Settlement monitoring method for pre-pressing construction of hanging basket

Abstract

The invention relates to the technical field of construction monitoring, in particular to a settlement monitoring method for hanging basket pre-pressing construction, and the method comprises the steps: dividing a monitoring region into a plurality of sub-monitoring regions, clustering load data, determining a load influence coefficient, a time-varying connection coefficient and a node stiffness coefficient, and calculating an abnormal settlement characterization value. The settlement tendency of each sub-monitoring area is judged; for the sub-monitoring area with the abnormal settlement tendency, acquiring temperature data to determine a temperature influence characteristic value, acquiring swing data to determine a swing influence characteristic value, and calculating a settlement influence parameter in combination with the abnormal settlement characterization value to adjust the settlement monitoring density and frequency; in response to the fact that the settlement influence parameter is larger than a settlement influence parameter threshold value, the settlement monitoring density and the monitoring frequency are improved. According to the invention, the monitoring density and frequency of different monitoring areas are adjusted in real time by carrying out pre-area division and targeted settlement monitoring on the monitoring areas, data generation is reduced, and the monitoring efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of construction monitoring technology, and in particular to a settlement monitoring method for preloading construction using hanging baskets. Background Technology

[0002] As a crucial safety control step in the cantilever construction of long-span bridges, the preloading of formwork is directly related to the verification of structural stability, control of elastic deformation, and construction safety through settlement monitoring. Traditional methods have significant limitations: the welded reaction frame structure has poor versatility; concentrated loading by jacks can easily induce localized damage; and manual leveling is greatly affected by environmental disturbances and cannot distinguish between elastic and inelastic deformations in real time. With technological advancements, settlement monitoring methods have evolved from traditional approaches such as surcharge preloading and jack counterloading to innovative methods such as automated load control systems, high-precision deformation monitoring technologies, and Internet of Things (IoT) integrated platforms, achieving significant progress. However, key technical challenges such as interference from complex construction environments, effective separation of elastic and inelastic deformations, and achieving a balance between technology and economy still require continued research and development.

[0003] Chinese Patent Publication No. CN118310478A discloses a method and system for monitoring bridge construction settlement. The method includes: during the construction of bridge piers, setting up laser rangefinders at a first preset position on each pier; setting up a reference laser rangefinder at a second preset position next to the first pier; determining a first distance, a first pitch angle, and a first yaw angle between the reference laser rangefinder and the laser rangefinder; determining a second distance, a second pitch angle, and a second yaw angle between the reference laser rangefinder and the laser rangefinder; determining a pier settlement score; setting up multiple third preset positions at the bottom of the bridge deck; determining a third distance, a third pitch angle, and a third yaw angle between the laser rangefinders; determining a bridge deck settlement score; and determining a bridge settlement score based on the pier settlement score and the bridge deck settlement score. According to this invention, the settlement of piers and the bridge deck can be monitored separately, comprehensively assessing the stability of the bridge structure, and the monitoring cost is low.

[0004] Chinese Patent Publication No. CN107255466A discloses a settlement monitoring method for hanging basket construction. The method includes: processing multiple tubular fiber Bragg grating monitoring components, each with multiple fiber Bragg gratings connected to a demodulator; after the construction of multiple piers, determining the number of tubular fiber Bragg grating monitoring components between adjacent piers based on their spacing; constructing a reference bridge block on each pier, and constructing connecting bridge blocks on both sides of the reference bridge block, with multiple connecting bridge blocks on the same side of the pier forming a bridge; simultaneously installing tubular fiber Bragg grating monitoring components along the construction direction to monitor the settlement of each bridge, until the connecting bridge blocks on adjacent piers are closed; and when constructing connecting bridge blocks symmetrical about the piers, analyzing the causes and adjusting the construction based on the settlement values ​​ω1 and ω2 of the bridge on one side of the pier before proceeding with further construction. This invention solves the problem of real-time settlement monitoring in hanging basket construction.

[0005] However, the following problems still exist in the existing technology.

[0006] When conducting settlement monitoring during preloading construction using hanging baskets, most methods involve directly deploying high-density sensors or using complex algorithms for analysis. The resulting massive amounts of monitoring data require complex analysis and processing, leading to significant redundancy in computing resources and consequently reducing the computational efficiency and real-time performance of settlement monitoring. Summary of the Invention

[0007] To address this issue, the present invention provides a settlement monitoring method for preloading construction using hanging baskets, which solves the problem that settlement monitoring during preloading construction using hanging baskets is mostly achieved by directly deploying high-density sensors or using complex algorithms for analysis. This results in massive amounts of monitoring data that require complex analysis and processing, leading to significant redundancy in computing resources and thus reducing the computational efficiency and real-time performance of settlement monitoring.

[0008] To achieve the above objectives, the present invention provides a settlement monitoring method for preloading construction using hanging baskets, comprising:

[0009] The preload points and non-smooth continuous sections within the monitoring area are obtained to divide the monitoring area into several sub-monitoring areas, and the load data and preload force data of each sub-monitoring area are obtained.

[0010] Cluster the load data to determine the load influence coefficient, determine the node stiffness coefficient based on the preload data, calculate the abnormal settlement characterization value by combining the load influence coefficient and the node stiffness coefficient, and determine the settlement tendency of each of the sub-monitoring areas.

[0011] For sub-monitoring areas with abnormal settlement tendency, temperature data is acquired to determine temperature influence characteristic values, and oscillation data is acquired to determine oscillation influence characteristic values. Settlement influence parameters are calculated in combination with the abnormal settlement characterization values ​​in order to adjust the settlement monitoring density and frequency.

[0012] In response to the settlement impact parameter being greater than the settlement impact parameter threshold, the settlement monitoring density and the monitoring frequency are increased;

[0013] The non-smooth continuous segment refers to the design engineering segment with protrusions or depressions during construction. The temperature data includes high-temperature surface data and low-temperature surface data. The oscillation data includes wind speed, windward area, and hanging basket height.

[0014] Furthermore, the process of dividing the monitoring area into several sub-monitoring areas includes,

[0015] Obtain a three-dimensional map of the monitoring area and label the three-dimensional coordinates of each pre-compression point and the non-smooth continuous segment;

[0016] The monitoring area is divided according to the non-smooth continuous segment;

[0017] The pre-pressure points in each area after division are determined so that each area is divided into several sub-monitoring areas.

[0018] Furthermore, the process of clustering the load data to determine the load influence coefficient includes,

[0019] Determine the number of effective load data in the sub-monitoring area;

[0020] The ratio of the stated quantity to the total number of load data is determined as the load influence coefficient;

[0021] The effective load data refers to load values ​​that are greater than the preset load value.

[0022] Furthermore, the process of determining the node stiffness coefficient includes,

[0023] The resultant force is determined based on the preload of each of the sub-monitoring areas;

[0024] The ratio of the calculated resultant force to the running resultant force is determined as the nodal stiffness coefficient.

[0025] Furthermore, the process of calculating the abnormal settlement characterization value includes,

[0026] The ratio of the load influence coefficient to the reference load influence coefficient is determined as the load influence factor;

[0027] The ratio of the reference node stiffness coefficient to the node stiffness coefficient is determined as the constraint influence factor;

[0028] The weighted sum of the load influence factor and the constraint influence factor is determined to be the abnormal settlement characterization value.

[0029] Further, the determination of the settlement tendency of each of the sub-monitoring areas, wherein,

[0030] If the abnormal settlement characterization value is greater than the abnormal settlement characterization value threshold, then the settlement tendency is determined to be an abnormal settlement tendency.

[0031] If the abnormal settlement characterization value is less than or equal to the abnormal settlement characterization value threshold, then the settlement tendency is determined to be a normal settlement tendency.

[0032] Furthermore, the process of acquiring temperature data to determine the characteristic values ​​of temperature influence includes,

[0033] Temperature data of the sub-monitoring area is acquired to construct a two-phase temperature time-domain curve;

[0034] The ratio of the average difference between the corresponding values ​​of the two-phase temperature time-domain curves to the corresponding values ​​of the low-temperature time-domain curve is determined as the temperature influence characteristic value.

[0035] The two-phase temperature time-domain curves include a high-temperature time-domain curve and a low-temperature time-domain curve.

[0036] Furthermore, the process of acquiring oscillation data to determine the oscillation influence characteristic values ​​includes,

[0037] The ratio of the wind speed to the reference wind speed is determined as the wind speed influence factor;

[0038] The ratio of the windward area to the total area is determined as the area influence factor;

[0039] The ratio of the hanging basket height to the construction height is determined as the height influence factor;

[0040] The mean of the sum of the wind speed influence factor, the area influence factor, and the height influence factor is determined to be the swing influence characteristic value.

[0041] Furthermore, the process of calculating the settlement influence parameters includes,

[0042] The ratio of the temperature influence characteristic value to the reference temperature influence characteristic value is determined as the temperature influence factor;

[0043] The ratio of the swing influence characteristic value to the benchmark swing influence characteristic value is determined as the swing influence factor;

[0044] The ratio of the abnormal settlement characterization value to the benchmark abnormal settlement characterization value is determined as the settlement influence factor;

[0045] The weighted sum of the temperature influence factor, the oscillation influence factor, and the settlement influence factor is determined as the settlement influence parameter.

[0046] Furthermore, the adjustment of settlement monitoring density and frequency, wherein,

[0047] If the settlement impact parameter is greater than the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency are increased;

[0048] If the settlement impact parameter is less than or equal to the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency remain unchanged.

[0049] Compared with existing technologies, this invention divides the monitoring area into several sub-monitoring areas, clusters load data, determines the load influence coefficient, time-varying connection coefficient, and node stiffness coefficient, calculates abnormal settlement characterization values, and determines the settlement tendency of each sub-monitoring area. For sub-monitoring areas with abnormal settlement tendencies, temperature data is acquired to determine temperature influence characteristic values, and oscillation data is acquired to determine oscillation influence characteristic values. Settlement influence parameters are calculated based on the abnormal settlement characterization values ​​to adjust the settlement monitoring density and frequency. In response to the settlement influence parameter exceeding a threshold value, the settlement monitoring density and frequency are increased. This invention pre-divides the monitoring area, performs targeted settlement monitoring, and adjusts the monitoring density and frequency of different monitoring areas in real time, reducing data generation, minimizing computational power consumption, and improving monitoring efficiency.

[0050] In particular, considering the uneven settlement during preloading construction using hanging baskets, a three-dimensional map of the monitoring area is obtained in advance. This map is then analyzed to identify preloading points and non-smooth continuous sections within the monitoring area, dividing it into several sub-monitoring areas (such as abrupt changes in truss nodes, anchorage transition zones, and stiffness discontinuities). Based on this, sub-monitoring areas with independent deformation characteristics are further defined, providing a structured data foundation for subsequent regional settlement difference analysis. In practice, to achieve full coverage, a large-scale, high-density sensor network is typically deployed (e.g., ≥3 strain gauges or displacement sensors per square meter). However, high-density sensors generate massive amounts of data, leading to computational redundancy and reduced analysis efficiency. The settlement rate is significantly reduced. Based on this, the present invention considers pre-dividing several sub-monitoring areas. It is understood that the areas involved in the non-smooth continuous segments have geometric discontinuities, which cause the settlement rate of the area to be different from that of other areas, thus leading to uneven settlement. Furthermore, if there are several pre-compression points in the area, the uneven settlement phenomenon will be amplified. Therefore, the present invention divides the monitoring areas into sub-monitoring areas by analyzing the pre-compression points and non-smooth continuous segments in the monitoring area, realizing targeted monitoring design, providing a calculation basis for subsequent determination of sub-monitoring areas with abnormal settlement tendencies, reducing data generation, reducing computing power consumption, and improving monitoring efficiency.

[0051] In particular, addressing the abnormal settlement problem caused by the coupling of spatial torsional effects and node stiffness attenuation, this invention clusters the loads in the sub-monitoring area to determine the load influence coefficient and calculates the abnormal settlement characterization value in conjunction with the node stiffness coefficient. In reality, the monitoring area bears vertical loads (self-weight, construction load, preload), resulting in significant torque. This torsional effect leads to uneven distribution of support reactions or temporary support reactions inside and outside the construction monitoring area, causing abnormal settlement. Furthermore, sufficient preload can ensure the stiffness of each connection node. If there is insufficient preload, the settlement monitoring points will exhibit excessive, nonlinear, or unstable settlement values. Especially when the load increases, if the preload is insufficient, the settlement in this area may be significantly greater than expected. Based on this, this invention considers analyzing the load data and preload data of the sub-monitoring area to determine the abnormal settlement characterization value, thereby determining the settlement tendency of the sub-monitoring area. This provides a data basis for subsequently determining and adjusting the monitoring density and frequency, reducing data generation, reducing computational consumption, and improving monitoring efficiency.

[0052] In particular, for situations where settlement phenomena in sub-monitoring areas catalyzed by external environmental factors leading to abnormal settlement tendencies may affect construction, this invention calculates settlement impact parameters by determining temperature and oscillation impact characteristic values. This provides data and theoretical basis for adjusting settlement monitoring density and frequency. In reality, uneven sunlight distribution exists, leading to uneven heating in the monitoring area. Since construction materials are mostly steel and concrete, this uneven heating exacerbates temperature differences, resulting in different settlement phenomena in high-temperature and low-temperature areas. Furthermore, ambient wind causes the hanging basket to sway, and the greater the wind speed, the larger the windward area, and the higher the hanging basket, the greater the wind impact. The coupling of these factors further exacerbates uneven settlement on-site, affecting construction. Based on this, this invention considers calculating settlement impact parameters to analyze whether uneven settlement in sub-monitoring areas will affect the construction process, thereby adjusting settlement monitoring density and frequency, reducing data generation, minimizing computational consumption, improving monitoring efficiency, and ultimately improving construction efficiency. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the steps of a settlement monitoring method for preloading construction using hanging baskets, as described in an embodiment of the invention.

[0054] Figure 2 A logic block diagram for determining the settlement tendency of each of the sub-monitoring areas in an embodiment of the invention;

[0055] Figure 3 This is a logic block diagram illustrating the adjustment of settlement monitoring density and frequency in an embodiment of the invention. Detailed Implementation

[0056] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0057] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0058] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of a settlement monitoring method for preloading construction using hanging baskets, according to an embodiment of the invention. The settlement monitoring method for preloading construction using hanging baskets of the present invention includes:

[0059] Step S1: Obtain the preload points and non-smooth continuous segments within the monitoring area to divide the monitoring area into several sub-monitoring areas, and obtain the load data and preload data of each sub-monitoring area.

[0060] Step S2: Cluster the load data to determine the load influence coefficient, determine the node stiffness coefficient based on the preload data, calculate the abnormal settlement characterization value by combining the load influence coefficient and the node stiffness coefficient, and determine the settlement tendency of each sub-monitoring area.

[0061] Step S3: For the sub-monitoring area with abnormal settlement tendency, acquire temperature data to determine the temperature influence characteristic value, acquire oscillation data to determine the oscillation influence characteristic value, and calculate the settlement influence parameter in combination with the abnormal settlement characterization value to adjust the settlement monitoring density and frequency.

[0062] Step S4: In response to the settlement impact parameter being greater than the settlement impact parameter threshold, increase the settlement monitoring density and the monitoring frequency;

[0063] The non-smooth continuous segment refers to the design engineering segment with protrusions or depressions during construction. The temperature data includes high-temperature surface data and low-temperature surface data. The oscillation data includes wind speed, windward area, and hanging basket height.

[0064] Specifically, there are no restrictions on the methods for obtaining preloading points and non-smooth continuous sections. For example, they can be accurately extracted based on construction design drawings (including but not limited to structural details, reinforcement drawings, and settlement control point layout drawings). Of course, they can also be obtained based on real-time monitoring data at the construction site, feedback from geological survey reports, or construction process flow. This flexibility is intended to adapt to the actual needs of different engineering scenarios. The specific acquisition logic can be determined according to the specific conditions of the project, which will not be elaborated here.

[0065] Specifically, there are no restrictions on the method of obtaining load data. For example, theoretical design values ​​can be extracted from structural design documents (such as load calculation sheets, live load layout diagrams, and equipment load lists), or load data can be obtained from real-time monitoring during construction (such as strain gauge and pressure sensor readings). Of course, other methods can also be used, as long as they are reasonable. This will not be elaborated further.

[0066] Specifically, there are no restrictions on the method of obtaining prestressing force data. For example, theoretical target values ​​can be extracted from prestressing-specific design drawings and tensioning control parameters (such as design tension force and elongation limits). Alternatively, prestressing force data can be obtained from real-time measurement data of tensioning equipment during construction (such as jack pressure gauge readings, force values ​​or elongation fed back by sensors). Of course, the specific method can be determined flexibly according to the project's specific circumstances, available equipment, and technical conditions, as long as it ensures that prestressing force data can be obtained. This will not be elaborated further.

[0067] Specifically, there are no restrictions on the method of acquiring temperature data. For example, the temperature of the sub-monitoring area can be detected by infrared thermometers. Of course, other methods can also be used, as long as the temperature data can be acquired. This will not be elaborated further.

[0068] Specifically, the process of dividing the monitoring area into several sub-monitoring areas includes,

[0069] Obtain a three-dimensional map of the monitoring area and label the three-dimensional coordinates of each pre-compression point and the non-smooth continuous segment;

[0070] The monitoring area is divided according to the non-smooth continuous segment;

[0071] The pre-pressure points in each area after division are determined so that each area is divided into several sub-monitoring areas.

[0072] Specifically, there are no restrictions on the method of obtaining 3D drawings. They can be dynamically selected based on the project stage, data foundation, and technical conditions. For example, in the forward design stage, they can be directly generated by converting native CAD / BIM engineering drawings (such as 2D DWG blueprints and 3D Revit parametric models). Of course, they can also be obtained through other methods, as long as they are reasonable. This will not be elaborated further.

[0073] It is understandable that dividing the monitoring area into sub-monitoring areas using existing data avoids the need for multiple algorithms to divide the area, thereby reducing computing power consumption and improving monitoring efficiency.

[0074] Specifically, the steps for dividing the monitoring area based on the non-smooth continuous segments are as follows:

[0075] Define a rule area;

[0076] This rule region can completely enclose non-smooth continuous segments.

[0077] Specifically, the steps for dividing the area into several sub-monitoring zones based on the pre-compression point are as follows:

[0078] Identify the areas within the rule area that contain preloading points, and divide the areas with preloading points into individual areas;

[0079] Sub-monitoring areas include individual areas and other areas.

[0080] It is understandable that, in practice, the shape of the sub-monitoring area is determined to be a regular cube for ease of calculation. Of course, those skilled in the art can also determine it according to the actual situation, which will not be elaborated here.

[0081] Specifically, considering the uneven settlement during preloading construction using hanging baskets, a three-dimensional map of the monitoring area is pre-obtained. This map analyzes the preloading points and non-smooth continuous sections within the monitoring area to divide it into several sub-monitoring areas (such as abrupt changes in truss nodes, anchorage transition zones, and stiffness discontinuities). Based on this, sub-monitoring areas with independent deformation characteristics are further divided, providing a structured data foundation for subsequent regional settlement difference analysis. In practice, to achieve full coverage, a large-scale, high-density sensor network is typically deployed (e.g., ≥3 strain gauges or displacement sensors per square meter). However, high-density sensors generate massive amounts of data, leading to computational redundancy and analysis challenges. Efficiency is significantly reduced. Based on this, the present invention considers pre-dividing several sub-monitoring areas. It is understood that the areas involved in the non-smooth continuous segments have geometric discontinuities, resulting in different settlement velocities between these areas and other areas, thus leading to uneven settlement. Furthermore, if there are several pre-compression points in the area, the uneven settlement phenomenon will be amplified. Therefore, the present invention divides the monitoring areas into sub-monitoring areas by analyzing the pre-compression points and non-smooth continuous segments within the monitoring area, realizing targeted monitoring design. This provides a calculation basis for subsequently determining the sub-monitoring areas with abnormal settlement tendencies, reducing data generation, reducing computing power consumption, and improving monitoring efficiency.

[0082] Specifically, the process of clustering the load data to determine the load influence coefficient includes,

[0083] Determine the number of effective load data in the sub-monitoring area;

[0084] The ratio of the stated quantity to the total number of load data is determined as the load influence coefficient;

[0085] The effective load data refers to load values ​​that are greater than the preset load value.

[0086] Specifically, the preset load value is a pre-calculated value. Several load values ​​that cause settlement during construction are obtained in advance, and the average value of each load value is determined as the preset load value.

[0087] Specifically, the process of determining the nodal stiffness coefficients includes,

[0088] The resultant force is determined based on the preload of each of the sub-monitoring areas;

[0089] The ratio of the calculated resultant force to the running resultant force is determined as the nodal stiffness coefficient.

[0090] Specifically, the sum of the preload forces is determined to be the resultant force.

[0091] Specifically, the running resultant force is calculated in advance, and the total preload of the monitoring area is determined during several construction processes to determine several total resultant forces. The average value of each total resultant force is then determined as the running resultant force.

[0092] Specifically, the process of calculating the abnormal settlement characterization value includes,

[0093] The ratio of the load influence coefficient to the reference load influence coefficient is determined as the load influence factor;

[0094] The ratio of the reference node stiffness coefficient to the node stiffness coefficient is determined as the constraint influence factor;

[0095] The weighted sum of the load influence factor and the constraint influence factor is determined to be the abnormal settlement characterization value.

[0096] Specifically, the reference load influence coefficient is calculated in advance by obtaining the load influence coefficients corresponding to abnormal settlements that occur during several construction processes, and determining the average value of each load influence coefficient as the reference load influence coefficient.

[0097] Specifically, the stiffness coefficient of the reference node is calculated in advance, and the stiffness coefficient of the node corresponding to abnormal settlement during several construction processes is determined to be the average of the stiffness coefficients of each node as the stiffness coefficient of the reference node.

[0098] Specifically, the sum of the weighting coefficients of the load influence factor and the constraint influence factor is 1. When balancing the weighting coefficients of the load influence factor and the constraint influence factor, considering that the load has a significant impact on settlement, the weighting coefficient of the load influence factor is set to 0.6 and the weighting coefficient of the constraint influence factor is set to 0.4.

[0099] Specifically, addressing the abnormal settlement problem caused by the coupling of spatial torsional effects and node stiffness attenuation, this invention clusters the loads in the sub-monitoring area to determine the load influence coefficient and calculates the abnormal settlement characterization value in conjunction with the node stiffness coefficient. In reality, the monitoring area bears vertical loads (self-weight, construction load, preload), resulting in significant torque. This torsional effect leads to uneven distribution of support reactions or temporary support reactions inside and outside the construction monitoring area, causing abnormal settlement. Furthermore, sufficient preload can ensure the stiffness of each connection node. If the preload is insufficient, the settlement monitoring points will exhibit excessive, nonlinear, or unstable settlement values. Especially when the load increases, if the preload is insufficient, the settlement in this area may be significantly greater than expected. Based on this, this invention considers analyzing the load data and preload data of the sub-monitoring area to determine the abnormal settlement characterization value, thereby determining the settlement tendency of the sub-monitoring area. This provides a data basis for subsequently determining and adjusting the monitoring density and frequency, reducing data generation, reducing computational consumption, and improving monitoring efficiency.

[0100] Please see Figure 2 , Figure 2 This is a logic block diagram illustrating the determination of the settlement tendency of each of the sub-monitoring areas according to an embodiment of the invention. Specifically, the settlement tendency of each of the sub-monitoring areas is determined, wherein...

[0101] If the abnormal settlement characterization value is greater than the abnormal settlement characterization value threshold, then the settlement tendency is determined to be an abnormal settlement tendency.

[0102] If the abnormal settlement characterization value is less than or equal to the abnormal settlement characterization value threshold, then the settlement tendency is determined to be a normal settlement tendency.

[0103] Specifically, the abnormal settlement characterization value threshold represents a boundary where abnormal settlement occurs during construction. It is calculated in advance by obtaining the abnormal settlement characterization values ​​corresponding to several abnormal settlements that occur during construction. The product of the mean of each abnormal settlement characterization value and the settlement coefficient is determined as the abnormal settlement characterization value threshold. In practice, to improve the calculation accuracy, the settlement coefficient is determined to be 0.8.

[0104] Technical effect analysis

[0105] Specifically, the process of acquiring temperature data to determine the characteristic values ​​of temperature influence includes,

[0106] Temperature data of the sub-monitoring area is acquired to construct a two-phase temperature time-domain curve;

[0107] The ratio of the average difference between the corresponding values ​​of the two-phase temperature time-domain curves to the corresponding values ​​of the low-temperature time-domain curve is determined as the temperature influence characteristic value.

[0108] The two-phase temperature time-domain curves include a high-temperature time-domain curve and a low-temperature time-domain curve.

[0109] Specifically, the high-temperature time-domain curve is the temperature time-domain curve corresponding to the time spent on the sunny side exceeding the predetermined time, and the low-temperature time-domain curve is the temperature time-domain curve corresponding to the time spent on the shady side exceeding the predetermined time. It can be understood that the specific data of the predetermined time is not limited. In practice, the predetermined time is 1 / 3 of the total construction duration. Of course, those skilled in the art can determine it according to the actual situation, which will not be elaborated here.

[0110] Specifically, the process of acquiring oscillation data to determine the oscillation-affected characteristic values ​​includes,

[0111] The ratio of the wind speed to the reference wind speed is determined as the wind speed influence factor;

[0112] The ratio of the windward area to the total area is determined as the area influence factor;

[0113] The ratio of the hanging basket height to the construction height is determined as the height influence factor;

[0114] The mean of the sum of the wind speed influence factor, the area influence factor, and the height influence factor is determined to be the swing influence characteristic value.

[0115] Specifically, the benchmark wind speed is calculated in advance. Several wind speeds that will not affect the construction are obtained in advance, and the average of each wind speed is determined as the benchmark wind speed.

[0116] Specifically, the windward area is the sum of the areas of the monitored area that are exposed to wind during the windy period, and the total area is the area of ​​the monitored area.

[0117] Specifically, the process of calculating settlement impact parameters includes,

[0118] The ratio of the temperature influence characteristic value to the reference temperature influence characteristic value is determined as the temperature influence factor;

[0119] The ratio of the swing influence characteristic value to the benchmark swing influence characteristic value is determined as the swing influence factor;

[0120] The ratio of the abnormal settlement characterization value to the benchmark abnormal settlement characterization value is determined as the settlement influence factor;

[0121] The weighted sum of the temperature influence factor, the oscillation influence factor, and the settlement influence factor is determined as the settlement influence parameter.

[0122] Specifically, the baseline temperature influence characteristic value is calculated in advance. Several temperature influence characteristic values ​​corresponding to the impact of settlement on construction during the construction process are obtained in advance, and the average value of each temperature influence characteristic value is determined as the baseline temperature influence characteristic value.

[0123] Specifically, the baseline swing influence characteristic value is obtained in advance by pre-calculating several swing influence characteristic values ​​corresponding to the impact of settlement on construction during the construction process, and the average value of each swing influence characteristic value is determined as the baseline swing influence characteristic value.

[0124] Specifically, the benchmark abnormal settlement characterization value is the abnormal settlement characterization value corresponding to the benchmark load influence coefficient and the benchmark node stiffness coefficient.

[0125] Specifically, the sum of the weighting coefficients of the temperature influence factor, the sway influence factor, and the settlement influence factor is 1. When adjusting the weighting coefficients, considering that the temperature difference will cause huge uneven settlement, which will affect the construction, the weighting coefficient of the temperature influence factor is set to 0.4, the weighting coefficient of the sway influence factor is 0.3, and the weighting coefficient of the settlement influence factor is 0.3.

[0126] Specifically, this invention addresses the potential impact on construction caused by settlement phenomena in sub-monitoring areas where abnormal settlement tendencies are catalyzed by external environmental factors. By determining the characteristic values ​​of temperature and oscillation effects, settlement impact parameters are calculated, providing data and theoretical basis for adjusting settlement monitoring density and frequency. In practice, uneven sunlight exposure leads to uneven heating in the monitoring area. Since construction materials are primarily steel and concrete, this uneven heating exacerbates temperature differences, resulting in different settlement phenomena in high-temperature and low-temperature areas. Furthermore, ambient wind causes the formwork to sway, and the greater the wind speed, the larger the windward area, and the higher the formwork, the greater the wind impact. The coupling of these factors further intensifies uneven settlement on-site, affecting construction. Therefore, this invention calculates settlement impact parameters to analyze whether uneven settlement in sub-monitoring areas will affect the construction process, adjusting settlement monitoring density and frequency to reduce data generation, computational cost, and monitoring efficiency, thus ensuring construction efficiency.

[0127] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating the adjustment of settlement monitoring density and frequency according to an embodiment of the invention. Specifically, the settlement monitoring density and frequency are adjusted, wherein...

[0128] If the settlement impact parameter is greater than the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency are increased;

[0129] If the settlement impact parameter is less than or equal to the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency remain unchanged.

[0130] Specifically, the settlement impact parameter threshold represents a boundary value that the settlement will affect the construction. It is calculated in advance by obtaining the settlement impact parameters of several abnormal settlements that affect the construction. The product of the mean of each settlement impact parameter and the impact coefficient is determined as the settlement impact parameter threshold. In practice, in order to improve the calculation accuracy, the settlement coefficient is determined to be 0.9.

[0131] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0132] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A settlement monitoring method for preloading construction using hanging baskets, characterized in that, include: The preload points and non-smooth continuous sections within the monitoring area are obtained to divide the monitoring area into several sub-monitoring areas, and the load data and preload data of each sub-monitoring area are obtained. Cluster the load data to determine the load influence coefficient, determine the node stiffness coefficient based on the preload data, calculate the abnormal settlement characterization value by combining the load influence coefficient and the node stiffness coefficient, and determine the settlement tendency of each of the sub-monitoring areas. For sub-monitoring areas with abnormal settlement tendency, temperature data is acquired to determine the temperature influence characteristic value, and oscillation data is acquired to determine the oscillation influence characteristic value. Settlement influence parameters are calculated in combination with the abnormal settlement characterization values ​​in order to adjust the settlement monitoring density and frequency. In response to the settlement impact parameter being greater than the settlement impact parameter threshold, the settlement monitoring density and the monitoring frequency are increased; The non-smooth continuous segment refers to the design engineering segment with protrusions or depressions during construction. The temperature data includes high-temperature surface data and low-temperature surface data. The oscillation data includes wind speed, windward area, and hanging basket height.

2. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The process of dividing the monitoring area into several sub-monitoring areas includes: Obtain a three-dimensional map of the monitoring area and label the three-dimensional coordinates of each pre-compression point and the non-smooth continuous segment; The monitoring area is divided according to the non-smooth continuous segment; The pre-pressure points in each area after division are determined so that each area is divided into several sub-monitoring areas.

3. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The process of clustering the load data to determine the load influence coefficient includes: Determine the number of effective load data in the sub-monitoring area; The ratio of the stated quantity to the total number of load data is determined as the load influence coefficient; The effective load data refers to load values ​​that are greater than the preset load value.

4. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The process of determining the node stiffness coefficient includes: The resultant force is determined based on the preload of each of the sub-monitoring areas; The ratio of the calculated resultant force to the running resultant force is determined as the nodal stiffness coefficient.

5. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The process of calculating the abnormal settlement characterization value includes: The ratio of the load influence coefficient to the reference load influence coefficient is determined as the load influence factor; The ratio of the reference node stiffness coefficient to the node stiffness coefficient is determined as the constraint influence factor; The weighted sum of the load influence factor and the constraint influence factor is determined to be the abnormal settlement characterization value.

6. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The determination of the settlement tendency of each of the sub-monitoring areas, wherein... If the abnormal settlement characterization value is greater than the abnormal settlement characterization value threshold, then the settlement tendency is determined to be an abnormal settlement tendency. If the abnormal settlement characterization value is less than or equal to the abnormal settlement characterization value threshold, then the settlement tendency is determined to be a normal settlement tendency.

7. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The process of acquiring temperature data to determine the characteristic values ​​of temperature influence includes: Temperature data of the sub-monitoring area is acquired to construct a two-phase temperature time-domain curve; The ratio of the average difference between the corresponding values ​​of the two-phase temperature time-domain curves to the corresponding values ​​of the low-temperature time-domain curve is determined as the temperature influence characteristic value. The two-phase temperature time-domain curves include a high-temperature time-domain curve and a low-temperature time-domain curve.

8. The settlement monitoring method for preloading construction using hanging baskets according to claim 7, characterized in that, The process of acquiring oscillation data to determine the oscillation-affected characteristic values ​​includes, The ratio of the wind speed to the reference wind speed is determined as the wind speed influence factor; The ratio of the windward area to the total area is determined as the area influence factor; The ratio of the hanging basket height to the construction height is determined as the height influence factor; The mean of the sum of the wind speed influence factor, the area influence factor, and the height influence factor is determined to be the swing influence characteristic value.

9. The settlement monitoring method for preloading construction using hanging baskets according to claim 8, characterized in that, The process of calculating the settlement impact parameters includes: The ratio of the temperature influence characteristic value to the reference temperature influence characteristic value is determined as the temperature influence factor; The ratio of the swing influence characteristic value to the benchmark swing influence characteristic value is determined as the swing influence factor; The ratio of the abnormal settlement characterization value to the benchmark abnormal settlement characterization value is determined as the settlement influence factor; The weighted sum of the temperature influence factor, the oscillation influence factor, and the settlement influence factor is determined as the settlement influence parameter.

10. The settlement monitoring method for preloading construction using hanging baskets according to claim 1, characterized in that, The adjustment of settlement monitoring density and frequency, wherein... If the settlement impact parameter is greater than the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency are increased; If the settlement impact parameter is less than or equal to the settlement impact parameter threshold, then the settlement monitoring density and the monitoring frequency remain unchanged.

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