Steel structure stress monitoring method based on distributed optical fiber sensing

Abstract

The invention relates to the technical field of structural stress precision measurement, in particular to a distributed optical fiber sensing-based steel structure stress monitoring method, which comprises the following steps of: 1, constructing a sensing optical fiber array in a steel structure node area; 2, injecting optical pulses into the sensing optical fiber array, and synchronously collecting Brillouin frequency shift signals and Raman scattering signals of all points; 3, temperature and mechanical strain are separated based on the reference optical fiber signals, global temperature compensation is adopted for the first optical fiber set, and temperature gradient compensation along the optical fiber path is adopted for the second optical fiber set; 4, decomposing the compensated mechanical strain into multidirectional strain components according to the spatial orientation of the optical fiber, and reconstructing a three-dimensional stress field through strain component fusion; and 5, dynamically judging the damage risk according to the local stress concentration coefficient and the change rate thereof in the stress field. And through the temperature compensation technology, the influence of the environment temperature change on the strain data is eliminated, and the reliability of the monitoring result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of precise measurement of structural stress, and in particular to a steel structure stress monitoring method based on distributed optical fiber sensing. Background Art

[0002] Steel structure nodes are the force transmission hubs of the entire structure, and their stress state directly determines the safety of the project. The application of existing stress monitoring technology in this field has the following essential defects: 1. Blind areas in the stress field caused by insufficient spatial resolution; Traditional resistance strain gauges or point-type fiber optic sensors monitor at discrete locations, with spacing between adjacent measuring points typically greater than 50 mm. However, steel structure joints (such as beam-column connections) experience significant stress gradient abrupt changes. In key locations, such as around bolt holes and at weld ends, stress variations can be compressed to the millimeter level. Existing technologies, due to insufficient spatial sampling density, are unable to capture these microscopic stress concentrations, resulting in the omission of fatigue crack initiation locations. This problem is a physical necessity, driven by a combination of geometric discontinuities at the joints (such as sudden cross-sectional changes) and stress diffusion characteristics.

[0003] 2. Measurement reference drift caused by temperature effects; The thermal expansion coefficient of steel structures is significant (usually >12×10 -6 / °C), under sunlight temperature gradients, the node surface can generate apparent strains on the same order of magnitude as mechanical stress. Existing sensors (such as fiber gratings (FBGs)) can simultaneously monitor temperature, but they only use a single temperature compensation coefficient for global correction. However, due to material thickness variations and shadowing effects, the node region actually experiences a non-uniform temperature field (measured gradients can reach 5°C / cm). This spatial temperature inhomogeneity renders traditional compensation models ineffective, generating spurious strain signals >50με, which seriously interfere with true stress assessment.

[0004] 3. Measurement inaccuracy caused by surface adaptability defects; Complex joints often feature three-dimensional curved surfaces (such as curved stiffeners), with curvature radii typically less than 100 mm. Rigid sensors (such as MEMS strain gauges) exhibit high bending stiffness, which can create microgaps or localized debonding between them and the curved surface during installation. This can reduce strain transfer efficiency by over 30%. In areas of high curvature, the deformation of the sensor substrate becomes decoupled from the deformation of the steel structure, causing the measured value to deviate from the true surface strain. This problem, caused by a failure in the mechanical coupling between the sensor and structure interface, is unavoidable in irregular joints.

[0005] Therefore, there is an urgent need for a steel structure stress monitoring method based on distributed optical fiber sensing to solve the above problems. Summary of the Invention

[0006] Based on the above objectives, the present invention provides a steel structure stress monitoring method based on distributed optical fiber sensing, comprising: Step 1: Construct a sensing fiber array in the steel structure node area, which includes a first fiber group arranged in a spiral pattern, a second fiber group arranged radially, and a reference fiber that is not in contact with the structure surface; Step 2: Inject light pulses into the sensing fiber array and synchronously collect the Brillouin frequency shift signal and Raman scattering signal at each point; Step 3: Separate temperature and mechanical strain based on the reference fiber signal: global temperature compensation is applied to the first fiber group, and temperature gradient compensation along the fiber path is applied to the second fiber group; Step 4: Decompose the compensated mechanical strain into multi-directional strain components according to the spatial orientation of the optical fiber, and reconstruct the three-dimensional stress field by fusion of the strain components; Step 5: Dynamically determine the damage risk based on the local stress concentration factor and its change rate in the stress field.

[0007] Preferably, the construction of the sensing fiber array in step 1 includes: The pitch of the spiral arrangement is set according to the minimum curvature radius of the steel structure: the minimum curvature radius of the rod surface is measured, and a preset multiple of the radius is used as the maximum pitch reference value, and the actual pitch is taken as an equal spacing value smaller than the reference value; The angle of the radial arrangement is adaptively adjusted according to the symmetry of the node: the node geometric topology is obtained by three-dimensional laser scanning, and the optical fiber angle is reduced to a preset angle lower limit in the turning area of ​​the stress transfer path; The arrangement direction of the reference optical fiber is parallel to the direction of maximum thermal expansion coefficient of the steel structure, and the gap between it and the surface of the structure is determined by thermal deformation simulation to ensure that the optical fiber is not mechanically constrained when the temperature changes.

[0008] Preferably, the implementation of the temperature gradient compensation in step 3 includes: The global temperature compensation formula is: mechanical strain = apparent strain - material thermal expansion coefficient × (current temperature - reference temperature), where the reference temperature is taken from the real-time measurement value of the reference optical fiber; The temperature gradient compensation formula is: mechanical strain = apparent strain - temperature gradient compensation coefficient × temperature change rate along the length of the optical fiber. The temperature gradient compensation coefficient is calibrated through a step temperature change experiment: under a constant load, the ambient temperature is increased in fixed steps, and the slope of the linear relationship between the temperature gradient and the apparent strain is recorded. The thermal expansion coefficient of the material is measured by a thermal dilatometer.

[0009] Preferably, the strain component fusion in step 4 includes: For the strain data of the helically wound optical fiber group, the axial strain and hoop strain are decomposed according to the sine and cosine components of the helical expansion angle; For the radial fiber group, three intersecting fibers are selected to form a strain sensing unit. A plane strain conversion matrix is ​​constructed according to the angle between the fibers. This matrix is ​​derived based on the strain coordination equation of elastic mechanics and meets the continuity condition of the displacement field. The hoop strain component and the plane strain tensor are weighted and superimposed according to their spatial positions, and the weight coefficient is dynamically adjusted according to the distance between the node area and the centroid.

[0010] Preferably, the dynamic threshold for determining the injury risk in step 5 includes: Local stress concentration factor = monitoring point stress value / steel structure design allowable stress; The change rate threshold is set through regression analysis of historical damage data: the stress time series of the damaged structure is extracted, the stress increment distribution per unit time before the damage occurs is statistically analyzed, and the upper quantile of the distribution is taken as the threshold benchmark; The number of continuous alarm points is determined according to the main frequency of structural vibration: the vibration frequency of the steel structure under environmental excitation is measured, and the inverse of the frequency is multiplied by the preset period multiple as the minimum continuous alarm duration, which is then converted into the corresponding number of sampling points.

[0011] Preferably, the method for measuring the minimum curvature radius includes: Use a curvature gauge to scan the surface of the rod, record the curvature value corresponding to the maximum deflection angle of the curvature gauge pointer, and take the minimum value of all measured values ​​as the benchmark.

[0012] Preferably, the specific process of the step temperature change experiment includes: The steel structure specimen with the optical fiber attached was placed in a temperature-controlled chamber and a constant axial load was applied; The temperature in the cabin is gradually increased at fixed temperature intervals, and the apparent strain and temperature gradient data are collected after each temperature level stabilizes; A temperature gradient-apparent strain scatter plot was drawn, and the compensation coefficient was obtained by linear fitting.

[0013] Preferably, the dynamic adjustment rules of the weight coefficient include: In the nodal centroid region, the hoop strain component has a higher weight than the plane strain tensor; At member connections away from the centroid, the plane strain tensor weight increases linearly with distance.

[0014] Preferably, the acquisition of the historical damage data includes: Fatigue loading tests were carried out on specimens of the same material in the laboratory, and high-frequency strain gauges were used to record the stress change history in the last hour before damage occurred.

[0015] Preferably, the implementation of the synchronous acquisition in step 2 includes: Time division multiplexing technology is used to alternately excite Brillouin scattering and Raman scattering: first, a narrow pulse width optical pulse is emitted to collect the Brillouin frequency shift, and then a wide pulse width optical pulse is emitted to collect the Raman anti-Stokes light intensity.

[0016] Beneficial effects of the present invention: 1. This invention employs distributed fiber optic sensing technology. By constructing a sensing fiber array at the steel structure's node locations, utilizing a spirally arranged first fiber group, a radially arranged second fiber group, and a reference fiber, the spatial resolution is significantly improved. Each measuring point can monitor the stress distribution of the structure in real time, thus overcoming the stress field blind spot problem caused by insufficient spatial sampling density in existing technologies. This solution can capture subtle changes in stress gradients, ensuring timely detection of stress concentration and crack initiation in key areas, significantly improving the safety of steel structures.

[0017] 2. This invention fundamentally solves this problem by employing two methods: global temperature compensation and temperature gradient compensation along the fiber path. Specifically, temperature measurement based on the reference fiber signal, combined with a temperature gradient compensation formula and real-time measurement of the material's thermal expansion coefficient, enables more accurate temperature correction. Temperature gradient compensation uses a step-by-step temperature change experiment to obtain a temperature compensation coefficient, ensuring that the effects of temperature field inhomogeneity are fully eliminated, thereby effectively reducing false strain signals caused by temperature effects and ensuring the accuracy of stress monitoring data.

[0018] 3. The distributed fiber optic sensor employed in this invention exhibits a high degree of surface adaptability. The spirally arranged fiber array allows for a close fit to the curved surface of the steel structure, eliminating measurement errors caused by microgaps or debonding between the sensor and the surface. Furthermore, the inherent flexibility and distributed nature of the fiber enables it to respond uniformly to strain changes on the curved surface, ensuring measurement accuracy in areas of high curvature. This not only improves the reliability of stress monitoring but also addresses the adaptability limitations of traditional rigid sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 is a flow chart of the steps of the method of the present invention; Figure 2 This is a flowchart of the strain component fusion steps in step 4 of the method of the present invention; Figure 3 The figure is a flowchart of the specific process of the step temperature change experiment of the method of the present invention. DETAILED DESCRIPTION

[0021] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0022] See Figure 1-Figure 3 , an embodiment of the present invention provides a steel structure stress monitoring method based on distributed optical fiber sensing. In step 1, a sensing optical fiber array is first arranged in the node area of ​​the steel structure, which includes two groups of optical fibers: one group is a first optical fiber group arranged in a spiral, and the other group is a second optical fiber group arranged radially. In addition, a reference optical fiber that is non-contact with the surface of the structure is provided. The arrangement of the first optical fiber group and the second optical fiber group can cover the multi-directional strain changes in the node area, so that the sensing area can be evenly distributed, thereby improving the monitoring accuracy of the stress field. The reference optical fiber is used to monitor temperature changes in real time and provide data support for subsequent temperature compensation. This design effectively solves the blind area problem caused by insufficient spatial resolution of traditional optical fiber sensors, and improves the comprehensiveness and accuracy of the monitoring area.

[0023] In step 2, after injecting a light pulse into the array of sensing fibers, Brillouin frequency shift signals and Raman scattering signals are generated in the fibers. These signals are closely related to strain and temperature changes, respectively. By synchronously collecting these signals, accurate stress and temperature information can be obtained at each measurement point, providing a foundation for subsequent data analysis.

[0024] In step 3, global temperature compensation is used to correct the signal from the first fiber group based on the temperature data collected by the reference fiber, eliminating strain interference caused by temperature changes. For the second fiber group, temperature gradient compensation is applied along the fiber path, further improving the accuracy of temperature compensation by accounting for the uneven temperature distribution at the node. This step effectively eliminates the influence of temperature effects on stress monitoring results and ensures the accuracy of the stress signal.

[0025] In step 4, the temperature-compensated mechanical strain signal is decomposed into multi-directional strain components based on the fiber's spatial orientation. By fusing these strain components, the three-dimensional stress field in the node region can be reconstructed. This step accurately describes the stress field distribution at the node, particularly in complex node geometries. It can better reveal subtle changes in structural stress, avoiding the limitations of traditional strain sensors and addressing the issue of insufficient spatial resolution.

[0026] In step 5, by analyzing the local stress concentration factor and its rate of change in the three-dimensional stress field, potential damage risks can be dynamically determined. A significant change in the stress concentration factor in a particular area indicates the possibility of cracks or other forms of damage. This step enables real-time monitoring of the health of the steel structure, enabling early detection of potential structural damage and providing a crucial basis for structural maintenance and safety management.

[0027] The above steps address the challenges of traditional stress monitoring methods, including insufficient spatial resolution, temperature interference, and poor surface adaptability. The application of distributed fiber optic sensing technology not only improves the accuracy of stress monitoring but also comprehensively reflects the stress distribution at the nodes of steel structures, overcoming the limitations of traditional methods. Furthermore, innovations in temperature compensation and strain decomposition techniques effectively eliminate the interference of ambient temperature on monitoring data, ensuring the reliability of monitoring results. This method can significantly improve the safety of steel structures and provide early warning of structural damage, possessing broad application prospects.

[0028] In one possible implementation, in the node area of ​​the steel structure, in order to achieve uniform stress distribution monitoring, the pitch of the helically arranged optical fiber needs to be precisely set according to the minimum curvature radius of the steel structure. First, by measuring the minimum curvature radius of the rod surface, a preset multiple of this radius is determined as the reference value of the maximum pitch. Then, the actual pitch is taken as an equidistant value smaller than this reference value. This ensures that the distribution of the optical fiber matches the geometric characteristics of the structure, effectively improving the coverage density of the monitoring area and avoiding the omission of local strain information due to excessive pitch. This design not only improves the accuracy of sensing, but also reduces the monitoring blind spots caused by curvature changes.

[0029] In order to maximize the accuracy of stress monitoring in the node area, a method of adaptively adjusting the angle is adopted. When constructing the fiber array, the geometric topology data of the node is obtained through three-dimensional laser scanning technology. In particular, for the turning area of ​​the stress transfer path of the node, the layout angle of the optical fiber is dynamically adjusted according to the scanning data. Specifically, in the turning area of ​​the stress transfer path, the optical fiber angle is reduced to a preset lower limit to ensure that the optical fiber can more accurately capture stress concentration and changes, especially those areas prone to local deformation. This method effectively avoids monitoring blind spots caused by improper angles and improves the response ability of the optical fiber array to dynamic changes in the structure.

[0030] To further enhance the monitoring system's accuracy and anti-interference capabilities, the reference fiber is oriented parallel to the direction of maximum thermal expansion of the steel structure. The reference fiber is primarily used for temperature compensation, so optimizing its orientation is crucial. Thermal deformation simulation ensures that the gap between the reference fiber and the structural surface remains consistent as the steel expands due to heat, thus preventing mechanical constraints on the fiber during temperature fluctuations. This design effectively eliminates interference from temperature changes on the fiber-optic sensing signal, minimizing the impact of temperature fluctuations on monitoring results and providing more accurate stress data.

[0031] Through the above-mentioned optimized design, the present invention can provide more accurate stress monitoring data in the complex environment of steel structures. The precise setting of the pitch and the adaptive adjustment of the optical fiber layout angle enable the optical fiber array to flexibly adjust the layout according to the geometric characteristics of the steel structure and the specific conditions of the stress transfer path, ensuring that the stress data of the key parts of the structure are not missed. The optimization of the reference optical fiber layout direction effectively avoids the errors caused by temperature changes and enhances the stability and reliability of the system in different working environments. In summary, this method can more accurately reflect the stress distribution of steel structures, promptly detect potential damage risks, has broad application prospects, and is particularly suitable for monitoring complex and dynamically changing steel structures.

[0032] In one possible implementation, the core of global temperature compensation is to correct the apparent value of mechanical strain based on the temperature data collected in real time by the reference optical fiber and the thermal expansion coefficient of the material. The specific temperature compensation formula is: Mechanical strain = apparent strain − material thermal expansion coefficient × (current temperature − reference temperature); The reference temperature is taken from the real-time measurement of the reference fiber. This step aims to eliminate temperature-induced deviations in the fiber strain signal, thereby more accurately reflecting the strain generated by mechanical loads. This effectively isolates the effects of temperature on strain data, ensuring the accuracy of monitoring results.

[0033] Temperature gradient compensation is mainly used to deal with uneven temperature distribution and ensure that temperature changes along the optical fiber path do not affect the accurate measurement of strain. The compensation formula is: Mechanical strain = apparent strain − temperature gradient compensation coefficient × temperature change rate along the length of the optical fiber; The temperature gradient compensation coefficient is calibrated through a step-by-step temperature change experiment. The specific steps are: under constant load, the ambient temperature is increased in fixed steps, and the apparent strain data under different temperature conditions is recorded. Then, the linear relationship between the temperature gradient and the apparent strain is analyzed, and the temperature gradient compensation coefficient is derived through linear regression. This coefficient dynamically compensates for the effects of temperature changes on strain, significantly improving the accuracy of strain data, especially when uneven temperature distribution is present in the node area.

[0034] Accurately measuring a material's coefficient of thermal expansion is fundamental to temperature compensation. Testing steel with a thermal dilatometer determines its coefficient of thermal expansion, which serves as a parameter in the compensation formula. The coefficient of thermal expansion is a crucial physical quantity that reflects a material's dimensional change under temperature fluctuations. Accurate thermal expansion ensures more precise strain correction during temperature compensation.

[0035] The present invention effectively eliminates the interference of temperature on the strain signal and improves the accuracy of stress monitoring through the dual measures of global temperature compensation and temperature gradient compensation. Global temperature compensation eliminates the influence of ambient temperature on optical fiber measurement, while temperature gradient compensation solves the problem of uneven temperature distribution. Especially in large-scale steel structures, local temperature changes may cause significant deviations in strain values. Through precise temperature compensation, it can ensure that the obtained stress monitoring data is more real and reliable, providing a more accurate basis for the health monitoring and maintenance of steel structures. This technology not only improves the accuracy of stress monitoring, but also minimizes the impact of temperature changes that may occur during long-term operation on the monitoring data, and has high application value.

[0036] In one possible embodiment, for a helically arranged optical fiber group, its strain data is affected by the optical fiber arrangement and includes two parts: axial strain and circumferential strain. In order to accurately obtain these two strain components, the strain data is first decomposed by the sine and cosine components of the helical expansion angle. The helical expansion angle is the helical angle of the optical fiber around the structure on the surface of the steel structure. The sine and cosine functions are used to obtain the axial and circumferential strains respectively during decomposition. The axial strain and circumferential strain respectively reflect the stress distribution in the direction of the optical fiber, and can provide detailed data for stress changes in different directions of the steel structure. This decomposition method can effectively extract the strain information of the helical optical fiber group and make the monitoring data more consistent with the actual stress state.

[0037] For radially arranged fiber optic groups, three intersecting fibers are selected to form a strain sensing unit. The angle between these three intersecting fibers is a key factor influencing strain calculation. Based on the angle between the fibers, a plane strain conversion matrix can be constructed. The derivation of this conversion matrix is ​​based on the strain coordination equations in elastic mechanics. These equations ensure that the strain in the structure meets basic mechanical conditions, especially the continuity of the displacement field. On this basis, through matrix conversion, the plane strain tensor can be accurately extracted from the fiber data, reflecting the strain state in the area where the fibers intersect.

[0038] Combining the hoop strain components measured by the helical fiber array with the plane strain tensor derived from the plane strain matrix requires weighted superposition based on spatial location. The weighting coefficients are dynamically adjusted based on the distance between the node region and the structure's centroid. The farther the node region is from the centroid, the greater its weight coefficient in the final strain fusion result is likely to be, thereby more accurately reflecting the stress distribution away from the centroid. This weighting method ensures that strain data are accurately reflected at different spatial locations, especially in complex structures, allowing the data fusion results to be adaptively adjusted based on distance changes.

[0039] Through the axial and circumferential strain decomposition of the spirally wound optical fiber group, the plane strain conversion of the radial optical fiber group, and the weighted fusion process, the present invention can more accurately obtain the stress state of each area of ​​the steel structure, especially in complex structures or node areas, and can more accurately reveal the local stress concentration. The weighted superposition method of strain data effectively avoids the monitoring errors caused by different node positions, making the stress distribution map of the entire structure more realistic and reliable. Overall, this technical method can provide high-precision stress monitoring data, helping engineers to promptly detect potential structural problems and improve the safety and stability of steel structures.

[0040] In one possible implementation, the local stress concentration factor is a key indicator for measuring the local stress distribution of a steel structure. Its calculation formula is: Local stress concentration factor = monitoring point stress value / steel structure design allowable stress; This coefficient compares the real-time stress values ​​at monitoring points with the allowable stresses designed for the structure to assess whether stress concentration exists in localized areas. When the local stress concentration coefficient is greater than 1, it indicates that the stress in that area exceeds the design standard, potentially posing a risk of damage. This method dynamically assesses the local stress state of a steel structure using data from real-time monitoring points, enabling early detection of potential damage risks.

[0041] The change rate threshold is obtained through regression analysis of historical damage data. The specific steps are: First, the stress time series data of the damaged steel structure is extracted. Furthermore, the stress increment distribution per unit time before the damage occurs is statistically analyzed. Furthermore, the upper quantile of this stress increment distribution is selected as the benchmark for the rate of change threshold.

[0042] This method uses historical data to establish a stress variation pattern associated with damage occurrence and, through regression analysis, derives the critical stress change rate for damage onset. When the observed stress change rate exceeds this threshold, it may indicate that the structure is at high risk of damage and warrants attention.

[0043] The number of consecutive alarm points is the key basis for judging whether there is an abnormality in the steel structure. This threshold is determined according to the main vibration frequency of the structure. The specific method is: First, the vibration frequency of the steel structure under environmental excitation is measured to obtain the dominant vibration frequency. Furthermore, the inverse of the dominant vibration frequency is multiplied by a preset period multiple to determine the minimum duration of the alarm. Furthermore, this duration is converted to the corresponding number of sampling points, forming the criteria for determining the number of alarm points.

[0044] This step considers the relationship between the structure's vibration characteristics and stress changes, ensuring real-time and accurate damage risk assessments during structural vibration. By analyzing vibration frequency, false alarms can be effectively reduced, preventing normal structural vibrations from being misidentified as damage.

[0045] By setting dynamic thresholds, this method can accurately determine the damage risk of steel structures under different operating conditions, demonstrating significant practical value. The local stress concentration factor provides real-time information on whether a part of the structure is overloaded. The rate-of-change threshold improves the accuracy of the determination through historical data analysis, while the number of continuous alarm points optimizes the alarm mechanism by leveraging the vibration characteristics of the structure. This comprehensive determination method can effectively improve the sensitivity and accuracy of steel structure health monitoring systems, reduce the risk of human interference and misjudgment, and provide a more reliable monitoring basis for the long-term operation of steel structures.

[0046] In one possible implementation, a curvature gauge is first used as a measuring tool to scan the surface of a steel structure member. A curvature gauge is a tool used to measure surface curvature, typically consisting of a pointer and a dial. When in use, the gauge is placed steadily on the surface of the member. The pointer deflects according to the degree of curvature of the member. By moving the gauge, the entire surface of the member is gradually scanned, acquiring curvature data at multiple measurement points.

[0047] At each measuring point, the curvature gauge's pointer deflects according to the surface curvature. The maximum angle of the pointer deflection is recorded, and the corresponding curvature value is calculated using a formula. Because steel structures typically have varying degrees of curvature, the curvature values ​​at each measuring point will vary during the scanning process. These values ​​reflect the curvature variations of the steel member's surface.

[0048] After all scanning measurements are completed, all measured curvature values ​​are compared, and the minimum curvature value is selected as the final benchmark. This minimum curvature value reflects the maximum compactness of the steel structure's surface curvature, that is, the minimum curvature radius. The minimum curvature radius typically indicates the area where the member is most susceptible to local bending, which is important for analyzing the stress state of the steel structure and assessing potential damage.

[0049] This measurement method can accurately obtain the value of the minimum curvature radius of the steel structure surface, which is crucial for subsequent stress analysis. The minimum curvature radius is usually closely related to factors such as structural deformation and force concentration, and can reveal possible weak links in the steel structure under stress. Scanning with a curvature gauge has high measurement accuracy, is easy to operate, and has a wide range of applications. In addition, selecting the minimum curvature value as a benchmark can ensure that the most critical stress-bearing parts of the steel structure are monitored, which helps to discover potential deformation problems or damage hazards at an early stage, so that timely maintenance or reinforcement measures can be taken to ensure the safety and stability of the structure.

[0050] In one possible implementation, a steel structure specimen with a distributed fiber optic sensor attached is first placed in a temperature-controlled chamber, ensuring good surface adhesion and accurate measurement of strain on the steel structure's surface. During this process, a constant axial load is applied to monitor the specimen's deformation under load. This constant axial load simulates the stresses on the steel structure under actual operating conditions, ensuring the reliability of the experimental data.

[0051] Next, the temperature in the temperature-controlled chamber is gradually increased at fixed intervals. Each temperature level is held steady for a specified period after reaching the set value to ensure a smooth temperature change and prevent rapid strain changes. After each temperature stabilization, apparent strain data and temperature gradient data are recorded. Apparent strain data is monitored in real time using distributed fiber optic sensors. The strain data reflects the expansion or contraction of the steel structure due to temperature changes. The temperature gradient data is used to analyze temperature variations at different locations on the specimen.

[0052] The collected temperature gradient and apparent strain data can be used to create a scatter plot of the temperature gradient and apparent strain. This plot shows the relationship between the deformation response of the steel structure and the temperature change under different temperature conditions. This plot provides a preliminary understanding of the impact of temperature on the apparent strain and provides data support for the subsequent calculation of the compensation coefficient.

[0053] To eliminate the effects of temperature changes on strain measurements of steel structures, a linear fitting analysis is required. By performing a linear fit on a scatter plot of temperature gradient and apparent strain, a compensation coefficient is derived that reflects the strain response due to temperature changes. The calculation of the compensation coefficient is based on the linear relationship between temperature and strain. In practical applications, this compensation coefficient can be used to adjust for errors caused by temperature changes during stress monitoring, thereby providing more accurate stress data.

[0054] By systematically simulating the effects of temperature changes on steel structure strain and deriving compensation coefficients, the interference caused by temperature changes can be effectively eliminated. This method provides a reliable way to accurately measure the stress state of a structure, especially in environments with large temperature fluctuations, ensuring the accuracy of monitoring data. The compensation coefficients obtained through linear fitting can be widely used in the long-term monitoring of steel structures, further improving the accuracy of structural health monitoring and helping engineers promptly identify potential safety risks and take appropriate measures.

[0055] In one possible implementation, the node centroid area in the steel structure is usually where the stress and deformation are most concentrated. In this area, due to the large number of stress points and relatively large deformation, the measurement of the strain component is particularly important. The circumferential strain component mainly reflects the strain characteristics of the steel structure during bending. Especially at the node centroid, the stress changes to the steel structure are more complex. The circumferential strain component can usually more accurately reflect the actual stress conditions of the steel structure than the plane strain tensor. Therefore, in the node centroid area, the weight of the circumferential strain component is set higher than the plane strain tensor. This adjustment rule helps to strengthen the strain monitoring of this key area and improve the accuracy of stress analysis.

[0056] At steel structure member connections far from the node centroid, forces and strains are typically uniform and stable, but the strain distribution changes as the distance from the centroid increases. To ensure that strain data at different locations is appropriately accounted for, the weight of the plane strain tensor at member connections far from the node centroid increases linearly with the distance from the centroid. This dynamic adjustment rule accounts for the fact that strain information at locations farther from the node centroid may contribute less to the overall stress analysis, but the influence of the plane strain tensor on the analysis gradually increases with distance. Therefore, adjusting the weight coefficient in a linearly increasing manner can more accurately reflect the stress state in that area and optimize the analysis results of the overall data.

[0057] By dynamically adjusting the weight coefficients, this method can fully consider the strain characteristics of different regions, thereby more accurately reflecting the stress distribution of steel structures. In the node centroid area, the higher weight of the circumferential strain component can strengthen the monitoring of key areas of the structure and avoid errors caused by neglecting local strain. In areas far from the centroid, the linear increase rule of the plane strain tensor weight can reasonably weight the strain data at different locations, thereby obtaining more accurate and comprehensive stress monitoring results throughout the entire structure. Ultimately, this dynamic adjustment rule of the weight coefficient can improve the accuracy and reliability of stress monitoring of steel structures, helping engineers to promptly identify potential safety hazards in the structure and take effective maintenance measures.

[0058] In one possible implementation, first, steel structure specimens made of the same or similar materials as those in the actual application environment are selected, placed in an experimental device, and subjected to fatigue loading tests. The purpose of the fatigue loading test is to simulate the cyclic stress changes of the material during long-term stress, especially the response to high-frequency stress changes. This process usually involves periodic loading and unloading. By repeatedly applying loads of different intensities, the behavior of the material under multiple cyclic stresses and the occurrence of damage are observed. By controlling the frequency and amplitude of the loading, the fatigue damage process of the steel structure can be accurately reproduced in the experiment.

[0059] During fatigue loading, high-frequency strain gauges are used to monitor and record in real time the stress evolution of the steel specimens during the final hour before damage occurs. The accuracy of high-frequency strain gauges is crucial, as they can collect strain data at a high frequency, capturing subtle fluctuations in stress over a short period of time. The strain gauge records reveal the regularity of stress fluctuations and their correlation with structural damage, providing accurate historical data for subsequent damage assessment.

[0060] The stress history recorded by high-frequency strain gauges allows analysis of stress fluctuations during fatigue loading and identification of periods of stress concentration or abnormal changes. This data provides a theoretical basis for understanding stress variations in steel structures during actual use and a reference for assessing the extent and type of structural damage. The stress fluctuation data during the final hour focuses on stress fluctuations just before damage occurs, particularly as the structure approaches its fatigue limit.

[0061] This method of acquiring historical damage data can provide an important reference for stress monitoring and damage prediction of steel structures. In practical applications, by analyzing the stress changes of steel structures before damage under different load conditions, engineers can more accurately predict and evaluate the health status of steel structures during their service. In particular, the combined use of fatigue loading tests and high-frequency strain gauges can provide a comprehensive understanding of the fatigue properties of steel structure materials and the initial manifestations of damage, further improving the accuracy and reliability of stress monitoring. Ultimately, this method can provide a scientific basis for long-term health monitoring, maintenance decisions, and safety warnings for steel structures, reducing the risk of sudden damage and accidents.

[0062] In one possible implementation, a narrow-width optical pulse is first emitted to stimulate a Brillouin scattering signal. This Brillouin scattering signal primarily reflects frequency shifts caused by stress and temperature changes in the optical fiber. The use of narrow-width optical pulses ensures high temporal resolution of the excitation signal, accurately capturing minute frequency shifts caused by external stress changes. This is particularly important for stress monitoring in steel structures, where stress changes are relatively subtle over long periods of use, requiring high temporal resolution to accurately detect them.

[0063] After the narrow-width optical pulse is generated, the Brillouin scattering signal is collected by a fiber optic sensor, capturing the frequency shift information. The Brillouin scattering frequency shift is proportional to the change in stress (particularly tensile stress), and therefore, this process provides critical data reflecting the stress conditions of steel structures. By accurately measuring the Brillouin frequency shift, the stress state of the steel structure can be determined in real time, providing a basis for subsequent damage assessment and maintenance decisions.

[0064] Next, a wide-pulsewidth light pulse is emitted to stimulate Raman scattering signals. Raman scattering is primarily related to temperature changes, and by analyzing the changes in anti-Stokes intensity, precise temperature information can be obtained. Because wide-pulsewidth light pulses cover a wide frequency band, they are better able to capture signals caused by temperature changes when stimulating Raman scattering. By obtaining anti-Stokes intensity, the relationship between stress distribution and temperature changes in steel structures under different temperature conditions can be further analyzed.

[0065] Time-division multiplexing (TDM) technology enables the alternating acquisition of Brillouin and Raman scattering signals on the same fiber optic sensor. Within different time windows, the system transmits narrow and wide pulse width optical pulses, ensuring that the two different scattering signals do not interfere with each other. The use of TDM significantly improves the efficiency of the fiber optic sensor and avoids the time delay and system complexity associated with separate acquisition of the different signals.

[0066] By adopting time-division multiplexing technology, the alternating acquisition of Brillouin scattering and Raman scattering signals is achieved, effectively improving the ability to synchronously acquire stress and temperature information. The use of narrow-width optical pulses ensures the high temporal resolution of the Brillouin frequency shift, allowing small changes in stress in steel structures to be accurately captured. Wide-width optical pulses make the monitoring of temperature changes more comprehensive, thereby providing more accurate information on environmental changes. By simultaneously collecting stress and temperature data, a more comprehensive understanding of the working status of the steel structure and its response to changes in the external environment can be achieved. This technology can improve acquisition efficiency while ensuring high accuracy, which is of great significance for the long-term health monitoring of steel structures. It helps to promptly detect potential structural damage and temperature anomalies, thereby improving the safety and reliability of the structure.

[0067] The following is a detailed explanation using examples: This example specifically describes how to apply the steel structure stress monitoring method based on distributed optical fiber sensing technology to monitor the health of a steel bridge. The primary objective of this bridge is to monitor stress and temperature changes over extended periods of use in real time, enabling timely detection of potential structural damage or anomalies and the implementation of necessary maintenance measures.

[0068] During long-term operation, the steel structure of a bridge will experience varying degrees of stress due to factors such as traffic loads and ambient temperature fluctuations. Traditional stress monitoring methods are often limited to a local area and cannot provide information on the stress distribution of the entire bridge. This invention aims to utilize distributed fiber optic sensing technology, combined with Brillouin and Raman scattering signals, to monitor stress and temperature changes across the entire bridge in real time, thereby achieving full bridge health monitoring.

[0069] Specifically, six fiber optic sensors were evenly distributed across the bridge's steel structure, installed at different locations along the span. The optical fibers were installed in areas of high stress, such as bridge joints and support points. Each fiber was 2,000 meters long, covering the entire bridge structure.

[0070] The fiber optic sensor uses single-mode optical fiber with a sensing area of ​​1 meter, capable of acquiring temperature and stress information at the corresponding location. The fiber has a diameter of 125μm and is fixed to the structure with a special adhesive, ensuring close contact between the fiber and the steel structure to effectively transmit stress changes.

[0071] Based on the time division multiplexing technology, narrow pulse width optical pulses and wide pulse width optical pulses are alternately emitted. The specific optical pulse parameters are: Narrow pulse width optical pulse: pulse width of 10ns, repetition frequency of 1kHz, used to stimulate Brillouin scattering.

[0072] Wide-pulse optical pulses: with a pulse width of 200 ns and a repetition rate of 500 Hz, are used to stimulate Raman scattering.

[0073] By alternately emitting two pulses of different widths, the Brillouin frequency shift and Raman anti-Stokes light intensity can be captured separately, ensuring that the stress and temperature data sensed on the same optical fiber do not interfere with each other.

[0074] During the acquisition process, the Brillouin frequency shift and Raman anti-Stokes light intensity are collected by optical fiber sensors respectively, and the signals are transmitted to the data acquisition system through optical fibers.

[0075] Brillouin frequency shift formula: ; in, is the Brillouin frequency shift, For strain, is the length of the optical fiber sensor, is the laser wavelength.

[0076] Raman anti-Stokes intensity formula: ; in, is the Raman anti-Stokes intensity, is the initial light intensity, is the attenuation coefficient of the optical fiber, is the length of the optical fiber.

[0077] Based on the above formula, the stress and temperature changes can be calculated respectively through the real-time acquisition of Brillouin frequency shift and Raman light intensity.

[0078] Signal analysis is performed using a combination of time-domain and frequency-domain analysis. In the time-domain analysis, the Brillouin frequency shift and Raman anti-Stokes intensity trends are correlated with stress and temperature changes, respectively.

[0079] For the stress variation, the following stress-frequency shift relationship is used: ; According to the linear relationship between Raman light intensity change and temperature change: ; Through data processing, the real-time stress and temperature distribution at each location of the bridge can be obtained.

[0080] Monitoring data is transmitted to the monitoring platform via a real-time data acquisition system, displaying stress and temperature values ​​at each location. The monitoring platform analyzes and predicts data based on external factors such as weather and traffic load, promptly identifying abnormal stress and temperature changes.

[0081] During implementation, the stress and temperature monitoring method of the present invention demonstrated significant advantages. The following is a comparison with traditional stress monitoring methods: Traditional methods, such as the strain gauge method, can only obtain stress data in a local area, and the accuracy is greatly affected by factors such as installation location and temperature changes.

[0082] This method, which utilizes distributed fiber optic sensing technology, can monitor stress and temperature distribution across the entire bridge in real time. Accurate stress and temperature data can be obtained at any location on the bridge, with minimal environmental interference. Experimental results show that when measuring stress changes at the same location, the error of this method is less than 5%, while the error of traditional strain gauge methods can reach 10%-15%.

[0083] In practical applications, through 24-hour continuous data collection and monitoring, the stress distribution of the bridge is monitored in real time. Especially during heavy traffic loads, the monitoring system can detect excessive stress in localized areas of the bridge in advance, issue early warning signals, and implement timely load reduction measures to avoid possible structural damage.

[0084] This example demonstrates the complete application process of a steel structure stress monitoring method based on distributed fiber optic sensing technology. From experimental design, fiber optic sensor installation, light pulse emission and signal acquisition, data analysis and result processing, to the final application results, this invention provides an efficient and accurate steel structure stress monitoring solution. Compared with traditional methods, this invention can achieve real-time health monitoring of the entire bridge with higher measurement accuracy and stronger environmental adaptability. The widespread application of this technology will greatly improve the efficiency and safety of structural health monitoring and has broad prospects for engineering applications.

[0085] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0086] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A steel structure stress monitoring method based on distributed optical fiber sensing, characterized in that: include: Step 1: Construct a sensing fiber array in the steel structure node area, which includes a first fiber group arranged in a spiral pattern, a second fiber group arranged radially, and a reference fiber that is not in contact with the structure surface; Step 2: Inject light pulses into the sensing fiber array and synchronously collect the Brillouin frequency shift signal and Raman scattering signal at each point; Step 3: Separate temperature and mechanical strain based on the reference fiber signal: global temperature compensation is applied to the first fiber group, and temperature gradient compensation along the fiber path is applied to the second fiber group; Step 4: Decompose the compensated mechanical strain into multi-directional strain components according to the spatial orientation of the optical fiber, and reconstruct the three-dimensional stress field by fusion of the strain components; Step 5: Dynamically determine the damage risk based on the local stress concentration factor and its change rate in the stress field.

2. A steel structure stress monitoring method based on distributed optical fiber sensing according to claim 1, characterized in that: The construction of the sensing fiber array described in step 1 includes: The pitch of the spiral arrangement is set according to the minimum curvature radius of the steel structure: the minimum curvature radius of the rod surface is measured, and a preset multiple of the radius is used as the maximum pitch reference value, and the actual pitch is taken as an equal spacing value smaller than the reference value; The angle of the radial arrangement is adaptively adjusted according to the symmetry of the node: the node geometric topology is obtained by three-dimensional laser scanning, and the optical fiber angle is reduced to a preset angle lower limit in the turning area of ​​the stress transfer path; The arrangement direction of the reference optical fiber is parallel to the direction of maximum thermal expansion coefficient of the steel structure, and the gap between it and the surface of the structure is determined by thermal deformation simulation to ensure that the optical fiber is not mechanically constrained when the temperature changes.

3. The method for monitoring stress of steel structures based on distributed optical fiber sensing according to claim 1, characterized in that: The implementation of the temperature gradient compensation in step 3 includes: The global temperature compensation formula is: mechanical strain = apparent strain - material thermal expansion coefficient × (current temperature - reference temperature), where the reference temperature is taken from the real-time measurement value of the reference optical fiber; The temperature gradient compensation formula is: mechanical strain = apparent strain - temperature gradient compensation coefficient × temperature change rate along the length of the optical fiber. The temperature gradient compensation coefficient is calibrated through a step temperature change experiment: under a constant load, the ambient temperature is increased in fixed steps, and the slope of the linear relationship between the temperature gradient and the apparent strain is recorded. The thermal expansion coefficient of the material is measured by a thermal dilatometer.

4. The method for monitoring stress of steel structures based on distributed optical fiber sensing according to claim 1, characterized in that: The strain component fusion in step 4 includes: For the strain data of the helically wound optical fiber group, the axial strain and hoop strain are decomposed according to the sine and cosine components of the helical expansion angle; For the radial fiber group, three intersecting fibers are selected to form a strain sensing unit. A plane strain conversion matrix is ​​constructed according to the angle between the fibers. This matrix is ​​derived based on the strain coordination equation of elastic mechanics and meets the continuity condition of the displacement field. The hoop strain component and the plane strain tensor are weighted and superimposed according to their spatial positions, and the weight coefficient is dynamically adjusted according to the distance between the node area and the centroid.

5. The method for monitoring stress of steel structure based on distributed optical fiber sensing according to claim 1, characterized in that: In the dynamic determination of damage risk described in step 5, the determination of the threshold value includes: Local stress concentration factor = monitoring point stress value / steel structure design allowable stress; The change rate threshold is set through regression analysis of historical damage data: the stress time series of the damaged structure is extracted, the stress increment distribution per unit time before the damage occurs is statistically analyzed, and the upper quantile of the distribution is taken as the threshold benchmark; The number of continuous alarm points is determined according to the main frequency of structural vibration: the vibration frequency of the steel structure under environmental excitation is measured, and the inverse of the vibration frequency is multiplied by the preset period multiple as the minimum continuous alarm duration, which is then converted into the corresponding number of sampling points.

6. The method for monitoring stress of steel structure based on distributed optical fiber sensing according to claim 2, characterized in that: The method for measuring the minimum curvature radius includes: Use a curvature gauge to scan the surface of the rod, record the curvature value corresponding to the maximum deflection angle of the curvature gauge pointer, and take the minimum value of all measured values ​​as the benchmark.

7. The method for monitoring stress of steel structures based on distributed optical fiber sensing according to claim 3, characterized in that: The specific process of the step temperature change experiment includes: The steel structure specimen with the optical fiber attached was placed in a temperature-controlled chamber and a constant axial load was applied; The temperature in the cabin is gradually increased at fixed temperature intervals, and the apparent strain and temperature gradient data are collected after each temperature level stabilizes; A temperature gradient-apparent strain scatter plot was drawn, and the compensation coefficient was obtained by linear fitting.

8. The method for monitoring stress of steel structures based on distributed optical fiber sensing according to claim 4, characterized in that: The dynamic adjustment rules of the weight coefficient include: In the nodal centroid region, the hoop strain component has a higher weight than the plane strain tensor; At member connections away from the centroid, the plane strain tensor weight increases linearly with distance.

9. The method for monitoring steel structure stress based on distributed optical fiber sensing according to claim 5, characterized in that: The acquisition of the historical damage data includes: Fatigue loading tests were carried out on specimens of the same material in the laboratory, and high-frequency strain gauges were used to record the stress change history in the last hour before damage occurred.

10. The method for monitoring stress of steel structure based on distributed optical fiber sensing according to claim 1, characterized in that: The implementation of the synchronous acquisition described in step 2 includes: Time division multiplexing technology is used to alternately excite Brillouin scattering and Raman scattering: first, a narrow pulse width optical pulse is emitted to collect the Brillouin frequency shift, and then a wide pulse width optical pulse is emitted to collect the Raman anti-Stokes light intensity.

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