Intelligent monitoring method for outrigger structure climbing frame attached lattice column

Abstract

The application discloses an intelligent monitoring method for a cantilever structure climbing frame attached lattice column, and relates to the field of cantilever structure climbing frame attached lattice column, which comprises the following steps: constructing a critical instability database of the lattice column, providing a basis for monitoring data linkage critical state, determining three-dimensional core load parameters and test thresholds in combination with the actual stress field of the lattice column, carrying out indoor orthogonal critical instability tests based on the three-dimensional load parameters, collecting lateral deflection, vertical displacement, column axial force and strain data of the lattice column under different load combinations, on-site monitoring and data collection, and differentiating sensor arrangement for key monitoring points. By adopting miniaturized and high adaptability sensors and implementing differentiated encryption arrangement, in combination with multi-channel synchronous collection and data preprocessing, the application is beneficial to obtaining micro-deformation and internal force data that cannot be captured by traditional manual observation, is convenient for solving the safety hazards caused by data loss, and provides complete data support for subsequent evaluation.

Description

Technical Field

[0001] This invention relates to the field of cantilever climbing scaffold attached lattice columns, and particularly to an intelligent monitoring method for cantilever climbing scaffold attached lattice columns. Background Technology

[0002] Cantilevered climbing scaffolding with attached lattice columns is typically a type of external scaffolding that is supported and attached to the engineering structure via cantilevering, and climbs up floor by floor as construction progresses.

[0003] The lattice columns attached to the climbing scaffold are core load-bearing components, and their stability directly determines construction safety. Therefore, monitoring of these lattice columns is necessary. However, due to the complex construction environment and numerous obstructions such as construction equipment and scaffolding around the lattice columns, it is difficult to effectively deploy monitoring equipment at some critical monitoring points, including the connection nodes between the lattice columns and cantilever beams and the lower stress concentration sections of the lattice columns. Consequently, monitoring is primarily conducted by visual observation, supplemented by data monitoring. This approach easily leads to the failure to obtain data on micro-deformations and internal forces that occur during the use of the lattice columns, posing a significant safety hazard. Even when monitoring equipment is deployed in some locations, such as using traditional automated monitoring methods like laser rangefinders and fiber optic strain sensors, this method is not only costly but also fails to link the monitoring data with the critical state database of the lattice columns, making accurate assessment based on critical states impossible. Therefore, it is necessary to use intelligent monitoring methods to monitor the lattice columns. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring method for the attachment of lattice columns to a cantilevered climbing scaffold, comprising:

[0006] Step 1: Construct a critical instability database for lattice columns to provide a basis for monitoring data linkage to critical states. Combine the actual stress scenarios of lattice columns to determine the three-dimensional core load parameters and test thresholds. Conduct indoor orthogonal critical instability tests based on the three-dimensional load parameters. Collect lateral deflection, vertical displacement, column axial force and strain data of lattice columns under different load combinations. Establish a three-dimensional numerical simulation model of lattice columns. Use the corrected and qualified numerical simulation model to conduct large-sample simulation tests, generate basic data, and obtain the critical instability database for lattice columns.

[0007] Step 2: On-site monitoring deployment and data acquisition. Sensors are deployed differently for key monitoring points. Data from each sensor is collected in real time using a multi-channel synchronous data acquisition instrument, and the data is preprocessed and transmitted to the cloud data processing platform.

[0008] Step 3: Data Inversion and Safety Assessment. The preprocessed deformation parameters and internal force parameters of the monitoring points of the lattice column are imported into the constructed critical instability database of the lattice column. The current stress state of the lattice column is determined by preliminary screening and precise matching. The average value of the three candidate stress state parameters with the smallest spatial distance is taken as the current three-dimensional stress state of the lattice column. Based on the current stress state, the corresponding safety factor is matched in the critical instability database, and the safety state of the lattice column is dynamically assessed.

[0009] Preferably, the determination of the three-dimensional core load parameters and test thresholds in step one specifically includes setting vertical load, horizontal load, and eccentricity as three-dimensional load parameters. The vertical load range covers construction conditions from no load to full load, the horizontal load range covers additional loads under light to strong wind conditions, and the eccentricity range covers eccentricity caused by the offset of the lattice column. The foundation test values ​​for the vertical load under construction conditions of no load, normal construction, and full load are set to 80kN, 110kN, and 140kN, respectively, with a foundation test step size of 10kN. The foundation test values ​​for the horizontal load under light wind, moderate wind, and strong wind conditions are set to 20kN, 50kN, and 80kN, respectively, with a foundation test step size of 5kN. The foundation test values ​​for the eccentricity of no eccentricity, slight offset, moderate offset, and significant offset are set to 0mm, 50mm, 100mm, and 150mm, respectively, with a foundation test step size of 10mm.

[0010] Preferably, when collecting lateral deflection, vertical displacement, column axial force, and strain data in step one, the load is determined to be the critical instability load under the corresponding load combination when any of the following critical conditions are met:

[0011] When the lateral deflection increment is greater than 0.4 mm for three consecutive 1-second sampling periods;

[0012] The fluctuation amplitude of the column axial force exceeds 8% of the current stable axial force value, and the duration is >4s;

[0013] The strain value exceeds 85% of the yield strain of the lattice column material;

[0014] The vertical displacement change within a single sampling period is greater than 0.3 mm.

[0015] Preferably, the establishment of the three-dimensional numerical simulation model of the lattice column in step one includes using the material properties of the lattice column measured in indoor tests. These material properties include elastic modulus, Poisson's ratio, and yield strength. The numerical simulation model is then corrected through mesh refinement, boundary condition correction, and model optimization at component connections. The mesh refinement requires densification at the connection nodes between the lattice column and the cantilever beam, and in the lower and middle stress concentration areas. The large-sample simulation test in step one is conducted using high-performance computing equipment. Each data record in the critical instability database of the lattice column in step one contains three-dimensional load data. The system includes load parameters, lattice column deformation parameters, internal force parameters, and a safety factor. The lattice column deformation parameters include lateral deflection and vertical displacement. The internal force parameters include column axial force and strain. The safety factor is defined as the ratio of the lattice column's ultimate bearing capacity to the current load. A safety factor > 1.0 is defined as a stable state, a safety factor < 1.0 as an unstable state, and a safety factor = 1.0 as a critical unstable state. Based on all data points with a safety factor = 1.0 in the critical unstable database, a three-dimensional spatial curve fitting algorithm is used to fit and generate a critical unstable state envelope that can divide the stable and unstable regions of the lattice column.

[0016] Preferably, in step two, the monitoring points for differentiated sensor deployment at key monitoring locations include the cantilever beam connection nodes and the lower and middle stress concentration sections. The deployed sensors include laser displacement gauges, fiber optic strain gauges, inclinometers, accelerometers, and wind speed sensors. The laser displacement gauges are deployed at the top, middle, and bottom of the lattice column, and beside the connection nodes. These laser displacement gauges are used to monitor the lateral deflection and vertical displacement of the key areas. The fiber optic strain gauges are deployed in groups of two every 1.2m along the height of the lattice column. The fiber optic strain gauges are placed on the two orthogonal sides of the column, and denser near the connection nodes and in the lower and middle stress concentration sections, with each group spaced at 0.8m apart. The fiber optic strain gauges are used to collect strain data in key areas to calculate the axial force of the column. The inclinometers are placed on the top of the lattice column to monitor the overall tilt angle. The accelerometers are placed in the middle of the lattice column where collisions are likely to occur, and are used to identify construction disturbances. The wind speed sensors are placed on the unobstructed side of the top of the lattice column to collect on-site wind load data.

[0017] Preferably, step two further includes:

[0018] Construction disturbance identification is based on acceleration sensor data. When the instantaneous acceleration value is detected to the set value, it is determined to be a construction collision disturbance. The safety assessment of deformation and internal force data during this period is suspended until the acceleration value returns to within the base value and the duration is greater than the set duration, and then the assessment is restarted.

[0019] Wind load impact correction is performed based on wind speed sensor data. When the real-time wind speed is set to a certain value, the monitored lateral deflection data is corrected according to the wind speed value and the wind load conversion model. The wind load conversion model involves formulas including:

[0020]

[0021] in, This is the standard value of wind load. For height The wind vibration coefficient at the location, This is the wind load shape coefficient. For height The wind pressure height variation coefficient at that location. This is the basic wind pressure.

[0022] Preferably, the deformation parameters and internal force parameters in step three are lateral deflection, vertical displacement, and column axial force, respectively. The preliminary screening in step three specifically includes eliminating stress states that deviate from the current monitoring parameter range by more than 20% based on the range of deformation and internal force parameters corresponding to each stress state in the critical instability database of lattice columns. The precise matching in step three specifically includes calculating the spatial distance between the current monitoring parameters and the parameters corresponding to the candidate stress states, quickly finding the six candidate stress states that are closest to the current monitoring parameters, and simultaneously spatially segmenting the set of candidate stress states.

[0023] Preferably, the dynamic assessment of the safety status of the lattice column in step three specifically includes:

[0024] When the safety factor is ≥1.6, the lattice column is determined to be in a completely safe state, and the regular monitoring frequency is maintained;

[0025] When 1.4 ≤ safety factor < 1.6, the lattice column is determined to be in a state of mild concern, triggering a blue alert, adjusting the monitoring frequency, and checking whether there are any illegal loads around the lattice column.

[0026] When 1.2 ≤ safety factor < 1.4, the lattice column is determined to be in a mild warning state, triggering a yellow warning, increasing the monitoring frequency, and inspecting the tightness of the bolts at the connection nodes of the lattice column;

[0027] When 1.0 ≤ safety factor < 1.2, the lattice column is determined to be in a moderate warning state, triggering an orange warning. Construction work related to the lattice column is immediately suspended, and additional sensors are deployed at key monitoring points.

[0028] When the safety factor is less than 1.0, the lattice column is determined to be in a severe warning state, triggering a red alert. The emergency response plan is immediately activated, and the personnel are evacuated to a safe area. Temporary reinforcement measures, such as setting up diagonal supports and increasing counterweight balance, are adopted to reinforce the lattice column.

[0029] Preferably, step three further includes continuous monitoring of the safety factor changes of the lattice column after the early warning is issued:

[0030] If the safety factor rises to the threshold for lifting the corresponding warning level, then the frequency of normal construction and monitoring will be gradually restored.

[0031] If the safety factor does not recover or continues to decline, the measures need to be optimized and reassessed. At the same time, the monitoring data, stress state inversion results, measures, and safety factor change trends should be fed back to the critical instability database of the lattice column to update the database. Based on the feedback data, the parameters of the numerical simulation model should be further corrected.

[0032] Preferably, updating the database specifically includes classifying the feedback monitoring data and stress state data according to load parameter intervals and supplementing them into the database records of the corresponding intervals. When the number of records for a certain load parameter interval exceeds a set number, the local critical instability envelope of that interval is refitted. The numerical simulation model correction specifically includes adjusting the elastic modulus of the material and the constraint stiffness of the boundary conditions in the model based on the feedback data, so that the error between the safety factor calculated by the model and the safety factor inverted from the actual monitoring is less than 5%.

[0033] The technical effects and advantages of this invention are as follows:

[0034] (1) By adopting miniaturized, highly adaptable sensors and implementing differentiated encrypted deployment, combined with multi-channel synchronous acquisition and data preprocessing, this invention is conducive to obtaining micro-deformation and internal force data that cannot be captured by traditional manual observation, which facilitates the solution of safety hazards caused by data loss and provides complete data support for subsequent evaluation.

[0035] (2) This invention constructs a critical instability database by utilizing indoor experiments and numerical simulations, conducts large-sample simulations, replaces some high-cost field experiments, and imports field monitoring data into the database, which is conducive to realizing the linkage assessment of monitoring data with critical instability envelope and safety factor. It does not rely on expensive dedicated monitoring systems, greatly reduces costs and improves assessment accuracy.

[0036] (3) This invention utilizes a critical instability database and a three-dimensional envelope to preliminarily screen and accurately match the inversion of the stress state, replacing the traditional single threshold alarm. At the same time, it introduces an acceleration sensor and a wind speed sensor to eliminate interference and combines multi-level safety factor threshold dynamic evaluation, which helps to make the evaluation results fit the actual stress scenario, reduce the false alarm rate, and prevent the missed risk handling opportunity due to false alarm fatigue.

[0037] (4) This invention utilizes early warning and graded disposal measures to quickly respond to risks, and the disposal effect can be verified by continuous monitoring. At the same time, the monitoring, inversion and disposal data are fed back to the database to update the data and correct the numerical model, which not only improves the accuracy of subsequent assessment, but also covers multiple working conditions such as vertical load and eccentricity through the database, which is conducive to adapting to the monitoring needs of lattice columns under different construction scenarios, and facilitates the dynamic optimization and wide application of safety management. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the intelligent monitoring method for the attached lattice columns of the cantilevered climbing frame of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention provides, for example Figure 1 The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold, as shown, includes the following specific steps:

[0041] Step 1: Construct a critical instability database for lattice columns to provide a basis for monitoring data and linking critical states. Combine the actual stress scenarios of the lattice columns to determine the three-dimensional core load parameters and test thresholds. Based on the three-dimensional load parameters, conduct indoor orthogonal critical instability tests, collecting lateral deflection, vertical displacement, column axial force, and strain data of the lattice columns under different load combinations. Establish a three-dimensional numerical simulation model of the lattice columns. The three-dimensional numerical simulation model of the lattice columns is established using general-purpose finite element numerical simulation software, such as ABAQUS or ANSYS. The operation mode of general-purpose finite element numerical simulation software is based on discretization-modeling-solution logic: first, according to the actual structure of the lattice column (columns, girders, and connection nodes), it is discretized into a finite number of discrete components using solid elements or beam elements. The calculation is performed using small units, each assigned material properties (elastic modulus and Poisson's ratio, etc.) that match the actual structure. These units are then connected through nodes to reconstruct the overall structural form. Boundary conditions (such as fixed-end constraints) and load parameters (vertical / horizontal loads and eccentricity) consistent with those used in the field are set. This transforms the mechanical analysis of the complex structure into stress and displacement calculations at the unit nodes. Subsequent model corrections focus on data matching optimization: based on measured deformation (lateral deflection and vertical displacement) and internal force (axial force and strain) data from indoor experiments, the accuracy of the unit mesh, the connection model, and boundary constraint parameters are adjusted. Through iterative calculations, the error between the simulation results and experimental data is controlled within a preset range, ensuring that the model accurately reflects the actual stress and deformation characteristics of the lattice column, laying the foundation for subsequent large-sample modeling. This study aims to provide a reliable foundation for building a critical instability database. Using a modified and qualified numerical simulation model, large-sample simulation experiments will be conducted to generate basic data. This basic data will be supplemented using data interpolation algorithms, specifically cubic spline interpolation or Kriging interpolation. Cubic spline interpolation constructs a smooth curve composed of piecewise cubic polynomials between known discrete data points, ensuring the continuity of the first and second derivatives of adjacent polynomials at connection points, thus guaranteeing the overall smoothness of the curve. This method solves for the coefficients of each piecewise polynomial by satisfying the constraints of accurate data point interpolation (the curve passes through all known points) and derivative continuity, thereby achieving accurate estimation of data at unknown points. This method is particularly suitable for critical instability databases of lattice columns, based on finite simulation experiment data. To supplement the deformation, internal forces, and safety factors under intermediate load combinations and ensure data continuity and smooth transition, the Rickin interpolation method, based on regionalized variable theory, quantifies the variability of spatial data distribution by analyzing the spatial correlation of known data points and constructing a semi-variogram model. Then, based on the spatial distance and correlation weights between the unknown point and all known points, a weighted estimate of the unknown point is calculated. This method not only provides interpolation results but also assesses estimation errors. It is suitable for lattice column databases, accurately supplementing data gaps when there are spatial distribution differences in monitoring data corresponding to different load parameters (vertical load, horizontal load, and eccentricity), while ensuring the reliability of data estimation. This provides high-quality data support for subsequent safety factor assessments. In large-sample simulation experiments…The vertical load step size was adjusted to 4kN (33 gradients in total), the horizontal load step size was adjusted to 3kN (32 gradients in total), and the eccentricity step size was adjusted to 5mm (40 gradients in total), generating 33×32×40=42240 sets of basic data, thus obtaining the critical instability database for lattice columns;

[0042] Step Two: On-site Monitoring Deployment and Data Acquisition. Sensors are strategically deployed at key monitoring points. A multi-channel synchronous data acquisition unit (MCU) collects data from each sensor in real time. The MCU achieves synchronized data acquisition from multiple sensors through hardware clock synchronization and parallel signal acquisition. Its principle is as follows: a built-in high-precision crystal oscillator generates a unified reference clock signal, which is then distributed to all acquisition channels, ensuring that each channel triggers data sampling at the same time. Simultaneously, for output signals from different types of sensors such as laser displacement gauges and fiber optic strain gauges (e.g., analog voltage signals, digital pulse signals), the built-in signal conditioning modules (e.g., amplification, filtering, analog-to-digital conversion) convert the signals into digital signals, which are then transmitted to the core processor via a parallel data bus. This avoids the time delay caused by single-channel serial acquisition, ensuring the stability of the lattice column. Consistency of multi-dimensional data such as key area deformation and internal force in the time dimension lays the foundation for subsequent data correlation analysis. During the data acquisition process, the acquisition instrument also improves data reliability through real-time verification and caching mechanisms: on the one hand, it performs real-time verification on the digital signals acquired by each channel (such as verifying data integrity and whether the signal amplitude is within a reasonable range). If an anomaly is found (such as signal loss caused by sensor disconnection), it immediately marks and triggers a retry acquisition. On the other hand, it temporarily stores the synchronously acquired digital data in a high-speed cache area. After a collection cycle is completed, it is then transmitted in batches to the cloud data processing platform via Ethernet to avoid packet loss or errors during data transmission. This ensures that subsequent preprocessing steps such as 3σ criterion outlier removal and wind load correction can be carried out based on complete and synchronous original data, and data preprocessing is performed before being transmitted to the cloud data processing platform.

[0043] Step 3: Data Inversion and Safety Assessment. The preprocessed deformation parameters and internal force parameters of the monitoring points of the lattice column are imported into the constructed critical instability database of the lattice column. The current stress state of the lattice column is determined by preliminary screening and precise matching. The average value of the three candidate stress state parameters with the smallest spatial distance is taken as the current three-dimensional stress state of the lattice column. Based on the current stress state, the corresponding safety factor is matched in the critical instability database, and the safety state of the lattice column is dynamically assessed.

[0044] Specifically, the determination of the three-dimensional core load parameters and test thresholds in step one includes setting vertical load, horizontal load, and eccentricity as three-dimensional load parameters. The vertical load range covers construction conditions from no load to full load, the horizontal load range covers additional loads under light to strong wind conditions, and the eccentricity range covers the eccentricity caused by the offset of the lattice column. The foundation test values ​​for the vertical load under construction conditions of no load, normal construction, and full load are set to 80kN, 110kN, and 140kN, respectively, with a foundation test step size of 10kN. The foundation test values ​​for the horizontal load under light wind, moderate wind, and strong wind conditions are set to 20kN, 50kN, and 80kN, respectively, with a foundation test step size of 5kN. The foundation test values ​​for eccentricity under no eccentricity, slight offset, moderate offset, and significant offset are set to 0mm, 50mm, 100mm, and 150mm, respectively, with a foundation test step size of 10mm.

[0045] Furthermore, when collecting lateral deflection, vertical displacement, column axial force, and strain data in step one, the load is determined to be the critical instability load under the corresponding load combination if any of the following critical conditions are met:

[0046] When the lateral deflection increment is greater than 0.4 mm for three consecutive 1-second sampling periods;

[0047] The fluctuation amplitude of the column axial force exceeds 8% of the current stable axial force value, and the duration is >4s;

[0048] The strain value exceeds 85% of the yield strain of the lattice column material;

[0049] The vertical displacement change within a single sampling period is greater than 0.3 mm.

[0050] Specifically, the establishment of the three-dimensional numerical simulation model of the lattice column in step one includes using the material properties of the lattice column measured in indoor tests. A total of 120 sets of indoor orthogonal critical instability tests can be carried out, including 48 sets of basic tests and 72 sets of extended tests. During the tests, displacement gauges with an accuracy of ±0.04mm and strain gauges with a sampling frequency of 60Hz are used to collect data, and the failure modes of the lattice column under different load combinations are recorded. The material properties of the lattice column include elastic modulus, Poisson's ratio, and yield strength. The numerical simulation model is corrected through mesh refinement adjustment, boundary condition correction, and model optimization of component connections. Mesh refinement adjustment specifically involves adjusting the mesh size of the connection nodes between the lattice column and the cantilever beam, and the middle and lower stress concentration sections, from the initial 12mm to 4-6mm, while maintaining the mesh size of non-critical areas at 8-10mm. Component connection model optimization specifically involves adjusting the initial rigid connection model to a flexible connection model that considers bolt preload and contact surface friction coefficient, so that the strain distribution at the connection is similar to that in the indoor tests. The agreement between the measured values ​​and the actual values ​​is >88%. The mesh refinement adjustment needs to be carried out by densifying the connection nodes between the lattice column and the cantilever beam and the stress concentration section in the middle and lower part. The large-sample simulation test in step one is carried out by high-performance computing equipment, which is a parallel workstation with 80 cores and 384GB of memory. Each data record in the critical instability database of the lattice column in step one contains three-dimensional load parameter values, lattice column deformation parameters, internal force parameters and safety factor. The lattice column deformation parameters include lateral deflection and vertical displacement, and the internal force parameters include column axial force and strain. The safety factor is defined as the ratio of the ultimate bearing capacity of the lattice column to the current load. It is stipulated that a safety factor >1.0 is a stable state, a safety factor <1.0 is an unstable state, and a safety factor =1.0 is a critical instability state. Based on all data points with a safety factor =1.0 in the critical instability database, a three-dimensional space curve fitting algorithm is used to fit and generate a critical instability state envelope that can divide the stable and unstable regions of the lattice column.

[0051] Specifically, in step two, the differentiated sensor deployment at key monitoring points includes the cantilever beam connection nodes and the lower and middle stress concentration sections. The deployed sensors include laser displacement gauges, fiber optic strain gauges, inclinometers, accelerometers, and wind speed sensors. Laser displacement gauges are deployed at the top, middle, and bottom of the lattice columns, and beside the connection nodes. These gauges monitor lateral deflection and vertical displacement in key areas. Fiber optic strain gauges are deployed in sets every 1.2m along the column height, with each set consisting of two strain gauges. Each set of fiber optic strain gauges is deployed on two orthogonal sides of the column, with denser deployment near the connection nodes and in the lower and middle stress concentration sections. The spacing between each set is adjusted to 0.8m. Fiber optic strain gauges are used to collect strain data in key areas to calculate the axial force of the column. Inclinometers are deployed at the top of the lattice column to monitor the overall tilt angle. Accelerometers are deployed in the middle of the lattice column at the collision-prone parts to identify construction disturbances. Wind speed sensors are deployed on the unobstructed side of the top of the lattice column to collect on-site wind load data.

[0052] Furthermore, step two also includes:

[0053] Construction disturbance identification is based on acceleration sensor data. When the instantaneous acceleration value is detected to the set value, it is determined to be a construction collision disturbance. The safety assessment of deformation and internal force data during this period is suspended until the acceleration value returns to within the base value and the duration is greater than the set duration, and then the assessment is restarted.

[0054] Wind load impact correction is performed based on wind speed sensor data. When the real-time wind speed is set, the monitored lateral deflection data is corrected according to the wind speed value and the wind load conversion model. The wind load conversion model is established based on the formulas for wind load calculation in GB50009-2012 "Code for Design of Building Structures". The formulas involved in the wind load conversion model include:

[0055]

[0056] in, This is the standard value of wind load. For height The wind vibration coefficient at the location, This is the wind load shape coefficient. For height The wind pressure height variation coefficient at that location. As the basic wind pressure, based on the standard specifications, the measured wind speed on site is converted into a quantifiable wind load value, providing a scientific basis for correcting the monitoring data of the lattice column. In the formula, Based on meteorological data of the project site, and The effects of height and lattice column structure form on wind load are considered separately. Reflecting the dynamic fluctuation characteristics of wind load, this formula calculates the actual wind load acting on the lattice column, which can accurately isolate the additional deformation (such as lateral deflection) caused by wind load in the monitoring data, eliminate environmental wind interference, ensure that the subsequent safety factor assessment is based on the actual stress and deformation data of the lattice column, and improve the monitoring accuracy.

[0057] Furthermore, the deformation and internal force parameters in step three are lateral deflection, vertical displacement, and column axial force, respectively. The preliminary screening in step three specifically includes eliminating stress states whose deviation from the current monitoring parameter range exceeds 20% based on the deformation and internal force parameter ranges corresponding to each stress state in the critical instability database of lattice columns. The precise matching in step three specifically includes calculating the spatial distance between the current monitoring parameters and the corresponding parameters of the candidate stress states. This spatial distance is calculated using the Euclidean distance method. The principle of the Euclidean distance method is that in n-dimensional space, by calculating the sum of the squares of the differences in the corresponding dimensional coordinates of two data points, and then taking the arithmetic square root of the sum, we obtain... The straight-line distance between two points is used to quantify the similarity of data points. In the inversion of the stress state of latticed columns, this method uses the currently monitored deformation parameters (lateral deflection, vertical displacement) and internal force parameters (column axial force) as monitoring points in three-dimensional space, and the parameters corresponding to each stress state in the critical instability database as candidate points. By calculating the Euclidean distance between the two, the smaller the distance, the closer the stress states are, providing a quantitative basis for subsequent accurate matching of the current stress state. The method quickly finds the 6 candidate stress states closest to the current monitoring parameters, and simultaneously performs spatial segmentation on the candidate stress state set. In the preliminary screening step, the criterion for a deviation exceeding 20% ​​is: |(current monitoring parameter value - median value of database parameter range) / median value of database parameter range| > 20%. In the accurate matching step, when calculating the Euclidean distance, lateral deflection, vertical displacement, and column axial force are assigned weight coefficients of 0.4, 0.3, and 0.3, respectively, and the weighted Euclidean distance is calculated.

[0058] Specifically, step three, the dynamic assessment of the safety status of the lattice column, includes:

[0059] When the safety factor is ≥1.6, the lattice column is determined to be in a completely safe state, and the regular monitoring frequency is maintained;

[0060] When 1.4 ≤ safety factor < 1.6, the lattice column is determined to be in a state of mild concern, triggering a blue alert, adjusting the monitoring frequency, and checking whether there are any illegal loads around the lattice column.

[0061] When 1.2 ≤ safety factor < 1.4, the lattice column is determined to be in a mild warning state, triggering a yellow warning, increasing the monitoring frequency, and inspecting the tightness of the bolts at the connection nodes of the lattice column;

[0062] When 1.0 ≤ safety factor < 1.2, the lattice column is determined to be in a moderate warning state, triggering an orange warning. Construction work related to the lattice column is immediately suspended, and additional sensors are deployed at key monitoring points.

[0063] When the safety factor is less than 1.0, the lattice column is determined to be in a severe warning state, triggering a red alert. The emergency response plan is immediately activated, and the personnel are evacuated to a safe area. Temporary reinforcement measures, such as setting up diagonal supports and increasing counterweights, are adopted to reinforce the lattice column. In the temporary reinforcement measures, the diagonal supports are made of Q355 steel with a cross-sectional dimension of not less than 100×100mm. Both ends of the supports are reliably connected to the middle of the lattice column and the main structure of the building, respectively. The added counterweights are sandbags, with a single sandbag weighing 25±1kg. The counterweights are placed away from the areas where the stress of the lattice column is concentrated.

[0064] Furthermore, step three also includes continuous monitoring of changes in the safety factor of the lattice column after the early warning response:

[0065] If the safety factor rises to the threshold for lifting the corresponding warning level, then the frequency of normal construction and monitoring will be gradually restored.

[0066] If the safety factor does not recover or continues to decline, the handling measures need to be optimized and reassessed. At the same time, the monitoring data, stress state inversion results, handling measures, and safety factor change trends should be fed back to the critical instability database of the lattice column and the database should be updated. Specifically, the updated database includes classifying the fed-back monitoring data and stress state data according to load parameter intervals and adding them to the database records of the corresponding intervals. When the number of records for a certain load parameter interval exceeds the set number, the local critical instability envelope of that interval should be refitted. Based on the feedback data, the parameters of the numerical simulation model should be further corrected. Specifically, the numerical simulation model correction includes adjusting the elastic modulus of the material and the constraint stiffness of the boundary conditions in the model based on the feedback data, so that the error between the safety factor calculated by the model and the safety factor inverted by the actual monitoring is less than 5%.

[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold, characterized in that: The specific steps include the following: Step 1: Construct a critical instability database for lattice columns to provide a basis for monitoring data linkage to critical states. Combine the actual stress scenarios of lattice columns to determine the three-dimensional core load parameters and test thresholds. Conduct indoor orthogonal critical instability tests based on the three-dimensional load parameters. Collect lateral deflection, vertical displacement, column axial force and strain data of lattice columns under different load combinations. Establish a three-dimensional numerical simulation model of lattice columns. Use the corrected and qualified numerical simulation model to conduct large-sample simulation tests, generate basic data, and obtain the critical instability database for lattice columns. Step 2: On-site monitoring deployment and data acquisition. Sensors are deployed differently for key monitoring points. Data from each sensor is collected in real time using a multi-channel synchronous data acquisition instrument, and the data is preprocessed and transmitted to the cloud data processing platform. Step 3: Data Inversion and Safety Assessment. The preprocessed deformation parameters and internal force parameters of the monitoring points of the lattice column are imported into the constructed critical instability database of the lattice column. The current stress state of the lattice column is determined by preliminary screening and precise matching. The average value of the three candidate stress state parameters with the smallest spatial distance is taken as the current three-dimensional stress state of the lattice column. Based on the current stress state, the corresponding safety factor is matched in the critical instability database, and the safety state of the lattice column is dynamically assessed.

2. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: The determination of the three-dimensional core load parameters and test thresholds in step one specifically includes setting vertical load, horizontal load, and eccentricity as three-dimensional load parameters. The vertical load range covers construction conditions from no load to full load, the horizontal load range covers additional loads under light to strong wind conditions, and the eccentricity range covers eccentricity caused by the offset of the lattice column. The foundation test values ​​for the vertical load under construction conditions of no load, normal construction, and full load are set to 80kN, 110kN, and 140kN, respectively, with a foundation test step size of 10kN. The foundation test values ​​for the horizontal load under light wind, moderate wind, and strong wind conditions are set to 20kN, 50kN, and 80kN, respectively, with a foundation test step size of 5kN. The foundation test values ​​for the eccentricity of no eccentricity, slight offset, moderate offset, and significant offset are set to 0mm, 50mm, 100mm, and 150mm, respectively, with a foundation test step size of 10mm.

3. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: When collecting lateral deflection, vertical displacement, column axial force, and strain data in step one, the load is determined to be the critical instability load under the corresponding load combination if any of the following critical conditions are met: When the lateral deflection increment is greater than 0.4 mm for three consecutive 1-second sampling periods; The fluctuation amplitude of the column axial force exceeds 8% of the current stable axial force value, and the duration is >4s; The strain value exceeds 85% of the yield strain of the lattice column material; The vertical displacement change within a single sampling period is greater than 0.3 mm.

4. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: The establishment of the three-dimensional numerical simulation model of the lattice column in step one includes using the material properties of the lattice column measured in indoor tests. These material properties include elastic modulus, Poisson's ratio, and yield strength. The numerical simulation model is then corrected through mesh refinement, boundary condition correction, and model optimization at component connections. The mesh refinement specifically targets the connection nodes between the lattice column and the cantilever beam, as well as the lower-middle stress concentration areas. The large-sample simulation test in step one is conducted using high-performance computing equipment. Each data record in the critical instability database of the lattice column in step one contains three-dimensional load parameters. The data includes numerical values, deformation parameters of the lattice column, internal force parameters, and a safety factor. The deformation parameters of the lattice column include lateral deflection and vertical displacement. The internal force parameters include column axial force and strain. The safety factor is defined as the ratio of the ultimate bearing capacity of the lattice column to the current load. It is stipulated that a safety factor > 1.0 indicates a stable state, a safety factor < 1.0 indicates an unstable state, and a safety factor = 1.0 indicates a critical unstable state. Based on all data points with a safety factor = 1.0 in the critical unstable database, a three-dimensional spatial curve fitting algorithm is used to fit and generate a critical unstable state envelope that can divide the stable and unstable regions of the lattice column.

5. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: The monitoring points for differentiated sensor deployment at key monitoring locations in step two include the cantilever beam connection nodes and the lower and middle stress concentration sections. The deployed sensors include laser displacement gauges, fiber optic strain gauges, inclinometers, accelerometers, and wind speed sensors. The laser displacement gauges are deployed at the top, middle, and bottom of the lattice column, and beside the connection nodes. These laser displacement gauges are used to monitor the lateral deflection and vertical displacement of the key areas. The fiber optic strain gauges are deployed in groups of two every 1.2m along the height of the lattice column. The fiber optic strain gauges are installed on the two orthogonal sides of the column, with denser spacing near the connection nodes and in the lower and middle stress concentration sections. The spacing between each group is adjusted to 0.8m. The fiber optic strain gauges are used to collect strain data in key areas to calculate the axial force of the column. The inclinometers are installed at the top of the lattice column to monitor the overall tilt angle. The accelerometers are installed in the middle of the lattice column where collisions are likely to occur. The accelerometers are used to identify construction disturbances. The wind speed sensors are installed on the unobstructed side of the top of the lattice column to collect on-site wind load data.

6. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: Step two also includes: Construction disturbance identification is based on acceleration sensor data. When the instantaneous acceleration value is detected to the set value, it is determined to be a construction collision disturbance. The safety assessment of deformation and internal force data during this period is suspended until the acceleration value returns to within the base value and the duration is greater than the set duration, and then the assessment is restarted. Wind load impact correction is performed based on wind speed sensor data. When the real-time wind speed is set to a certain value, the monitored lateral deflection data is corrected according to the wind speed value and the wind load conversion model. The wind load conversion model involves formulas including: in, This is the standard value of wind load. For height The wind vibration coefficient at the location, This is the wind load shape coefficient. For height The wind pressure height variation coefficient at that location, This is the basic wind pressure.

7. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: The deformation parameters and internal force parameters in step three are lateral deflection, vertical displacement, and column axial force, respectively. The preliminary screening in step three specifically includes eliminating stress states that deviate from the current monitoring parameter range by more than 20% based on the range of deformation and internal force parameters corresponding to each stress state in the critical instability database of lattice columns. The precise matching in step three specifically includes calculating the spatial distance between the current monitoring parameters and the corresponding parameters of the candidate stress states, quickly finding the 6 candidate stress states that are closest to the current monitoring parameters, and simultaneously spatially segmenting the set of candidate stress states.

8. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: The dynamic assessment of the safety status of the lattice column in step three specifically includes: When the safety factor is ≥1.6, the lattice column is determined to be in a completely safe state, and the regular monitoring frequency is maintained; When 1.4 ≤ safety factor < 1.6, the lattice column is determined to be in a state of mild concern, triggering a blue alert, adjusting the monitoring frequency, and checking whether there are any illegal loads around the lattice column. When 1.2 ≤ safety factor < 1.4, the lattice column is determined to be in a mild warning state, triggering a yellow warning, increasing the monitoring frequency, and inspecting the tightness of the bolts at the connection nodes of the lattice column; When 1.0 ≤ safety factor < 1.2, the lattice column is determined to be in a moderate warning state, triggering an orange warning. Construction work related to the lattice column is immediately suspended, and additional sensors are deployed at key monitoring points. When the safety factor is less than 1.0, the lattice column is determined to be in a severe warning state, triggering a red alert. The emergency response plan is immediately activated, and the personnel are evacuated to a safe area. Temporary reinforcement measures, such as setting up diagonal supports and increasing counterweight balance, are adopted to reinforce the lattice column.

9. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 1, characterized in that: Step three also includes continuous monitoring of changes in the safety factor of the lattice column after the early warning response: If the safety factor rises to the threshold for lifting the corresponding warning level, then the frequency of normal construction and monitoring will be gradually restored. If the safety factor does not recover or continues to decline, the measures need to be optimized and reassessed. At the same time, the monitoring data, stress state inversion results, measures, and safety factor change trends should be fed back to the critical instability database of the lattice column to update the database. Based on the feedback data, the parameters of the numerical simulation model should be further corrected.

10. The intelligent monitoring method for the attached lattice columns of a cantilevered climbing scaffold according to claim 9, characterized in that: The database update specifically includes classifying the feedback monitoring data and stress state data according to load parameter intervals and supplementing them into the database records of the corresponding intervals. When the number of records for a certain load parameter interval exceeds a set number, the local critical instability envelope of that interval is refitted. The numerical simulation model correction specifically includes adjusting the elastic modulus of the material and the constraint stiffness of the boundary conditions in the model based on the feedback data, so that the error between the safety factor calculated by the model and the safety factor inverted from the actual monitoring is less than 5%.

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