# A Review of the T-Stub Components for the Analysis of Bolted Moment Joints
## 5 Informational Model  
Different methods of studying the T-stub were presented (analytical, experimental testing and numerical models). With the recent advances in statistical, machine learning and artificial intelligence techniques, more sophisticated approximation models have been developed in engineering domain investigations [78].  
Informational models could be developed based on different methods such as fuzzy logic methods, neural networks (NN), artificial NN (ANN), evolutionary algorithms, support vector regression (SVR), probabilistic reasoning, Bayesian methods and statistical learning procedures, etc. These methods are covered by soft computing (SC) techniques,which is an emerging and more or less established family of problem-stating and problem-solving methods that aims to mimic natural intelligence [79].  
The artificial neural network (ANN) is among the most widely known approximation models. Various studies were done about the beam to column connection. For instance, in 1996, [PERSON] and [PERSON] [80] estimated the response of a cast-in-situ beam to column joint (a beam to column joint that is made of concrete) based on 34 tests. This research aimed to demonstrate a concept and a methodology of parallel distributed processing-based learning in artificial neural networks.  
In 1997, another group of researchers, [PERSON] et al. [81], proposed a two-stage neural network approach for the elastoplastic analysis of steel structures with semi-rigid connections. In the first stage, the moment-rotation law is obtained as a quadratic programming problem (QPP) by the first neural network, based on the perceptron model, from the six experimental test results. In the second stage, the second NN, based on the Hopfield model, resolved the resulting QPP. This research work demonstrated that ANN models are also able to accurately estimate the response of single web-angle bolted con- nections.  
In 1997, [PERSON] et al. [82] used ANN to predict the moment-rotation curve of the minor axis of end-plate connection between the beam and column. The results of 20 tests provided the training of the ANN, and the variables that were taken into account were the column depth of section, flange thickness and web thickness. The beam flange breadth, depth of the section. The connection number of bolts and plate thickness.  
In 2005, [PERSON] et al. [83] studied the behaviour of the welded end-plate and double angle connections by ANN. The author addressed that the initial stiffness and the bending moment resistance were accurately estimated with only two ANNs for each connection type.  
Most of the research studied the whole connection, such as the end-plate connection, since this method appeared. However, in 2014, [PERSON] et al. [78] studied the T-stub component by a numerical-informational method and predicted the response of the component. This hybrid approach constitutes a fusion between the FE method and metamodels based on soft computing (SC). The numerical informational method's methodology starts when a representative dataset (geometrical parameters and mechanical properties) of T-stub configurations are generated using design of computer experiments (DoCE). Then this representative dataset is used as inputs in a refined FE model to run a parametric simulation. Afterwards, the simulations provide the force-displacement curves of the dataset and are stored then. These curves are characterised into a set of physical meaning parameters, and this information is used in the following learning process. The last process consists of training and testing metamodels by using a combination of SC techniques based on SVR and genetic algorithms (GA), which were employed to achieve overall and parsimonious metamodels. Finally, the parsimonious metamodels were tested against new data to assess both their prediction capacity and generalisation ability, see Figure 30.