparison of the performance indices showed that the accuracy of the SVR model is slightly higher than the ANN model for predicting the coefficient of the moment redistribution. (4) Efficiencies of the ANN and SVR model were compared with six structural design codes. Despite the conservative nature of the code methods, most predicted values with the design code were significantly lower than experimental results since the range of the mean values and the COV for the ratio \(\beta_{\text{pre}}/\beta_{\text{exp}}\) were 0.35-1.35 and 0.62-0.84, respectively, whereas the mean values of \(\beta_{\text{pre}}/\beta_{\text{exp}}\) for the two proposed models were closer to 1.0 and the COV values of \(\beta_{\text{pre}}/\beta_{\text{exp}}\) were 0.11 and 0.05, respectively. The predicted values closely agreed with the experimental results. ANN and SVR can be used as effective methods for the design of statically indeterminate RC structures because they can allow structural designers to accurately predict the degree of moment redistribution without conducting costly and time-consuming confirmatory experiments. Further parametric studies can be carried out to analyze the impacts of input parameters on moment redistribution to help the designers make more rational use of these parameters. Based on the large amount of results obtained from the models with respect to various input values, mathematical  
Figure 15: Comparison of the ratio between the experimental and predicted results using different methods: (a) ANN; (b) SVR; (c) CSA A23.3-14; (d) BS 8100-1:1997; (e) AS 3600-2009; (f) DIN 1045-1:2008; (g) fib Model Code 2010 and EN 1992-1-1:2004; (h) ACI 318-14.  
formulae with a certain safe guarantee rate can be established in future research, which may provide convenience for the application of the designers and references for the design codes. Nevertheless, the developed models are valid within the range of variables investigated in this study and the local invariance of some input parameters may lead to slightly negative effects on the accuracy and modeling capability of the developed algorithms.