Digital-based load-bearing beam structure bearing stability evaluation method

Abstract

The present application relates to the technical field of balance test of structural components, and is a digital-based bearing stability evaluation method for bearing beam structure, specifically comprising: constructing a virtual reality feedback model; performing internal micro-damage analysis caused by physical continuity attenuation, extracting internal implicit peeling index obtained through internal micro-damage analysis, and combining left-right symmetrical support trimming fluctuation data obtained through real-time weight measurement to import into oblique displacement offset evaluation analysis to correct bearing stability; evaluating the deterioration of interface structure coupling stiffness; according to the oblique displacement offset index and the structure interface stability evaluation index, jointly determining the abnormality, obtaining a comprehensive collapse estimation index and performing risk warning. The present application solves the problem in the prior art that for the bearing beam with specific stress characteristics of components, there is a lack of effective technical means for early warning of sudden instability (such as oblique section shear failure) caused by internal damage accumulation before visible damage or significant deformation occurs.

Description

Technical Field

[0001] This invention relates to the field of structural component balance testing technology, and is a digital-based method for evaluating the load-bearing stability of load-bearing beam structures. Background Technology

[0002] In existing technologies, the monitoring and evaluation of the load-bearing stability of reinforced concrete or high-performance concrete (such as UHPC) load-bearing beam structures generally focus on macroscopic mechanical responses, such as inferring the degradation of the overall stiffness or strength of the structure by monitoring changes in deflection, strain, or vibration frequency. However, these methods lack effective perception and early warning capabilities for structural instability precursors induced by the evolution of microscopic defects within specific materials, which precede macroscopic deformation. This is especially true in large-span, thin-walled load-bearing beam structures, where local materials with unique gradation characteristics (such as Yellow River sand with extremely low fineness modulus) are used. When preparing UHPC, the material exhibits a special response mechanism that differs from conventional aggregates under the coupled effects of complex environments (such as non-uniform heat flow, high humidity, or long-term dynamic load). For example, in a high-temperature fire scenario, the vapor pressure accumulated by the pore water inside the ultra-fine particles of Yellow River sand due to heat vaporization can trigger micro-bursts (thermal trap effect), resulting in high-frequency, micro-amplitude hidden physical damage inside the material, such as micro-crack initiation and interface weakening. This damage is not uniformly dissipated, but rather selectively evolves and accumulates along the principal stress path inside the structure (especially in the key compression bar area where shear force is transmitted in the oblique section). Existing assessment systems struggle to quantify the dynamics of microscopic damage related to material gradation strength, and are unable to reveal how it drives the gradual loss of macroscopic stability by altering the internal force transmission mechanism of the structure (e.g., causing imperceptible shifts in the axial force trajectory of the main compression members). Therefore, for load-bearing beams with material-specific and component-specific stress characteristics, there is often a lack of effective technical means to provide early warning of sudden instability (such as oblique section shear failure) caused by the accumulation of internal damage before visible damage or significant deformation occurs. This constitutes a prominent technical blind spot and safety hazard in the existing field of load-bearing beam structural health monitoring. Summary of the Invention

[0003] The technical problem to be solved by this invention is that, in the prior art, there is a lack of effective technical means to provide early warning of sudden instability (such as oblique section shear failure) caused by the accumulation of internal damage in load-bearing beams with specific stress characteristics before visible damage or significant deformation occurs. This invention proposes a digital-based method for assessing the load-bearing stability of load-bearing beam structures.

[0004] To achieve the above objectives, the technical solution of the present invention, based on a digital method for evaluating the load-bearing stability of load-bearing beam structures, includes the following steps:

[0005] S1: Collect multi-dimensional characteristic data and real-time symmetrical reaction force weighing data of the coupled environment of the non-uniform energy field injected into the Yellow River sand UHPC load-bearing beam through the data acquisition terminal, and construct a virtual reality feedback model of internal damage stratification.

[0006] S2: Obtain the transient dynamic energy response sequence of the Yellow River sand UHPC beam, import it into the internal damage strategy to conduct internal micro-damage analysis caused by the decay of physical continuity, and simultaneously simulate the distribution pattern of particle size distribution after being affected by the injection of non-uniform energy field on the virtual reality feedback model.

[0007] S3: Extract the internal hidden stripping index obtained from S2 through internal micro-loss analysis, and combine it with the left and right symmetrical support balance fluctuation data obtained from real-time weight measurement reaction force, and import it into the oblique displacement deflection evaluation analysis for bearing stability calibration.

[0008] S4: Obtain local induced signal data generated inside the load-bearing beam due to the drift of the interface connection characteristics of Yellow River sand and import it into the mechanical brittle coupling analysis to evaluate the deterioration of the interface structure connection stiffness.

[0009] S5: The oblique displacement eccentricity index calculated by S3 and the structural interface stability evaluation index calculated by S4 are jointly imported into the instability and collapse risk assessment strategy to make a joint anomaly judgment and obtain the overall collapse prediction index of the oblique section of the load-bearing beam.

[0010] S6: Based on the overall collapse prediction index finally generated in S5, issue feedback and risk warnings on the stability failure of the load-bearing beam structure.

[0011] Preferably, S1 includes:

[0012] S11. Real-time acquisition of the transient dynamic energy response sequence caused by the injection of ultra-fine particles of Yellow River sand into a non-uniform energy field. The transient dynamic energy response sequence includes the transient pulse peak signal and dynamic load frequency signal describing the energy jump of the structure, as well as the support reaction force fluctuation characteristics, and is stored in the first storage component.

[0013] S12. Obtain the deviation signal of the electrical properties of the fine-particle interface by means of the electromagnetic sensing circuit embedded in the shear span area of ​​the inclined section at the beam end. The deviation signal of the electrical properties of the fine-particle interface includes the current feedback gradient of the sensor circuit and the thickness of the surface embrittlement layer obtained therefrom, and stores it in the second storage component.

[0014] The strategy for obtaining the thickness of the surface embrittlement layer is as follows: First, an AC excitation signal with a stable frequency is applied to the electromagnetic sensing circuit, and the current response signal of the circuit is collected in real time. After analog-to-digital conversion, the current feedback gradient is extracted. The current feedback gradient is compared with the reference gradient under the pre-calibrated healthy state to obtain the gradient change rate. Then, combined with the mapping relationship between different carbonization layer thicknesses and gradient change rates established in advance through material experiments, the current surface embrittlement layer thickness is obtained by inversion and recorded as the surface embrittlement characteristic value.

[0015] S13. Extract the center of gravity offset and principal stress axis deflection coefficient of the left and right supports of the load-bearing beam under the energy flow of the non-uniform energy field through the real-time weighing compensation algorithm, and store them in the third storage component.

[0016] S14. Construct a virtual reality feedback model to characterize the internal implicit peeling index and oblique displacement of the load-bearing beam. This model dynamically maps the data collected in S11-S13 onto the 3D constructed beam oblique compression member force skeleton to achieve visualized monitoring of the damage state of the internal force transmission backbone of the oblique section. Through signal extraction, real-time mechanical reconstruction, VR interactive display, and immediate feedback of dangerous center of gravity shift, a simulation feedback model is formed.

[0017] Preferably, step S2 includes the following steps:

[0018] S21. Obtain the transient dynamic energy response sequence of all Yellow River sand collected within period T, driven by internal energy.

[0019] S22. The instantaneous dynamic intensity peak value and its occurrence frequency density of the force-bearing node extracted in the i-th extraction are imported into the internal implicit peeling index calculation formula to obtain the internal implicit peeling index. The calculation formula is:

[0020] ;

[0021] Where a is the mass distribution bias weighting factor for a specific aggregate ratio in the structural steady-state assessment; The instantaneous peak dynamic intensity of a single force-bearing node sampled for the i-th time reflects the energy level of the kinetic energy jump generated by the single force-bearing node under load excitation. This represents the background average value of physical disturbances caused by the injection of a non-uniform energy field under normal conditions. These are the response value limits set for the evaluation;

[0022] This represents the average kinetic response frequency for the current period. This represents the safe reference value for the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam under reference operating conditions. The safe reference fluctuation range for the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam.

[0023] Preferably, step S3 includes the following steps:

[0024] S31; Extract the internal hidden peeling index obtained by internal micro-damage analysis of S2, and simultaneously collect the center of gravity change component of the beam support resultant force fed back by the measuring table.

[0025] S32: The internal implicit stripping index and the current mid-span eccentricity are imported into the inclined section column offset prediction formula to calculate the oblique displacement eccentricity index. The calculation strategy is as follows:

[0026] ;

[0027] in, exp() is the oblique displacement eccentricity index, which characterizes the severity of the deviation of the main force transmission member inside the load-bearing beam from the design axis. The values ​​of gravity measured in real time are those deployed symmetrically at the left and right supports of the load-bearing beam. This represents the total current weight measurement. The equivalent principal stress axial moment offset moment value of the beam section is used to quantitatively characterize the degree of physical displacement of the main load-bearing spine (i.e. the trajectory of the inclined section main compression member) inside the load-bearing beam deviating from the original geometric design force center axis under the non-uniform energy field injection environment of the load-bearing beam and the additional eccentric torque component caused thereby. This represents the upper limit of the design eccentricity corresponding to the main force transmission axis of the inclined section.

[0028] Preferably, step S4 includes the following steps:

[0029] S41. Real-time monitoring of the surface embrittlement and damage characteristics and loop impedance difference of fine fly ash and ultra-fine sand under the coupled environment of non-uniform energy field injection;

[0030] The surface embrittlement characteristic value is the thickness of the surface embrittlement layer obtained in step S12;

[0031] The strategy for obtaining the loop impedance difference is as follows: an AC excitation signal with known amplitude and frequency is applied to the electromagnetic sensing loop, the current response signal of the loop is acquired synchronously, and the current equivalent impedance value of the loop is obtained based on Ohm's law. This value is compared with the reference impedance value obtained in advance through health status calibration. By comparison, the equivalent impedance offset was calculated. The impedance difference of the circuit is obtained and denoted as . ;

[0032] S42. Calculate the final structural interface stability evaluation index using the boundary connection failure judgment model. The formula is: ;

[0033] in, is the structural interface stability evaluation index, which characterizes the degree of weakening of the interface response between the internal steel fiber and the Yellow River sand matrix after the injection of a non-uniform energy field; R is the category of physical parameters monitored by the sensor. The evolution response weighting factor is used for different thicknesses of the surface embrittlement and damage layer. Let be the equivalent impedance offset of the r-th type of induction loop at time i; The theoretical impedance threshold for the interface continuity of the main feedback region under environmental calibration represents the reference electrical signal response value under healthy conditions.

[0034] Among them, the evolution response weight factor The value is determined based on the thickness range into which the surface embrittlement evolution characteristic value in S41 falls.

[0035] Preferably, S5 includes:

[0036] Extract the oblique displacement bias index output in step S3 and the structural interface stability evaluation index output in step S4 And substitute it into the formula for calculating the overall collapse prediction coefficient of the inclined section of the load-bearing beam:

[0037] ;

[0038] in, For the overall collapse prediction coefficient, The oblique geometric weighting parameter is used in the design stage of the shear span ratio of load-bearing components.

[0039] Preferably, S6 includes:

[0040] Obtain the overall collapse prediction coefficient, normalize the overall collapse prediction coefficient by dividing it by the collapse prediction critical threshold, and map it to the early warning dashboard. Specifically, this includes:

[0041] If the normalized result of the risk prediction value is within the range of (0, 0.4], it is identified as a stable system, and the near-surface random energy release caused by S2 is identified as a normal energy consumption perturbation.

[0042] If the normalized result of the risk simulation value is within the range of (0.4, 0.75], it is defined as the risk of asymmetric center of gravity drift. Combined with the posture of the compression bar calculated by S3, it is marked in red on the virtual model, and a collapse avoidance plan due to asymmetric stiffness reduction is issued.

[0043] If the normalized result of the risk prediction value exceeds 0.75, it is defined as a critical shear collapse state. At this time, the internal forces and the force transmission axis enter the main structure's performance collapse zone, and a forced indication of the limit of loss of bearing capacity is given.

[0044] A storage medium storing instructions that, when read by a computer, cause the computer to execute the digital-based load-bearing beam structure load-bearing stability assessment method.

[0045] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described digital-based method for assessing the load-bearing stability of load-bearing beam structures.

[0046] Compared with the prior art, the technical effects of the present invention are as follows:

[0047] 1. This invention enables early warning of macroscopic instability risk in load-bearing beam structures. By integrating high-precision dynamic weighing and multi-physics field sensing data, a quantitative assessment system is constructed that can sensitively capture the evolution of implicit peeling and interface embrittlement induced by specific material gradation defects (such as ultra-fine particles of Yellow River sand). This overcomes the limitation of traditional methods that rely solely on macroscopic deformation or strength degradation indicators, resulting in delayed early warning.

[0048] 2. This invention accurately depicts how microscopic damage causes a shift in the stress trajectory of the key stress path of the load-bearing beam—the inclined section main compression member, thus enabling targeted monitoring and prediction of the specific failure mode of inclined section shear failure.

[0049] 3. By constructing a mechanical digital twin that integrates real-time data and VR interaction, this invention not only provides intuitive visualization monitoring and risk location capabilities, but also forms a hierarchical early warning and decision support system based on the proposed comprehensive collapse prediction index. This provides a reliable technical tool for predictive maintenance, emergency intervention and safe operation and maintenance of major engineering structures in complex environments, and significantly improves the safety assurance level and intelligent management capabilities of infrastructure. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0051] Figure 1 This is a schematic flowchart of the digital-based load-bearing stability assessment method for load-bearing beam structures according to the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Example 1:

[0056] like Figure 1 As shown in the embodiment of the present invention, the method for assessing the load-bearing stability of a load-bearing beam structure based on digitalization is particularly noteworthy. This embodiment aims to demonstrate the specific implementation and effectiveness verification of the universal structural damage testing method provided by the present invention under a specific and stringent environmental load with non-uniform energy injection—namely, the standard fire temperature rise curve. This embodiment selects a Yellow River sand UHPC load-bearing beam as the test object because the damage mechanism of this material at high temperatures (such as micro-bursting and interface carbonization) is typical, and can fully verify the comprehensive monitoring capability of this method for multiple damage modes. It should be noted that the core purpose of this embodiment is not to assess the fire resistance rating of the material itself, but to demonstrate how to apply the testing framework of the present invention to achieve a real-time quantitative assessment of the overall stability of a fire-exposed load-bearing beam structure.

[0057] In this embodiment, a simply supported heavy beam made of ultra-high performance concrete (UHPC) using Yellow River sand is used as the test object. Its cross-section is a hollow thin-walled structure, and the fineness modulus of the Yellow River sand in the material is in the range of 0.5-1.5. The beam specimen is placed in a horizontal fire test furnace, and a fire environment simulated by the ISO 834 standard temperature rise curve is applied to its bottom and two sides. Simultaneously, 80% of its design load is maintained as a constant static load at the top of the beam. This environment constitutes the coupling environment of non-uniform energy field injection as referred to in the claims of this invention. It should be noted that in this embodiment, the coupling environment of non-uniform energy field injection specifically refers to the coupling of a non-uniform thermal flow field and a mechanical load field. This embodiment specifically includes the following steps:

[0058] S1: Collect multidimensional characteristic data and real-time symmetrical reaction force weighing data of the coupled environment of the non-uniform energy field injection of the Yellow River sand UHPC load-bearing beam through the data acquisition terminal, and construct a virtual reality feedback model to characterize the internal damage stratification caused by the non-uniform energy field injection of the hollow thin-walled beam.

[0059] S11. Real-time acquisition of the transient dynamic energy response sequence caused by the injection of ultra-fine particles of Yellow River sand into a non-uniform energy field. The transient dynamic energy response sequence includes the transient pulse peak signal and dynamic load frequency signal describing the energy jump of the structure, as well as the support reaction force fluctuation characteristics, and is stored in the first storage component.

[0060] S12. Obtain the deviation signal of the electrical properties of the fine-particle interface caused by the non-uniform energy trap effect through the electromagnetic sensing circuit embedded in the shear span area of ​​the inclined section at the beam end. The deviation signal of the electrical properties of the fine-particle interface includes the current feedback gradient of the sensor circuit and the thickness of the surface embrittlement layer inverted therefrom, and stores it in the second storage component.

[0061] The strategy for obtaining the thickness of the surface embrittlement layer is as follows: First, an AC excitation signal with a stable frequency is applied to the electromagnetic sensing circuit, and the current response signal of the circuit is collected in real time. After analog-to-digital conversion, the current feedback gradient is extracted. The current feedback gradient is compared with the reference gradient under the pre-calibrated healthy state to obtain the gradient change rate. Then, combined with the mapping relationship between different carbonization layer thicknesses and gradient change rates established in advance through material experiments, the current surface embrittlement layer thickness is obtained by inversion and recorded as the surface embrittlement characteristic value.

[0062] S13. Extract the center of gravity offset and principal stress axis deflection coefficient of the left and right supports of the load-bearing beam under the energy flow of the non-uniform energy field through the real-time weighing compensation algorithm, and store them in the third storage component.

[0063] S14. Construct a virtual reality feedback model to characterize the internal implicit peeling index and oblique displacement of the load-bearing beam. This model dynamically maps the data collected in S11-S13 onto the 3D constructed beam oblique compression member force skeleton to achieve visualized monitoring of the damage state of the internal force transmission backbone of the oblique section. Through signal extraction, real-time mechanical reconstruction, VR interactive display, and immediate feedback of dangerous center of gravity shift, a simulation feedback model is formed.

[0064] For example, in this embodiment, S14 further includes:

[0065] First, the collected high-frequency weighing pulse data, support reaction force fluctuation data, and surface embrittlement damage data are recombined through a multi-objective correlation algorithm to remove noise signals caused by convective energy of non-uniform energy fields and convert them into a discrete numerical format that can characterize the strength of the inclined section skeleton of the load-bearing beam.

[0066] Then, using a 3D graphics engine or BIM reverse modeling, a mechanical digital twin of the load-bearing beam under load in a non-uniform energy field is created. The internal implicit peeling index obtained in S2 is mapped to the damage cloud map inside the model, so that the capillary cavities formed by the micro-deterioration of the Yellow River sand are presented in the form of dynamic voids; the oblique displacement plugging index obtained in S3 is mapped to the principal stress compression axis of the model, visually displaying the bending or lateral displacement trajectory of the beam's true load-bearing centerline; the interface steady-state damaged area obtained in S4 is mapped to the delamination and spalling zone on the beam surface;

[0067] Then, the processed mechanical model is integrated into the VR operation and maintenance system. Operation and maintenance personnel can locate the oblique profile point where the structural effect evolution is most vulnerable after the injection of non-uniform energy field in the virtual reality environment, and trigger the pre-calculation of the force risk at this point through the controller to simulate and predict the reaction force balance.

[0068] Finally, based on the centroid of the oblique displacement calculated by the model, the system automatically assigns an enhanced monitoring frequency in that area and provides optimal position guidance for unloading the load or establishing a shear support.

[0069] S2: Obtain the transient dynamic energy response sequence of the Yellow River sand UHPC beam, import it into the internal damage strategy to conduct internal micro-damage analysis caused by the decay of physical continuity, and simultaneously simulate the distribution pattern of particle size distribution after being affected by the injection of non-uniform energy field on the virtual reality feedback model.

[0070] S2 includes:

[0071] S21: Within the set monitoring period T, dynamic weight-pulse data of the Yellow River sand UHPC beam under the action of a standard fire temperature rise curve are continuously collected using high-precision weighing sensors deployed on the left and right supports of the load-bearing beam. This raw data sequence is the transient dynamic energy response sequence excited by the internal energy of the Yellow River sand. For example, in this embodiment, this internal energy specifically refers to the huge vapor pressure potential energy accumulated by the heating and vaporization of pore water inside the ultra-fine particles of Yellow River sand under the thermal environment of a fire.

[0072] S22: Identify and extract the i-th valid quality pulse peak and its corresponding occurrence frequency characteristics. Then, substitute these characteristic values ​​into the formula for calculating the internal implicit stripping index to obtain the internal implicit stripping index. The specific calculation formula and the parameter explanation under this fire resistance test scenario are as follows:

[0073] ;

[0074] The specific meanings and physical significance of each parameter in the internal hidden stripping index calculation formula in the fire resistance test of this embodiment are as follows:

[0075] The value of a (i.e., the mass distribution bias weighting factor) is calibrated based on the refractory test data of the specific aggregate ratio (i.e., Yellow River sand with a fineness modulus of 0.5-1.5) used in this embodiment. It weighs the proportion of the contribution of pulse amplitude and frequency to the overall peeling damage and reflects the failure characteristics of this specific material at high temperature.

[0076] (i.e., the peak instantaneous dynamic intensity of a single force-bearing node sampled in the i-th time) is the amplitude of the i-th mass pulse extracted from the support sensor signal. In this embodiment, it directly originates from the impact signal generated by the micro-explosion of Yellow River sand particles in the high-temperature range of 300℃-500℃ due to the thermal trap effect causing a sharp increase in vapor pressure. It should be noted that... It quantifies the amount of kinetic energy released by a single micro-explosion event.

[0077] (That is, the average background value of physical disturbances due to the injection of a non-uniform energy field under normal conditions) In fire resistance testing, it refers to the statistical average value of the pulse amplitude measured in the initial stage of uniform and slow heating of the beam (e.g., before the temperature rises to 150°C), or in a stable heating state where the material has not experienced violent cracking. It should be noted that... This represents the non-destructive, background thermal noise level.

[0078] The (i.e., the response value limit set for evaluation) is an evaluation threshold pre-set based on a large amount of UHPC refractory test data of Yellow River sand.

[0079] (i.e., the average kinetic response frequency of the current period) represents the internal implicit stripping index in the calculation. Within the current time window or period T, the average number of micro-explosion pulse events occurring per unit time (e.g., per second) is statistically obtained. It reflects the distribution density of active damage points (i.e., Yellow River sand particles that are bursting) per unit area under the fire-exposed surface of the load-bearing beam, and is a key indicator of the internal thermal damage propagation rate.

[0080] (i.e., the safe reference value of the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam under the reference working condition) refers to the expected normal value of the mass pulse frequency of the Yellow River sand UHPC load-bearing beam under the reference working condition, such as under the design load and normal temperature environment, or when no chain explosion occurs in the early stage of fire, which is obtained by calibration through health status monitoring data.

[0081] (i.e., the safe reference fluctuation range of the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam) In this embodiment, it is the safe reference fluctuation range of the mass pulse frequency of the Yellow River sand UHPC load-bearing beam.

[0082] It should be noted that, in the fire resistance test scenario of this embodiment, the strategy for obtaining the internal implicit exfoliation index provided in this embodiment aims to quantify implicit exfoliation through the exfoliation impact kinetic energy (i.e., mass frequency and amplitude) specific to the ultrafine particles of Yellow River sand. The strategy involves using the exfoliation impact kinetic energy (i.e., the amplitude of the mass pulse) specific to the ultrafine particles of Yellow River sand. with frequency mean This implicit exfoliation can be quantified using [a specific method / mechanism]. It should be noted that the thermal trap effect formed in the Yellow River sand UHPC within the 300℃-500℃ range leads to its bursting exhibiting characteristics of small volume and high frequency. It can accurately capture and quantify this process, and remove the capillary-level physical losses that are ignored by ordinary assessment methods, thereby achieving accurate and dynamic assessment of the degree of high-temperature damage inside the UHPC of Yellow River sand.

[0083] S3: Extract the internal hidden stripping index obtained from S2 through internal micro-loss analysis, and combine it with the left and right symmetrical support balance fluctuation data obtained from real-time weight measurement reaction force, and import it into the oblique displacement deflection evaluation analysis for bearing stability calibration.

[0084] S3 includes the following steps:

[0085] S31; Extract the internal hidden peeling index obtained by internal micro-damage analysis of S2, and simultaneously collect the center of gravity change component of the beam support resultant force fed back by the measuring table.

[0086] S32: The internal implicit stripping index and the current mid-span eccentricity are imported into the inclined section column offset prediction formula to calculate the oblique displacement eccentricity index. The calculation strategy is as follows:

[0087] ;

[0088] in, exp() is the oblique displacement eccentricity index, which characterizes the severity of the deviation of the main force transmission member inside the load-bearing beam from the design axis. The values ​​of gravity measured in real time are those deployed symmetrically at the left and right supports of the load-bearing beam. This represents the total current weight measurement. The equivalent principal stress axial moment offset moment value of the beam section is used to quantitatively characterize the degree of physical displacement of the main load-bearing spine (i.e. the trajectory of the inclined section main compression member) inside the load-bearing beam deviating from the original geometric design force center axis under the non-uniform energy field injection environment of the load-bearing beam and the additional eccentric torque component caused thereby. This represents the upper limit of the design eccentricity corresponding to the main force transmission axis of the inclined section.

[0089] It should be noted that, based on material gradation defects This leads to a rapid change in the strength of the internal principal compression members of the load-bearing beam, a unique component, thereby triggering a structural stress reorganization, most directly manifested as the offset of the support reactions. Incorporating this as a correction term into the exponential structure allows for the sensitive capture of precursors to oblique through-shear caused by the accumulation of micro-exfoliation. This enables the calculation of the force transmission system trajectory drift due to internal crystallization collapse using only minute deviations in the support gravity moment.

[0090] S4: Obtain local induced signal data generated inside the load-bearing beam due to the drift of the interface connection characteristics of Yellow River sand and import it into the mechanical brittle coupling analysis to evaluate the deterioration of the interface structure connection stiffness.

[0091] S41: By pre-embedding an electromagnetic sensing circuit in the shear span area of ​​the inclined section at the beam end, the surface embrittlement characteristics of the Yellow River sand UHPC material are monitored in real time under a standard fire environment (i.e., the non-uniform thermal energy field injection coupling environment in this embodiment). In this embodiment, this embrittlement specifically refers to the charred carbonized layer generated on the matrix surface composed of fine fly ash and ultra-fine sand after being subjected to high temperature.

[0092] It should be noted that as the fire resistance test proceeds, the surface of the UHPC undergoes a chemical change due to the high temperature, forming a dense but brittle carbide glass phase (i.e., a charred glass phase). The formation and thickening of this material significantly alters its dielectric properties and conductivity, thereby causing a characteristic shift in the impedance of the inductive circuit. This shift... This refers to surface embrittlement and damage.

[0093] S42: Calculate the final structural interface stability evaluation index using the boundary connection failure judgment model. The formula is: ;

[0094] The specific meanings and physical significance of each parameter in the formula for calculating the structural interface stability evaluation index in this embodiment of the fire resistance test are as follows:

[0095] (i.e., the structural interface stability evaluation index) In this embodiment, this structural interface stability evaluation index is specifically used to quantitatively assess the degree of degradation of the interfacial bonding performance between the steel fibers and the matrix inside the Yellow River sand UHPC after being exposed to fire, that is, the severity of thermal brittle failure. The higher the value, the more severe the degradation of interface properties and the greater the overall brittleness of the material.

[0096] In this embodiment, R (i.e., the total number of physical parameter categories) mainly includes: 1) the temperature resistance distribution characteristics (i.e., impedance values ​​at different locations) directly measured by the electromagnetic sensing circuit; 2) the changes in induced charge captured by the auxiliary electrode; and 3) the relative humidity data obtained by the humidity sensor. It should be noted that these multiple categories of data together constitute a comprehensive description of the carbonization layer formation environment.

[0097] The evolution response weighting factor (i.e., the weighting factor for the r-th type of physical parameter) is a weighting factor set for that parameter. Its physical meaning lies in characterizing the sensitivity or contribution of different carbide layer thickness ranges to changes in that parameter. The value of is determined based on the thickness range into which the surface embrittlement evolution characteristic value in S41 falls. For example, in the early stages of carbonization, the induced charge parameter may be more sensitive; while when the carbonized layer reaches a certain thickness, the temperature resistance distribution may become the dominant signal. These physical parameter categories correspond to... The parameters are determined through prior refractory matching tests. Specifically, a database linking different carbonized layer thickness ranges to physical parameter sensitivity is established in advance through refractory matching tests. When S41 detects a specific surface embrittlement characteristic value in real time, the corresponding parameter weighting factor is automatically matched according to the thickness range in which the value falls. .

[0098] Let be the equivalent impedance shift of the r-th type of induction circuit at time i. It should be noted that, in this embodiment, the impedance shift is mainly driven by the formation and thickening of the carbonization layer and the resulting changes in the electrical properties of the material.

[0099] The theoretical impedance threshold for the interface continuity of the main feedback region under environmental calibration represents the reference electrical signal response value under healthy conditions.

[0100] S5: The oblique displacement eccentricity index calculated by S3 and the structural interface stability evaluation index calculated by S4 are jointly imported into the instability and collapse risk assessment strategy to make a joint anomaly judgment and obtain the overall collapse prediction index of the oblique section of the load-bearing beam.

[0101] The oblique displacement eccentricity index is used to reflect the stability of the load-bearing beam structure, and the structural interface stability evaluation index is used to reflect the degree of embrittlement of the surface layer of the Yellow River sand UHPC interface.

[0102] S5 includes:

[0103] Extract the oblique displacement bias index output in step S3 and the structural interface stability evaluation index output in step S4 And substitute it into the formula for calculating the overall collapse prediction coefficient of the inclined section of the load-bearing beam:

[0104] ;

[0105] in, For the overall collapse prediction coefficient, The oblique geometric weighting parameter is used in the design stage of the shear span ratio of load-bearing components.

[0106] It should be noted that, considering that even a small shift in the center of gravity can produce an extremely high collapse leverage effect when a structure undergoes locally triggered brittle evolution, this embodiment uses... Multiply The purpose is to accurately capture the brittle break-off point caused by the interaction between the load-bearing beam made of Yellow River sand under non-uniform energy response (exemplarily, under thermal response in this embodiment) and the turning of the center of gravity trajectory.

[0107] S6: Based on the overall collapse prediction index finally generated in S5, issue feedback and risk warnings on the stability failure of the load-bearing beam structure.

[0108] S6 includes:

[0109] Obtain the overall collapse prediction coefficient, normalize the overall collapse prediction coefficient by dividing it by the collapse prediction critical threshold, and map it to the early warning dashboard. Specifically, this includes:

[0110] If the normalized result of the risk prediction value is within the range of (0, 0.4], it is identified as a stable system, and the near-surface random energy release caused by S2 is identified as a normal energy consumption perturbation.

[0111] In the fire resistance test of this embodiment, this stage corresponds to the initial stage of fire heating or a stage with relatively low temperature. At this time, the sporadic and slight cracking of the surface particles of Yellow River sand UHPC is a normal physical reaction of the material at high temperature and has not yet posed a substantial threat to the overall stability of the structure.

[0112] In this embodiment, when the normalized result of the risk prediction value is within the range of (0, 0.4), the specific implementation strategy for the steady-state graded early warning of the load-bearing beam includes: maintaining the existing load unchanged and simultaneously tracking the support pulse sequence.

[0113] If the normalized result of the risk simulation value is within the range of (0.4, 0.75], it is defined as the risk of asymmetric center of gravity drift. Combined with the posture of the compression bar calculated by S3, it is marked in red on the virtual model, and a collapse avoidance plan due to asymmetric stiffness reduction is issued.

[0114] In the fire resistance test of this embodiment, this stage typically occurs after the fire has lasted for a period of time, when the temperature enters the 300℃-500℃ range. The thermal trapping effect inside the Yellow River sand has led to the development of implicit peeling and interfacial carbonization and embrittlement to a certain extent, which together cause an observable shift in the force transmission path inside the structure, resulting in an asymmetric weakening of the overall stiffness and a significant increase in the risk of collapse.

[0115] In this embodiment, when the normalized result of the risk prediction value is within the range of (0.4, 0.75), the specific implementation strategy for the steady-state graded early warning of the load-bearing beam includes: increasing the monitoring of the support points. The frequency of counterforce inversion monitoring.

[0116] If the normalized result of the risk prediction value exceeds 0.75, it is defined as a critical shear collapse state. At this time, the internal forces and the force transmission axis enter the main structure's performance collapse zone, and a forced indication of the limit of loss of bearing capacity is given.

[0117] In the fire resistance test of this embodiment, this stage corresponds to the continuous action of high fire temperature (such as around 600°C or above). The microscopic damage inside the material has been transformed into macroscopic cracks, the interfacial bonding performance has been severely degraded, the main force transmission path has been severely deviated and coincides with the area of ​​most severe damage, and the structure is in a critical state of oblique section shear collapse.

[0118] In this embodiment, when the normalized result of the risk prediction value exceeds 0.75, the specific implementation strategy for the steady-state classification early warning of the load-bearing beam includes: establishing emergency steel bracing through remote-controlled mechanical equipment.

[0119] For example, in this embodiment, the mass distribution bias weighting factor a and the evolution response weighting factor under different surface embrittlement layer thicknesses are... Oblique geometric weight parameters The method for determining the critical threshold for collapse prediction is as follows:

[0120] Failure data of the test beam with this specific Yellow River sand UHPC formulation was collected under full-process loading at 600℃. Then, the data were substituted into the initial calculation model to obtain a preliminary assessment level. Fifty senior experts in the field, specializing in high-temperature failure of materials and civil engineering structures, were engaged to cross-check and conduct multiple rounds of blind review of the critical collapse point and sensor fluctuation trends during the experiment. Finally, the true values ​​of physical collapse determined by the experts and the various indicators collected by the sensors were imported into nonlinear fitting software. The values ​​corresponding to the highest accuracy rate (confidence interval > 95%) achieved by the expert group analysis were taken as the basis for the values ​​of each specific material parameter in this system.

[0121] Example 2:

[0122] This embodiment aims to illustrate that the structural damage testing method based on multi-source sensor data described in this invention is also applicable to evaluating the performance degradation and damage accumulation of structures under long-term static or fatigue loads, demonstrating the universality of this method.

[0123] It should be noted that, compared to Example 1, the following adjustments have been made to this example. First, the evaluation environment has been adjusted as follows, specifically including:

[0124] Test object: A simply supported beam specimen made of the same Yellow River sand UHPC material as in Example 1, with identical dimensions and reinforcement.

[0125] Test environment: This embodiment was conducted in a standard laboratory environment with constant temperature and humidity (temperature 20±2℃, relative humidity 60±5%), without applying any external heat source.

[0126] Load conditions: Four-point bending fatigue loads were applied to the beam specimens, with load amplitudes ranging from 40% to 60% of the static ultimate bearing capacity, a loading frequency of 5 Hz, and a planned total number of cycles of 2 million to simulate the alternating stress state during long-term service.

[0127] Therefore, in this embodiment, the core of data acquisition is to monitor the changes in the dynamic response of the structure caused by the accumulation of fatigue damage, rather than thermal shock.

[0128] Weighing sensor data (corresponding to S1 / S2 in the instruction manual): Weighing sensors deployed at the supports continuously collect support reaction force fluctuation data. Under fatigue loading, due to the initiation and propagation of internal microcracks, the local stiffness of the beam changes, causing quasi-static drift and subtle fluctuations coupled with the loading frequency in the support reaction force distribution. These fluctuation characteristics are recorded and extracted in real time as input for analyzing internal damage.

[0129] Electromagnetic sensing loop data (corresponding to S4 in the specification): The sensing loop embedded in the beam end continuously monitors the interface impedance signal. Under long-term loads, the interface between the steel fibers and the matrix inside the UHPC may gradually debond, and the material itself may develop microcracks. These factors will alter the electromagnetic induction characteristics of the loop, manifesting as a trend of impedance drift and transient response changes at peak load. In this embodiment, this signal is used as an indicator of interface performance degradation.

[0130] Furthermore, in this embodiment, the core parameter calculation formulas defined in the claims of this invention are used, but their physical meaning is redefined for long-term load scenarios as follows:

[0131] Internal Hidden Delamination Index: In the long-term static or fatigue loading scenarios of this embodiment, this index no longer characterizes thermally induced micro-explosion, but quantifies the loss of internal material continuity caused by the accumulation of fatigue microcracks. The mass pulse characteristic in the calculation formula is replaced by the amplitude of the energy dissipation characteristic frequency band related to fatigue damage extracted from the support reaction force fluctuation signal, and an increase in the index indicates that fatigue damage is accumulating internally.

[0132] Material Response Evaluation Index: This index no longer characterizes thermal brittleness, but rather quantifies the degree of debonding at the steel fiber-matrix interface under cyclic stress and the fatigue damage of the matrix itself. This index is calculated by analyzing the change patterns of the inductive loop impedance signal relative to the initial healthy state (such as increased nonlinearity and phase angle shift).

[0133] The oblique displacement smothering index and the overall collapse prediction coefficient remain unchanged in their calculation methods and formulas. Under long-term loads, they are used to quantify the trend of structural internal force path deviation due to damage accumulation, and the overall instability risk of the structure after considering both internal damage and interface degradation, respectively. The threshold settings will be calibrated based on fatigue test databases.

[0134] Compared to Example 1, this example fully demonstrates the broad applicability of the testing method proposed in this invention. Its core advantage lies in the fact that, through a unified framework based on multi-source sensor data fusion and characteristic parameter calculation, it is possible to quantitatively test and assess the structural damage caused by different damage mechanisms (such as high temperature, fatigue, impact, etc.). In the test scenario of Example 1, the damage-causing factor is high temperature, and the sensitive signals are mass pulse and temperature resistance change.

[0135] In this embodiment (i.e., long-term load test), the damaging factor is cyclic stress, and the sensitive signals are reaction force fluctuation and impedance drift.

[0136] Both methods achieve a unified quantitative description and risk assessment of structural damage status from microscopic to macroscopic levels, thus verifying that the present invention is a general structural health monitoring and safety testing method, rather than one specific to a particular application scenario.

[0137] Example 3:

[0138] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0139] The processor executes the aforementioned digital-based method for assessing the load-bearing stability of load-bearing beam structures by calling computer programs stored in memory.

[0140] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the digital-based load-bearing stability assessment method for load-bearing beam structures provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted in this embodiment.

[0141] Example 4:

[0142] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0143] When the computer program runs on the computer device, it causes the computer device to perform the aforementioned digital-based method for assessing the load-bearing stability of load-bearing beam structures.

[0144] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0145] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0147] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0148] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0149] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A digital-based method for assessing the load-bearing stability of load-bearing beam structures, characterized in that, The method includes: S1: Collect multi-dimensional characteristic data and real-time symmetrical reaction force weighing data of the coupled environment of the non-uniform energy field injection of the Yellow River sand UHPC load-bearing beam, and construct a virtual reality feedback model; S2: Obtain the transient dynamic energy response sequence of the Yellow River sand UHPC beam, import it into the internal damage strategy to conduct internal micro-damage analysis caused by the decay of physical continuity, and simultaneously simulate the distribution pattern of particle size distribution after being affected by the injection of non-uniform energy field on the virtual reality feedback model. S3: Extract the internal hidden stripping index obtained from S2 through internal micro-loss analysis, and combine it with the left and right symmetrical support balance fluctuation data obtained from real-time weight measurement reaction force, and import it into the oblique displacement deflection evaluation analysis for bearing stability calibration. S3 includes the following steps: S31: Extract the internal hidden peeling index obtained by S2 through internal micro-damage analysis, and simultaneously collect the center of gravity change component of the beam support resultant force fed back by the measuring table. S32: The internal implicit stripping index and the current mid-span eccentricity are imported into the inclined section column offset prediction formula to calculate the oblique displacement eccentricity index. The calculation strategy is as follows: ； in, exp() is the oblique displacement eccentricity index, which characterizes the severity of the deviation of the main force transmission member inside the load-bearing beam from the design axis. The values ​​of gravity measured in real time are those deployed symmetrically at the left and right supports of the load-bearing beam. This represents the total current weight measurement. The value of the equivalent principal stress axial moment offset moment of the beam section; This represents the upper limit of the allowable eccentricity for the main force transmission axis of the inclined section. S4: Obtain local induced signal data generated inside the load-bearing beam due to the drift of the interface connection characteristics of Yellow River sand and import it into the mechanical brittle coupling analysis to evaluate the deterioration of the interface structure connection stiffness. S4 includes the following steps: S41. Real-time monitoring of the surface embrittlement and damage characteristics and loop impedance difference of fine fly ash and ultra-fine sand under the coupled environment of non-uniform energy field injection; The surface embrittlement characteristic value is the thickness of the surface embrittlement layer obtained in step S12; The strategy for obtaining the loop impedance difference is as follows: an AC excitation signal with known amplitude and frequency is applied to the electromagnetic sensing loop, the current response signal of the loop is acquired synchronously, and the current equivalent impedance value of the loop is obtained based on Ohm's law. This value is compared with the reference impedance value obtained in advance through health status calibration. Compare and calculate the equivalent impedance offset. The impedance difference of the circuit is obtained and denoted as . ; S42. Calculate the final structural interface stability evaluation index using the boundary connection failure judgment model. The formula is: ; in, is the structural interface stability evaluation index, which characterizes the degree of weakening of the interface response between the internal steel fiber and the Yellow River sand matrix after the injection of a non-uniform energy field; R is the category of physical parameters monitored by the sensor. The evolution response weighting factor is used for different thicknesses of the surface embrittlement and damage layer. Let be the equivalent impedance offset of the r-th type of induction loop at time i; The theoretical impedance threshold for the interface continuity of the main feedback region under environmental calibration represents the reference electrical signal response value under healthy conditions. Among them, the evolution response weight factor The value is determined based on the thickness range into which the surface embrittlement evolution characteristic value in S41 falls; S5: The oblique displacement eccentricity index calculated by S3 and the structural interface stability evaluation index calculated by S4 are jointly imported into the instability and collapse risk assessment strategy to make a joint anomaly judgment and obtain the overall collapse prediction index of the oblique section of the load-bearing beam. S6: Based on the overall collapse prediction index finally generated in S5, issue feedback and risk warnings on the stability failure of the load-bearing beam structure.

2. The method for evaluating the load-bearing stability of a load-bearing beam structure based on digitalization according to claim 1, characterized in that, S1 includes: S11. Real-time acquisition of the transient dynamic energy response sequence caused by the injection of ultra-fine particles of Yellow River sand into a non-uniform energy field. The transient dynamic energy response sequence includes the transient pulse peak signal and dynamic load frequency signal describing the energy jump of the structure, as well as the support reaction force fluctuation characteristics, and is stored in the first storage component. S12. Obtain the deviation signal of the electrical properties of the fine-particle interface by means of the electromagnetic sensing circuit embedded in the shear span area of ​​the inclined section at the beam end. The deviation signal of the electrical properties of the fine-particle interface includes the current feedback gradient of the sensor circuit and the thickness of the surface embrittlement and damage layer, and stores it in the second storage component. The strategy for obtaining the thickness of the surface embrittlement layer is as follows: First, an AC excitation signal with a stable frequency is applied to the electromagnetic sensing circuit, and the current response signal of the circuit is collected in real time. After analog-to-digital conversion, the current feedback gradient is extracted. The current feedback gradient is compared with the reference gradient under the pre-calibrated healthy state to obtain the gradient change rate. Then, combined with the mapping relationship between different carbonization layer thicknesses and gradient change rates established in advance through material experiments, the current surface embrittlement layer thickness is obtained by inversion and recorded as the surface embrittlement characteristic value. S13. Extract the center of gravity offset and principal stress axis deflection coefficient of the left and right supports of the load-bearing beam under the energy flow of the non-uniform energy field through the real-time weighing compensation algorithm, and store them in the third storage component. S14. Construct a virtual reality feedback model to characterize the internal implicit peeling index and oblique displacement of the load-bearing beam. This model dynamically maps the data collected in S11-S13 onto the 3D constructed beam oblique compression member force skeleton. At the same time, through signal extraction, real-time mechanical reconstruction, VR interactive display, and instant feedback of dangerous center of gravity shift, a simulation feedback model is formed.

3. The method for evaluating the load-bearing stability of a load-bearing beam structure based on digitalization according to claim 2, characterized in that, S2 includes the following steps: S21. Obtain the transient dynamic energy response sequence of all Yellow River sand collected within period T, driven by internal energy. S22. The instantaneous dynamic intensity peak value and its occurrence frequency density of the force-bearing node extracted in the i-th extraction are imported into the internal implicit peeling index calculation formula to obtain the internal implicit peeling index. The calculation formula is: ； Where a is the mass distribution bias weighting factor for a specific aggregate ratio in the structural steady-state assessment; The instantaneous peak value of the dynamic intensity of a single force-bearing node sampled in the i-th time; This represents the background average value of physical disturbances caused by the injection of a non-uniform energy field under normal conditions. These are the response value limits set for the evaluation; This represents the average kinetic response frequency for the current period. This represents the safe reference value for the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam under reference operating conditions. The safe reference fluctuation range for the kinetic energy response frequency of the Yellow River sand UHPC load-bearing beam.

4. The method for evaluating the load-bearing stability of a load-bearing beam structure based on digitalization according to claim 3, characterized in that, S5 includes: Extract the oblique displacement bias index output in step S3 and the structural interface stability evaluation index output in step S4 And substitute it into the formula for calculating the overall collapse prediction coefficient of the inclined section of the load-bearing beam: ； in, For the overall collapse prediction coefficient, The oblique geometric weighting parameter is used in the design stage of the shear span ratio of load-bearing components.

5. The method for evaluating the load-bearing stability of a load-bearing beam structure based on digitalization according to claim 4, characterized in that, S6 includes: Obtain the overall collapse prediction coefficient, normalize the overall collapse prediction coefficient by dividing it by the collapse prediction critical threshold, obtain the risk prediction value, and map it to the early warning dashboard. Specifically, this includes: If the normalized result of the risk prediction value is within the range of (0, 0.4], it is identified as a stable system, and the near-surface random energy release caused by S2 is identified as a normal energy consumption perturbation. If the normalized result of the risk simulation value is within the range of (0.4, 0.75], it is defined as the risk of asymmetric center of gravity drift. Combined with the posture of the compression bar calculated by S3, it is marked in red on the virtual model, and a collapse avoidance plan due to asymmetric stiffness reduction is issued. If the normalized result of the risk prediction value exceeds 0.75, it is defined as a critical shear collapse state. At this time, the internal forces and the force transmission axis enter the main structure's performance collapse zone, and a forced indication of the limit of loss of bearing capacity is given.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the digital-based method for evaluating the load-bearing stability of load-bearing beam structures as described in any one of claims 1-5.

7. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the digital-based load-bearing stability assessment method for load-bearing beam structures as described in any one of claims 1-5.

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