Innovative methods for assessing the technical condition of bridge structures

Abstract

Bridges worldwide are aging while the loads they carry continue to increase, making the monitoring of structural condition a critical engineering challenge. Traditional visual inspections conducted at annual or multiannual intervals remain useful but are insufficient for the early detection of corrosion processes and microcracks that may compromise structural integrity.

This article examines modern approaches to bridge condition monitoring that are already applied in engineering practice. These approaches include continuous sensor systems with real time data transmission, non destructive testing methods, and the use of unmanned aerial vehicles equipped with optical cameras and LiDAR systems.

The study demonstrates that the application of big data analytics and machine learning algorithms enables more accurate prediction of structural degradation and remaining service life. At the same time, several challenges persist, including high implementation costs, ongoing equipment maintenance requirements, and the complexity of interpreting large volumes of heterogeneous monitoring data.

The main conclusion is that the integration of sensor networks, digital monitoring platforms, and robotic inspection technologies significantly improves the reliability of bridge operation and enables proactive maintenance planning. Such an integrated approach supports earlier intervention, reduces the risk of sudden failures, and contributes to safer and more sustainable infrastructure management.

Keywords

bridge monitoring, non destructive testing, technical condition, sensors, structural reliability, artificial intelligence, integral assessment

1. Introduction

Bridge structures are critical elements of transportation systems, and their safe operation directly affects the functioning of society. At the same time, a significant proportion of existing bridges were constructed several decades ago and are gradually approaching the end of their design service life. The accumulation of damage caused by material fatigue, corrosion, increasing traffic intensity, and environmental воздействия leads to a gradual reduction in the load bearing capacity of structural elements.

Traditional bridge maintenance practice is based on scheduled visual inspections, typically conducted annually or once every few years, combined with instrumental surveys performed at relatively long intervals. However, numerous cases of sudden bridge failures and collapses worldwide demonstrate that periodic regulatory inspections alone are insufficient to ensure structural safety ^[4][7]. More effective methods of damage detection and condition monitoring are required to identify early stage defects and prevent аварийные situations in advance.

With the rapid development of technology in recent years, two key directions have emerged in the field of inspection and monitoring of engineering structures. The first direction is the advancement of structural health monitoring and non destructive testing methods, including the deployment of sensors, data acquisition networks, and automated condition assessment systems ^[10]. The second direction focuses on the implementation of intelligent solutions in the design of new bridges, involving digitalization and embedded self diagnostic systems aimed at reducing future maintenance costs. The focus of this article is on the first direction, namely innovative approaches to technical inspection and monitoring of existing bridge structures.

Recent advances in measurement systems, electronics, and computing technologies have created new opportunities for continuous monitoring of bridge performance. Modern technical solutions enable real time measurement of key structural parameters such as deformations, vibrations, crack opening, and stress levels, with data transmitted to control centers for analysis. For example, automated structural monitoring platforms such as the ArchiSense system have been developed for continuous condition assessment of buildings and structures ^[1].

At the same time, new criteria and indices have been proposed for integrated quantitative evaluation of structural deterioration and stability. In particular, reference ^[1] describes an original methodology for calculating the structural functional stability index, which assigns an integral technical condition score to a building or structure based on a combination of diagnostic parameters. The application of such indices provides engineers with a practical tool for comparing the condition of different structures and justifying repair prioritization.

The purpose of this article is to summarize modern methods for assessing the technical condition of bridges and to demonstrate their practical application through representative examples. The following sections address: (1) innovative bridge monitoring systems that provide continuous control of key parameters; (2) advanced non destructive testing methods for materials and structural components; (3) robotic and remote inspection technologies that extend the capabilities of visual assessment; and (4) integrated indices and digital models for comprehensive evaluation of bridge reliability.

The discussion section presents a comparative analysis of the reviewed methods and highlights their advantages and limitations, including the application of artificial intelligence algorithms for data processing. The final section formulates conclusions regarding the importance of implementing these innovations to enhance the safety and durability of bridge infrastructure.

2. Methods of Bridge Condition Assessment

Structural Health Monitoring Systems for Bridges

Structural health monitoring refers to continuous automated control of technical parameters of a structure during its operational life. In the context of bridges, monitoring enables real time observation of deck deformations, deflections and settlements of spans, displacements and inclinations of piers, traffic induced vibrations, changes in material condition, and other indicators that characterize structural performance. The primary objective is early detection of anomalies and degradation processes, which supports timely intervention to prevent failures and optimize maintenance activities.

System composition and architecture. A typical monitoring system consists of a network of sensors installed on key bridge elements, data acquisition and transmission devices, and software for data storage, processing, and visualization ^[4][7]. Depending on the measured parameters, different types of sensors are used, including strain gauges for deformation measurement, accelerometers for vibration monitoring, inclinometers, crack opening sensors, temperature sensors, humidity sensors, corrosion sensors, and others. In modern systems, the number of sensors may reach dozens or even hundreds. These systems operate in automated mode with data transmission via wired or wireless communication channels.

An important direction of development is the integration of intelligent data processing algorithms that enable noise filtering, sensor fault diagnostics, and prevention of false alarms ^[7]. Practical implementations show that automated monitoring systems can detect hazardous deviations months before they become visible during traditional visual inspections. In addition, long term archiving of large data volumes, such as settlement histories, temperature effects, and dynamic responses, creates a valuable database for subsequent analysis and expert assessment.

Monitored parameters. Modern monitoring systems address a wide range of control tasks. One of the most important is the monitoring of deformations and displacements, including measurement of span deflections, horizontal shifts, and pier inclinations with high precision, down to fractions of a millimeter. This is achieved by combining several methods. Stationary laser rangefinders and level sensors track sagging and settlements, inclinometers measure angular rotations of structural elements, and geodetic instruments such as electronic total stations periodically record the coordinates of control points on the bridge.

Another key task is the assessment of dynamic characteristics. Accelerometers register vibrations caused by traffic loads, wind gusts, and seismic disturbances. Frequency spectrum analysis of these vibrations allows identification of natural frequencies of the structure and monitoring of their changes over time. Such changes may indicate stiffness reduction, crack development, or degradation of structural connections. These data are complemented by strain measurements of the stress strain state. Electrical resistive or fiber optic strain gauges are installed on steel girders, bridge decks, retaining elements, and stress concentration zones. They continuously measure microstrain levels, typically within several microstrain, enabling estimation of current stress levels in structural materials.

Such measurements are particularly effective for prestressed reinforced concrete elements, where monitoring of prestress losses and crack development is critical. Crack opening in concrete can be measured using linear displacement sensors or fiber optic Bragg grating sensors. Corrosion activity is assessed using specialized sensors that measure electrochemical parameters of reinforcement, indicating the onset of corrosion processes.

Implementation examples. In recent years, significant experience has been accumulated worldwide in equipping major bridges with comprehensive monitoring systems. One example is the Alexander Nevsky combined road and tram bridge over the Neva River in Saint Petersburg, one of the largest movable bridges in the city. Between 2018 and 2020, an automated structural monitoring system was developed and implemented for this bridge.

The system includes several subsystems based on different physical principles: a geodetic monitoring subsystem with optical sensors and markers for tracking displacements of spans and piers, a vibration diagnostics subsystem consisting of a network of accelerometers installed on the spans, a strain monitoring subsystem with deformation sensors placed in critical cross sections of girders, and a meteorological data module measuring temperature, wind speed, and related parameters. All subsystems are integrated within a unified software platform that implements alert prioritization logic. The system analyzes data in combination and notifies maintenance personnel when critical thresholds are exceeded.

As a result of this implementation, deviations in the behavior of one of the spans, including uneven pier settlements, were detected at an early operational stage. This allowed timely adjustment of load distribution and prevented further defect development. This case demonstrates the effectiveness of comprehensive monitoring. The combination of multiple sensor types provides a holistic view of the bridge structural condition and significantly improves monitoring reliability.

It should be noted that the regulatory framework is also evolving toward mandatory monitoring for critical bridge structures. National standards and sector specific regulations require the installation of monitoring systems on large span bridges, unique structures, and bridges located in seismic regions ^[8]. This trend highlights the growing importance of monitoring technologies in ensuring infrastructure safety.

Non Destructive Testing Methods for Bridge Structures

Non destructive testing represents a set of methods for investigating materials and structural elements without compromising their integrity. For bridge structures, such methods are particularly important during inspections where it is necessary to identify hidden defects in steel or concrete without opening or dismantling the structure. Recent technological advances have significantly expanded the capabilities of non destructive testing, improving both accuracy and informational value.

Ultrasonic testing. This classical method has gained renewed relevance due to digital technologies. The principle is based on the generation of ultrasonic waves and analysis of their propagation through a material. Portable ultrasonic thickness gauges and flaw detectors enable detection of cracks, delaminations, incomplete weld penetration, and hidden voids in concrete that are not visible to the naked eye. Ultrasonic testing is widely used for inspection of welded joints in girders and orthotropic bridge decks.

Modern phased array ultrasonic devices allow scanning of large material volumes at multiple angles, producing two dimensional and three dimensional images of the internal structure ^[7]. Achievable resolution is on the order of 0.1 to 2 millimeters in depth for defect detection. This method is particularly valuable for inaccessible areas such as anchor zones of prestressed tendons, embedded elements, and internal welds in box girders. Its main advantages include rapid results displayed in real time and full data documentation for subsequent analysis. Limitations include the need for good surface contact and qualified personnel for interpretation of results.

Acoustic emission method. This dynamic non destructive testing technique is increasingly applied in structural monitoring. It is based on the registration of elastic waves generated spontaneously in materials during the initiation and development of defects such as microcracks. Highly sensitive acoustic sensors are installed on the structure to capture characteristic acoustic signals. The appearance of these signals indicates active cracking or plastic deformation processes.

For bridges, this method is particularly promising during load testing and long term monitoring, as it enables detection of crack initiation well before visible manifestations occur. Advances in low noise instrumentation and signal filtering algorithms have improved reliability by reducing interference from wind, traffic, and other external sources. Acoustic emission monitoring has been applied, for example, to cable elements and welded joints of steel bridges, where crack activity detected under load was later confirmed by ultrasonic inspection.

Radiographic and electromagnetic methods. For thick steel elements such as truss node connections in large bridges, radiographic inspection using X rays or gamma radiation may be applied. This method provides highly informative images of internal metal structure but requires complex equipment and strict safety measures, which limits its use on operating bridges.

In reinforced concrete structures, ground penetrating radar and other electromagnetic scanning methods are increasingly employed. Portable radar systems enable scanning of concrete elements to visualize reinforcement layout, ducts, and zones of material discontinuity. Such tools help assess reinforcement corrosion based on changes in reflected signals and detect voids or defects in concrete placement.

Thermal inspection. Infrared thermography is another advanced diagnostic tool. Thermal cameras detect anomalies in temperature distribution, which are often associated with hidden defects or material heterogeneity. For example, thermal images of concrete surfaces can reveal areas of increased moisture due to evaporation effects or zones of protective layer delamination, where thermal conductivity and inertia differ from intact material.

In bridge applications, thermal imaging is used to monitor bearing overheating, friction in expansion joints, and uniformity of asphalt pavement heating during placement. Modern thermal cameras offer high spatial resolution and temperature sensitivity on the order of hundredths of a degree. When combined with unmanned aerial vehicles, they enable efficient inspection of large bridge spans and pylons.

The integrated use of the described non destructive testing methods in combination with structural monitoring systems provides the most comprehensive understanding of bridge condition. Continuous monitoring may identify zones of potential concern based on sensor anomalies, after which targeted non destructive testing confirms and details the nature of the defect. This integrated approach significantly improves diagnostic reliability and supports data driven maintenance planning.

Robotic and Remote Inspection Technologies

Visual inspection remains a necessary component of bridge condition assessment, as it allows detection of surface level defects such as cracks, concrete spalling, corrosion, and visible deformations. However, many parts of bridge structures are difficult or unsafe for inspectors to access directly, including high spans, undersides of decks, and cable elements of cable stayed bridges. As a result, remote and robotic inspection technologies have gained increasing importance in recent years, significantly extending human inspection capabilities.

Unmanned aerial vehicles. Drones are increasingly adopted for bridge inspection due to their high maneuverability and ability to carry various sensing devices ^[4]. They enable comprehensive aerial surveys of bridge structures, capturing high resolution photographs and videos of all structural elements. This substantially reduces the need for scaffolding, temporary platforms, or expensive inspection vehicles.

Modern inspection drones are equipped not only with high resolution optical cameras, but also with infrared thermal cameras for thermal assessment, LiDAR modules for laser scanning and generation of three dimensional models, and in some cases ultrasonic thickness gauges for contact measurements. Within the framework of the European AEROBI project, a specialized aerial robotic system was developed featuring a multi segment manipulator arm capable of performing contact based bridge inspection.

This robotic platform can precisely position itself near concrete girders and bridge piers, perform laser scanning of surfaces to detect cracks, measure crack widths, and when required, establish physical contact with the structure to carry out ultrasonic testing of crack depth and concrete cover thickness. According to the developers, the system automatically integrates measurement results into the bridge analytical model and evaluates load bearing capacity.

The effectiveness of drone based inspection has already been demonstrated on numerous structures. In Japan and the United States, drones equipped with optical cameras are used for periodic inspection of large bridge trusses. Comparative analysis of image sequences over time allows engineers to track crack propagation and corrosion development. In Austria, a project has been implemented in which drones perform laser scanning of complex arch bridges in mountainous regions, producing accurate three dimensional models on which engineers subsequently mark detected defects. These examples show that robotic inspection significantly improves coverage and level of detail, enabling access to areas unreachable by conventional means.

Machine vision systems and artificial intelligence. The large volumes of image and video data generated during bridge inspections require automated analysis. Computer vision systems are being actively developed to automatically detect defects in structural images. Neural network based algorithms are trained to identify cracks by characteristic linear patterns, assess corrosion severity based on color and surface texture, and detect concrete loss by analyzing exposed reinforcement contours.

Such systems can process thousands of images, highlighting potentially critical areas for detailed review by engineers. Intelligent algorithms are also applied to analyze monitoring sensor data, including detection of anomalous signals and prediction of defect evolution based on trend analysis. This field increasingly integrates traditional engineering models with artificial intelligence methods.

Knowledge driven approaches based on physical models and engineering rules offer high interpretability and stability but may not fully capture complex degradation processes. In contrast, data driven approaches based on machine learning and large datasets can identify hidden relationships and provide more accurate predictions, but often lack transparency and require extensive high quality data. Current research suggests that combining these approaches into hybrid systems is particularly promising. In such systems, outputs generated by neural networks are constrained and validated using physical laws and engineering limitations.

In practical bridge diagnostics, elements of artificial intelligence are already being applied. In China, several bridges employ fatigue degradation prediction programs based on combined sensor data and traffic load models. In Europe, expert decision support systems are under development to assist in maintenance planning, aggregating operational experience from thousands of bridge structures. These developments indicate that robotic inspection and artificial intelligence technologies will play a central role in the future of bridge condition assessment.

Integral Indicators and Comprehensive Assessment

The increasing volume and diversity of data generated by modern monitoring systems create the need for aggregation and representation in a form that is clear and usable for decision makers, including engineers, operating organizations, and management authorities. One effective approach is the calculation of integral indicators of bridge technical condition. The core idea is to derive a single condition index from multiple measured parameters and identified defects, enabling ranking of structures according to the degree of deterioration and associated risk.

An example of such an approach is the structural functional stability index methodology developed for assessing the reliability of building structures ^[1]. Although this index was originally designed for civil buildings, its conceptual framework can be adapted for bridge assessment. The index is calculated based on a weighted scoring of the condition of individual structural elements and components, taking into account their importance for overall structural stability.

The final aggregated score incorporates material strength indicators such as residual load bearing capacity of girders, the extent of defect development including crack depth and corrosion area, monitoring results such as deviations in deflections and vibration frequencies from design values, as well as indirect factors including load intensity, structure age, and operating conditions. Each factor is assigned a weighting coefficient, and the integral score is calculated using a defined formula.

Such an integral technical condition index allows classification of a bridge into predefined categories, for example serviceable, serviceable with limitations, unserviceable requiring repair, or emergency condition. For buildings assessed using the structural functional stability index, values exceeding a critical threshold indicated emergency status and the need for immediate strengthening or decommissioning. The introduction of a similar index for bridges could significantly simplify condition control by reducing hundreds of monitoring parameters and inspection results to a single numerical value or condition class. At the same time, development of such a methodology represents a complex scientific and engineering challenge that requires extensive failure data and expert calibration of weighting factors.

Another important direction of integral assessment is the creation of bridge digital twins, which are detailed analytical models linked to monitoring data ^[3]. The digital twin concept assumes that each physical bridge is associated with a virtual counterpart, such as a finite element model, into which field measurement results including deformations, stresses, and vibration modes are regularly incorporated. The model is calibrated to the actual structural condition and can be used to predict behavior under various load scenarios and future degradation processes.

In essence, the digital twin acts as an information integrator. It enables engineers to test hypotheses, for example estimating bridge performance after ten years of continued corrosion, and to evaluate the effectiveness of mitigation measures such as strengthening or load reduction. This supports scientifically grounded and quantitative decision making. Elements of such digital modeling are already used in practice, where results of dynamic bridge testing are compared with analytical models, and discrepancies are interpreted as indicators of stiffness reduction or structural damage. With the expansion of permanent monitoring systems, the integration between monitoring data and analytical models is expected to intensify.

Finally, the integral approach is also implemented at the level of infrastructure management systems. Many countries operate automated bridge management systems in which each bridge is assigned a unique technical condition profile. These systems accumulate all available information, from inspection and repair records to real time sensor data, and generate a condition index. For example, the United States uses the National Bridge Inventory rating scale from 0 to 9, which directly influences maintenance funding decisions. In Europe, bridge reliability assessment increasingly incorporates risk indicators based on monitoring data.

Russian practice is also moving in this direction through the development of regional bridge databases, where mechanisms for automated assignment of technical condition categories based on integrated assessment of inspection and monitoring results are expected to be implemented as part of smart city and digital infrastructure initiatives ^[2]. Overall, integral indicators serve as a critical link between advanced monitoring technologies and practical actions aimed at ensuring the long term reliability and safety of bridge structures.

3. Discussion

The innovative methods reviewed significantly enhance the capabilities of engineers in assessing the technical condition of bridge structures. At the same time, their implementation is associated with both technical and organizational economic challenges. This section discusses the advantages and limitations of modern approaches, as well as their potential for further development.

Advantages of integrated monitoring. The primary advantage of modern monitoring systems lies in their ability to detect problems at an early stage. Previously, defects became apparent only after visible manifestations such as excessive settlement, cracking, or corrosion. Today, sensor systems can signal deviations long before such symptoms appear. Automation enables continuous monitoring rather than episodic inspections conducted once every several years. This substantially increases safety. For example, monitoring systems can detect the onset of uneven pier settlement through inclinometer data, allowing traffic restrictions to be introduced before a critical condition develops.

The economic effect is also significant. Preventing failures and performing planned minor repairs is considerably less expensive than emergency reconstruction after a collapse. According to various estimates, implementation of monitoring systems can reduce unplanned repair costs by a factor of three to five. In addition, load optimization, such as restricting heavy vehicle traffic during unfavorable weather conditions based on sensor data, can extend the service life of a bridge by several years without structural strengthening. Another important benefit is the creation of a documented operational history. All data are archived, providing objective evidence of structural behavior in the event of disputes or incident investigations.

Limitations and challenges. The first major limitation is cost. Equipping a large bridge with a full scale monitoring system requires substantial investment, including hundreds of sensors, communication infrastructure, servers, and specialized software. Not all asset owners can afford such systems, particularly for secondary bridge networks. Second, system operation requires qualified personnel. Data must not only be collected but also correctly interpreted. The number of specialists trained in structural monitoring remains limited, and many operating organizations lack experience working with large data sets.

Third, sensor reliability and calibration present challenges. Equipment must operate for long periods under harsh environmental conditions, including moisture, low temperatures, and vibration. Failure of even a portion of sensors may lead either to missed critical warnings or to false alarms. Regular verification and calibration of hardware are therefore required, increasing operational costs. Fourth, large data volumes necessitate effective data filtering. Millions of measurements per month require automated processing algorithms, which is where artificial intelligence becomes essential. However, adoption of AI is constrained by regulatory conservatism. Responsible authorities tend to trust traditional analytical methods and engineering judgment more than black box neural networks. Accumulation of successful case studies is required to overcome this skepticism.

Combined use of methods. The most promising approach involves combining different assessment techniques. No single method provides a complete picture. Monitoring offers high temporal resolution but captures indirect indicators of damage. Non destructive testing is accurate but episodic. Visual inspection covers surface defects but is subjective. Analytical models enable forecasting but depend on up to date data. Only by synthesizing these tools can a truly reliable assessment system be achieved.

In this context, experience with integrated monitoring systems is particularly valuable, where static, dynamic, and environmental effects are analyzed within a unified framework. Future developments are likely to focus on unified platforms capable of integrating sensor data, inspection results, and expert evaluations, and producing integral condition assessments and maintenance recommendations. In essence, this represents the concept of a digital bridge inspector that supports, rather than replaces, the practicing engineer in decision making.

Role of artificial intelligence. AI algorithms have the potential to play a transformative role in bridge monitoring data analysis. Neural network models trained on large data sets of vibration signals and damage cases are already capable of identifying the presence and approximate location of cracks in girders based on changes in dynamic response. The main advantage of AI lies in its ability to detect complex patterns that are not obvious through conventional analysis.

At the same time, purely data driven algorithms may exhibit instability. Therefore, the most likely direction of progress involves hybrid systems in which neural networks operate within the constraints of physical models. For example, a neural network may process raw sensor signals and identify suspicious zones, while an embedded expert system verifies whether the detected anomaly is consistent with feasible structural damage scenarios. This approach allows exploitation of machine learning capabilities without sacrificing transparency and engineering validity. In the near future, commercial monitoring software integrating AI components is expected to become more widespread.

Regulatory and technical framework. Development of standards governing the application of new methods is a critical factor. Currently, many innovative inspection techniques are applied in experimental or research contexts, and their results are interpreted through expert judgment. For broader adoption, regulations must recognize the use of automated monitoring data in assigning bridge condition categories and formally approve methods such as acoustic emission testing for bridge structures.

In Russia, updates to construction standards and regulations in the 2020s have begun to incorporate provisions for remote inspection methods and monitoring systems. Major infrastructure owners are also developing internal guidelines for applying digital and monitoring technologies. Over time, this will reduce regulatory barriers and promote standardization of approaches.

In summary, innovative assessment methods have already demonstrated practical effectiveness. However, their full potential can only be realized through comprehensive implementation that accounts for organizational factors. It is necessary not only to equip bridges with sensors, but also to train personnel, allocate funding for system maintenance, and adapt regulatory frameworks. The global trend is clear: digital and intelligent technologies represent the future of transport infrastructure.

4. Conclusion

Ensuring the reliability and long-term durability of bridge structures in the twenty-first century is no longer conceivable without the application of modern scientific and technological advances in diagnostics and monitoring. Based on the analysis of contemporary innovative methods for assessing bridge condition, the following conclusions can be drawn.

• Continuous structural monitoring using sensor networks and automated systems enables a transition from a reactive maintenance model, in which repairs are performed only after defects become evident, to a proactive condition management strategy. Monitoring systems detect the early formation of problems and provide the opportunity to intervene before defects reach a critical stage. Practical experience confirms that integrated monitoring delivers valuable results and helps prevent emergency situations.
• Modern non-destructive testing methods, including ultrasonic testing, acoustic emission monitoring, radiowave scanning, and infrared thermography, significantly expand inspection capabilities. These techniques complement monitoring systems by enabling detailed investigation of identified problem areas without damaging the structure. Their integration into routine inspections increases the reliability and accuracy of bridge diagnostics.
• Robotic inspection tools and digital technologies, such as unmanned aerial vehicles equipped with machine vision systems and neural network-based image analysis, elevate visual inspection to a new level. Difficult-to-access structural elements can now be examined quickly and safely, while large volumes of visual data can be processed efficiently using artificial intelligence. European initiatives, including the AEROBI project, as well as developments in other countries, demonstrate the high effectiveness of drones in bridge inspection practice.
• Integral indicators and digital models simplify interpretation of complex monitoring results. Integral condition indices, similar in concept to the structural functional stability index, together with the digital twin approach for bridges, may serve as a foundation for objective condition classification and quantitative decision making regarding maintenance and repair.

Implementation of the reviewed innovations is no longer a matter of the distant future. Many of these technologies are already applied in both international and domestic projects. Nevertheless, the transition from isolated pilot initiatives to large-scale adoption requires coordinated efforts at multiple levels. Regulatory frameworks must be adapted, operating personnel must be trained, and standardized solutions for equipping bridges with monitoring systems and integrating heterogeneous data into a unified information environment must be developed.

It is important to emphasize that innovative methods do not compete with one another but rather complement each other. Maximum effectiveness is achieved when they are combined within a unified bridge safety management strategy. Monitoring sensors can signal anomalies, non-destructive testing can clarify the nature of defects, drones can inspect inaccessible areas, and digital models can estimate residual service life. Together, this integrated toolkit provides engineers with a comprehensive understanding of structural condition and confidence in their decisions.

In conclusion, innovative methods for assessing the technical condition of bridges have proven to be powerful tools that significantly enhance the effectiveness of safety control and infrastructure management. Their continued development and integration into routine operational practice are essential for preventing failures, extending service life, and optimizing maintenance expenditures. Digitalization and intelligent technologies represent an inevitable path of progress for transport infrastructure, and bridge engineering and operation stand at the forefront of these transformations. The responsibility of the engineering community is to ensure the competent and responsible application of these technologies in the interest of public safety and sustainable development of transportation networks.