Crack monitoring and early warning method and system for continuous beam construction

Abstract

The application provides a continuous beam construction crack monitoring and early warning method and system, distributed sensing optical fibers are arranged at key stress sections in the continuous beam body construction stage to form a sensing network covering the beam body, and then the optical frequency domain reflection signals of the sensing network are collected; the wavelength domain difference characteristics between the beam body no-load reference signal and the optical frequency domain reflection signals are fitted to obtain the strain distribution profile along the optical fiber path in the beam body, and the suspicious sections with strain mutation of the beam body are identified according to the gradient change of the strain distribution profile; the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious section is extracted, and then the width quantitative value of the crack in each suspicious section is inversely calculated; and the width quantitative value is converted into the early warning level of the beam body. By adopting the scheme of the application, the local crack defect can be quickly responded in the long-distance continuous beam construction scene.

Description

Technical Field

[0001] This application relates to the field of continuous beam monitoring technology, and more specifically, to a method and system for monitoring and early warning of cracks during continuous beam construction. Background Technology

[0002] Continuous beams are key load-bearing components in modern bridges, high-rise buildings, and other large-scale infrastructure projects. Their construction quality directly affects the safety and durability of the structure. During the construction of continuous beams, micro-cracks are easily generated at critical sections due to factors such as concrete shrinkage, temperature changes, and load effects. If these cracks are not detected and intervened in time, they may expand and cause structural damage, or even threaten the overall safety. Therefore, real-time and accurate monitoring and early warning of cracks during the construction of continuous beams is an important link in ensuring project quality and long-term structural performance.

[0003] Currently, crack monitoring technology based on distributed optical fiber sensing has been widely applied, especially Optical Frequency Domain Reflectometry (OFDR) technology. Due to its advantages such as high spatial resolution and strong anti-interference capability, it is suitable for structural health monitoring. Traditional OFDR crack monitoring methods typically employ global cross-correlation calculations, locating and quantifying strain distribution by comparing the wavelength domain differences between the reference signal and the measured signal. However, in long-distance, large-scale continuous beam monitoring scenarios, the amount of data from the optical fiber under test is enormous, and the time complexity of the global cross-correlation algorithm is O(n^2). 2 The computational load is large and the processing speed is slow, resulting in a serious lack of real-time performance of the system. Especially during the construction process, cracks may only appear in local areas. Traditional methods still require complex calculations on all-fiber data, resulting in a waste of computing resources and response delays. It is difficult to meet the needs of rapid monitoring and early warning during the construction period. Therefore, how to respond quickly to local crack defects in the construction scenario of long-distance continuous beams has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a method and system for monitoring and early warning of cracks in continuous beam construction, which can quickly respond to local crack defects in long-distance continuous beam construction scenarios.

[0005] Firstly, this application provides a method for monitoring and early warning of cracks during continuous beam construction, including: Distributed sensing optical fibers are deployed at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, thereby collecting the optical frequency domain reflection signal of the sensing network. The structural temperature field information of the beam is collected by intelligent sensors. Based on the structural temperature field information, the optical frequency domain reflection signal is temperature compensated. The strain distribution profile along the optical fiber path in the beam is fitted by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature compensated optical frequency domain reflection signal. Based on the gradient change of the strain distribution profile, suspicious sections of the beam with abrupt strain changes are identified. For each suspicious segment, the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment is extracted, and each cross-correlation spectrum is locally smoothed to obtain the strain signal of each suspicious segment. Then, based on the strain signal of each suspicious segment, the axial strain of the distributed sensing fiber is inverted to the quantized value of the crack width in each suspicious segment. The beam's structural temperature field information is collected by intelligent sensors, and the warning level of the beam is determined based on the width quantization value.

[0006] In some embodiments, distributed sensing optical fibers are deployed at key stress sections during the construction phase of a continuous beam to form a sensing network covering the beam, and the optical frequency domain reflection signal of the sensing network is collected, specifically including: Determine the key stress-bearing sections of the beam based on the continuous beam design drawings; Plan the deployment path of distributed sensing optical fibers along the longitudinal and transverse directions of the beam to cover the key stress sections; The sensing fiber is attached to the surface of the beam along the aforementioned deployment path; All the completed sensing optical fibers are connected in series to the optical frequency domain reflection demodulation equipment to form a sensing network covering the beam. During the construction of the continuous beam, the optical frequency domain reflection signal of the sensor network is collected in real time through the optical frequency domain reflection demodulation equipment according to the preset sampling period.

[0007] In some embodiments, fitting the strain distribution profile along the fiber path in the beam using the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal specifically includes: The reference signal of the beam under no load is collected in advance through the sensor network before construction; The reference signal is converted to the wavelength domain to obtain the reference wavelength signal; The optical frequency domain reflection signal is converted to the wavelength domain to obtain the measured wavelength signal; Determine the differential characteristics between the reference wavelength signal and the temperature-compensated optical frequency domain reflection signal; The differential features are integrated along the time dimension to fit the strain distribution profile distributed along the deployment path of the sensor network.

[0008] In some embodiments, identifying suspicious sections of the beam with abrupt strain changes based on the gradient change of the strain distribution profile specifically includes: Determine the gradient sequence of the strain distribution profile; Based on the gradient sequence, multiple abrupt change points in the beam were identified, indicating a sudden change in strain. Based on the spatial location of all mutation points, clustering is performed on all mutation points to obtain multiple suspicious sections of the beam body where strain mutations exist.

[0009] In some embodiments, extracting the cross-correlation spectrum of the sub-signal segments within each suspected segment of the optical frequency domain reflection signal specifically includes: Select a suspicious segment as the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious segment from the reference wavelength signal as the reference wavelength sub-signal of the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious section from the measured wavelength signal as the measured wavelength sub-signal of the selected suspicious section; Cross-correlation analysis is performed on the reference wavelength sub-signal and the measured wavelength sub-signal to obtain the cross-correlation spectrum of the selected suspicious segment; Continue to determine the cross-correlation spectra of the remaining suspicious segments.

[0010] In some embodiments, performing local smoothing on each cross-correlation spectrum to obtain the strain signal for each suspicious segment specifically includes: The window length of the sliding window is determined based on the data length of each cross-correlation spectrum; The smoothed cross-correlation spectrum of each suspicious segment is obtained by performing a moving average on each cross-correlation spectrum through the sliding window. Multiple wavelength offsets relative to the zero-offset reference point were identified from the smooth cross-correlation spectrum of each suspicious segment; All wavelength offsets for each suspicious segment are converted into strain signals for each suspicious segment.

[0011] In some embodiments, determining the warning level of the beam based on the width quantification value specifically includes: Obtain historical data for width quantization values; The historical data of the width quantification value is input into a pre-set early warning classification model to obtain the early warning level of the beam.

[0012] Secondly, this application provides a continuous beam construction crack monitoring and early warning system, comprising: The acquisition module is used to deploy distributed sensing optical fibers at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, and then acquire the optical frequency domain reflection signal of the sensing network. The processing module is used to collect the structural temperature field information of the beam through intelligent sensors, perform temperature compensation on the optical frequency domain reflection signal based on the structural temperature field information, fit the strain distribution profile along the optical fiber path in the beam by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal, and identify suspicious sections of the beam with abrupt strain changes based on the gradient change of the strain distribution profile. The processing module is also used to extract the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment for each suspicious segment, perform local smoothing processing on each cross-correlation spectrum to obtain the strain signal of each suspicious segment, and then invert the axial strain of the distributed sensing fiber to the width quantization value of the crack in each suspicious segment based on the strain signal of each suspicious segment. The execution module is used to collect the structural temperature field information of the beam through intelligent sensors and determine the warning level of the beam based on the width quantization value.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for monitoring and early warning of cracks in continuous beam construction.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring and early warning of cracks in continuous beam construction.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The continuous beam construction crack monitoring and early warning method and system provided in this application firstly deploys distributed sensing optical fibers at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, and then collects the optical frequency domain reflection signal of the sensing network; intelligent sensors collect the structural temperature field information of the beam, and perform temperature compensation on the optical frequency domain reflection signal based on the structural temperature field information; and fits the strain distribution profile along the optical fiber path in the beam by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal, and identifies suspicious sections of the beam with abrupt strain changes based on the gradient change of the strain distribution profile; for each suspicious section, the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious section is extracted, and each cross-correlation spectrum is locally smoothed to obtain the strain signal of each suspicious section; then, based on the strain signal of each suspicious section, the axial strain of the distributed sensing optical fibers is inverted to the quantized width value of the crack in each suspicious section; the structural temperature field information of the beam is collected by intelligent sensors, and the early warning level of the beam is determined based on the quantized width value.

[0016] Therefore, this application significantly improves the real-time performance of the system while ensuring monitoring accuracy by optimizing the signal processing flow. Firstly, it utilizes wavelength domain differential features to quickly fit the strain distribution profile. Then, it replaces the traditional global cross-correlation operation with a linear operation of differential integration, reducing the time complexity from O(n2) to O(n2). 2 The computational complexity is reduced to O(n), enabling efficient identification of suspicious sections with abrupt strain changes globally. This allows for rapid screening of all-fiber data, avoiding redundant calculations in non-strain regions. Furthermore, for the identified suspicious sections, sub-signal segments are extracted for cross-correlation spectrum analysis. Local smoothing improves the signal-to-noise ratio, thus accurately retrieving the quantified crack width. This combines rapid global positioning with precise local analysis. The former achieves high-speed preliminary positioning through differential integration, while the latter uses cross-correlation spectrum analysis in suspicious areas to ensure measurement accuracy. Thus, the system performs computationally intensive cross-correlation operations only in sections where cracks may occur, significantly reducing the overall computational load while ensuring high-precision quantification of strain and crack width in key areas. This scheme is particularly suitable for long-distance, multi-point crack monitoring scenarios during continuous beam construction, enabling rapid response and accurate early warning even with massive amounts of data, significantly improving the practicality and engineering application value of the monitoring system. In summary, the scheme proposed in this application enables rapid response to local crack defects in long-distance continuous beam construction scenarios. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a continuous beam construction crack monitoring and early warning method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the acquisition of optical frequency domain reflection signals according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of strain distribution profile according to some embodiments of this application; Figure 4 This is a structural schematic diagram of a continuous beam construction crack monitoring and early warning system according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a method for monitoring and early warning of cracks in continuous beam construction, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a continuous beam construction crack monitoring and early warning method according to some embodiments of this application. The continuous beam construction crack monitoring and early warning method mainly includes the following steps: In step 101, distributed sensing optical fibers are laid out at the key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, thereby collecting the optical frequency domain reflection signal of the sensing network.

[0020] In some embodiments, reference Figure 2 The figure is an exemplary flowchart illustrating the acquisition of optical frequency domain reflection signals according to some embodiments of this application. In this application, distributed sensing optical fibers are deployed at key stress sections during the construction phase of a continuous beam to form a sensing network covering the beam. The acquisition of optical frequency domain reflection signals from this sensing network can be achieved through the following steps: In step 1011, the key stress-bearing sections of the beam are determined according to the continuous beam design drawings; In step 1012, the deployment path of distributed sensing optical fibers along the longitudinal and transverse directions of the beam to cover the key stress section is planned; In step 1013, the sensing optical fiber is attached to the surface of the beam along the deployment path; In step 1014, all the completed sensing optical fibers are connected in series to the optical frequency domain reflection demodulation device to form a sensing network covering the beam. In step 1015, during the construction of the continuous beam, the optical frequency domain reflection signal of the sensor network is collected in real time through the optical frequency domain reflection demodulation device according to the preset sampling period.

[0021] In specific implementation, the key stress sections of the beam can be determined according to the design drawings of the continuous beam in the following way: First, obtain the design drawings and structural calculation model of the continuous beam, and identify the key areas where the internal forces such as bending moment, shear force, and torque are concentrated. These areas usually include, but are not limited to: the mid-span section of each span (bearing the maximum positive bending moment), the intermediate support section (bearing the maximum negative bending moment), the section with large shear force near the support (bearing the maximum shear force), the section where the section changes abruptly (such as the beam height change, the prestressed anchorage zone), and the location of the significant temporary load during the construction stage (such as the hanging basket anchorage point, the closure section). These areas are marked as key stress sections. In other embodiments, the key stress sections may also include other areas on the continuous beam where the internal forces such as bending moment, shear force, and torque are concentrated, which will not be elaborated here.

[0022] It should be noted that, in this application, the critical stress section refers to the area on a continuous beam where internal forces such as bending moment, shear force, and torque are concentrated.

[0023] In practice, the deployment path of the distributed sensing optical fibers covering the key stress sections along the longitudinal and transverse directions of the beam can be implemented in the following way: For longitudinal deployment, at least one longitudinal sensing optical fiber is deployed along the central axis of the top and bottom plates of the beam at each determined key stress section to monitor the tensile and compressive strain caused by positive and negative bending moments. For transverse deployment, multiple transverse sensing optical fibers are deployed at equal intervals along the beam width direction at the key stress sections to monitor the transverse stress distribution and uneven deformation, ensuring that the optical fibers form a continuous and closed monitoring loop, and avoiding routing in locations that may be mechanically damaged or covered during later construction.

[0024] In practice, the sensing optical fiber can be attached to the beam surface along the layout path in the following way: First, the layout path is polished to remove the laitance and form a rough, clean surface, and dust and oil are removed. Then, a layer of special epoxy resin structural adhesive is applied along the marked layout path. Before the adhesive initially sets, the sensing optical fiber (usually a strain sensing optical fiber with a tight sheath) is embedded in the adhesive. Finally, a protective adhesive is applied to the surface of the optical fiber for temporary fixation. After the adhesive is fully cured, a strong bond is formed.

[0025] In practice, all the deployed sensing fibers are connected in series to the optical frequency domain reflection demodulation device to form a sensing network covering the beam. This can be achieved in the following way: using a fiber optic fusion splicer, the ends of each independently deployed sensing fiber are spliced ​​sequentially to form a physically continuous fiber optic link. The splice points need to be protected with heat shrink tubing. Then, the two ends (start and end) of this series link are connected to the laser output port and signal receiving port of the optical frequency domain reflection demodulation device respectively through standard fiber optic patch cords, thus forming a working distributed sensing network.

[0026] In specific implementation, during the construction of continuous beams, the optical frequency domain reflection signal of the sensor network can be collected in real time through the optical frequency domain reflection demodulation equipment according to the preset sampling period. This can be achieved in the following way: In the control software of the optical frequency domain reflection demodulation equipment, a continuous automatic acquisition task is set: The sampling period is dynamically adjusted according to the construction stage (e.g., once every few hours during the static curing period, while it can be increased to once per minute or per second during key processes such as prestressing tensioning and concrete pouring). The sampling parameters include, but are not limited to, the sweep frequency range (e.g., 1540nm-1560nm), sweep frequency speed, spatial resolution (e.g., 1cm), etc. The equipment automatically triggers the sweep frequency laser and collects the original beat frequency interference signal according to the set period, and uses the collected original beat frequency interference signal as the optical frequency domain reflection signal of the sensor network. The sampling parameters can be set according to actual needs and are not limited here.

[0027] In step 102, the structural temperature field information of the beam is collected by a smart sensor. Based on the structural temperature field information, the optical frequency domain reflection signal is temperature compensated. The strain distribution profile along the optical fiber path in the beam is fitted by the wavelength domain difference feature between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal. Based on the gradient change of the strain distribution profile, suspicious sections of the beam with abrupt strain changes are identified.

[0028] In specific implementation, the structural temperature field information of the beam can be collected by intelligent sensors in the following way: the intelligent sensors are distributed digital temperature sensors, and the structural temperature field information is collected by deploying distributed digital temperature sensors inside the beam (embedded in concrete). The deployment path of the distributed digital temperature sensors is the same as the deployment path of the distributed sensing optical fibers in the sensor network.

[0029] In some embodiments, temperature compensation of the optical frequency domain reflection signal based on the structural temperature field information can be achieved by the following steps: Obtain the reference temperature; Obtain the temperature sensitivity coefficient of the distributed sensing fiber optic cable; The optical frequency domain reflection signal is converted to the wavelength domain to obtain the measured wavelength signal; The measured wavelength signal is compensated based on the structural temperature field information, the reference temperature, and the temperature sensitivity coefficient to obtain the compensated optical frequency domain reflection signal.

[0030] In practice, the temperature sensitivity coefficient of the distributed sensing fiber can be obtained in the following way: the temperature sensitivity coefficient can be obtained directly from the product manual of the distributed sensing fiber. This temperature sensitivity coefficient refers to the Rayleigh scattering characteristic wavelength shift of the sensing fiber caused by a unit temperature change.

[0031] In specific implementation, the optical frequency domain reflection signal is converted to the wavelength domain to obtain the measured wavelength signal in the following manner: First, a fast Fourier transform is performed on the optical frequency domain reflection signal to convert it from the time domain to the range domain, obtaining the backscattering intensity distribution along the sensor network deployment path. Then, a Hanning window function is used to window the backscattering intensity distribution in the range domain. Each sliding window corresponds to a preset spatial resolution unit on the sensor network. An inverse fast Fourier transform is performed on each windowed signal segment to convert it back to the wavelength domain, obtaining the Rayleigh scattering spectrum of the corresponding spatial location. All Rayleigh scattering spectra are arranged in spatial order to form a complete measured wavelength signal. This measured wavelength signal is a two-dimensional data, with each row of data corresponding to a spatial resolution unit. That is, for the same spatial location, each column of data corresponds to a sampling time. The preset spatial resolution unit can be set according to actual needs. For example, in this application, the spatial resolution unit is set to 1 cm.

[0032] In specific implementation, the measured wavelength signal is compensated based on the structural temperature field information, the reference temperature, and the temperature sensitivity coefficient to obtain the compensated optical frequency domain reflection signal. This can be achieved in the following way: First, the current temperature value corresponding to each spatial location in the structural temperature field information is obtained and compared with the reference temperature of the corresponding location recorded when acquiring the reference reference signal. The temperature change at each spatial location is calculated. Second, based on the temperature sensitivity coefficient KT (unit: pm / °C) of the distributed sensing fiber, the temperature change at each spatial location is converted into the corresponding temperature-induced wavelength offset, i.e., the product of the temperature sensitivity coefficient and the temperature change. Then, from the measured wavelength signal converted from the optical frequency domain reflection signal, the wavelength offset caused by the temperature at each spatial resolution unit, i.e., each spatial location, is subtracted to eliminate the influence of temperature change on the wavelength signal, thereby obtaining the temperature-compensated optical frequency domain reflection signal.

[0033] In some embodiments, reference Figure 3 The figure is an exemplary flowchart illustrating the determination of strain distribution profile according to some embodiments of this application. The strain distribution profile along the fiber optic path in the beam can be determined by fitting the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal using the following steps: In step 1021, the reference signal of the beam under no load is collected in advance through the sensor network before construction; In step 1022, the reference signal is converted to the wavelength domain to obtain a reference wavelength signal; In step 1023, the differential characteristics between the reference wavelength signal and the temperature-compensated optical frequency domain reflection signal are determined; In step 1024, the differential features are integrated along the time dimension to fit the strain distribution profile distributed along the deployment path of the sensing network.

[0034] In specific implementation, the pre-collection of the reference signal of the beam under no load before construction through the sensor network can be achieved in the following way: after the main structure of the continuous beam is completed and all temporary construction loads (such as formwork and supports) have been removed, the beam is only bearing its own weight and is in a period of relatively stable temperature, for example, when the ambient temperature change is ≤ ±1℃ / h and the internal temperature difference of the beam is ≤ ±0.5℃, or directly before sunrise in the early morning, a complete data acquisition is performed on the sensor network through the constructed optical frequency domain reflection demodulation equipment. During the acquisition, it is ensured that there are no additional live loads such as construction vehicles or personnel on the bridge, and the current ambient temperature is recorded as the reference temperature. The original beat frequency interference signal obtained in this acquisition is initially demodulated by the equipment to generate full-link distributed reflection intensity and phase data, which is then stored as the reference signal. Furthermore, it should be noted that the internal temperature of the beam when the reference reference signal is acquired is the reference temperature in this application.

[0035] It should be noted that the reference signal in this application is signal data used to characterize the initial state of the beam under no external construction load.

[0036] In specific implementation, the reference signal is converted to the wavelength domain to obtain the reference wavelength signal, which can be achieved in the following way: First, a fast Fourier transform is performed on the reference signal to convert it from the time domain to the range domain, obtaining the backscattering intensity distribution along the sensor network deployment path. Then, a Hanning window function is used to window the backscattering intensity distribution in the range domain, with each sliding window corresponding to a preset spatial resolution unit on the sensor network. An inverse fast Fourier transform is performed on each windowed signal segment to convert it back to the wavelength domain, obtaining the Rayleigh scattering spectrum at the corresponding spatial location. All Rayleigh scattering spectra are arranged in spatial order to form a complete reference wavelength signal. This reference wavelength signal is a two-dimensional data, where the preset spatial resolution unit can be set according to actual needs. For example, in this application, the spatial resolution unit is set to 1 cm.

[0037] It should be noted that the reference wavelength signal and the measured wavelength signal have the same data dimension and the same data volume in this application.

[0038] In a specific implementation, the differential characteristics between the reference wavelength signal and the temperature-compensated optical frequency domain reflection signal can be determined in the following way: the absolute difference between the temperature-compensated optical frequency domain reflection signal and the reference wavelength signal is used as the differential characteristics between the reference wavelength signal and the measured wavelength signal.

[0039] It should be noted that the differential features in this application are two-dimensional data used to describe the distribution of the wavelength shift of the Rayleigh scattering spectrum caused by strain. The two-dimensional data is stored in matrix form, where each row of the matrix corresponds to a spatial resolution unit in the sensor network, and each column corresponds to a sampling time.

[0040] In a specific implementation, integrating the differential features along the time dimension to fit the strain distribution profile distributed along the sensor network deployment path can be achieved in the following way: For the matrix corresponding to the differential features, for each row in the matrix, i.e., the data corresponding to each spatial resolution unit, numerical integration is performed along the direction of all columns in that row, i.e., the direction of the time dimension, and the obtained integration result is used as the strain quantization value of the corresponding spatial resolution unit. Finally, all strain quantization values ​​are arranged according to the row order of the matrix corresponding to the differential features, and the resulting sequence is used as the strain distribution profile distributed along the sensor network deployment path.

[0041] It should be noted that the strain distribution profile in this application is a sequence representing the continuous change of strain magnitude along the fiber optic cable routing path.

[0042] In some embodiments, identifying suspicious sections of the beam with abrupt strain changes based on the gradient change of the strain distribution profile can be achieved using the following steps: Determine the gradient sequence of the strain distribution profile; Based on the gradient sequence, multiple abrupt change points in the beam were identified, indicating a sudden change in strain. Based on the spatial location of all mutation points, clustering is performed on all mutation points to obtain multiple suspicious sections of the beam body where strain mutations exist.

[0043] In a specific implementation, the gradient sequence of the strain distribution profile can be determined in the following way: for each internal data point in the strain distribution profile sequence, calculate the difference between it and the previous data point, divide the difference by the spatial resolution unit, and then use the quotient as the gradient at each internal data point. Finally, arrange all the gradients in order to obtain the gradient sequence of the strain distribution profile.

[0044] In specific implementation, the identification of multiple abrupt strain points in the beam based on the gradient sequence can be achieved in the following way: First, calculate the standard deviation of the absolute values ​​of all gradient values ​​in the gradient sequence. Then, take the data points corresponding to gradient values ​​that are more than three times the standard deviation as abrupt strain points in the beam.

[0045] In specific implementation, clustering all mutation points based on their spatial locations to obtain multiple suspicious segments of the beam with strain mutations can be achieved in the following way: merge mutation points that are spatially adjacent and spaced less than ten spatial resolution units apart into the same set. The continuous spatial range covered by each set is defined as an independent suspicious segment, thereby obtaining multiple suspicious segments of the beam with strain mutations.

[0046] It should be noted that the principle of step 102 is that when the sensing fiber is subjected to external strain, it causes an additional phase change ϕ1 in the measurement optical signal propagating in the fiber. During signal processing, through specific windowing and frequency shifting operations, the components of the reference signal and the measurement signal within the target space window can be converted into wavelength domain forms, respectively. The measurement signal is the product of the reference signal and a factor containing this phase change. After differential and modulo operations on these two wavelength domain signals, the result is a quantity proportional to cos(ϕ0+ϕ1)−cos(ϕ0), where ϕ0 is the initial phase. Integrating this result over the entire time length... The integral value is directly determined by the phase change ϕ1. The key is that when external strain exists (ϕ1≠0), the integral result will inevitably be non-zero, while in the absence of strain (ϕ1=0), the integral result will inevitably be zero. Therefore, by sequentially calculating the integral value of each spatial window and observing its distribution, the region where the integral value shows a significant peak corresponds to the location of strain on the optical fiber, thus achieving precise positioning. Therefore, this application rapidly obtains the strain distribution profile by directly integrating along the wavelength dimension after differentiating the reference and measured wavelength signals. This linear calculation path of difference and integration has a time complexity of O(n), while the traditional cross-correlation calculation requiring point-by-point sliding comparison of the entire spectrum has a time complexity of O(n). 2 Therefore, the method in this application achieves an order-of-magnitude improvement in data processing efficiency, meeting the needs of long-distance real-time monitoring.

[0047] In step 103, for each suspicious segment, the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment is extracted, and each cross-correlation spectrum is locally smoothed to obtain the strain signal of each suspicious segment. Then, based on the strain signal of each suspicious segment, the axial strain of the distributed sensing fiber is inverted to the quantized value of the crack width in each suspicious segment.

[0048] In some embodiments, extracting the cross-correlation spectrum of the sub-signal segments within each suspected segment of the optical frequency domain reflection signal can be achieved using the following steps: Select a suspicious segment as the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious segment from the reference wavelength signal as the reference wavelength sub-signal of the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious segment from the measured wavelength signal as the measured wavelength sub-signal of the selected suspicious segment; Cross-correlation analysis is performed on the reference wavelength sub-signal and the measured wavelength sub-signal to obtain the cross-correlation spectrum of the selected suspicious segment; Continue to determine the cross-correlation spectra of the remaining suspicious segments.

[0049] In specific implementation, the cross-correlation analysis of the reference wavelength sub-signal and the measured wavelength sub-signal to obtain the cross-correlation spectrum of the selected suspicious segment can be achieved in the following way: First, the reference wavelength sub-signal and the measured wavelength sub-signal are respectively normalized to zero mean. Then, the cross-correlation function is calculated for the two normalized signals, and the calculated cross-correlation function is used as the cross-correlation spectrum of the selected suspicious segment.

[0050] In some embodiments, the strain signal for each suspected segment can be obtained by locally smoothing each cross-correlation spectrum using the following steps: The window length of the sliding window is determined based on the data length of each cross-correlation spectrum; The smoothed cross-correlation spectrum of each suspicious segment is obtained by performing a moving average on each cross-correlation spectrum through the sliding window. Multiple wavelength offsets relative to the zero-offset reference point were identified from the smooth cross-correlation spectrum of each suspicious segment; All wavelength offsets for each suspicious segment are converted into strain signals for each suspicious segment.

[0051] In specific implementation, the window length of the sliding window can be determined based on the data length of each cross-correlation spectrum in the following way: For a cross-correlation spectrum sequence with N discrete points, according to the known sliding window parameter setting rules in the field of signal processing, the sliding window length L is set to a value in the range of 1 / 10 to 1 / 20 of the sequence length N, and the closest odd number in this range is selected as the final window length. The purpose of selecting an odd number is to ensure that the window has a unique center position, so that the result of the moving average calculation can accurately correspond to the data point at the center of the window. This achieves effective smoothing of random noise and avoids distortion of the main peak feature of the cross-correlation spectrum due to excessive smoothing caused by an excessively long window length. For example, when the cross-correlation spectrum sequence length N=1000, 1 / 10 of N is 100 and 1 / 20 is 50. Selecting the closest odd number in this range can determine L as 51 or 101. Those skilled in the art can adaptively adjust it in this range according to the actual signal noise level.

[0052] In specific implementation, the smooth cross-correlation spectrum of each suspicious segment can be obtained by performing a moving average on each cross-correlation spectrum through the sliding window in the following way: For each cross-correlation spectrum, a sliding window of length L is placed at the beginning of the cross-correlation spectrum sequence, the arithmetic mean of all L data points in the window is calculated, and the average value is assigned to the data point corresponding to the center position of the window. Then the window is slid backward by one data point, and the above average value calculation process is repeated until the window covers the entire cross-correlation spectrum sequence.

[0053] It should be noted that, in this application, the wavelength offset is a parameter value that characterizes the overall shift of the Rayleigh scattering spectrum on the wavelength axis caused by local strain in the optical fiber.

[0054] In practical implementation, converting all wavelength offsets of each suspicious segment into strain signals for that segment can be achieved as follows: For each suspicious segment, based on the fundamental principles of fiber optic sensing, there is a linear relationship between the wavelength offset Δλ and the fiber axial strain ε, i.e., ε = (1 / K) × (Δλ / λ), where K is the strain sensitivity coefficient of the fiber, which can be directly obtained from the product manual of the sensing fiber. For example, for standard silica fiber, the typical value is approximately 0.78 με. -1 λ is the center wavelength of the light source, which is preset by the system. By substituting the calculated wavelength offset Δλ into this formula, the strain value ε of the corresponding spatial point can be directly calculated. By traversing all the calculation points in the suspicious section, a set of discrete data points characterizing the strain distribution along the section is obtained, which is the strain signal of the suspicious section.

[0055] It should be noted that the strain signal in this application is a data sequence used to characterize the magnitude of the local axial strain of the sensing fiber and its spatial distribution along the suspected section.

[0056] In some embodiments, the inversion of the axial strain of the distributed sensing fiber to the quantized width of the crack in each suspected segment based on the strain signal of each suspected segment can be achieved by the following steps: The strain signal of each suspicious section is fitted with a function along the spatial distribution of each suspicious section to obtain a strain function describing the continuous strain change within each suspicious section; A theoretical correlation between the axial strain of an optical fiber and the crack opening displacement is established based on the strain-displacement relationship model in mechanics of materials. Substituting each strain function into the theoretical correlation, the distribution of crack opening displacement along the crack length in each suspicious section is obtained; The width of the crack perpendicular to its extension direction in each suspicious section of the beam is determined by the distribution of crack opening displacement along the crack length within the suspicious section.

[0057] In practice, the strain signal of each suspicious section is fitted with a function along the spatial distribution of each suspicious section to obtain the strain function describing the continuous change of strain in each suspicious section. This can be achieved in the following way: For each suspicious section, the corresponding strain signal and the spatial position of each data point in the strain signal are obtained. Then, the value of the data point is used as the dependent variable and the spatial position is used as the independent variable. The least squares method in the prior art is used to fit a curve, which is the strain function of the continuous change of strain in the corresponding suspicious section.

[0058] In specific implementation, the theoretical correlation between the axial strain of the optical fiber and the crack opening displacement based on the strain-displacement relationship model in mechanics of materials can be achieved in the following way: the existing model between the axial strain of the optical fiber and the crack opening displacement based on the strain-displacement relationship model in mechanics of materials can be used as the theoretical correlation. For example, the conversion model between optical fiber strain and crack displacement mentioned in the literature "Method and Theory for Conversion of Distributed Fiber-Optic Strains to Crack Opening Displacements" can be used. In other embodiments, other existing models between the axial strain of the optical fiber and the crack opening displacement can also be used, which are not limited here.

[0059] In practice, the distribution of crack opening displacement along the crack length in each suspected section can be obtained by substituting each strain function into the theoretical correlation. This can be achieved by substituting the strain function of each suspected section into the theoretical correlation and solving it. The result obtained is then used as the distribution of crack opening displacement along the crack length in the corresponding suspected section.

[0060] In practice, the determination of the quantified value of the width of the crack perpendicular to its extension direction in each suspicious section of the beam by the distribution of the crack opening displacement along the crack length in the suspicious section can be achieved in the following way: For each suspicious section, the distribution of the crack opening displacement along the crack length in each suspicious section is integrally integrated, and the interval of integration is the range of the corresponding suspicious section. Then, the integration result is divided by the length of the corresponding suspicious section, and the final value is used as the quantified value of the width of the crack perpendicular to its extension direction in the corresponding suspicious section.

[0061] It should be noted that the width quantification value in this application is a parameter value used to measure the width of cracks in a continuous beam.

[0062] In step 104, the warning level of the beam is determined based on the width quantification value.

[0063] In some embodiments, determining the warning level of the beam based on the width quantization value can be achieved using the following steps: Obtain historical data for width quantization values; The historical data of the width quantification value is input into a pre-set early warning classification model to obtain the early warning level of the beam.

[0064] In practice, the historical data of width quantization value can be obtained in the following way: during the construction of continuous beam, the steps 101-103 in this application are repeated multiple times to obtain multiple width quantization values, and the width quantization value in the most recent 24 hours is used as the historical data of width quantization value. Similarly, the structural temperature field information in the most recent 24 hours is used as the historical data of structural temperature field information.

[0065] In specific implementation, the historical data of the quantified width value is input into a pre-set early warning classification model to obtain the early warning level of the beam. This can be achieved in the following way: the historical data of the quantified width value is input into a pre-set early warning classification model to obtain the early warning level of the beam. The pre-set early warning classification model is set according to engineering specifications and engineering needs. For example, it can refer to the control standard for crack width in concrete structures in the bridge design and maintenance specifications to set multiple early warning thresholds. That is, when all crack widths are less than 0.10mm and the average crack width growth rate within 24 hours is less than 0.01mm / h, the early warning level is determined to be Level I; when crack widths are between 0.10mm and 0.20mm and the average crack width growth rate within 24 hours is between 0.01 and 0.05mm / h, the early warning level is determined to be Level II; when crack widths exceed 0.20mm and the average crack width growth rate within 24 hours is greater than 0.05mm / h, the early warning level is determined to be Level III. In other embodiments, the early warning classification model can also be set according to other standards, which are not limited here.

[0066] In another aspect, in some embodiments, this application provides a continuous beam construction crack monitoring and early warning system, with reference to... Figure 4 The figure is a schematic diagram of the structure of a continuous beam construction crack monitoring and early warning system 400 according to some embodiments of this application. The continuous beam construction crack monitoring and early warning system 400 includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to lay distributed sensing optical fibers at key stress sections during the construction stage of continuous beams to form a sensing network covering the beam, and then acquire the optical frequency domain reflection signal of the sensing network. Processing module 402 in this application is mainly used to collect the structural temperature field information of the beam through a smart sensor, perform temperature compensation on the optical frequency domain reflection signal based on the structural temperature field information, fit the strain distribution profile along the optical fiber path in the beam by the wavelength domain difference feature between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal, and identify suspicious sections of the beam with a sudden strain change based on the gradient change of the strain distribution profile. It should be noted that the processing module 402 in this application is also used to extract the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment for each suspicious segment, perform local smoothing processing on each cross-correlation spectrum to obtain the strain signal of each suspicious segment, and then invert the axial strain of the distributed sensing fiber to the width quantization value of the crack in each suspicious segment based on the strain signal of each suspicious segment. The execution module 403 in this application is mainly used to collect the structural temperature field information of the beam through intelligent sensors and determine the warning level of the beam based on the width quantization value.

[0067] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for monitoring and early warning of cracks in continuous beam construction.

[0068] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a method for monitoring and early warning of cracks in continuous beam construction, according to some embodiments of this application. The method for monitoring and early warning of cracks in continuous beam construction in the above embodiments can... Figure 5 The computer device 500 shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0069] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0070] The communication bus 502 can be used to transmit information between the aforementioned components.

[0071] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0072] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. In the above embodiment, the continuous beam construction crack monitoring and early warning method can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0073] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0074] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0075] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0076] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring and early warning of cracks in continuous beam construction.

[0077] In summary, the continuous beam construction crack monitoring and early warning method and system disclosed in this application firstly involves deploying distributed sensing optical fibers at key stress sections during the continuous beam construction phase to form a sensing network covering the beam, thereby collecting the optical frequency domain reflection signal of the sensing network; then, the structural temperature field information of the beam is collected by intelligent sensors, and temperature compensation is performed on the optical frequency domain reflection signal based on the structural temperature field information; finally, the strain along the optical fiber path in the beam is fitted by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal. The beam is analyzed to identify suspicious sections with abrupt strain changes based on the gradient changes of the strain distribution profile. For each suspicious section, the cross-correlation spectrum of the sub-signal segments of the optical frequency domain reflection signal within each suspicious section is extracted. The cross-correlation spectrum is locally smoothed to obtain the strain signal of each suspicious section. Then, based on the strain signal of each suspicious section, the axial strain of the distributed sensing fiber is inverted to quantify the width of the crack within each suspicious section. The structural temperature field information of the beam is collected by intelligent sensors, and the warning level of the beam is determined based on the width quantization value.

[0078] Therefore, this application significantly improves the real-time performance of the system while ensuring monitoring accuracy by optimizing the signal processing flow. Firstly, it utilizes wavelength domain differential features to quickly fit the strain distribution profile. Then, it replaces the traditional global cross-correlation operation with a linear operation of differential integration, reducing the time complexity from O(n2) to O(n2). 2 The computational complexity is reduced to O(n), enabling efficient identification of suspicious sections with abrupt strain changes globally. This allows for rapid screening of all-fiber data, avoiding redundant calculations in non-strain regions. Furthermore, for the identified suspicious sections, sub-signal segments are extracted for cross-correlation spectrum analysis. Local smoothing improves the signal-to-noise ratio, thus accurately retrieving the quantified crack width. This combines rapid global positioning with precise local analysis. The former achieves high-speed preliminary positioning through differential integration, while the latter uses cross-correlation spectrum analysis in suspicious areas to ensure measurement accuracy. Thus, the system performs computationally intensive cross-correlation operations only in sections where cracks may occur, significantly reducing the overall computational load while ensuring high-precision quantification of strain and crack width in key areas. This scheme is particularly suitable for long-distance, multi-point crack monitoring scenarios during continuous beam construction, enabling rapid response and accurate early warning even with massive amounts of data, significantly improving the practicality and engineering application value of the monitoring system. In summary, the scheme proposed in this application enables rapid response to local crack defects in long-distance continuous beam construction scenarios.

[0079] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0080] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for monitoring and early warning of cracks during continuous beam construction, characterized in that, include: Distributed sensing optical fibers are deployed at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, thereby collecting the optical frequency domain reflection signal of the sensing network. The structural temperature field information of the beam is collected by intelligent sensors. Based on the structural temperature field information, the optical frequency domain reflection signal is temperature compensated. The strain distribution profile along the optical fiber path in the beam is fitted by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature compensated optical frequency domain reflection signal. Based on the gradient change of the strain distribution profile, suspicious sections of the beam with abrupt strain changes are identified. For each suspicious segment, the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment is extracted, and each cross-correlation spectrum is locally smoothed to obtain the strain signal of each suspicious segment. Then, based on the strain signal of each suspicious segment, the axial strain of the distributed sensing fiber is inverted to the quantized value of the crack width in each suspicious segment. The warning level of the beam is determined based on the width quantification value.

2. The method as described in claim 1, characterized in that, Distributed sensing optical fibers are deployed at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam. The acquisition of optical frequency domain reflection signals from this sensing network specifically includes: Determine the key stress-bearing sections of the beam based on the continuous beam design drawings; Plan the deployment path of distributed sensing optical fibers along the longitudinal and transverse directions of the beam to cover the key stress sections; The sensing fiber is attached to the surface of the beam along the aforementioned deployment path; All the completed sensing optical fibers are connected in series to the optical frequency domain reflection demodulation equipment to form a sensing network covering the beam. During the construction of the continuous beam, the optical frequency domain reflection signal of the sensor network is collected in real time through the optical frequency domain reflection demodulation equipment according to the preset sampling period.

3. The method as described in claim 1, characterized in that, The strain distribution profile along the fiber optic path in the beam is fitted using the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal. Specifically, this includes: The reference signal of the beam under no load is collected in advance through the sensor network before construction; The reference signal is converted to the wavelength domain to obtain the reference wavelength signal; Determine the differential characteristics between the reference wavelength signal and the temperature-compensated optical frequency domain reflection signal; The differential features are integrated along the time dimension to fit the strain distribution profile distributed along the deployment path of the sensor network.

4. The method as described in claim 1, characterized in that, Based on the gradient change of the strain distribution profile, the suspected sections of the beam with abrupt strain changes are identified, specifically including: Determine the gradient sequence of the strain distribution profile; Based on the gradient sequence, multiple abrupt change points in the beam were identified, indicating a sudden change in strain. Based on the spatial location of all mutation points, clustering is performed on all mutation points to obtain multiple suspicious sections of the beam body where strain mutations exist.

5. The method as described in claim 1, characterized in that, Extracting the cross-correlation spectrum of the sub-signal segments within each suspicious segment of the optical frequency domain reflection signal specifically includes: Select a suspicious segment as the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious segment from the reference wavelength signal as the reference wavelength sub-signal of the selected suspicious segment; Extract the signal segment corresponding to the selected suspicious section from the measured wavelength signal as the measured wavelength sub-signal of the selected suspicious section; Cross-correlation analysis is performed on the reference wavelength sub-signal and the measured wavelength sub-signal to obtain the cross-correlation spectrum of the selected suspicious segment; Continue to determine the cross-correlation spectra of the remaining suspicious segments.

6. The method as described in claim 1, characterized in that, Local smoothing of each cross-correlation spectrum yields the strain signal for each suspicious segment, specifically including: The window length of the sliding window is determined based on the data length of each cross-correlation spectrum; The smoothed cross-correlation spectrum of each suspicious segment is obtained by performing a moving average on each cross-correlation spectrum through the sliding window. Multiple wavelength offsets relative to the zero-offset reference point were identified from the smooth cross-correlation spectrum of each suspicious segment; All wavelength offsets for each suspicious segment are converted into strain signals for each suspicious segment.

7. The method as described in claim 1, characterized in that, Determining the warning level of the beam based on the width quantification value specifically includes: Obtain historical data for width quantization values; The historical data of the width quantification value is input into a pre-set early warning classification model to obtain the early warning level of the beam.

8. A continuous beam construction crack monitoring and early warning system, characterized in that, include: The acquisition module is used to deploy distributed sensing optical fibers at key stress sections during the construction phase of the continuous beam to form a sensing network covering the beam, and then acquire the optical frequency domain reflection signal of the sensing network. The processing module is used to collect the structural temperature field information of the beam through intelligent sensors, perform temperature compensation on the optical frequency domain reflection signal based on the structural temperature field information, fit the strain distribution profile along the optical fiber path in the beam by the wavelength domain difference characteristics between the unloaded reference signal of the beam and the temperature-compensated optical frequency domain reflection signal, and identify suspicious sections of the beam with abrupt strain changes based on the gradient change of the strain distribution profile. The processing module is also used to extract the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious segment for each suspicious segment, perform local smoothing processing on each cross-correlation spectrum to obtain the strain signal of each suspicious segment, and then invert the axial strain of the distributed sensing fiber to the width quantization value of the crack in each suspicious segment based on the strain signal of each suspicious segment. The execution module is used to collect structural temperature field information of the beam through intelligent sensors and determine the warning level of the beam based on the width quantization value.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the continuous beam construction crack monitoring and early warning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the continuous beam construction crack monitoring and early warning method as described in any one of claims 1 to 7.

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