A method, device, and medium for early warning of loosening of scaffolding and fasteners.

Abstract

This application discloses a method, device, and medium for early warning of loosening of scaffolding and fasteners, relating to the field of construction safety technology. The method includes: installing micro-sensors at the scaffolding connection fasteners to collect reference physical signals of the fasteners in a reference stable state and establishing a reference model; during the construction phase, collecting real-time physical signals and performing finite element statics and dynamics based on the reference physical signals and real-time physical signals to generate residual signals; simulating the loosening evolution process of the fasteners under alternating loads based on the residual signals, evaluating the estimated remaining preload and remaining life of the fasteners, as well as the corresponding risk level; and generating predictive early warning signals and strategies based on the risk level. This application, through the above method, enables the prediction of the remaining life and development trend of fasteners in the early stages of loosening, transforming passive alarms into proactive predictions, greatly improving the timeliness of early warning.

Description

Technical Field

[0001] This application relates to the field of construction safety technology, and in particular to a method, equipment and medium for early warning of loosening of scaffolding and fasteners. Background Technology

[0002] In the construction industry, scaffolding, as an important temporary support structure, directly affects the safety of construction workers and the smooth progress of the project. Scaffolding fasteners, as key components of the scaffolding structure, are crucial for ensuring the overall stability of the scaffolding. Currently, monitoring the condition of fasteners mainly relies on regular manual inspections, which has drawbacks such as low efficiency, strong subjectivity, and inability to detect problems in real time.

[0003] In recent years, some sensor-based monitoring systems have emerged, but most of them can only provide alarms for macroscopic deformation or overload, lacking the ability to early identify and accurately predict microscopic hazards such as loose fasteners. These systems cannot adapt to dynamic environments such as material creep and load changes, resulting in a high false alarm rate. Especially in large-scale construction projects, where the number of scaffolding structures is enormous and their structures are complex, manual inspections cannot fully cover all connecting fasteners, leading to the failure to detect potential loosening hazards in a timely manner. This can potentially cause serious safety accidents such as scaffolding collapses. Furthermore, these systems fail to link the status of individual fasteners with the overall safety of the scaffolding, making it difficult to provide effective predictive maintenance decision support.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] Existing technologies cannot 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 fastener failure on the overall structural safety. Summary of the Invention

[0006] This application provides a method, device, and medium for early warning of loosening of scaffolding and fasteners, which can solve the problems in the prior art that it cannot adapt to dynamic environments such as load changes in scaffolding connections, cannot provide early and accurate predictive warning of fastener loosening, and cannot assess the impact of fastener failure on the overall structural safety.

[0007] In a first aspect, embodiments of this application provide a method for early warning of loosening of scaffolding and fasteners. The method includes: installing microsensors at the fastener connection points of the scaffolding to collect reference physical signals of the fasteners under a reference stable state, and establishing a reference model. 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. The loosening evolution process of the fasteners under alternating loads is simulated based on the residual signals to evaluate the estimated residual preload and remaining life of the fasteners, as well as the corresponding risk level. Based on the risk level, predictive early warning signals and strategies are generated.

[0008] In one implementation of this application, the reference physical signal of the fastener in a reference stable state is collected, and a reference model of the fastener is established. Specifically, this includes: applying test excitations of multiple amplitudes to the scaffolding in the reference stable state of the fastener; recording the response signals of the microsensors under the test excitations of multiple amplitudes; extracting features from the response signals and calculating the feature distribution interval, and using the statistical boundary of the distribution interval as the reference model.

[0009] In one implementation of this application, during the construction phase, real-time physical signals are acquired, and finite element static and dynamic simulations are performed and compared with the reference physical signals to generate residual signals. Specifically, this includes: in response to new real-time physical signal packets, 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, performing static simulation to calculate the theoretical strain distribution of the fastener under the current working condition, and performing dynamic modal analysis to calculate the theoretical vibration response; synchronizing the theoretical strain distribution and theoretical vibration response signals with the acquired 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-physics residual signals used for the prognostic model.

[0010] In one implementation of this application, the loosening evolution process of the fastener under alternating load is simulated based on the residual signal, and the estimated values ​​of the remaining preload and remaining life of the fastener, as well as the corresponding risk level, are evaluated. Specifically, this includes: establishing the dynamic equation of the bolted connection structure, taking the attenuation of the frictional torque between the threaded pairs as the core mechanism of preload degradation; inverting the current frictional torque parameters based on the residual signal; iteratively solving the dynamic equation based on the frictional torque parameters, predicting the evolution trajectory of the preload, and obtaining the estimated values ​​of the remaining preload and remaining life in a continuous time series.

[0011] In one implementation of this application, the method further includes: relaxing the fasteners from a rigid connection to a hinged connection in a digital twin until complete failure; calculating the redistribution of internal forces and the maximum displacement change of the overall scaffold structure in each relaxation step, and determining the risk level by combining the estimated value of the remaining preload and the estimated value of the remaining life.

[0012] In one implementation of this application, after generating predictive warning signals and strategies based on risk levels, the method further includes: continuously monitoring the time-varying energy envelope of the residual signal; if the first derivative of the energy envelope exceeds a dynamic threshold, determining that an abnormal impact load event has occurred; triggering a digital twin to perform instantaneous load response simulation, evaluating the degree of damage to the remaining lifetime estimate based on Miner's linear cumulative damage rule, and updating the remaining lifetime estimate.

[0013] In one implementation of this application, predictive early warning signals and strategies are generated based on the risk level, specifically including: outputting maintenance instructions such as immediate tightening, planned maintenance, simultaneous inspection of adjacent components, or partial unloading of loads based on the risk level; binding the maintenance instructions to the mobile terminal of the maintenance personnel, and pushing the optimal inspection path.

[0014] In one implementation of this application, after generating predictive early warning signals and strategies based on risk levels, the method further includes: collecting the actual fastening parameters of the fasteners after maintenance; calculating the deviation between the actual fastening parameters and the estimated values ​​of the remaining preload and remaining life to obtain the model prediction error; if the model prediction error exceeds the accuracy threshold, correcting the characteristic distribution range of the benchmark model, as well as the material property parameters of the finite element simulation model and the friction coefficient of the preload degradation dynamic equation.

[0015] Secondly, embodiments of this application also provide a loosening early warning device for scaffolding and fasteners. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which enable the at least one processor to: install microsensors at the scaffolding connection fasteners, 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. During the construction phase, collect real-time physical signals and perform finite element statics and dynamics based on the reference 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 early warning signals and strategies based on the risk level.

[0016] Thirdly, this application also provides a non-volatile computer storage medium for loosening early warning of scaffolding and fasteners, storing computer-executable instructions. The computer-executable instructions are configured to: install microsensors at the scaffolding connection fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model, the reference 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 reference physical signals and 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 value of the remaining preload and the estimated value of the remaining life of the fasteners, as well as the corresponding risk level; and generate predictive early warning signals and strategies based on the risk level.

[0017] This application provides a method, device, and medium for early warning of loosening of scaffolding and fasteners. By integrating digital twins and failure physics models, it can predict the remaining life and development trend of fasteners in the early stages of loosening, transforming passive alarms into proactive predictions and greatly improving the timeliness of early warnings. Employing a dynamic health benchmark model and multiphysics residual analysis eliminates environmental interference and individual differences. Combined with parameter inversion and iterative solutions, the assessment results of remaining preload and lifespan are more scientific and accurate. System safety is enhanced: by online simulation of the impact of individual fastener failure on the overall structure, a comprehensive risk assessment from component to system is achieved, providing managers with precise decision-making basis and effectively preventing collapse accidents. The generated differentiated maintenance strategies and optimal inspection paths greatly improve the targeting and efficiency of maintenance work, reduce operation and maintenance costs, and realize intelligent management. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A flowchart illustrating a method for early warning of loosening of scaffolding and fasteners provided in this application embodiment;

[0020] Figure 2 This is a schematic diagram of the internal structure of a loosening early warning device for scaffolding and fasteners provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This application provides a method, device, and medium for early warning of loosening of scaffolding and fasteners, which solves the problems in the prior art that it cannot adapt to dynamic environments such as load changes in scaffolding connections, cannot provide early and accurate predictive warning of fastener loosening, and cannot assess the impact of fastener failure on the overall structural safety.

[0023] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a method for early warning of loosening of scaffolding and fasteners, provided as an embodiment of this application. Figure 1 As shown in the figure, the present application provides a method for early warning of loosening of scaffolding and fasteners, which specifically includes the following steps:

[0025] Step 10: Install micro sensors at the scaffold connection couplers to collect the reference physical signals of the couplers in the reference stable state, and establish a reference model. The reference physical signals include strain data, vibration acceleration data, and relative displacement data.

[0026] In this step, a standard is established for each fastener. Physical signals are collected and modeled when the fastener is in an absolutely stable state. Real-time signals in subsequent construction stages can be compared with this to accurately identify anomalies caused by loosening. Strain data: The greater the preload, the greater the strain; when loose, the strain will decrease significantly. Vibration acceleration data: When stable, the vibration frequency is high and the amplitude is small; when loose, the frequency decreases and the amplitude increases. Relative displacement data: When stable, the displacement approaches 0; when loose, the displacement fluctuates significantly with the load.

[0027] As an optional embodiment, the reference physical signal of the fastener in the reference stable state is collected, and the reference model of the fastener is established. Specifically, it may include: Step 101: Apply test excitation of multiple amplitudes to the scaffolding in the reference stable state of the fastener.

[0028] In this step, standard counterweights, each weighing 10 kg, are stacked in stages on the scaffold horizontal members. Design loads of 30%, 50%, 80%, 100%, and 150% are applied. For example, if the design load is 200 kg, the corresponding weights are 60 kg, 100 kg, 160 kg, 200 kg, and 300 kg. Each load level is maintained for 5 minutes to simulate static loads such as material stacking. Further, alternating load excitation is performed: a small vibrating table is fixed to the scaffold uprights, and a sinusoidal alternating load is applied. The frequency covers typical frequencies of personnel movement and wind loads during construction, and the amplitude is graded at 0.1g, 0.3g, and 0.5g (g is the acceleration due to gravity). Each frequency and amplitude level lasts for 3 minutes to simulate dynamic loads.

[0029] Step 102: Record the response signals of the microsensor under multiple amplitude test excitations.

[0030] In this step, a resistance strain gauge, such as the BX120-3AA type with a sensitivity of 2.0±1%, is selected as the strain sensor and is attached to the head of the fastener bolt to monitor the axial strain of the bolt. The arc-shaped inner wall of the fastener in contact with the steel pipe is used to monitor the contact strain, and 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 to the top cover of the fastener, and the laser head is aimed at the surface of the steel pipe below to monitor the relative sliding displacement between the fastener and the steel pipe.

[0031] Step 103: Extract features from the response signal and calculate the feature distribution interval, using the statistical boundary of the distribution interval as the benchmark model.

[0032] In this step, the peak strain values ​​under each load level in the previous steps are taken: the maximum strain of the bolt head and the average steady-state strain, such as the average value within 5 minutes after the load stabilizes; the root mean square acceleration of the vibration acceleration is extracted to reflect the vibration intensity and the peak acceleration is extracted to reflect the impact degree; the maximum displacement deviation, the maximum difference from the initial position, the displacement fluctuation range, and the difference between the maximum and minimum values ​​are extracted from the relative displacement data.

[0033] Furthermore, Fourier transform can be performed on the vibration acceleration data to extract the dominant frequency and amplitude. Due to the rigid connection, the dominant frequency of the fastener is usually higher, decreasing after loosening. Statistical analysis can be performed on the values ​​of the same feature under the same load amplitude but different test numbers. For example, if the strain peak values ​​of three tests are 120με, 125με, and 118με, their 95% confidence intervals can be calculated. By fitting a normal distribution and taking the mean ± 1.96 times the standard deviation, the normal range of the feature can be obtained, such as 115-130με. In this way, the 95% confidence intervals of all features can be integrated to form a multi-dimensional constraint model: Strain: 100-150με (under 30% design load), 180-230με (under 100% design load); Vibration dominant frequency: 3.5-4.5Hz (under 0.3g alternating load); Relative displacement: ≤0.05mm (under all loads). The upper and lower boundaries of these intervals are the baseline thresholds. If subsequent real-time signals exceed these ranges, they are identified as potential loosening anomalies.

[0034] Step 20: During the construction phase, real-time physical signals are acquired, and finite element statics and dynamics are performed based on the reference physical signals and the real-time physical signals to generate residual signals.

[0035] In this step, by comparing the on-site measured signals with the digital twin simulation signals, the subtle anomalies caused by the loosening of the fasteners are captured. 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 stage, so that the residual signals can more accurately reflect the essential differences caused by the loosening.

[0036] As an optional embodiment, during the construction phase, real-time physical signals are acquired, and finite element static and dynamic simulations and comparisons are performed based on the reference physical signals and the real-time physical signals to generate residual signals. Specifically, this may include: Step 201: In response to a new real-time physical signal packet, load environment information is extracted, and the boundary conditions and load settings of the finite element simulation model in the digital twin are updated.

[0037] In this step, the load type is identified from the vibration acceleration signal. If the signal spectrum is concentrated in 1-2Hz, it is determined to be a personnel walking load; if it is in 2-5Hz and lasts for a long time, it is determined to be a wind load. The load amplitude is inferred from the strain data. Through the load-strain calibration relationship in the benchmark model, such as 300kg static load corresponding to 200με strain, the current load size is calculated in real time. The measured strain of 250με is used to infer the load of 375kg.

[0038] In this way, environmental parameters are directly extracted, and 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 load settings apply the extracted load type, whether it is static or dynamic, its amplitude, and direction to the corresponding position in the model. Static load is applied to the middle of the horizontal bar, and wind load is applied to the top of the vertical bar. The material properties are adjusted according to the temperature data. The elastic modulus of Q235 steel changes with temperature: for every 1°C increase, E decreases by about 0.02%.

[0039] Then, if the real-time signal packet shows that the current static load is 300kg, the wind load is 0.3g (3Hz), and the temperature is 28℃, the finite element model is set synchronously as follows: the horizontal bar is subjected to a vertical force of 3000N, the vertical bar is subjected to an alternating load of 0.3g (3Hz) in the horizontal direction, and the material elastic modulus is corrected according to 28℃, which is 0.16% lower than the reference value of 20℃.

[0040] Step 202: Based on the updated model, perform static simulation to calculate the theoretical strain distribution of the fastener under the current working condition, and perform dynamic modal analysis to calculate the theoretical vibration response.

[0041] In this step, the scaffolding is modeled according to its actual dimensions, and a fine mesh is used for mesh generation. The contact type between the fasteners and steel pipes, and between the bolts and fasteners, is set to frictional contact, with the friction coefficient calibrated based on the baseline model, corresponding to a stable state. The material properties can be Q235 steel for the steel pipes (E=206GPa, μ=0.3) and cast iron for the fasteners (E=110GPa, μ=0.25), depending on the actual situation. The static analysis module of ANSYS Mechanical is used. The axial strain of the fastener bolts and the contact between the fasteners and steel pipes are analyzed. The radial strain distribution at the contact interface is output, and the strain value corresponding to the sensor position is output for easy comparison later. The theoretical strain time-domain curve is shown, such as the bolt head theoretical strain remaining stable at 220με over time under a static load of 300kg. The first 10 natural frequencies of the model are solved, and the first 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 updated alternating wind load of 3Hz, and the acceleration time-domain curve in the same direction as the sensor is shown, such as the theoretical root mean square acceleration of 0.08g.

[0042] Step 203: Synchronize the theoretical strain distribution and theoretical vibration response signals with the acquired real-time strain and real-time vibration signals on the time scale, and perform point-by-point differential calculation.

[0043] In this step, the finite element simulation results are interpolated at the same time intervals, using the timestamp of the real-time signal as the benchmark. Timestamp matching ensures a one-to-one correspondence between the theoretical and real-time signals at the same point in time. For each time point, the strain residual is calculated by subtracting the real-time strain value from the theoretical strain value; for example, theoretical strain = 220 με, real-time strain = 180 με, residual = +40 με. For each time point, the vibration response residual is calculated by combining the theoretical and real-time acceleration values; for example, theoretical acceleration = 0.08 g, real-time acceleration = 0.12 g, residual = -0.04 g. If the real-time signal is missing due to sensor failure, it is supplemented by interpolation using the average of the first three cycles to avoid data gaps.

[0044] Step 204: Generate strain residual signals and vibration response residual signals respectively, which together constitute the multiphysics residual signals used for the prognostic model.

[0045] In this step, a time-domain curve is plotted with time on the horizontal axis and the residual value (με) on the vertical axis, such as the residual fluctuation over 10 consecutive minutes. The vibration response residual signal is also a time-domain curve, with the unit being g (gravitational acceleration). The residual signal is filtered to remove high-frequency noise. If the fastener is loose, the strain residual is usually a positive deviation, with theoretical strain > real-time strain, due to the decrease in preload leading to a reduction in actual deformation. The vibration residual is usually a negative deviation, with theoretical acceleration < real-time acceleration, due to the increased vibration caused by loosening. If both show deviations in the expected direction, it can be considered a valid anomaly, eliminating false alarms from a single signal. Strain deviations caused by temperature will not be accompanied by vibration deviations.

[0046] Step 30: Simulate the loosening evolution process of the fastener under alternating load based on the residual signal, and evaluate the estimated value of the remaining preload and remaining life of the fastener, as well as the corresponding risk level.

[0047] It is understood that the loads borne by scaffolding during the construction phase, such as personnel movement and material handling, are periodic alternating loads. These loads cause the frictional torque between the threaded pairs of fastener bolts to gradually decrease, which is the main cause of preload degradation.

[0048] As an optional embodiment, the loosening evolution process of the fastener under alternating load 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, it may include: Step 301: Establish the dynamic equation of the bolt connection structure and take the attenuation of the frictional torque between the threaded pairs as the core mechanism of preload degradation.

[0049] In this step, the connection between the fastener bolt and nut is simplified to a bolt and nut threaded pair system, and the maintenance of the preload F depends on the frictional torque M between the threaded pairs. f The relationship between the two is: M f =F•d2 / 2•tan(λ+ρ v), where: d2 is the thread pitch diameter, d2 = 10.863 mm for M12 bolts, λ is the thread helix angle, approximately 1.87° for M12 bolts, ρ v The equivalent friction angle of the threaded pair is related to the surface roughness; the dynamic equations are established considering the alternating load F. dyn The frictional torque decreases with the number of cycles N under the action of (t), and the equation is as follows:

[0050]

[0051] Where: k is the attenuation coefficient, which is related to the material wear characteristics; F0 is the initial preload; n is the load exponent; and F is the alternating load. dyn The larger (t) is, the faster the frictional torque decays; the larger the initial frictional torque M is... f The larger the value, the higher the decay rate, resulting in accelerated wear.

[0052] When the fastener loosens, the actual contact area between the threaded parts decreases, the coefficient of friction decreases, and M... f This reduces the preload, causing the preload to decrease with the number of cycles according to the above equation.

[0053] Step 302: Invert the current friction torque parameters based on the residual signal.

[0054] In this step, the abstract signal difference of the residual signal is transformed into the specific mechanical parameter of the friction torque, establishing a bridge between the signal and the physical state. The strain residual Δε, and the theoretical strain based on the steady state M... f0 The difference between the calculated real-time strain and the theoretical 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 frictional torque: Δa∝(M f 0-M f ) / M f Loosening leads to increased vibration. The inversion algorithm uses the least squares method, taking the residual signal as input, to fit and obtain the current friction torque M. f :

[0055] Define the objective function:

[0056]

[0057] The Mf value that minimizes J through iterative solution is the current friction torque parameter.

[0058] If the mean strain residual is +40με, which is greater than the measured value, it indicates a decrease in preload. If the mean vibration residual is -0.04g, which is greater than the measured value, it indicates increased loosening. The current M can be obtained through fitting. f =12N•m, initial Mf0=20N•m, has decayed by 40%.

[0059] Step 303: Based on the friction torque parameters, iteratively solve the dynamic equations to predict the evolution trajectory of the preload and obtain the estimated values ​​of the remaining preload and remaining life in the continuous time series.

[0060] In this step, based on the current frictional torque, the future preload decay trend is predicted, and the remaining load that can be withstood is quantified: the remaining preload and the remaining safe service life.

[0061] Input parameters: Current M f Alternating load amplitude F dyn (t) Attenuation coefficient k, calibrated using a reference model, e.g., k = 5 × 10 -6 ;

[0062] Divide future time into hourly intervals and calculate the number of load cycles N per hour; update M time-by-time according to the dynamic equations in step 301. f And through M f The conversion relationship with F yields the residual preload F. t F at any time t t (Unit: kN), for example, if calculated, Ft = 18 kN after 12 hours and Ft = 15 kN after 24 hours; Remaining lifetime estimate: the time difference from the current moment until Ft drops to the first safe threshold (e.g., 10 kN), for example, when F... t =10kN corresponds to a time of 48h, so the remaining lifetime is 48h.

[0063] As an optional embodiment, the method may further include: step 304: relaxing the fastener in the digital twin from a rigid connection step to a hinged connection until complete failure.

[0064] In this step, the impact of loose fasteners on the overall structure of the scaffold is simulated to avoid assessing only a single fastener and ignoring the risk of a chain reaction caused by local failure.

[0065] Based on the remaining preload F t The ratio of the initial preload force F0 to the loosening force can be divided into four stages: rigid connection: F t ≥80% F 0， Sufficient preload, no relative slippage; semi-rigid connection: 50%F0≤F t <80% F 0， Slightly loose, with minor slippage; hinged state: 20%F0≤F t <50% F 0， Significantly loose, can rotate; completely failed: F t <20% F 0， The bolts came loose, rendering the connection ineffective.

[0066] In the finite element model, by adjusting the contact stiffness between the fastener and the steel pipe, the stiffness can be 1×10⁻⁶ for a rigid connection. 8 N / m, stiffness 1×10 when hinged 5 N / m, to achieve stepped relaxation, simulating mechanical behavior at different stages.

[0067] Step 305: Calculate the redistribution of internal forces and the maximum displacement change of the overall scaffold structure during each relaxation stage. Combine the estimated value of the remaining preload and the estimated value of the remaining life to determine the risk level.

[0068] In this step, the status of a single fastener is linked to the overall structural safety, enabling an upgraded assessment from local evaluation to systemic risk assessment.

[0069] Calculate the changes in axial force and bending moment of adjacent fasteners after each relaxation stage. After a fastener enters the hinged state, the axial force of three adjacent fasteners increases by 20%. Calculate the maximum displacement based on the maximum deflection at the top or mid-span of the scaffolding; for example, a deflection of 5mm for rigid connections increases to 15mm for hinged connections. The risk level classification standard is combined with the remaining preload F. t Remaining lifespan T t and structural response, such as low risk: F t ≥80%F0, T t ≥72h, internal force change <10%, displacement <10mm.

[0070] Step 40: Generate predictive early warning signals and strategies based on the risk level.

[0071] As an optional embodiment, predictive early warning signals and strategies are generated based on the risk level, which may specifically include: Step 401: Based on the risk level, output maintenance instructions for immediate tightening, planned maintenance, simultaneous inspection of adjacent components, or partial unloading; Step 402: Bind the maintenance instructions to the mobile terminal of the maintenance personnel and push the optimal inspection path.

[0072] In this step, maintenance instructions are strictly linked to risk levels to avoid over-maintenance and under-maintenance; optimal path planning reduces inspection time, which can significantly improve maintenance efficiency, especially in large scaffolding and super high-rise building scaffolding; instruction push, execution feedback and digital twin linkage facilitate later project review and model optimization.

[0073] As an optional embodiment, after generating predictive warning signals and strategies based on risk levels, the method may further include: continuously monitoring the time-varying energy envelope of the residual signal; if the first derivative of the energy envelope exceeds a dynamic threshold, determining that an abnormal impact load event has occurred; triggering a digital twin to perform instantaneous load response simulation, evaluating the degree of damage to the remaining lifetime estimate based on Miner's linear cumulative damage rule, and updating the remaining lifetime estimate.

[0074] In this step, the sum of the energy of the strain residual and the vibration residual is calculated through a sliding window, and changes in signal intensity are captured in real time. The energy envelope can intuitively reflect the difference between normal loosening and sudden impact.

[0075] Calculate the first derivative of the instantaneous rate of change of the energy envelope and compare it with 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. Trigger the finite element model and calculate the instantaneous stress response of the fastener thread pair, such as the maximum contact stress, by inferring the load parameter amplitude and duration based on the peak impact energy. The impact stress corresponds to the fatigue life. Refer to the material's SN curve. A single impact is equivalent to one cycle. Calculate the damage increment. Correct the original remaining life using the remaining allowable damage ratio. For example, if the impact causes a 0.01 increase in damage, the original 24-hour life may be shortened to 23.6 hours.

[0076] As an optional embodiment, after generating predictive warning signals and strategies based on risk levels, the method may further include: collecting the actual fastening parameters of the fasteners after maintenance; calculating the deviation between the actual fastening parameters and the estimated values ​​of remaining preload and remaining life to obtain the model prediction error; if the model prediction error exceeds the accuracy threshold, correcting the characteristic distribution range of the benchmark model, as well as the material property parameters of the finite element simulation model and the friction coefficient of the preload degradation kinetic equation.

[0077] In this step, the torque value and measured preload after re-tightening can be obtained through torque and preload conversion or measured with a dedicated instrument; record the remaining life at the time of maintenance completion, such as an actual remaining life of 18 hours instead of the previously estimated 24 hours. Compare the actual tightening parameters with the previous estimated values ​​of remaining preload and remaining life, and calculate the deviation: correct the characteristic distribution range, such as the original strain reference range of 100-150με, which actually needs to be adjusted to 90-140με due to fastener wear; update material properties, such as correcting the elastic modulus of steel pipe from 206GPa to 200GPa due to corrosion; adjust the friction coefficient of the thread pair, such as correcting it from 0.35 to 0.32 to match the actual preload decay rate.

[0078] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a loosening early warning device for scaffolding and fasteners, the structure of which is as follows: Figure 2 As shown.

[0079] Figure 2 This is a schematic diagram of the internal structure of a loosening early warning device for scaffolding and fasteners, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0080] At least one processor 201;

[0081] And a memory 202 that is communicatively connected to at least one processor;

[0082] The memory 202 stores instructions executable by at least one processor. These instructions are executed by at least one processor 201 to enable the processor 201 to: install microsensors at the scaffold connection fasteners, 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. During the construction phase, the processor collects real-time physical signals and performs finite element statics and dynamics based on the reference physical signals and the real-time physical signals to generate residual signals. Based on the residual signals, the processor simulates the loosening evolution process of the fasteners under alternating loads, evaluates the estimated residual preload and remaining life of the fasteners, and the corresponding risk level. Based on the risk level, the processor generates predictive early warning signals and strategies.

[0083] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for early warning of loosening of scaffolding and fasteners stores computer-executable instructions. The computer-executable instructions are configured to: install micro-sensors at the scaffolding connection fasteners to collect reference physical signals of the fasteners in a reference stable state and establish a reference model, the reference 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 reference physical signals and 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, as well as the corresponding risk level; and generate predictive early warning signals and strategies based on the risk level.

[0084] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0085] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0086] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0092] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for early warning of loosening of scaffolding and fasteners, characterized in that, The method includes: Microsensors are installed at the scaffolding connection fasteners to collect the reference physical signals of the fasteners in a reference stable state and to establish a reference model. The reference physical signals include strain data, vibration acceleration data and relative displacement data. During the construction phase, real-time physical signals are acquired, and finite element static and dynamic simulations are performed and compared with the reference physical signals to generate residual signals, specifically including: In response to new real-time physical signal packets, load environment information is extracted, and the boundary conditions and load settings of the finite element simulation model in the digital twin are updated. 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. The theoretical strain distribution and theoretical vibration response are synchronized with the acquired real-time strain and real-time vibration signals on the time scale, and point-by-point differential calculations are performed. The strain residual signal and the vibration response residual signal are generated separately, and together they constitute the multiphysics residual signal used for the prediction model. Based on the residual signal, the loosening evolution process of the fastener under alternating load is simulated to evaluate the estimated remaining preload and remaining life of the fastener, as well as the corresponding risk level, specifically including: A dynamic equation for the bolted connection structure is established, with the attenuation of frictional torque between threaded pairs as the core mechanism of preload degradation; The current friction torque parameters are retrieved based on the residual signal; Based on the friction torque parameters, the dynamic equations are iteratively solved to predict the evolution trajectory of the preload, and the estimated values ​​of the remaining preload and remaining life in a continuous time series are obtained. In the digital twin, the fastener is relaxed from a rigid connection step to a hinged connection until it completely fails; Calculate the redistribution of internal forces and the maximum displacement change of the overall scaffold structure during each relaxation stage. Combine the estimated remaining preload and the estimated remaining life to determine the risk level. Based on the risk level, predictive early warning signals and strategies are generated.

2. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that, The process of acquiring the reference physical signal of the fastener in a reference stable state and establishing the reference model of the fastener specifically includes: With the fastener reference stable state, test excitations of multiple amplitudes are applied to the scaffolding; Record the response signals of the microsensor under multiple amplitude test excitations; Features are extracted from the response signal and the feature distribution interval is calculated, with the statistical boundary of the distribution interval serving as the benchmark model.

3. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that, After generating predictive early warning signals and strategies based on the risk level, the method further includes: Continuously monitor the time-varying energy envelope of the residual signal; If the first derivative of the energy envelope exceeds the dynamic threshold, it is determined that an abnormal impact load event has occurred. The digital twin is triggered to perform an instantaneous load response simulation. Based on Miner's linear cumulative damage rule, the damage degree of the event to the remaining lifetime estimate is evaluated, and the remaining lifetime estimate is updated.

4. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that, The step of generating predictive early 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 unloading of load. The maintenance instructions are bound to the mobile terminal of the maintenance personnel, and the optimal inspection route is pushed.

5. The method for early warning of loosening of scaffolding and fasteners according to claim 1, characterized in that, After generating predictive early warning signals and strategies based on the risk level, the method further includes: Collect the actual tightening parameters of the fasteners after maintenance; The deviation between the actual fastening parameters and the estimated values ​​of remaining preload and remaining life is calculated to obtain the model prediction error. If the prediction error of the model exceeds the accuracy threshold, the characteristic distribution range of the benchmark model, as well as the material property parameters and friction coefficient of the preload degradation dynamic equation of the finite element simulation model, are corrected.

6. A loosening early warning device for scaffolding and fasteners, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Perform the steps of a loosening warning method for scaffolding and fasteners as described in any one of claims 1-5.

7. 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 set as follows: Perform the steps of a loosening warning method for scaffolding and fasteners as described in any one of claims 1-5.

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