## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.2 Geotechnical Engineering  
The domain of geotechnical engineering covers the subject of soil dynamics, geotechnical earthquake engineering, soil-foundation-structure interactions, soil improvement and reinforcement, analysis of the behavior of geostructures with simulations, deep foundations, geotechnical engineering of mining projects and environmental and geotechnical engineering. Soil classification based on shared characteristics is a cornerstone of geotechnical engineering. Testing in the lab and the field, both of which may be expensive and time-consuming, has led to this categorization. Each construction site has ground studies, which must be completed before any technical project can be designed. Artificial intelligence may play a crucial role in cutting down on the time and money needed for a proper site inspection program. For example, [41] evaluated the essential ability of machine learning models to classify soils based on cone penetration tests (CPT). A dataset of 1339 representative CPTs was used to test 24 different machine learning models, based on three different algorithms. The applied algorithms were support the vector machine, artificial neural network and random forest algorithms; the input variables included tip resistance, sleeve friction, friction ratio and depth; and the output variables included total vertical stresses, effective vertical stresses and hydrostatic pore pressure. Soil classes based on grain size distributions and soil types based on soil behavior are often used as reference points in the literature. The accuracy of each model's predictions and the time it takes to train are compared. Notably, the algorithm with the highest predictive ability for grain size distribution soil classes obtained around 75% accuracy, while the algorithm with the best predictive power for soil classes achieved about 97-99% accuracy. The best results for all targets were obtained with models using a random forest classifier. Evaluating soil liquefaction is challenging in geotechnical earthquake engineering. The capacity energy is related to initial soil factors such as the relative density, the initial appropriate confining pressure, fine contents and the soil textural properties, which have been the focus of several liquefaction evaluation processes and approaches. Traditional methods used to assess the liquefaction risk of sand deposits fall into one of three broad categories: stress, strain or energy. The energy-based approach has the edge over the other two because, unlike the focus- or stress-based methods, it accounts for the impacts of stress and strain concurrently. In [42], the amount of energy needed to cause liquefaction in the sand and silty sand was estimated by conducting comparative analyses of state-of-the-art artificial intelligence systems on suitable datasets. Specifically, analyses were carried out on a total of 405 previously published tests using soft computing approaches, including ridge, Lasso and LassoCV, random forest, eXtreme gradient boost (XGBoost) and multivariate adaptive regression splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sand. The performance measures for ridge and Lassoand LassoCV models were all below 0.6, reflecting the shortage of linear regression methods in handling the complex data mapping in high-dimensional datasets. RF, XGBoost and MARS models were capable of capturing the nonlinear relationships involving a multitude of variables with interaction among each other without making any specific assumption about the underlying functional relationship between the input variables and the response. Using the results of ridge, Lasso and LassoCV, random forest, XGBoost and MARS for a cross-validation, it can be found that the capacity energy, Log(W), was most sensitive to Dr, the relative importance of which was almost 100% for all five machine learning methods. The R\({}^{2}\) score of the testing set for the MARS model was the same as the XGBoost model, and even higher than the RF model.