## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.3 Structural Constructions  
The domain of structural constructions covers the subjects of applied methods of analysis and design of linear and surface carriers; reinforced concrete structures for everyday and seismic actions; prestressed concrete structures for normal and seismic activities; special reinforced and prestressed concrete structures; and control and interventions in structures, metal structures, metal bridges, wooden structures, light structures and masonry structures for every day and seismic actions. Advanced machine learning algorithms have been successfully applied in many areas of modelling seismic structures and, more generally, in predicting structural damage from single earthquake events, ignoring the effect of seismic sequences. In [43], a neural network approach was applied to determine the expected ultimate structural damage of a reinforced concrete frame under natural and artificial ground motion sequences. Sequential earthquakes consisting of two seismic events were used. Specifically, 16 known measures of ground motion intensity and the structural damage caused by the first earthquake were considered characteristics of the problem. In contrast, the final structural damage was the goal. After the first seismic events and after the seismic sequences, the damage indices' actual values were calculated through a nonlinear time history analysis. The machine learning model was trained using the dataset generated from artificial arrangements, while the predictive ability of the neural network was approximated using the natural seismic lines. In this paper, a holistic single-hidden layer feed forward network (ShLFFN) shallow architecture approach with N neurons in the hidden layer, randomly chosen input weights and random values of bias constants on the hidden layer neurons was used, while the output weights were computed by a single matrix multiplication, which automates and optimally solves the problem. The proposed approach learns N samples with accuracy, while the learning speed was even thousands of times faster than conventional feed forward networks, as the training was not based on time-consuming, repetitive processes such as the back propagation algorithm, which changes the weights of the neural network by estimating the quadratic error between the target vectors and the actual network outputs for all training samples, which are entered into the network in a random serial manner and for many repetitions (epochs). An important advantage gained by the proposed system is the fact that it offers a more efficient and stable prediction model, since the overall behavior of multiple MLP ANNs is less noisy than a single one, and in any case, it reduces the overall risk of particularly inaccurate values. The study in question is a promising application of the method of modelling multiple seismic sequences for the final prediction of the structural damage of a building, offering highly accurate results [44]. Multiple nonlinear time history evaluations utilizing various incidence angles are necessary to determine the angle at which possible seismic damage is at a maximum (critical angle). Thus, the rise in seismic excitation is a crucial consideration in assessing the seismic response of structures. In addition, several accelerograms should be used to analyze the seismic reaction, as advised by seismic codes [45]. As a result, it takes longer to complete the project. [46] presented a technique for critical angle estimation that uses multi-layer neural networks to drastically cut down on computation time. The general concept is to identify situations in which the seismic damage category is higher due to the acute angle than it would be due to the application of seismic motion along the structural axis of the structure. This is accomplished by formulating and resolving the issue as a pattern recognition problem. Inputs to the networks were the ratios of seismic parameter values along the two components of the horizontal seismic files and correctly chosen structural parameters.