## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.5 Mechanics Engineering  
The mechanics engineering domain covers continuum mechanics, solid body kinematics and dynamics, the strength of materials, experimental mechanics, fracture mechanics, the theory of plasticity and viscoelasticity and theoretical methods for calculating linear and surface vectors. In particular, the modelling of fracture energy investigation methodologies utilized to investigate the fracture performance of concrete structures/beams is a hot topic of study because of the critical relevance of this topic to the practical implementation of concrete engineering works. While the fracture energy may be estimated and the fracture behavior of various concrete structures can be predicted, this is not always possible owing to the material's inherent properties and the intricacy of the fracture process. In [58], the researchers used various experimental methodologies, AI and associated optimization techniques to find a workable solution to fracture energy prediction issues. Multiple factors that influence the fracture energy and compressive strength of concrete were studied, and critical conclusions were gleaned for further study and experimental assessment. Specifically, in this study, artificial intelligence approaches were used to seek a feasible way to solve these prediction issues. Firstly, the ridge regression (RR), the classification and regression tree (CART) and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. Then, the hyperparameters were tuned with the particle swarm optimization (PSO) algorithm; the performances of these three optimum models were compared with the test dataset. The mean squared errors (MSEs) of the optimum RR, CART and GBRT models were 0.0447, 0.0164 and 0.0111, respectively, which indicated that their performances were excellent. Compared with the RR and CART models, the hybrid model constructed with GBRT and PSO appeared to be the most accurate and generalizable, both of which are significant for prediction work. The relative importance of the variables that influence the fracture energy of concrete was obtained, and the compressive strength was found to be the most significant variable. One of the most basic composite materials with excellent properties is fiber-reinforced concrete, the application of which is constantly expanding in multiple technical projects. However, its mixed design is mainly based on extensive experimentation, the effectiveness of which has been tested. For example, [59] deals with the concept of using metallic and non-metallic fibers in concrete. In this research, GI (galvamized iron) and chopped jute fibers were used to develop an FRC material to study the possible improvement in the 28 day strength. Clumping of fibers at high fiber amounts caused mixing and casting problems. These problems became even more severe when long fibers were used at a high fiber dosage amount. In this study, different compositions of jute (0.1%, 0.2% and 0.3%) and GI fibers (1%, 1.5% and 2%) with different lengths were added to concrete. Significant increases in compressive and tensile strengths between plane concrete and fiber-reinforced concrete were found. Accordingly, one of the main priorities of the field in question is the study of the mechanical properties of composite materials used in constructions, their conventional failure criteria and their possible deformation states. Researchers in [60] implemented a machine learning model capable of predicting the fracture behavior of all possible subclasses of fiber-reinforced concrete, especially cementitious composites made with strain hardening. Specifically, five machine learning models were developed and their outputs were compared. These include artificial neural networks, the support vector machine, the classification and regression tree, the Gaussian process of regression and the extreme gradient boosting tree.