# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 1 Introduction
[33] present a probabilistic approach for evaluating subsidence phenomena impacting existing structures and infrastructure. The method integrates multitemporal interferometry synthetic aperture radar (MTInSAR) data with unmanned aerial vehicle (UAV) photogrammetry. Statistical distributions of spatiotemporal velocities derived from surveys are analyzed and assessed against a predefined limit state. The results are categorized into different scenarios to identify potential subsidence occurrences. The proposed methodology was initially validated on a real landslide, focusing on its vertical displacement component. Subsequently, it was tested on another case for risk prediction, demonstrating its effectiveness in providing early warnings to support the implementation of risk mitigation measures. [PERSON] and [PERSON] [34] proposed a model for assessing the bearing capacity of existing bridges, for which there is no design documentation and no possibility of conducting field tests. Since only limited information about the bridges is available, the basis of the study is the use of AI technology. The authors compared the performance of five regression algorithms: multiple linear regression (MLR), Decision Tree (DT), boosting tree (BT), Support Vector Machine (SVM), Gaussian process regression (GPR), and ANN model for estimating the calibration coefficient as a condition assessment of 82 bridges. Each of the AI models used in the proposed framework showed high predictive performance and can be used to assess the bearing capacity of bridges without design documentation. [PERSON] at al. [35] present ML-based tools designed to predict the reduction of prestressing force in prestressed reinforced concrete (PSC) box-girder bridges with unbonded tendons. The dataset used for model development was generated from experimental tests conducted on a scaled PSC box-girder. The results, analyzed using statistical metrics and an explainability approach, demonstrate that the proposed model effectively predicts prestressing force reduction for this type of bridge. The prediction is based on inputs, including the current prestressing force, the elastic modulus of the concrete, and the strain variation at specific cross-sections of the structure. The use of AI in the research and assessment of the technical condition of bridges has immense potential. It opens up new prospects for improving the efficiency and reliability of the infrastructure. However, using AI tools to solve this problem has several disadvantages. Table 1 systematizes the advantages and disadvantages of using AI tools according to data analysis, detection, automation, forecasting, inspection, and human factors. Analysis of the main trends of transport systems shows that the direction of their development can be characterized as a complication of the design and simplification of operation. Moreover, the main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature [39]. Given the complexity of modern engineering structures, it is impossible to predict and enter into the calculation scheme all possible operational situations in calculating and designing these structures, which can change during operation. In addition, during operation, both the characteristics of materials and the parameters of external influences and other factors accompanying the operation process change in a complex and unpredictable way. During operation, various types of defects and damages appear and develop in structures, which adversely affect the structure's operational condition. Therefore, ensuring the reliable functioning of newly constructed and existing transport structures requires the development and application of modern systems for the inspec