System for adaptive load distribution analysis in multi-span bridge structures using embedded structural sensors

Abstract

An adaptive load distribution analysis system in a multi-span bridge structure, comprising: a plurality of embedded sensors physically arranged in load-bearing components, including at least one girder, at least one deck slab, and at least one support interface. Each embedded sensor includes a strain gauge for generating electrical signals representing deformation, a vibration gauge for detecting dynamic structural response, a temperature gauge for providing thermal compensation data, a signal conditioning unit electrically connected to the sensor elements for amplification and noise filtering, an analog-to-digital converter for generating digitized measurement data, and a local processing unit for performing calibration adjustments and data validation.a data acquisition unit that is communicatively connected to the numerous embedded sensors and configured to receive synchronized measurement data; and a central processing unit that is operationally coupled with the data acquisition unit and configured to calculate the span-related load distribution over multiple spans of the bridge structure based on the received measurement data and to dynamically update the load distribution properties in response to changes in structural behavior.

Description

Technical field of the invention The present invention relates generally to the field of measurement technology in civil engineering and in particular to a system for adaptive load distribution analysis in real time in multi-span bridge structures by means of embedded structural sensors integrated into load-bearing components. The invention further relates to a structurally integrated device for acquiring, processing and transmitting mechanical response data under dynamic and static load conditions. Background of the invention Multi-span bridges are subject to complex and variable loads from vehicle traffic, environmental influences, thermal expansion, material aging, and fatigue. Conventional inspection and monitoring methods are largely based on periodic manual checks or externally mounted sensors, which offer only limited spatial resolution and detect structural anomalies with a delay. Such methods are inadequate for capturing distributed load changes across multiple spans and do not provide real-time insights into load redistribution due to local damage, support settlement, or changing boundary conditions. Furthermore, the lack of an integrated sensor infrastructure limits the correlation of internal stress states with external loading events, thus reducing the reliability of predictive maintenance and structural condition assessment.Therefore, there is a need for an integrated system that continuously analyzes the load distribution across multiple fields using integrated sensor elements that interact directly with the components. Multi-span bridges are critical components of transport infrastructure and are subject to highly variable and complex loads from vehicular traffic, environmental influences such as wind and temperature gradients, seismic activity, and long-term material fatigue. The load-bearing behavior of such bridges is determined by the interaction of multiple spans, supports, and load transfer mechanisms, resulting in an uneven distribution of stresses and strains within the structure. Accurate determination of the load distribution in these systems is essential to ensure structural safety, optimize maintenance strategies, and extend service life.Until now, bridge monitoring has relied on simplified analytical models and periodic inspection methods that assume an idealized load distribution between spans and often fail to account for real-world variations such as column settlement, stiffness loss, and local damage. With increasing traffic density and rising load magnitudes, the limitations of traditional monitoring approaches are becoming ever more apparent, necessitating the development of advanced systems capable of capturing the actual structural behavior under dynamic loading conditions. Conventional bridge condition monitoring systems predominantly use externally mounted sensors such as strain gauges, displacement transducers, accelerometers, and inclinometers. These sensors are typically installed on accessible structural surfaces and connected to central data acquisition systems via wired networks. While such configurations provide useful information about the overall structural behavior, they have some inherent limitations. Surface-mounted sensors are susceptible to environmental factors such as corrosion, moisture ingress, and temperature fluctuations, which can lead to signal distortion and reduced measurement accuracy over time. Furthermore, the placement of these sensors is often restricted by accessibility constraints, resulting in low sensor density. Consequently, local stress concentrations and internal load transfer mechanisms are not captured.Consequently, the recorded data may not represent the actual internal state of the structure, which can lead to incomplete or misleading assessments of the load distribution. Another widely used approach is regular manual inspections, in which trained personnel visually examine structural components for damage such as cracks, deformations, or corrosion. While these inspections are essential for identifying visible defects, they are inherently subjective and dependent on the inspector's expertise. Furthermore, manual inspections are performed at irregular intervals and therefore do not allow for continuous monitoring of load-bearing behavior. This limitation is particularly critical for multi-span bridges, where rapid load redistribution can occur due to short-term events such as heavy traffic or sudden changes in support conditions. The inability to detect such events in real time increases the risk of undetected structural damage and potential failures. In recent years, wireless sensor networks have been introduced as an alternative to traditional wired monitoring systems. They offer advantages in terms of ease of installation and scalability. These systems typically consist of distributed sensor nodes equipped with sensing elements, processing units, and wireless communication interfaces. While wireless sensor networks reduce cabling complexity, they also introduce new challenges regarding power management, data reliability, and network synchronization. Many wireless sensor nodes rely on battery power, which limits their operating time and necessitates regular battery replacements. For large bridge structures, such maintenance can be costly and logistically demanding.Furthermore, wireless communication in bridge environments is frequently affected by electromagnetic interference, signal attenuation due to building materials, and multipath propagation, leading to data loss or transmission delays. These problems can compromise the integrity of the acquired data and complicate accurate load distribution analysis. Advanced monitoring techniques are also exploring the use of fiber optic sensor technologies, such as fiber Bragg grating sensors. These offer high sensitivity and the ability to measure distributed strain along the fiber length. Although fiber optic sensors offer improved spatial resolution compared to discrete sensors, their use in bridge structures presents practical challenges. Installing fiber optic cables requires careful handling to avoid damage, and integrating these cables into existing structures is often complex and involves intervention. Furthermore, fiber optic systems typically require specialized readout equipment, increasing the overall cost and complexity of the monitoring system.The brittleness of glass fibers under mechanical stress and their sensitivity to bending further limit their applicability in demanding environments. Another category of existing solutions involves the use of dynamic weighing systems and traffic monitoring technologies to estimate the loads acting on bridge structures. These systems measure vehicle weights and axle configurations during bridge crossings, thus providing input data for analytical models to predict load distribution. However, such approaches are based on indirect estimates rather than direct measurements of structural behavior. Therefore, they cannot account for variations in structural stiffness, boundary conditions, and damage states that influence the actual load distribution across the spans. The accuracy of these systems thus depends on the validity of the underlying assumptions and models, which may not reflect the current state of the structure. Finite element modeling and simulation-based methods are frequently used to analyze load distribution in multi-span bridges. These methods involve creating detailed computational models that represent the geometry, material properties, and boundary conditions of the structure. While finite element models can provide valuable insights into structural behavior, they are inherently dependent on the accuracy of the input parameters and assumptions. In practice, obtaining precise information about material properties, support conditions, and existing damage is challenging, leading to discrepancies between model predictions and actual structural behavior. Furthermore, such models are typically static or quasi-static and may not be able to capture real-time dynamic effects associated with moving loads.Updating and calibrating finite element models using measurement data is computationally intensive and requires specialized expertise, which limits their practical applicability for continuous monitoring. Some modern systems attempt to integrate data-driven approaches, including machine learning, into the analysis of structural response data and the detection of anomalies. These systems use historical data to train predictive models capable of identifying deviations from normal behavior. However, the effectiveness of such approaches depends heavily on the availability of large, high-quality datasets that accurately represent a wide range of operating conditions. In many cases, the lack of sufficient training data, particularly for rare or extreme events, limits the reliability of these models. Furthermore, data-driven methods often operate as black-box systems, restricting interpretability and making it difficult for engineers to understand the underlying causes of detected anomalies.This lack of transparency can hinder decision-making and reduce confidence in system expenditures. Another limitation of existing solutions lies in their inability to adapt to changing structural conditions. Many monitoring systems are based on initial assumptions about load paths and structural behavior, which can change over time due to factors such as material wear, renovation, or environmental influences. Static monitoring configurations and rigid analytical models are not suitable for capturing such changes, resulting in reduced accuracy in load distribution analysis. The lack of adaptive mechanisms that can update analytical models using real-time data represents a significant gap in current technologies. Furthermore, most existing systems do not offer seamless integration of sensors, data acquisition, and data analysis. The separation of these components often leads to data processing delays and limits real-time analysis. In scenarios where the immediate detection of abnormal load distributions is critical, such delays can have serious consequences. Moreover, the lack of integrated sensors means that many systems rely on indirect or surface measurements, which do not fully capture the internal stress states of components. In summary, while various monitoring techniques have been developed for multi-span bridge structures, they all exhibit significant drawbacks. These include limited spatial resolution, susceptibility to environmental influences, reliance on indirect measurements, high installation and maintenance costs, and a lack of adaptability to changing structural conditions. These limitations underscore the need for a more robust and integrated solution that enables continuous, highly accurate, and adaptive analysis of load distribution using sensors integrated directly into the structural components. Summary of the invention The present invention provides a system for adaptive load distribution analysis in multi-span bridge structures. Several embedded structural sensors are integrated into critical load-bearing components such as girders, piers, deck slabs, and bearings. Each sensor detects mechanical parameters such as strain, stress, displacement, vibration, and temperature, and generates electrical signals representing local structural responses. The system further comprises a central processing unit that receives synchronized data streams from the sensors and calculates load distribution profiles across the spans using adaptive analysis methods. The load distribution models are dynamically adapted to the detected changes, enabling the real-time identification of abnormal load paths, overstressed areas, and potential structural damage. The main objective of the present invention is to provide a system for adaptive load distribution analysis in multi-span bridge structures using embedded structural sensors. The system is capable of continuously acquiring high-resolution mechanical response data directly from the load-bearing components in order to precisely determine the load distribution across multiple spans in real time. A further objective of the invention is to provide a structurally integrated sensor that can be embedded in bridge elements such as girders, piers, deck slabs, and bearing areas. This enables the direct measurement of internal stresses, strains, displacements, vibrations, and temperatures without relying on surface-mounted instruments, which are susceptible to environmental influences and measurement inaccuracies. A further objective of the invention is to provide a system with multiple distributed sensors at predetermined locations within the bridge structure. These sensors detect local changes in the load transfer mechanisms, thus enabling comprehensive mapping of the load paths and the identification of stress concentration zones within the bridge structure. Another objective of the invention is to provide a data acquisition device that synchronizes and aggregates data from multiple embedded sensors in real time. This ensures the temporal coherence of the measurements for a precise analysis of the dynamic load conditions caused by moving vehicles and environmental influences. A further objective of the invention is to provide a computer system for adaptive load distribution analysis. This system dynamically updates analytical models based on incoming sensor data, thus enabling the consideration of changes in structural behavior caused by material fatigue, column settlement, changes in boundary conditions, and cumulative fatigue effects. Another objective of the invention is the early detection of abnormal load distribution patterns, including uneven load distribution between spans and excessive stress accumulation in critical structural areas. This allows for timely maintenance measures and reduces the risk of structural failure. A further objective of the invention is to provide a sensor with internal signal conditioning and local data processing for performing calibration settings, noise reduction, and preliminary data validation before transmission. This improves data accuracy and reduces the load on central processing systems. Another objective of the invention is the integration of a reliable communication interface into the sensor for transmitting processed data to a remote or central monitoring system. This interface is designed for effective operation in electromagnetically complex environments, such as those typically found in large bridge structures. A further objective of the invention is to provide an energy management system within the embedded sensor. This system comprises energy harvesting elements that convert structural vibrations and ambient energy into electrical energy, thus enabling long-term autonomous operation without frequent maintenance or external power supply. Another objective of the invention is to ensure mechanical compatibility between the sensor and the surrounding structural material through an adhesive bond. This enables the precise transmission of strain and deformation to the sensor elements, thereby maintaining measurement accuracy throughout the bridge's entire service life. A further objective of the invention is to provide a system that correlates structural response data with external loading events, thereby enabling a more accurate interpretation of the load distribution behavior under various traffic and environmental conditions. Another objective of the invention is to enable long-term trend analyses through continuous data acquisition, which allows for the detection of progressive structural deterioration such as crack formation, propagation, and loss of stiffness. A further objective of the invention is to provide a scalable and modular system architecture that can be implemented both in newly constructed bridges and by retrofitting existing bridges, thus extending its applicability to a wide range of infrastructure scenarios. A final objective of the invention is to provide an integrated and reliable solution for structural monitoring that overcomes the limitations of existing methods by combining embedded sensors, real-time data acquisition, adaptive data processing, and autonomous operation in a unified system, thereby enabling precise and continuous load distribution analysis in multi-span bridges. BRIEF DESCRIPTION OF THE IMAGE These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. 1 shows a block diagram of a quantum-optimized system for detecting fake news. Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention. It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof. References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment. The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components. Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Fig. 1 shows a block diagram of a system for adaptive load distribution analysis in a multi-span bridge structure. The system 100 comprises: several embedded sensors (102) arranged in load-bearing components such as at least one girder, at least one deck slab, and at least one bearing surface. Each sensor includes a strain gauge for generating electrical signals representing deformation, a vibration gauge for detecting the dynamic structural response, a temperature gauge for providing data for thermal compensation, a signal conditioning unit electrically connected to the sensors for amplification and noise filtering, an analog-to-digital converter for generating digitized measurement data, and a local processing unit for calibration and data validation.A data acquisition unit (104) is connected to the embedded sensors and receives synchronized measurement data. A central processing unit (106) is operationally coupled to the data acquisition unit and is configured to calculate the span-related load distribution over multiple spans of the bridge structure based on the received measurement data and to dynamically update the load distribution properties in response to changes in the structural response. In one embodiment, each embedded sensor device (102) further comprises a hermetically sealed housing made of a corrosion-resistant material, dimensioned to allow mechanical embedding in concrete or metal components. The housing has an interface configured to maintain strain compatibility with the surrounding material, ensuring accurate transmission of mechanical deformation to the sensor elements. In one embodiment, the strain measuring element comprises a plurality of strain gauges arranged in mutually orthogonal orientations within the embedded sensor device to detect multidirectional stress components, wherein the local processing unit is configured to resolve the multidirectional signals into principal stress values ​​that are representative of local load conditions. In one embodiment, the vibration sensor element comprises a microelectromechanical accelerometer configured to measure structural acceleration over at least two axes, and wherein the local processing unit is configured to derive frequency domain characteristics of the measured vibration signals in order to identify dynamic load effects caused by moving traffic. In one embodiment, the signal processing unit comprises a multi-stage amplifier circuit and a low-pass filter arrangement configured to eliminate high-frequency noise components associated with electromagnetic interference and structural resonance artifacts. In one embodiment, the data acquisition unit (104) is configured to perform the time synchronization of the plurality of embedded sensors by means of a clock alignment mechanism, so that measurement data corresponding to different locations of the bridge structure are aligned in time to enable an accurate correlation of the load distribution. In one embodiment, the central processing unit (106) is configured to determine the load distribution by correlating measured strain and displacement data with structural geometry parameters and support conditions stored in a memory unit and iteratively refining the load distribution calculations based on continuous data updates. In one embodiment, the central processing unit (106) is further configured to detect an abnormal load redistribution by comparing the load distribution values ​​in real time with the base distribution patterns and generating a warning signal when the deviations exceed a predetermined threshold that indicates a structural imbalance. In one embodiment, each embedded sensor device further comprises a communication interface unit configured to transmit digitized measurement data to the data acquisition unit via a wired transmission medium, with shielding elements in place to minimize signal attenuation and interference within the bridge structure. In one embodiment, each embedded sensor device further comprises an energy management unit with an energy harvesting element configured to convert mechanical vibrations of the bridge structure into electrical energy, and an energy storage element configured to store the harvested energy to supply the sensor elements, the signal conditioning unit, and the local processing unit. The adaptive load distribution analysis system for a multi-span bridge structure operates through the coordinated interaction of embedded sensors, a data acquisition unit, and a central processing unit. This unit executes a continuously updated analysis procedure to determine the load distribution across the spans. Each sensor is physically integrated into a component, ensuring that the mechanical deformation of the surrounding material is transmitted to the internal sensor elements without loss. During operation, the strain sensors generate electrical signals proportional to the local deformation in various directions, while the vibration sensors produce time-varying signals corresponding to the acceleration of the component caused by dynamic loading. Simultaneously, the temperature sensor generates thermal data used to compensate for temperature-related variations in the strain measurements.These analog signals are first processed in the signal conditioning unit. There, amplifier circuits adjust the signal strength to a measurable range, and filters attenuate noise components caused by electromagnetic interference and high-frequency resonances of the component. The processed signals are then fed to the analog-to-digital converter unit, which samples the signals at a predefined sampling rate to generate discrete digital representations of the measurement parameters. The sampling rate is adaptively selected based on the captured dynamic properties of the structure. Higher sampling rates are used during transient load events, and lower rates in steady-state conditions, to optimize energy consumption. The digitized data are then processed in the local processing unit of each embedded sensor. Calibration coefficients stored in local memory are applied to correct sensor nonlinearities and offset errors. A drift compensation routine is executed by integrating temperature data into a compensation function that adjusts the strain values ​​to account for thermal expansion effects.This ensures that the measured deformation corresponds to the actual mechanical load and not to environmental influences. Following calibration, the local processing unit performs initial data validation by comparing incoming measurements with predefined operating thresholds and historical trends stored in the device. Measurements that deviate significantly from the expected ranges are flagged and tagged with metadata indicating potential anomalies. The processed data is then packaged into packets along with timestamps generated by an internal clock synchronized with the data acquisition unit. The communication interface transmits the packetized data over a wired or wireless medium, employing error detection and correction coding to ensure data integrity during transmission through the structurally complex and electromagnetically susceptible bridge environment. The data acquisition unit receives data streams from the numerous embedded sensors and synchronizes the received data using a clock. This mechanism ensures that measurements at different locations within the structure occur simultaneously, thus enabling a precise correlation of structural responses across multiple regions. The data acquisition unit also performs buffering operations to compensate for fluctuations in the data transmission rate. During periods of high data acquisition activity, incoming data is temporarily stored and then carefully and sequentially forwarded to the central processing unit. The central processing unit performs an adaptive analysis procedure to determine the load distribution in the bridge structure. First, structural parameters such as geometry, spans, material properties, and support conditions are retrieved from a stored structural model. Using these parameters, the central processing unit establishes a basic relationship between measured strain values ​​and internal forces in the components. This relationship is based on equilibrium conditions and compatibility requirements that determine the load transfer between adjacent spans and supports. After receiving synchronized measurement data, the central processing unit calculates the local stress values ​​from the strain measurements based on material laws. These local stress values ​​are then integrated along the component length to determine the internal force components, including bending moments and shear forces. The system also evaluates displacement and rotation characteristics by integrating strain distributions and correlating them with acceleration data derived from vibrations. By combining static and dynamic measurements, the system creates a comprehensive representation of the structural behavior under the current loading conditions. The calculated internal force components are then distributed across multiple spans to determine the load distribution ratios. These ratios indicate the proportion of the total load borne by each span. This distribution process involves solving a system of equilibrium conditions that considers load continuity between supports and the compatibility of deflections between adjacent spans. The central processing unit iteratively refines the solution by minimizing the discrepancies between measured values ​​and those calculated from the structural model. This iterative refinement is achieved through an adaptive update, where structural parameters such as stiffness coefficients and boundary conditions are adjusted based on the measurement data. The adaptive update calculates an error value representing the difference between measured and predicted structural reactions. This error value is used to adjust the model parameters to minimize deviations in subsequent iterations. Over time, the structural model converges to a representation that accurately reflects the bridge's actual condition, including the effects of material fatigue, support settlements, and changes in load transfer mechanisms. This continuous model update enables the system to maintain high accuracy in load distribution analysis, even when the structure changes. In addition to determining the load distribution, the central processing unit performs anomaly detection by comparing the load distribution patterns in real time with reference patterns from normal operation. Deviations exceeding predefined thresholds are analyzed to identify potential causes such as local stiffness reductions, overloading, or component damage. The system generates warning signals as soon as such anomalies are detected, thus providing crucial information for maintenance and inspection measures. The system also includes a temporal analysis component that examines trends in load distribution over extended periods. By evaluating historical data stored in a memory, the system detects gradual changes in the load distribution between the fields, which can indicate progressive structural damage. This long-term analysis enables the early detection of problems such as cracking and propagation, corrosion-related weakening, and foundation settlement, thus supporting predictive maintenance strategies. The system also includes a correlation that links structural response data with external loading events. By analyzing patterns in vibration signals and strain changes, the central processing unit identifies characteristics of moving loads, including magnitude, velocity, and distribution across the lanes. This information is used to refine load distribution calculations, taking into account the dynamic effects of vehicle traffic. The correlation increases the accuracy of the analysis by distinguishing between transient load effects and sustained structural changes. Each embedded sensor's energy management is handled by a power harvesting system that converts mechanical vibrations of the bridge into electrical energy. This harvested energy is stored and regulated to ensure a stable power supply for the sensors, data processing, and communication systems. The technology in the local processing unit adjusts operating parameters such as sampling rate and transmission frequency to the available energy level, thus guaranteeing uninterrupted operation. Overall, the system operates as a closed, adaptive analysis system in which embedded sensors continuously deliver highly precise data, the data acquisition unit ensures synchronized and reliable data transmission, and the central processing unit performs a continuously evolving analysis that accurately determines the load distribution in multi-span bridge structures. The integration of real-time data acquisition, adaptive model updates, and anomaly detection enables the system to precisely and reliably assess structural behavior under various loading conditions. In one embodiment, the invention provides a structurally embeddable sensor element designed as a compact, hermetically sealed unit that can be embedded in a bridge component during construction or retrofitted into an existing structure by precise fitting. The device comprises an outer housing made of corrosion-resistant alloy material that withstands the compressive and tensile forces occurring in reinforced concrete or steel components. The outer housing contains a sensor unit with several orthogonally arranged strain gauges for detecting multiaxial stress states, a vibration sensor configured as a microelectromechanical accelerometer, and a temperature sensor to compensate for thermal influences on the strain measurements. The sensor unit is electrically connected to an internal signal conditioning circuit that includes amplifier stages, noise filters, and an analog-to-digital conversion circuit for generating high-resolution digital representations of the measurement parameters. An integrated processing unit performs local preprocessing of the acquired data, including calibration, drift compensation, and preliminary anomaly detection. The device also features a communication interface for wired or wireless transmission of the processed data to an external data acquisition system. This interface incorporates shielding and error correction circuitry to ensure reliable data transmission in electromagnetically noisy environments, such as those typically found on bridge structures. The device also features an energy management system with an energy harvesting element for converting structural vibrations into electrical energy and an energy storage element for storing the harvested energy for continuous operation. The device is mechanically anchored to the component by means of an adhesive layer. This layer ensures strain compatibility between the sensor and the surrounding material, thus enabling the precise transmission of mechanical deformations to the sensor elements. In operation, numerous embedded sensors are distributed across multiple spans of a bridge structure at predetermined positions corresponding to areas of expected stress concentration, such as span centers, support points, and load transfer zones. Each sensor continuously monitors local structural responses and transmits the processed data to a data acquisition unit located either within the bridge structure or in a remote monitoring facility. The data acquisition unit is configured to aggregate time-synchronized data streams from all sensors and store the data in a structured format suitable for further analysis. A computing unit is operationally coupled to the data acquisition unit and performs an adaptive load distribution analysis by applying mathematical models that relate measured strains and displacements to the internal force distributions across the spans. The computing unit dynamically updates these models based on incoming data, taking into account changes in boundary conditions, material properties, and environmental influences. The system also includes a correlation mechanism that links detected structural reactions with external load events, such as vehicle movements, thus enabling the identification of load redistribution phenomena. Upon detecting an abnormal load distribution, such as uneven stress accumulation across adjacent fields or excessive load concentration at specific supports, the system generates warning signals indicating potential structural problems. Furthermore, the system is configured to perform long-term trend analyses by comparing historical data with current measurements to detect progressive structural damage such as crack propagation, material fatigue, and settlement of the supporting structure. Thanks to its adaptive nature, the system can refine its analytical models over time, thereby improving the accuracy of load distribution predictions and increasing the reliability of structural monitoring. Integrating embedded sensors into the components ensures direct measurement of internal mechanical conditions, thus eliminating the inaccuracies of surface-mounted sensors. Distributing the sensors across multiple spans allows for comprehensive monitoring of the bridge structure, enabling precise mapping of load paths and early detection of anomalies. The system enables continuous, real-time monitoring of load distribution in multi-span bridge structures, thus allowing predictive maintenance and reducing the risk of structural failure. The use of integrated sensors ensures highly precise data acquisition and long-term reliability, even under harsh environmental conditions. The system's adaptive analysis capability allows for a dynamic response to changing structural conditions, thereby improving the accuracy and reliability of the load distribution analysis. Furthermore, the sensors' energy recovery function enables autonomous operation, thus reducing power supply maintenance requirements. The present invention relates to the structural monitoring and instrumentation of structures, in particular a system for adaptive load distribution analysis in multi-span bridge structures using embedded structural sensors. The invention aims at integrating structurally embedded sensors into load-bearing components of bridge structures to acquire mechanical response data in real time and to process this data through a coordinated arrangement of sensor, data acquisition, and computing units. This enables the determination of the dynamic and static load distribution across multiple spans. The invention further relates to architectures for embedded sensors, signal conditioning arrangements, synchronized data acquisition techniques, and adaptive analytical processing for evaluating structural behavior under varying load and environmental conditions. The present invention relates to the structural monitoring and instrumentation of structures, in particular a system for adaptive load distribution analysis in multi-span bridge structures using embedded structural sensors. The invention aims at integrating structurally embedded sensors into load-bearing components of bridge structures to acquire mechanical response data in real time and to process this data through a coordinated arrangement of sensor, data acquisition, and computing units. This enables the determination of the dynamic and static load distribution across multiple spans. The invention further relates to architectures for embedded sensors, signal conditioning arrangements, synchronized data acquisition techniques, and adaptive analytical processing for evaluating structural behavior under varying load and environmental conditions. The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims. The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A block diagram of a quantum-optimized system for detecting fake news. 102 Multiple embedded sensors. 104 Data acquisition unit. 106 Central processing unit.

Claims

An adaptive load distribution analysis system in a multi-span bridge structure, comprising: a plurality of embedded sensors physically arranged in load-bearing components, including at least one girder, at least one deck slab, and at least one support interface. Each embedded sensor includes a strain gauge for generating electrical signals representing deformation, a vibration gauge for detecting dynamic structural response, a temperature gauge for providing thermal compensation data, a signal conditioning unit electrically connected to the sensor elements for amplification and noise filtering, an analog-to-digital converter for generating digitized measurement data, and a local processing unit for performing calibration adjustments and data validation.a data acquisition unit that is communicatively connected to the numerous embedded sensors and configured to receive synchronized measurement data; and a central processing unit that is operationally coupled with the data acquisition unit and configured to calculate the span-related load distribution over multiple spans of the bridge structure based on the received measurement data and to dynamically update the load distribution properties in response to changes in structural behavior. System according to claim 1, wherein each embedded sensor device further comprises a hermetically sealed housing made of a corrosion-resistant material, dimensioned to allow mechanical embedding in concrete or metal components. The housing has an interface configured to maintain strain compatibility with the surrounding material to ensure accurate transmission of mechanical deformation to the sensor elements. System according to claim 1, wherein the strain measuring element comprises a plurality of strain gauges arranged in mutually orthogonal orientations within the embedded sensor device to detect multidirectional stress components, and wherein the local processing unit is configured to resolve the multidirectional signals into principal stress values ​​that are representative of local load conditions. System according to claim 1, wherein the vibration sensor element comprises a microelectromechanical accelerometer configured to measure structural acceleration over at least two axes, and wherein the local processing unit is configured to derive frequency domain characteristics of the measured vibration signals in order to identify dynamic load effects caused by moving traffic. System according to claim 1, wherein the signal processing unit comprises a multi-stage amplifier circuit and a low-pass filter arrangement configured to eliminate high-frequency noise components associated with electromagnetic interference and structural resonance artifacts. System according to claim 1, wherein the data acquisition unit is configured to implement the time synchronization of the plurality of embedded sensors by means of a clock alignment mechanism such that measurement data corresponding to different locations of the bridge structure are time-aligned to enable an accurate correlation of the load distribution. System according to claim 1, wherein the central processing unit is configured to determine the load distribution by correlating measured strain and displacement data with structural geometry parameters and support conditions stored in a storage unit, and iteratively refines the load distribution calculations based on continuous data updates. System according to claim 1, wherein the central processing unit is further configured to detect an abnormal load redistribution by comparing the load distribution values ​​in real time with the base distribution patterns, and generates a warning signal when the deviations exceed a predetermined threshold indicating a structural imbalance. System according to claim 1, wherein each embedded sensor device further comprises a communication interface unit configured to transmit digitized measurement data to the data acquisition unit via a wired transmission medium with shielding elements to minimize signal attenuation and interference within the bridge structure. System according to claim 1, wherein each embedded sensor element further comprises an energy management unit comprising an energy harvesting element for converting mechanical vibrations of the bridge structure into electrical energy and an energy storage element for storing the harvested energy to supply the sensor elements, the signal conditioning unit and the local processing unit.

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