Loosening early warning method and device for scaffold and fastener and medium

Abstract

The invention discloses a loosening early warning method and device for a scaffold and a fastener and a medium, and relates to the technical field of building construction safety. The method comprises the following steps: installing a microsensor at a scaffold connecting fastener, collecting a reference physical signal of the fastener in a reference stable state, and establishing a reference model; in the construction stage, real-time physical signals are collected, finite element statics and dynamics are carried out according to the reference physical signals and the real-time physical signals, and residual signals are generated; simulating a loosening evolution process of the fastener under an alternating load according to the residual signal, and evaluating a residual pre-tightening force estimated value and a residual life estimated value of the fastener and a corresponding risk level; and according to the risk level, generating a predictive early warning signal and strategy. According to the method, the residual life and the development trend of the fastener can be predicted in the initial loosening stage of the fastener, passive alarm is changed into active prediction, and the timeliness of early warning is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of building construction safety technology, and in particular to a loosening warning method, equipment and medium for scaffolding and fasteners. Background Art

[0002] In the construction industry, scaffolding, as an important temporary support structure, is directly related to the safety of construction workers and the smooth progress of the project. Scaffolding fasteners, as key components of the scaffolding structure, ensure that their connections are secure and secure, ensuring the overall stability of the scaffolding. Currently, monitoring the status of fasteners relies primarily on regular manual inspections, a method that suffers from inefficiency, subjectivity, and the inability to detect problems in real time.

[0003] In recent years, a number of sensor-based monitoring systems have emerged, but most are only capable of providing macro-deformation or overload alarms, lacking the ability to early identify and accurately predict micro-hazards such as fastener loosening. These systems are unable to adapt to dynamic environments such as material creep and load variations, resulting in high false alarm rates. This is particularly true in large-scale construction projects, where the scaffolding is numerous and complex, making it difficult for manual inspections to fully cover all fastener connections. This can result in potential loosening hazards going undetected, potentially leading to serious safety incidents such as scaffold collapse. Furthermore, these systems fail to integrate the status of individual fasteners with the overall safety of the scaffolding, making it difficult to provide effective predictive maintenance decision support.

[0004] Through the above analysis, the problems and defects of the existing technology are as follows: The existing technology is unable to adapt to dynamic environments such as load changes in scaffolding connections, cannot provide early and accurate predictive warnings of fastener loosening, and cannot assess the impact of its failure on the overall structural safety. Summary of the Invention

[0005] The embodiments of the present application provide a loosening warning method, device and medium for scaffolding and fasteners, which can solve the problems in the prior art that the scaffolding connection cannot adapt to dynamic environments such as load changes, cannot provide early and accurate predictive warning of fastener loosening, and cannot evaluate the impact of its failure on the overall structural safety.

[0006] In the first aspect, an embodiment of the present application provides a loosening warning method for scaffolding and fasteners, the method comprising: installing microsensors at the scaffolding connecting fasteners, collecting baseline physical signals of the fasteners in a baseline stable state, and establishing a baseline model, the baseline physical signals including strain data, vibration acceleration data, and relative displacement data; during the construction phase, collecting real-time physical signals, and performing finite element statics and dynamics based on the baseline physical signals and the real-time physical signals to generate residual signals; simulating the loosening evolution process of the fasteners under alternating loads based on the residual signals, and evaluating the estimated residual preload and remaining life of the fasteners, as well as the corresponding risk level; generating predictive warning signals and strategies based on the risk level.

[0007] In one implementation of the present application, a reference physical signal of the fastener in a reference stable state is collected, and a reference model of the fastener is established, specifically including: applying test excitations of multiple amplitudes to the scaffolding when the fastener is in a reference stable state; recording the response signals of the microsensor under multiple amplitude test excitations; extracting features from the response signal and calculating the feature distribution interval, and using the statistical boundary of the distribution interval as the reference model.

[0008] In one implementation of the present application, during the construction phase, real-time physical signals are collected, and finite element statics and dynamics simulations and comparisons are performed based on the baseline physical signals and the real-time physical signals to generate residual signals, specifically including: extracting load environment information in response to the new real-time physical signal package, and updating the boundary conditions and load settings of the finite element simulation model in the digital twin; performing static simulation based on the updated model, calculating the theoretical strain distribution of the fastener under the current working conditions, and performing dynamic modal analysis to calculate the theoretical vibration response; synchronizing the theoretical strain distribution and the theoretical vibration response signals with the collected real-time strain and real-time vibration signals on a time scale, and performing point-by-point differential calculations; generating strain residual signals and vibration response residual signals respectively, which together constitute the multi-physical field residual signals used for the prognostic model.

[0009] In one implementation of the present application, the loosening evolution process of the fastener under alternating loads is simulated based on the residual signal, and the residual preload estimate and the remaining life estimate of the fastener, as well as the corresponding risk level, are evaluated, specifically including: establishing a dynamic equation of the bolted connection structure, and taking the friction torque attenuation between the thread pairs as the core mechanism of preload degradation; inverting the current friction torque parameters based on the residual signal; iteratively solving the dynamic equation based on the friction torque parameters, predicting the evolution trajectory of the preload, and obtaining the residual preload estimate and the remaining life estimate of the continuous time series.

[0010] In one implementation of the present application, the method also includes: relaxing the fasteners from a rigid connection to a hinged connection until complete failure in the digital twin; calculating the internal force redistribution and maximum displacement change of the overall structure of the scaffolding in each stage of relaxation, and combining the estimated value of the residual preload and the estimated value of the remaining life to determine the risk level.

[0011] In one implementation of the present application, after generating predictive warning signals and strategies based on the risk level, the method also includes: continuously monitoring the time-varying energy envelope of the residual signal; if the first-order derivative of the energy envelope exceeds the dynamic threshold, it is determined that an abnormal impact load event has occurred; triggering the digital twin to perform instantaneous load response simulation, evaluating the damage degree of the event to the remaining life estimate based on Miner's linear cumulative damage law, and updating the remaining life estimate.

[0012] In one implementation of the present application, predictive warning signals and strategies are generated based on the risk level, specifically including: outputting maintenance instructions for immediate tightening, planned maintenance, simultaneous inspection of adjacent components, or partial load unloading based on the risk level; binding the maintenance instructions to the maintenance personnel's mobile terminal, and pushing the optimal inspection path.

[0013] In one implementation of the present application, after generating predictive warning signals and strategies based on the risk level, the method also includes: collecting actual tightening parameters of the fasteners after maintenance; performing deviation calculations on the actual tightening parameters and the estimated values ​​of the remaining preload and remaining life to obtain a model prediction error; if the model prediction error exceeds an accuracy threshold, correcting the characteristic distribution interval of the baseline model, as well as the material property parameters of the finite element simulation model and the friction coefficient of the preload degradation dynamics equation.

[0014] In a second aspect, an embodiment of the present application also provides a loosening warning device for scaffolding and fasteners, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to: install microsensors at the scaffolding connecting fasteners, collect baseline physical signals of the fasteners in a baseline stable state, and establish a baseline model, the baseline physical signals including strain data, vibration acceleration data, and relative displacement data; during the construction phase, collect real-time physical signals, and perform finite element statics and dynamics based on the baseline physical signals and the real-time physical signals to generate residual signals; simulate the loosening evolution process of the fasteners under alternating loads based on the residual signals, evaluate the estimated residual preload and the estimated remaining life of the fasteners, as well as the corresponding risk level; generate predictive warning signals and strategies based on the risk level.

[0015] On the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for loosening warning of scaffolding and fasteners, which stores computer executable instructions, and the computer executable instructions are set to: install micro sensors at the scaffolding connecting fasteners, collect baseline physical signals of the fasteners in a baseline stable state, and establish a baseline model, the baseline physical signals include strain data, vibration acceleration data and relative displacement data; during the construction phase, collect real-time physical signals, and perform finite element statics and dynamics based on the baseline physical signals and the real-time physical signals to generate residual signals; simulate the loosening evolution process of the fasteners under alternating loads based on the residual signals, and evaluate the residual preload estimate and remaining life estimate of the fasteners, as well as the corresponding risk level; generate predictive warning signals and strategies based on the risk level.

[0016] The embodiments of the present application provide a loosening warning method, equipment and medium for scaffolding and fasteners. By integrating digital twins and failure physical models, it can predict the remaining life and development trend of fasteners at the early stage of loosening, changing passive alarms to active predictions, greatly improving the timeliness of warnings; adopting dynamic health benchmark models and multi-physical field residual analysis, it eliminates environmental interference and individual differences, and combines parameter inversion and iterative solution to make the evaluation results of residual preload and life more scientific and accurate; enhance system safety: by online deduction of the impact of the failure of a single fastener on the overall structure, a full range of risk assessment from components to systems is achieved, providing managers with accurate decision-making basis, and effectively preventing collapse accidents; the generated differentiated maintenance strategies and optimal inspection paths greatly improve the pertinence and efficiency of maintenance work, reduce operation and maintenance costs, and realize intelligent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flowchart of a loosening warning method for scaffolding and fasteners provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a loosening warning device for scaffolding and fasteners provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] The embodiments of the present application provide a loosening warning method, device and medium for scaffolding and fasteners, which solve the problems in the prior art that the scaffolding connection cannot adapt to dynamic environments such as load changes, cannot provide early and accurate predictive warning of fastener loosening, and cannot evaluate the impact of its failure on the overall structural safety.

[0020] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a flow chart of a loosening warning method for scaffolding and fasteners provided in an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a loosening warning method for scaffolding and fasteners, which specifically includes the following steps: Step 10: Install micro sensors at the scaffolding fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model. The reference physical signals include strain data, vibration acceleration data, and relative displacement data. In this step, a standard is established for each fastener. By collecting physical signals and building a model when the fastener is in an absolutely stable state, the real-time signals in the subsequent construction phase can be compared with this, so as to accurately identify anomalies caused by loosening; the greater the preload of the strain data, the greater the strain; the strain will drop significantly when loosening; the vibration acceleration data has a high frequency and a small amplitude when it is stable; the frequency decreases and the amplitude increases when it is loose; the relative displacement data approaches 0 when it is stable; the displacement increases significantly with load fluctuations when loosening.

[0022] As an optional embodiment, collecting a reference physical signal of a fastener in a reference stable state and establishing a reference model of the fastener may specifically include: Step 101: applying test excitations of multiple amplitudes to the scaffold in a reference stable state of the fastener.

[0023] In this step, standard counterweights (10 kg each) are stacked in stages on the scaffolding crossbars. Loads of 30%, 50%, 80%, 100%, and 150% of the design load are applied. For example, if the design load is 200 kg, the corresponding loads are 60 kg, 100 kg, 160 kg, 200 kg, and 300 kg. Each load level is maintained for 5 minutes, simulating static loads such as material stacking. Furthermore, alternating load excitation is performed using a small vibration table fixed to the scaffolding uprights. Sinusoidal alternating loads are applied, with frequencies covering the typical frequencies of personnel movement and wind loads during construction. Amplitudes are graded at 0.1 g, 0.3 g, and 0.5 g (g is the acceleration of gravity). Each frequency and amplitude level lasts for 3 minutes, simulating dynamic loads.

[0024] Step 102: Record the response signal of the microsensor under multiple amplitude test excitations.

[0025] In this step, the strain sensor uses a resistance strain gauge, which can be type BX120-3AA with a sensitivity of 2.0±1%. It is pasted on the head of the fastener bolt to monitor the axial strain of the bolt, and the curved inner wall where the fastener contacts the steel pipe to monitor the contact strain. It is sealed with waterproof glue to adapt to the construction environment; the vibration acceleration sensor is fixed to the middle of the outer side of the fastener through a magnetic base to collect vibration acceleration in the axial, lateral and vertical directions; the relative displacement sensor is fixed on the fastener cover, and the laser head is aimed at the surface of the steel pipe below to monitor the relative sliding displacement of the fastener and the steel pipe.

[0026] Step 103: extract features from the response signal and calculate the feature distribution interval, using the statistical boundary of the distribution interval as a benchmark model.

[0027] In this step, the peak strain values ​​under each load level in the above steps are taken: the maximum strain of the bolt head, the average steady-state strain, for example, the average value within 5 minutes after the load stabilizes; the vibration acceleration data is extracted to obtain the root mean square acceleration to reflect the vibration intensity, and the peak acceleration to reflect the impact degree; the relative displacement data is extracted to obtain the maximum displacement deviation, the maximum difference from the initial position, the displacement fluctuation range, and the difference between the maximum and minimum values.

[0028] Furthermore, the vibration acceleration data can be Fourier transformed to extract the dominant frequency and amplitude. Due to the rigid connection of fasteners, the dominant frequency is typically high, but it decreases after loosening. The values ​​of the same feature under the same load amplitude and different test times are statistically analyzed. For example, if the strain peaks for three tests were 120 με, 125 με, and 118 με, respectively, their 95% confidence intervals are calculated. Using a normal distribution fit, the mean ±1.96 times the standard deviation is taken to determine the normal range for this feature, such as 115-130 με. In this way, the 95% confidence intervals of all features are integrated to form a multidimensional constraint model: strain: 100-150 με (at 30% design load) and 180-230 με (at 100% design load); dominant vibration frequency: 3.5-4.5 Hz (at 0.3 g alternating load); and relative displacement: ≤0.05 mm (at all loads). The upper and lower boundaries of these intervals are the benchmark thresholds. If the subsequent real-time signal exceeds this range, it is determined to be a potential loosening anomaly.

[0029] Step 20: During the construction phase, real-time physical signals are collected, and finite element statics and dynamics are performed based on the baseline physical signals and the real-time physical signals to generate residual signals.

[0030] In this step, subtle anomalies caused by loosening of fasteners are captured by comparing the actual measured signals on site with the simulated signals of the digital twin. Compared with directly comparing the real-time signals with the benchmark model, which is easily affected by environmental interference, the introduction of finite element simulation can dynamically adapt to the complex load changes during the construction phase, so that the residual signal can more accurately reflect the essential differences caused by loosening.

[0031] As an optional embodiment, during the construction phase, real-time physical signals are collected, and finite element statics and dynamics simulations and comparisons are performed based on the baseline physical signals and the real-time physical signals to generate residual signals. Specifically, the following may be included: Step 201: In response to the new real-time physical signal package, load environment information is extracted, and the boundary conditions and load settings of the finite element simulation model in the digital twin are updated.

[0032] In this step, the load type is identified from the vibration acceleration signal. If the signal spectrum is concentrated in the 1-2 Hz range, it is determined to be a pedestrian load; if it is in the 2-5 Hz range and lasts for a long time, it is determined to be a wind load. The load amplitude is inferred from the strain data. Using the load-strain calibration relationship in the benchmark model, for example, a 300 kg static load corresponds to a 200 με strain, the current load magnitude is calculated in real time. A measured strain of 250 με indicates a load of 375 kg. In this way, environmental parameters are directly extracted, and the boundary conditions are updated according to the actual support status of the scaffolding, including whether the bottom is fixed and whether the top is constrained. The constraint type of the model is updated; the load setting loads the extracted load type, static load or dynamic load, amplitude, and direction to the corresponding position of the model, with static load loaded in the middle of the crossbar and wind load loaded on the top of the vertical pole; the material properties are adjusted according to the temperature data, and the elastic modulus of Q235 steel varies with temperature: for every 1°C increase, E decreases by about 0.02%.

[0033] Then, if the real-time signal package shows that the current static load is 300kg, the wind load is 0.3g (3Hz), and the temperature is 28°C, the finite element model is synchronously set: the horizontal bar applies a vertical force of 3000N, and the vertical pole applies a 0.3g alternating load (3Hz) in the horizontal direction. The material elastic modulus is corrected to 28°C, which is 0.16% lower than the 20°C baseline value.

[0034] Step 202: Based on the updated model, static simulation is performed to calculate the theoretical strain distribution of the fastener under the current working condition, and dynamic modal analysis is performed to calculate the theoretical vibration response.

[0035] In this step, the scaffolding is modeled according to its actual size, and refined meshing is used for mesh division. The contact type between the fastener and the steel pipe, and between the bolt and the fastener is set to friction contact. The friction coefficient is calibrated based on the baseline model, corresponding to the stable state. The material properties can be Q235 steel (E=206GPa, μ=0.3) for the steel pipe and cast iron (E=110GPa, μ=0.25) for the fastener, depending on the actual situation. The static analysis module of ANSYS Mechanical is used. The axial strain of the fastener bolt, the contact between the fastener and the steel pipe, and the friction coefficient of the fastener are calculated. The radial strain distribution of the touch interface is output, and the strain value corresponding to the sensor position is output for subsequent comparison. For example, under a static load of 300kg, the theoretical strain of the bolt head remains stable at 220με over time. The first 10 natural frequencies of the model are solved, and the first-order frequency of the fastener is usually 4-5Hz. Based on the modal superposition method, the vibration acceleration response of the fastener is calculated under the action of the updated alternating load wind load of 3Hz, and the acceleration time domain curve in the same direction as the sensor is shown, such as a theoretical acceleration root mean square of 0.08g.

[0036] Step 203: The theoretical strain distribution and the theoretical vibration response signal are synchronously aligned with the collected real-time strain and real-time vibration signals on a time scale, and point-by-point difference calculation is performed.

[0037] In this step, the finite element simulation results are interpolated at the same time interval, using the real-time signal's timestamp as a benchmark. Timestamp matching ensures a one-to-one correspondence between the theoretical and real-time signals at the same time point. For each time point, the strain residual calculates the theoretical strain minus the real-time strain, e.g., theoretical 220με, real-time 180με, residual +40με. For each time point, the vibration response residual calculates the theoretical acceleration and real-time acceleration, e.g., theoretical 0.08g, real-time 0.12g, residual -0.04g. If the real-time signal is missing due to sensor failure, the mean of the previous three cycles is interpolated to supplement the data to avoid data gaps.

[0038] Step 204: Generate a strain residual signal and a vibration response residual signal respectively, and together form a multi-physics field residual signal for the prognostic model.

[0039] In this step, a time-domain curve is plotted with time as the horizontal axis and the residual value (με) as the vertical axis, for example, residual fluctuations over 10 consecutive minutes. The vibration response residual signal is also plotted in the time domain, expressed in g (gravitational acceleration). The residual signal is filtered to remove high-frequency noise. If the fastener is loose, the strain residual is typically positive, with the theoretical strain greater than the real-time strain, indicating a decrease in actual deformation due to a decrease in preload. The vibration residual is typically negative, with the theoretical acceleration less than the real-time acceleration, indicating increased vibration due to looseness. If both deviations coincide with the expected direction, it can be considered a valid anomaly, eliminating false positives from a single signal. Temperature-induced strain deviations are not associated with vibration deviations.

[0040] Step 30: Simulate the loosening evolution process of the fastener under alternating loads based on the residual signal, and evaluate the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level.

[0041] It is understood that the loads borne by the scaffolding during the construction phase, such as people moving around and material handling, are all periodic alternating loads. This type of load will cause the friction torque between the thread pairs of the fastener bolts to gradually decay, which is the main cause of the degradation of the preload force.

[0042] As an optional embodiment, the loosening evolution process of the fastener under alternating loads is simulated based on the residual signal to evaluate the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level. Specifically, the process may include: Step 301: Establishing a dynamic equation for the bolted connection structure, and taking the attenuation of the friction torque between the thread pairs as the core mechanism of preload degradation; In this step, the connection between the fastener bolt and nut is simplified to a bolt and nut thread pair system. The maintenance of the preload force F depends on the friction torque M between the thread pairs. f , the relationship between the two is: M f =F•d2 / 2•tan(λ+ρ v), where: d2 is the thread pitch diameter, M12 bolt d2 = 10.863mm, λ is the thread lead angle, M12 bolt is about 1.87°, ρ v is the equivalent friction angle of the thread pair, which is related to the surface roughness; the dynamic equation is established considering the alternating load F dyn Under the action of (t), the friction torque decays with the number of cycles N, and the equation form is:

[0043] Where: k is the attenuation coefficient, which is related to the wear characteristics of the material, F0 is the initial preload, n is the load index, and the alternating load F dyn The larger (t), the faster the friction torque decays; the larger the initial friction torque M f The larger it is, the higher the attenuation rate is, and the wear accelerates the effect.

[0044] When the fastener is loose, the actual contact area between the thread pairs decreases, the friction coefficient decreases, and the M f Reduce, and then make the preload force decay with the number of cycles according to the above equation.

[0045] Step 302: Invert the current friction torque parameters according to the residual signal.

[0046] In this step, the abstract signal difference of the residual signal is converted into the specific mechanical parameters of the friction torque, building a bridge between the signal and the physical state. The strain residual Δε, the theoretical strain is based on the stable state M f0 The difference between the real-time strain and the actual strain is positively correlated with the preload loss: Δε∝(F0-F) / F0; the vibration residual Δa: the difference between the real-time vibration acceleration and the theoretical value, is positively correlated with the decrease in friction torque: Δa∝(M f 0-M f ) / M f 0 looseness leads to increased vibration. The inversion algorithm uses the least squares method, takes the residual signal as input, and fits the current friction torque M f : Set the objective function:

[0047] The Mf value that minimizes J is solved iteratively, which is the current friction torque parameter.

[0048] If the mean value of strain residual is +40με, the theory is greater than the actual measurement, indicating that the preload has decreased. If the mean value of vibration residual is -0.04g, the actual measurement is greater than the theory, indicating that the looseness has intensified, the current M can be obtained by fitting. f =12N•m, initial Mf0=20N•m, has attenuated by 40%.

[0049] Step 303: Iteratively solve the dynamic equation according to the friction torque parameter to predict the evolution trajectory of the preload force and obtain the estimated value of the residual preload force and the estimated value of the residual life in a continuous time series.

[0050] In this step, based on the current friction torque, the future attenuation trend of the preload is predicted, and the remaining load that can be sustained is quantified: the remaining preload force; and the remaining safe use time: the remaining lifespan.

[0051] Input parameters: Current M f , alternating load amplitude F dyn (t), the attenuation coefficient k, calibrated by the benchmark model, such as k = 5 × 10 -6 ; Divide the future time into hourly intervals and calculate the number of load cycles N per hour; update M in each time period according to the dynamic equation of step 301. f and through M f The conversion relationship with F gives the residual preload F t . F at any time t t (Unit: kN), such as Ft = 18kN after 12h, Ft = 15kN after 24h; Remaining life estimation value: the time difference from the current moment to when Ft drops to the first safety threshold (such as 10kN), such as when F t =10kN, the corresponding time is 48h, and the remaining life is 48h.

[0052] As an optional embodiment, the method may further include: Step 304: relaxing the fastener from a rigid connection to a hinged connection in the digital twin until it completely fails.

[0053] In this step, the impact of loose fasteners on the overall structure of the scaffold is simulated to avoid evaluating only a single fastener and ignoring the risk of a chain reaction caused by local failure.

[0054] According to the residual preload F t The ratio of the initial preload force F0 can be used to divide the loosening into 4 stages: Rigid connection: F t ≥80% F 0， Sufficient preload force, no relative sliding; semi-rigid connection: 50%F0≤F t <80% F 0， Slightly loose, with slight sliding; hinged state: 20%F0≤F t <50% F 0， Significantly loose, can rotate; completely failed: F t <20% F 0， The bolts are detached and lose their connection function.

[0055] In the finite element model, by adjusting the contact stiffness between the fastener and the steel pipe, the stiffness of the rigid connection can be 1×10 8 N / m, stiffness when hinged 1×10 5 N / m, to achieve step relaxation and simulate mechanical behavior at different stages.

[0056] Step 305: Calculate the internal force redistribution and maximum displacement change of the scaffolding structure in each relaxation order, and determine the risk level by combining the estimated value of the remaining preload and the estimated value of the remaining life.

[0057] In this step, the status of individual fasteners is associated with the overall structural safety, enabling an upgraded judgment from local assessment to system risk.

[0058] Calculate the changes in axial force and bending moment of adjacent fasteners after each relaxation. After the fastener enters the hinged state, the axial force of the three adjacent fasteners increases by 20%. The maximum displacement is calculated as the maximum deflection at the top or mid-span of the scaffolding. For example, the deflection is 5mm in a rigid connection and increases to 15mm in a hinged state. The risk level classification standard is combined with the residual preload F. t , Remaining life T t and structural response, such as low risk: F t ≥80%F0, T t ≥72h, internal force change <10%, displacement <10mm.

[0059] Step 40: Generate predictive warning signals and strategies based on risk levels.

[0060] As an optional embodiment, predictive warning signals and strategies are generated according to the risk level, which may specifically include: Step 401: outputting maintenance instructions for immediate tightening, planned maintenance, simultaneous inspection of adjacent components, or partial load unloading according to the risk level; Step 402: binding the maintenance instructions to the maintenance personnel's mobile terminal and pushing the optimal inspection path.

[0061] In this step, maintenance instructions are strictly bound to risk levels to avoid over-maintenance and under-maintenance; optimal path planning reduces inspection time, especially for large scaffolding and the exterior frames of super-high-rise buildings, which can significantly improve maintenance efficiency; instruction push, execution feedback and digital twins are linked to facilitate later project review and model optimization.

[0062] As an optional embodiment, after generating predictive warning signals and strategies based on the risk level, the method may also include: continuously monitoring the time-varying energy envelope of the residual signal; if the first-order derivative of the energy envelope exceeds the dynamic threshold, determining that an abnormal impact load event has occurred; triggering the digital twin to perform transient load response simulation, evaluating the damage degree of the event to the remaining life estimate based on Miner's linear cumulative damage law, and updating the remaining life estimate.

[0063] In this step, the energy sum of the strain residual and vibration residual is calculated using a sliding window, capturing changes in signal strength in real time. The energy envelope can intuitively reflect the difference between normal loosening and sudden impact.

[0064] The first-order derivative of the instantaneous rate of change of the energy envelope is calculated and compared to a dynamic threshold, such as three standard deviations of energy fluctuations over the past 10 minutes. If three consecutive sampling points exceed the threshold and the energy increases compared to before the impact, it is determined to be an abnormal impact. The finite element model is triggered, and the load parameter amplitude and duration are inferred based on the peak impact energy. The instantaneous stress response of the fastener thread pair, such as the maximum contact stress, is calculated. The impact stress corresponds to the fatigue life. The material SN curve is consulted, and a single impact is equivalent to one cycle. The damage increment is calculated. The original remaining life is corrected using the remaining allowable damage ratio. If the impact causes the damage to increase by 0.01, the original 24-hour life may be shortened to 23.6 hours.

[0065] As an optional embodiment, after generating predictive warning signals and strategies based on the risk level, the method may further include: collecting actual tightening parameters of the fasteners after maintenance; calculating the deviation between the actual tightening parameters and the estimated remaining preload and the estimated remaining life to obtain a model prediction error; if the model prediction error exceeds an accuracy threshold, correcting the characteristic distribution interval of the baseline model, as well as the material property parameters of the finite element simulation model and the friction coefficient of the preload degradation dynamics equation.

[0066] In this step, the torque value and measured preload after retightening can be converted using torque and preload conversion or measured using specialized instruments. The remaining life upon completion of maintenance is recorded, such as the actual remaining life of 18 hours, rather than the previously estimated 24 hours. The actual tightening parameters are compared with the previously estimated remaining preload and remaining life values ​​to calculate deviations. The characteristic distribution interval is corrected, such as the original strain reference interval of 100-150με, which needs to be adjusted to 90-140με due to fastener wear. Material properties are updated, such as the elastic modulus of a steel pipe is corrected from 206GPa to 200GPa due to corrosion. The friction coefficient of the threaded pair is adjusted, such as from 0.35 to 0.32, to match the actual preload decay rate.

[0067] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a loosening warning device for scaffolding and fasteners, the structure of which is as follows: Figure 2 shown.

[0068] Figure 2 This is a schematic diagram of the internal structure of a loosening warning device for scaffolding and fasteners provided in an embodiment of the present application. Figure 2 As shown, the equipment includes: at least one processor 201; and, a memory 202 communicatively coupled to the at least one processor; Among them, the memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to: install microsensors at the scaffolding connecting fasteners, collect baseline physical signals of the fasteners in a baseline stable state, and establish a baseline model, the baseline physical signals include strain data, vibration acceleration data and relative displacement data; during the construction phase, collect real-time physical signals, and perform finite element statics and dynamics based on the baseline physical signals and the real-time physical signals to generate residual signals; simulate the loosening evolution process of the fasteners under alternating loads based on the residual signals, evaluate the estimated residual preload and remaining life of the fasteners, and the corresponding risk level; generate predictive warning signals and strategies based on the risk level.

[0069] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for early warning of loosening of scaffolding and fasteners stores computer-executable instructions, wherein the computer-executable instructions are configured to: install microsensors at scaffolding fasteners to collect baseline physical signals of the fasteners in a baseline stable state and establish a baseline model, wherein the baseline physical signals include strain data, vibration acceleration data, and relative displacement data; during the construction phase, collect real-time physical signals, and perform finite element statics and dynamics based on the baseline physical signals and the real-time physical signals to generate residual signals; simulate the loosening evolution process of the fasteners under alternating loads based on the residual signals, evaluate the estimated residual preload and remaining life of the fasteners, and the corresponding risk level; and generate predictive early warning signals and strategies based on the risk level.

[0070] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0071] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0072] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0080] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A loosening warning method for scaffolding and fasteners, characterized in that: The method comprises: Installing micro sensors at the scaffolding fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model, wherein the reference physical signals include strain data, vibration acceleration data, and relative displacement data; During the construction phase, real-time physical signals are collected, and finite element statics and dynamics are performed based on the reference physical signals and the real-time physical signals to generate residual signals; Simulating the loosening evolution process of the fastener under the alternating load according to the residual signal, and evaluating the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level; Based on the risk level, predictive warning signals and strategies are generated.

2. A method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that: The collecting of the reference physical signal of the fastener in the reference stable state and establishing a reference model of the fastener specifically includes: applying a test excitation of multiple amplitudes to the scaffold in a state where the fastener reference is stable; Recording the response signals of the microsensor under multiple amplitude test excitations; Features are extracted from the response signal and a feature distribution interval is calculated, with the statistical boundary of the distribution interval being used as the benchmark model.

3. A method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that: During the construction phase, real-time physical signals are collected, and finite element statics and dynamics simulations and comparisons are performed based on the reference physical signals and the real-time physical signals to generate residual signals, specifically including: Responding to new real-time physical signal packages, extracting load environment information and updating the boundary conditions and load settings of the finite element simulation model in the digital twin; Based on the updated model, a static simulation is performed to calculate the theoretical strain distribution of the fastener under the current working condition, and a dynamic modal analysis is performed to calculate the theoretical vibration response; The theoretical strain distribution and the theoretical vibration response signal are synchronously aligned with the collected real-time strain and real-time vibration signals on a time scale, and a point-by-point difference calculation is performed; The strain residual signal and the vibration response residual signal are generated separately, and together they constitute the multi-physics field residual signal used in the prognostic model.

4. A method for early warning of loosening of scaffolding and fasteners according to claim 3, characterized in that: The simulation of the loosening evolution process of the fastener under the alternating load based on the residual signal, and the evaluation of the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level, specifically includes: The dynamic equation of the bolt connection structure is established, and the friction torque attenuation between the thread pairs is regarded as the core mechanism of preload degradation; Inverting the current friction torque parameter according to the residual signal; The dynamic equation is iteratively solved according to the friction torque parameter to predict the evolution trajectory of the preload force, thereby obtaining a continuous time series of residual preload force estimation values ​​and residual life estimation values.

5. A method for early warning of loosening of scaffolding and fasteners according to claim 4, characterized in that: The method further comprises: In the digital twin, the fastener is gradually relaxed from a rigid connection to a hinged connection until it completely fails; The internal force redistribution and maximum displacement change of the scaffolding overall structure in each order of relaxation are calculated, and the risk level is determined by combining the estimated value of the residual preload and the estimated value of the remaining life.

6. The method for early warning of loosening of scaffolding and fasteners according to claim 3, characterized in that: After generating a predictive warning signal and strategy based on the risk level, the method further includes: continuously monitoring the time-varying energy envelope of the residual signal; If the first-order derivative of the energy envelope exceeds a dynamic threshold, it is determined that an abnormal impact load event has occurred; The digital twin is triggered to perform transient load response simulation, the damage degree of the event to the estimated remaining life is evaluated based on Miner's linear cumulative damage law, and the estimated remaining life is updated.

7. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that: Generating predictive warning signals and strategies based on the risk level specifically includes: Based on the risk level, output maintenance instructions such as immediate tightening, planned maintenance, simultaneous inspection of adjacent components, or partial load shedding; The maintenance instruction is bound to the maintenance personnel's mobile terminal, and the optimal inspection route is pushed.

8. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that: After generating predictive warning signals and strategies based on the risk level, the method further includes: collecting actual fastening parameters of the fastener after maintenance; Calculating the deviation between the actual tightening parameters and the estimated value of the remaining preload and the estimated value of the remaining service life to obtain a model prediction error; If the model prediction error exceeds an accuracy threshold, the characteristic distribution interval of the benchmark model, the material property parameters of the finite element simulation model, and the friction coefficient of the preload degradation dynamics equation are modified.

9. A loosening warning device for scaffolding and fasteners, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Installing micro sensors at the scaffolding fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model, wherein the reference physical signals include strain data, vibration acceleration data, and relative displacement data; During the construction phase, real-time physical signals are collected, and finite element statics and dynamics are performed based on the reference physical signals and the real-time physical signals to generate residual signals; Simulating the loosening evolution process of the fastener under the alternating load according to the residual signal, and evaluating the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level; Based on the risk level, predictive warning signals and strategies are generated.

10. A non-volatile computer storage medium for early warning of loosening of scaffolding and fasteners, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Installing micro sensors at the scaffolding fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model, wherein the reference physical signals include strain data, vibration acceleration data, and relative displacement data; During the construction phase, real-time physical signals are collected, and finite element statics and dynamics are performed based on the reference physical signals and the real-time physical signals to generate residual signals; Simulating the loosening evolution process of the fastener under the alternating load according to the residual signal, and evaluating the estimated value of the remaining preload and the estimated value of the remaining life of the fastener, as well as the corresponding risk level; Based on the risk level, predictive warning signals and strategies are generated.

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