# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 1 Introduction
Technical condition is a set of quantitative and qualitative indicators that determine the operational suitability of the structure. The bridge has seven structural components: superstructure, supports, foundations, scaffolding, roadway, approaches, and regulating structures. After inspection of the bridge components, the detected defects, damage, and other signs of degradation are compared with the data in the classification tables. The standard consists of 20 tables relating to all the most important components of the bridge. After comparing the actual condition with the data in the table, each component is assigned a corresponding condition from 1 to 5 (1--in good condition, 2--partially in good condition, 3--acceptably in good condition, 4--almost unfit for operation, 5--unfit for operation). In Poland, the \"Instructions for conducting road inspections of engineering structures\" [19] determine the frequency, purpose, and method of conducting and documenting the inspection. Engineering structures are inspected to identify threats to the safety of their use, maintain them in proper technical and aesthetic condition, and prevent excessive deterioration of their functional properties and technical efficiency. To unify the method of assessing engineering structures by different inspectors, the \"Principles of using the scale of point assessments of the technical condition and suitability for use of road engineering structures\" [20] (hereunder referred to as the Principles) was introduced. The rules contain instructions, advice, and examples for bridge inspectors to follow when assessing individual elements of engineering structures during basic and extended inspections. The technical condition of individual components of engineering structures is assessed on a six-point scale according to the following criteria  
* Adequate condition, score 5, no damage or contamination that could be detected during inspection;
* Satisfactory condition, score 4, the first symptoms of damage or contamination that impair the aesthetic appearance are detected;
* Alarming condition, score 3, detected damage that, if not eliminated, will shorten the period of safe operation;
* Insufficient condition, score 2, detected damage that reduces the usefulness of the structure but can be eliminated;
* Pre-emergency condition, score 1, irreversible damage is detected, which excludes the suitability of the facility;
* In an emergency condition, score 0, the facility is destroyed or does not exist. The rules provide for inspecting and assessing the technical condition of 21 bridge structure components. The rating of the entire facility is determined as the lowest value from several options:  
* Arithmetic mean of all evaluated components;
* Assessment of the bridge structure;
* Assessment of the main beam structure;
* Arithmetic mean of the assessment of supports and pillars. The article's next section will present the process of creating a dataset and AI models for assessing bridge decks, span structures, and piers and abutments produced from reinforced concrete. Bridges are an important infrastructure element and require constant attention to ensure the safety and efficiency of transportation systems. Traditional methods of surveying and assessing the technical condition of bridges have their limitations, so the use of artificial intelligence (AI) is gaining popularity in solving this problem [13]. Numerous studies and publications indicate the active interest of scientists and practitioners in the use of artificial intelligence in the system of monitoring the technical condition of bridges. They demonstrate the potential of artificial intelligence for detecting defects, predicting service life, and optimizing repair planning. Using artificial intelligence to monitor bridges' technical condition is appropriate and has many advantages. It allows for improved monitoring efficiency, ensures the accuracy of assessing the condition of transport structures, predicts possible damage, and improves maintenance and repair planning.