# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 1 Introduction
[PERSON] [21] examines the application of artificial intelligence methods, particularly neural networks, genetic algorithms, and deep learning, for monitoring the technical condition of bridges. The study results show the effectiveness of using artificial intelligence in assessing the condition of bridge structures. [PERSON] and [PERSON] [22] examined various artificial intelligence methods and algorithms used to analyze the technical condition of bridges. The authors discuss the advantages and limitations of different approaches and emphasize the need for further research to improve bridge monitoring systems. The study [23] considers unmanned aerial vehicles (drones) and deep learning methods for bridge defect detection. The authors explore different neural network architectures and image processing algorithms for automatic defect detection based on drone video recordings. [PERSON] and [PERSON] [24] proposed using an ANN model to predict the condition of bridges based on the condition assessment of the bridge's structural components. The input level of the model consisted of eight parameters: current bridge age, average daily traffic, design, load, main structure design, superstructure design, number of main spans, percentage of daily truck use, and growth rate. [PERSON] [25] used statistical analysis to identify factors affecting the wear of bridge pavement. After recognizing the eleven most important factors, the author proposed an ANN model for wear prediction. The developed model accurately predicted the condition of the bridge deck and can provide necessary information for maintenance planning and decision-making. [PERSON] et al. [26] proposed a method based on the concept of artificial intelligence to assess the condition of bridges and optimize their maintenance. The method includes data integration, condition assessment, and maintenance optimization. A neural network models bridges' degradation and quantifies the maintenance scheme's impact on the future condition. Monitoring structures' health and assessing bridges' technical condition is becoming increasingly important. Conventional inspections with personnel are expensive, time-consuming, and often risky. [PERSON] et al. [27] proposed an automatic method for diagnosing data anomalies for SHM based on a multimodal deep neural network. To improve the detection accuracy, two-dimensional and one-dimensional sensor data characteristics are combined into a multimodal deep neural network. The authors studied and analyzed six types of training conditions to solve the problem of unbalanced training data. The optimal model showed an accuracy of 95%, which confirms the possibility of applying the proposed AI-based method to assess the condition of structures. [PERSON] et al. [28] developed an automated method for bridge inspection using AI. The proposed method assesses the cracking condition in reinforced concrete bridge decks. A deep learning-based workflow integrated with image classification and semantic segmentation methods extracts information from images for further crack condition assessment. Experimental results demonstrate the effectiveness of this workflow. The stiffness separation method has been widely applied to improve the efficiency of damage identification in large-scale truss structures, including long-span steel truss bridges, by significantly reducing calculation time [29; 30]. This approach enables partial-model-based analysis, making it particularly effective for complex and large-scale systems [31]. [PERSON] et al. [32] proposed a two-stage deep learning-based framework for intelligent infrastructure management for bridge condition assessment in developing countries. The study detected and classified cracks based on their severity level, performed crack segmentation and measured their geometric parameters under different lighting conditions. Local infrastructure monitoring and rehabilitation agencies can use the proposed model to assess technical conditions regularly. [PERSON] et al.