## 1 Introduction
In addition to the experimental studies, many theoretical methods have been proposed to investigate the effective behavior of continuous RC members. [PERSON] et al. [8] developed a structural mechanics-based mathematical model for moment redistribution in continuous members, which was combined with the shear-friction approach [9], partial-interaction theory [10], and rigid body displacement. Through the application of this model, they concluded that moment redistribution increased with the decrease in the bond strength and the increase in the diameter of steel bars and concrete confinement. To study the entire nonlinear behavior of RC continuous beams, a finite element model based on the moment-curvature relationship and the Timoshenko beam theory was established by [PERSON] et al. [11]. By using this model, the effect of many factors, such as the concrete strength, the relationship between the tensile reinforcement ratios at critical negative and positive moment regions, the relative stiffness, and the concrete confinement on moment redistribution were studied comprehensively. As mentioned above, the mechanics of moment redistribution are incredibly complicated. The aim of this study was to propose a new model for accurate prediction of the moment redistribution that considers the various influential parameters as comprehensively as possible, while being convenient for the applications of practical engineering. In recent years, artificial intelligence techniques of artificial neural networks (ANNs) and support vector machines (SVMs) have exhibited great potential for solving various problems in civil engineering. Different from most methods used in civil engineering, ANN and SVM algorithms do not rely on the existing theories about structural mechanism, but employ high precision fitting to match the results to the real values as closely as possible [12]. These two models have been successfully applied to several areas in structural engineering, such as structural analysis [13; 14; 15], prediction of the shear resistance in beams [16; 17; 18; 19] and compressive strength of columns [20; 21; 22; 23], displacement determination of RC buildings [24], and estimation of the compressive strength for various types of concrete [25; 26; 27; 28; 29]. More recently, they were used to understand the behavior of structures under extreme conditions. Based on a back-propagation ANN, [PERSON] [30] developed a fracture model to predict the fracture parameters of cementitious materials. Compared with the common non-linear fracture mechanics approaches, the ANN model was more reliable. In a separate study, [PERSON] [31] established an ANN model for determining the ultimate moment capacity of RC slabs in fire. The prediction values were compared with the results provided by the ultimate moment capacity equation, which showed the ANN model had a high degree of accuracy. To ensure the realistic fire performance of steel structures, [PERSON] [32] used ANN to derive temperature-dependent thermal and mechanical material models for structural steel. The applicability of the models was validated using numerical case studies with a highly nonlinear finite element model. The results indicated that the proposed ANN models can significantly enhance the current state of structural fire design by developing a uniform representation of material properties at elevated temperatures. However, few studies have focused on the prediction of the moment redistribution in the statically indeterminate RC structures using artificial intelligence techniques. In this paper, an experimental study on the moment redistribution in 24 continuous RC beams and 12 continuous RC frames with various design parameters was presented. Furthermore, two models of ANN and SVM were developed using MATLAB software (MathWorks, Neddick, MA, USA) to predict the coefficient of moment redistribution in the statically RC indeterminate structures. In total, 111 experimental datasets were gathered to construct the models and a description of the development procedure is provided here.