# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 2 Methods
### The Applied AI Methods  
This study used the following AI methods: Support Vector Machine, Decision Trees, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. Boosting is an ensemble learning method that improves the predictive power of a model by combining multiple weak learners to form a strong one. The key idea behind boosting is to train models sequentially, where each new model attempts to correct the errors made by the previous ones. XGBoost (Extreme Gradient Boosting) is a highly efficient and scalable implementation of gradient boosting, which has gained immense popularity due to its exceptional performance in various machine learning tasks [41]. In regression tasks, XGBoost operates by optimizing a loss function through an iterative process, where each step adds a new model to minimize residual errors. Unlike traditional gradient boosting, XGBoost introduces several enhancements, such as regularization techniques (L1 and L2), to prevent overfitting and ensure the model's generalization capability. This makes it particularly effective in handling noisy datasets or datasets with complex relationships. XGBoost also supports parallel computation during training, significantly speeding up the process by utilizing multiple processors. Another notable feature is its ability to handle missing values within the data, eliminating the need for manual imputation. Additionally, XGBoost allows for the use of custom loss functions, providing greater flexibility in tackling specific regression challenges. Thanks to its robust performance and flexibility, XGBoost has become a go-to algorithm for many competitive machine learning tasks, especially in structured data problems. Support Vector Machine (SVM) is a supervised learning method designed to identify the optimal hyperplane that separates a dataset into distinct classes [42]. This machine learning technique is frequently applied in tasks such as classification and regression analysis. The primary goal of an SVM is to determine the hyperplane that provides the maximum margin between classes in a dataset. The margin is the distance from the hyperplane to the nearest data points of each class, and the hyperplane maximizing this margin is selected as the most effective classifier. SVMs can address both linearly separable and non-linearly separable data by utilizing kernel functions, which map the data into a higher-dimensional space, enabling a linear hyperplane to achieve separation. These algorithms find applications across diverse domains, including computer vision, text classification, bioinformatics, and finance. For regression problems, the Support Vector Regression (SVR) algorithm, a variation of SVM, is employed [36]. Like SVM, SVR aims to find an optimal hyperplane, but instead of categorizing data, its objective is to predict a continuous output variable. SVR minimizes the error between predicted and actual values in the training data while maximizing the margin between the hyperplane and the data points. A set of support vectors--data points closest to the hyperplane--are used to construct the regression model. Decision Trees (DTs) are another type of supervised learning algorithm utilized for classification and regression [43]. They operate through a tree-like structure that models decisions and their potential outcomes. The tree comprises nodes representing decision points, branches indicating possible outcomes, and leaves that signify final results. The algorithm splits the dataset into smaller subsets at each node by selecting the feature that provides the best separation, using criteria such as information gain or Gini impurity. Splitting continues until a stopping condition is met, such as reaching a minimum sample size per node or a maximum tree depth. The resulting Decision Tree can predict outcomes for new data by traversing from the root node to a leaf node. Random Forest (RF), an extension of Decision Trees, is a supervised learning algorithm known for its accuracy and robustness in classification and regression tasks [44].