Constructional engineering deflection precision detection method and system for building quality identification

Abstract

The invention provides a building engineering deflection precision detection method and system for building quality identification, and relates to the field of building deflection monitoring. The method comprises the following steps: monitoring deflection data, wind load data and vehicle load data of a first target measuring point of a to-be-measured building; determining a first influence factor of the wind load on the deflection based on the deflection data, the wind load data and the vehicle load data; for each monitoring moment, determining a second target measuring point according to the wind load data of each first target measuring point, and determining a second influence factor of the deflection based on the deflection data of each second target measuring point; wherein the second influence factor is an influence factor except a wind load and a vehicle load; determining a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; and determining a final value of the deflection data based on the target impact factor. The influence of the wind load in the deflection monitoring process can be removed based on the target influence factor, and the monitoring precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building deflection monitoring, and particularly relates to a precise detection method and system for building engineering deflection for building quality appraisal. Background Art

[0002] Building quality appraisal refers to the process of comprehensively inspecting, testing, and evaluating the quality of building projects. In this process, by monitoring the deflection of building projects, it is ensured that the deformation of the structure during construction is within the allowable range of the design, and the construction safety and the final form of the structure meet the design requirements. Among them, the above-mentioned building engineering deflection can reflect the stress state of the structure and evaluate the stability and durability of the structure.

[0003] In the related art, when monitoring the deflection of building projects, since building projects are usually exposed to the natural environment, the wind will cause the bridge to vibrate and deform, and the deflection is greatly affected by the wind load. Especially in bad weather, under the influence of long-term strong wind, the deflection of the bridge may increase significantly. If the influence of the wind load is not considered during monitoring, or the deflection is not monitored under appropriate wind conditions, the obtained deflection data will be inaccurate, thus affecting the result of building quality appraisal. Summary of the Invention

[0004] In order to solve the problem in the related art that if the influence of the wind load is not considered during monitoring, or the deflection is not monitored under appropriate wind conditions, the obtained deflection data will be inaccurate, thus affecting the result of building quality appraisal, the present invention provides a precise detection method for building engineering deflection for building quality appraisal. The specific technical solution adopted is as follows: Determine the first target measurement point of the building to be measured, and monitor the deflection data, wind load data, and vehicle load data of the first target measurement point; Based on the deflection data, the wind load data, and the vehicle load data, determine the first influence factor of the wind load on the deflection; For each monitoring moment, determine the second target measurement point according to the wind load data of each first target measurement point, and determine the second influence factor of the deflection based on the deflection data of each second target measurement point; wherein, the second influence factor is the influence factor other than the wind load and the vehicle load; Based on the first influence factor and the second influence factor, determine the target influence factor of the wind load on the deflection; Based on the target influence factor, determine the final value of the deflection data to realize the quality appraisal of the building to be measured.

[0005] Correspondingly, the present invention also provides a precise detection system for building engineering deflection for building quality appraisal, which specifically includes: A data monitoring module, configured to determine a first target measuring point of a building to be measured, and monitor deflection data, wind load data, and vehicle load data of the first target measuring point; A data processing module, configured to determine a first influence factor of the wind load on the deflection based on the deflection data, the wind load data, and the vehicle load data; The data processing module is further configured to, for each monitoring moment, determine a second target measuring point according to the wind load data of each of the first target measuring points, and determine a second influence factor of the deflection based on the deflection data of each of the second target measuring points; wherein, the second influence factor is an influence factor other than the wind load and the vehicle load; The data processing module is further configured to determine a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; A quality appraisal module, configured to determine a final value of the deflection data based on the target influence factor, so as to implement quality appraisal of the building to be measured.

[0006] The present invention may have the following partial or all beneficial effects: In the method for precise detection of building engineering deflection for building quality appraisal provided by the present invention, a first target measuring point of a building to be measured is determined, and deflection data, wind load data, and vehicle load data of the first target measuring point are monitored; a first influence factor of the wind load on the deflection is determined based on the deflection data, the wind load data, and the vehicle load data; for each monitoring moment, a second target measuring point is determined according to the wind load data of each of the first target measuring points, and a second influence factor of the deflection is determined based on the deflection data of each of the second target measuring points; wherein, the second influence factor is an influence factor other than the wind load and the vehicle load; a target influence factor of the wind load on the deflection is determined based on the first influence factor and the second influence factor; a final value of the deflection data is determined based on the target influence factor, so as to implement quality appraisal of the building to be measured. The present invention determines a first influence factor of the wind load on the deflection based on the deflection data, the wind load data, and the vehicle load data monitored at the first target measuring point, determines a second target measuring point according to the wind load data of each of the first target measuring points, and determines a second influence factor of the deflection based on the deflection data of each of the second target measuring points, so as to exclude the interference of other influence factors other than the wind load and the vehicle load on the deflection monitoring, thereby a target influence factor of the wind load on the deflection (i.e., the final influence factor of the wind load on the deflection after removing the interference factors) can be determined based on the first influence factor and the second influence factor, and a final value of the deflection data is determined based on the target influence factor. By analyzing the degree of influence of the wind load on the deflection of each first target measuring point at each monitoring moment, the influence factor of the wind load on the deflection is determined and used as the basis for determining the final value of the deflection data, removing the influence of the wind load in the deflection monitoring process, and improving the accuracy of the deflection monitoring.

[0007] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0009] Figure 1 shows a flowchart of a method for precise detection of building engineering deflection for building quality appraisal according to an exemplary embodiment of the present disclosure; Figure 2 shows a schematic diagram of the deflection change in the method for precise detection of building engineering deflection for building quality appraisal according to an exemplary embodiment of the present disclosure; Figure 3 shows a schematic diagram of a system for precise detection of building engineering deflection for building quality appraisal according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manner, structure, features and effects of the method for precise detection of building engineering deflection for building quality appraisal proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0012] The following will specifically describe the specific solution of the method for precise detection of building engineering deflection for building quality appraisal provided by the present invention in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1 , which shows a flowchart of the method for precise detection of building engineering deflection for building quality appraisal provided by an embodiment of the present invention. As Figure 1 shown, the method for precise detection of building engineering deflection for building quality appraisal specifically includes the following steps: S110: Determine the first target measurement points of the building to be measured, and monitor the deflection data, wind load data, and vehicle load data of the first target measurement points; S120: Determine the first influence factor of the wind load on the deflection based on the deflection data, wind load data, and vehicle load data; S130: For each monitoring moment, determine the second target measurement points according to the wind load data of each first target measurement point, and determine the second influence factor of the deflection based on the deflection data of each second target measurement point; wherein, the above-mentioned second influence factor is the influence factor other than the wind load and the vehicle load; S140: Determine the target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; S150: Determine the final value of the deflection data based on the target influence factor to achieve the quality appraisal of the building to be measured.

[0014] Based on the deflection data, wind load data, and vehicle load data monitored at the first target measurement points, the present invention determines the first influence factor of the wind load on the deflection, determines the second target measurement points according to the wind load data of each first target measurement point, and determines the second influence factor of the deflection based on the deflection data of each second target measurement point, so as to exclude the interference of other influence factors other than the wind load and the vehicle load on the deflection monitoring. Thus, the target influence factor of the wind load on the deflection (i.e., the final influence factor of the wind load on the deflection after removing the interference factors) can be determined based on the first influence factor and the second influence factor, and the final value of the deflection data can be determined based on the target influence factor. By analyzing the degree of influence of the wind load on the deflection of each first target measurement point at each monitoring moment, the influence factor of the wind load on the deflection is determined and used as the basis for determining the final value of the deflection data, removing the influence of the wind load during the deflection monitoring process and improving the accuracy of the deflection monitoring.

[0015] Next, each step of the above-mentioned building engineering deflection precise detection method for building quality appraisal will be described in detail: In step S110, determine the first target measurement points of the building to be measured, and monitor the deflection data, wind load data, and vehicle load data of the first target measurement points.

[0016] In the embodiment of the present application, the above-mentioned building to be measured is a building that needs to be subjected to deflection detection. Exemplarily, the building to be measured can be a building project such as a bridge.

[0017] In the embodiment of the present application, the above-mentioned first target measurement points are the monitoring points selected on the building to be measured for monitoring the deflection, wind load, and vehicle load. Exemplarily, the above-mentioned first target measurement points can be arranged at the key stress-bearing parts of the building to be measured.

[0018] Taking the building to be measured as a bridge for example, the above-mentioned first target measurement points can be arranged at key stress-bearing parts of the bridge, such as the mid-span of the main beam, near the supports, and the cantilever ends. Specifically, measurement points (i.e., the above-mentioned first target measurement points) can be arranged at regular intervals (5 meters) in the mid-span area. For the area near the supports, measurement points (i.e., the above-mentioned first target measurement points) can be arranged at a position 0.2L (L is the span) away from the supports.

[0019] In the embodiments of the present application, deflection refers to the linear displacement of the axis or mid-plane of a structural member (such as a beam, slab, column, etc.) in the direction perpendicular to the axis or the linear displacement of the mid-plane of a plate or shell in the direction perpendicular to the mid-plane under the action of factors such as force or non-uniform temperature change. That is, when a member bends or deforms under force, the offset of a certain point in the vertical direction relative to the original position. During the construction process of building engineering, there are many scenarios where deflection detection is required. For example, when building a long-span bridge, due to the large structural span, it is prone to large deflections under its own weight and construction loads. Therefore, precise deflection detection is required to ensure that the deformation of the structure during construction is within the design allowable range and to ensure construction safety and that the final form of the structure meets the design requirements.

[0020] In the embodiments of the present application, wind load is the pressure and suction generated by the air flow on the structure. Specifically, the formation principle of the above-mentioned pressure and suction is as follows: When the air flows, it has a certain kinetic energy. When the wind encounters a structure such as a building, a pressure difference will be formed on the surface of the structure. On the windward side, the dynamic pressure of the wind is converted into pressure on the structure; on the leeward side, side surfaces, and other parts of the building, due to phenomena such as air flow separation and vortex, suction will be formed. In addition, the factors determining the magnitude of the wind load include factors such as wind speed, wind direction, and wind pressure. Among them, wind speed is the most important factor determining the magnitude of the wind load. Generally speaking, the greater the wind speed, the greater the wind load, and the wind load is proportional to the square of the wind speed.

[0021] In the embodiments of the present application, vehicle load refers to the various forces generated by vehicles when driving on structures such as roads or bridges, and is an important load factor that must be considered in the design of road and bridge engineering.

[0022] In the embodiments of the present application, the above-mentioned deflection data, wind load data, and vehicle load data are respectively the data obtained by monitoring the deflection, wind load, and vehicle load at the above-mentioned first target measurement points.

[0023] Specifically, the monitoring of the deflection data, wind load data, and vehicle load data of the above-mentioned first target measurement points can be achieved as follows: (1) Monitor the deflection at each first target measurement point to obtain deflection data. Specifically, select a suitable measuring instrument according to factors such as measurement accuracy and on-site environment, such as a level, total station, laser displacement sensor, etc., and calibrate the instrument before measurement; use the selected instrument to measure the deflection at each first target measurement point and record the measured data. Exemplarily, the monitoring time interval for each first target measurement point can be 15 minutes, and the example diagram of the deflection change determined according to the recorded deflection data can be as Figure 2 shown.

[0024] (2) Monitor the wind load at each first target measurement point to obtain wind load data. Specifically, in the monitoring process of this application embodiment, the wind speed is mainly measured, and its measurement process can be realized as follows: Install an anemometer at each first target measurement point and debug it to ensure that its connection is firm and the contact is good; Monitor the wind speed at each first target measurement point and record the measured data. Exemplarily, the monitoring time interval for each first target measurement point is 15 minutes.

[0025] (3) Monitor the vehicle load at each first target measurement point to obtain vehicle load data. Specifically, select a suitable sensor according to the load-bearing situation of the bridge, install the selected sensor at the first target measurement point and debug it; When a vehicle passes through the bridge, start the sensor and record the measured data. Exemplarily, the monitoring time interval for each first target measurement point can be 15 minutes.

[0026] In step S120, determine the first influence factor of the wind load on the deflection based on the deflection data, wind load data, and vehicle load data.

[0027] In the embodiment of the present application, the above first influence factor is the initial influence factor of the wind load on the deflection after removing the influence of the vehicle load.

[0028] In the embodiment of the present application, the above determination of the first influence factor of the wind load on the deflection based on the deflection data, wind load data, and vehicle load data can be realized as follows: Obtain the specified deflection value corresponding to the building to be measured; Determine the first difference between the deflection data corresponding to each first target measurement point at each monitoring moment and the specified deflection value, identify the monitoring moment when the first difference exceeds the preset threshold as the deflection abnormal moment, and identify the monitoring moment when the first difference does not exceed the preset threshold as the deflection normal moment; For the deflection data of each first target measurement point at each deflection abnormal moment, calculate the correlation coefficient between the deflection data and the corresponding wind load data; Determine the abnormal factor corresponding to each deflection abnormal moment and the synchronization coefficient between the deflection data and the wind load data based on the deflection data, wind load data, and vehicle load data; Determine the first influence factor based on the correlation coefficient, abnormal factor, and synchronization coefficient.

[0029] In the embodiments of the present application, the above-mentioned deflection specified value refers to the maximum deformation amount that the structure is allowed to generate under normal use conditions as specified in the structural design of the above-mentioned building to be measured, considering factors such as the type of structure, usage function, material properties, and relevant design standards and specifications.

[0030] Specifically, taking the above-mentioned building to be measured as a bridge as an example, the process of implementing the determination of the first difference between the deflection data corresponding to each first target measurement point at each monitoring moment and the deflection specified value based on the deflection specified value, and identifying the monitoring moment when the first difference exceeds the preset threshold as the deflection abnormal moment and the monitoring moment when the first difference does not exceed the preset threshold as the deflection normal moment is as follows: Determine the deflection specified value of the above-mentioned bridge, denoted as D. In the deflection data sequence monitored through the above step S110, determine the difference between the deflection data of each first target measurement point at each monitoring moment and the above-mentioned deflection specified value : Among them, represents the deflection data of each first target measurement point at the i-th monitoring moment. When is greater than 0, it indicates that the deflection data at this monitoring moment exceeds the above-mentioned deflection specified value, proving that there may be problems such as excessive wind load, vehicle overload, or other issues at this monitoring moment. That is, in the process of identifying whether the deflection is abnormal, the monitoring moment when is greater than 0 is identified as the deflection abnormal moment, and the monitoring moment when is less than or equal to 0 is identified as the deflection normal moment. Thus, the deflection data of each first target measurement point at all monitoring moments can be divided into two categories: normal and abnormal. There may be a greater impact of wind load at the deflection abnormal moment, and the impact of wind load may be smaller at the deflection normal moment.

[0031] In an embodiment of the present application, the above-mentioned correlation coefficient may be a Pearson correlation coefficient. After identifying the above-mentioned deflection anomaly moment, the specific implementation of calculating the correlation coefficient between the deflection data of each first target measurement point at each deflection anomaly moment and the corresponding wind load data may be as follows: For each type of deflection data of each first target measurement point (that is, the above-mentioned deflection anomaly moments and the deflection data at the deflection anomaly moments), calculate the Pearson correlation coefficient between its deflection data and the corresponding wind load data, denoted as p. The Pearson correlation coefficient is a statistical index used to measure the degree of linear correlation between two variables, and here it is used to represent the overall influence degree of the wind load on the deflection at the current normal deflection moment or the current abnormal deflection moment. The larger the value of the Pearson correlation coefficient, the greater the correlation between the deflection and the wind load, that is, the more likely it is that the deflection anomaly of the first target measurement point is caused by the wind load. It should be noted that the above determination of the Pearson correlation coefficient may also be performed only on the deflection data at each deflection anomaly moment.

[0032] In an embodiment of the present application, the above-mentioned anomaly factor is the anomaly factor of the current first target measurement point at each deflection anomaly moment. Exemplarily, taking the anomaly factor of the current first target measurement point at the i-th deflection anomaly moment as an example, this anomaly factor can be determined by the anomaly degree of the deflection of the current first target measurement point at the i-th deflection anomaly moment and the similarity of the deflection anomalies of the current first target measurement point at each deflection anomaly moment.

[0033] In an embodiment of the present application, the above-mentioned synchronization coefficient is used to characterize the synchronization degree between the wind load change and the deflection change. This synchronization coefficient determines whether the changes between the two are synchronized by analyzing the response of the deflection when the wind load changes at different monitoring moments. The more synchronized the changes are, the more it shows that the deflection anomaly is caused by the wind load.

[0034] Exemplarily, the above-mentioned determination of the anomaly factor corresponding to each deflection anomaly moment and the synchronization coefficient between the deflection data and the wind load data based on the deflection data, the wind load data, and the vehicle load data may be implemented as follows: For each first target measurement point, perform clustering processing on the deflection anomaly moments based on the vehicle load data, and divide the deflection anomaly moments into multiple clustering clusters; among them, the magnitudes of the vehicle load data corresponding to the deflection anomaly moments in the same clustering cluster are the same; for each clustering cluster, determine a deflection difference sequence based on the first differences corresponding to the deflection data, calculate the second differences between adjacent first differences in the deflection difference sequence, and determine the anomaly factor corresponding to each deflection anomaly moment based on the first differences and the second differences; for all the deflection anomaly moments in each clustering cluster, determine the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data, and determine the synchronization coefficient between the deflection data and the wind load data based on the wind load change curve and the deflection change curve.

[0035] Among them, for all the deflection anomaly moments in each clustering cluster, to determine the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data, exemplarily, the following can be achieved: for each deflection anomaly moment in each clustering cluster, unify the dimensions of the deflection data and the corresponding wind load data; perform curve fitting based on the dimension-unified deflection data and wind load data to obtain the wind load change curve and the deflection change curve, and plot them in the same coordinate system.

[0036] The above-mentioned determination of the synchronization coefficient between the deflection data and the wind load data based on the wind load change curve and the deflection change curve, exemplarily, can be achieved as follows: determine all the first extreme points of the wind load change curve and the first maximum point among the first extreme points, as well as all the second extreme points of the deflection change curve and the second maximum point among the second extreme points; for each first maximum point, determine the average value of the differences between the first maximum point and its two adjacent data points before and after, and determine the weight of the first maximum point based on the average value; for the corresponding moment of each first maximum point, among the monitoring moments corresponding to the second maximum points of the deflection change curve, determine the target moment with the smallest time interval between the corresponding moment of the first maximum point and the target moment, and calculate the target time difference between the corresponding moment of the first maximum point and the target moment; determine the synchronization coefficient based on the number of the first maximum points, the weights of the first maximum points, and the target time difference.

[0037] Next, in a specific embodiment, the process of determining the anomaly factor corresponding to each deflection anomaly moment and the synchronization coefficient between the deflection data and the wind load data is described in detail: Since the vehicle loads at each monitoring moment are different, in the process of determining the first influence factor of the wind load on the deflection, it is necessary to exclude the influence of the vehicle load, and this process can be achieved as follows: for each first target measuring point, cluster all the deflection anomaly moments based on the magnitudes of the vehicle loads at the deflection anomaly moments of the first target measuring point, and divide all the deflection anomaly moments into several clustering clusters. Specifically, the vehicle load data corresponding to the deflection anomaly moments included in each clustering cluster are the same.

[0038] After dividing the above-mentioned deflection anomaly moments into multiple clustering clusters based on the vehicle load data, the embodiments of the present application are based on the difference between the above-mentioned deflection and the specified value Determine the anomaly factor of each of the above-mentioned deflection anomaly moments. Specifically, the following can be achieved: for the deflection difference sequence of each clustering cluster (the deflection difference sequence is a sequence composed of the differences between the deflection data and the deflection specified value at each abnormal deflection moment in the clustering cluster), calculate each in the deflection difference sequence And the next adjacent The difference between them: , wherein, the above is used to represent changes. The smaller its value is, the closer the difference between the corresponding two adjacent deflection data is proved; calculate all in the deflection difference sequence; calculate the anomaly factor of each deflection anomaly moment as follows: wherein, represents the anomaly factor of the i-th deflection anomaly moment of the first target measuring point; represents the difference between the i-th deflection anomaly moment and the specified deflection value, that is, the degree of deflection anomaly; represents the total number of deflection anomaly moments corresponding to the first target measuring point; represents the overall anomaly degree of all deflection anomaly moments of the first target measuring point. The smaller this value is, the more similar the anomaly degrees of each deflection anomaly moment are proved. In addition, from the above formula, it can also be obtained that: the anomaly degree of the current deflection anomaly moment (the i-th deflection anomaly moment) is smaller, the anomaly factor of the i-th deflection anomaly moment is smaller, that is, the smaller the value of is, and vice versa,

[0039] Furthermore, the synchronization coefficient of the above deflection data and wind load data can be determined through the following steps: S101: For each deflection anomaly moment in each clustering cluster, determine the wind load sequence corresponding to each deflection anomaly moment, unify the dimensions of the deflection data and the corresponding wind load data of all deflection anomaly moments, perform curve fitting according to the deflection data and wind load data after dimension unification, and draw the deflection change curve and wind load change curve of each deflection anomaly moment in the same coordinate system.

[0040] S102: Determine the number of all extreme points in the above deflection change curve and wind load change curve, and record the number of extreme points of the deflection change curve as j1 and the number of extreme points of the wind load change curve as j2.

[0041] In the embodiments of the present application, the more consistent the number of extreme points j1 of the above deflection change curve and the number of extreme points j2 of the wind load change curve are, the higher the possibility that the deflection changes with the wind load is proved.

[0042] S103: Based on the wind load variation curve, for each maximum point in the wind load variation curve, determine the average value of the absolute values of the differences between the maximum point and its two adjacent data points before and after, denoted as A, which is used to represent the change rate of the wind load at this maximum point. The larger the value of A and the larger the wind load data corresponding to this maximum value, the greater the influence of the wind load at this maximum point on the deflection. Therefore, the weight of each maximum point in the wind load variation curve can be determined by the following formula: where q represents the weight of each maximum point in the wind load variation curve; represents the wind load magnitude at this maximum point; represents the maximum value of the wind load; represents the relative magnitude of the wind load at this maximum point.

[0043] S4: For the monitoring moment corresponding to the i-th maximum point in the wind load variation curve, record the minimum time interval between the monitoring moment corresponding to the i-th maximum point in the wind load variation curve and all the monitoring times corresponding to the maximum points in the deflection variation curve as . Specifically, assume that the monitoring moment corresponding to the i-th maximum point in the wind load variation curve is ti, and there are 10 maximum points in the above-mentioned deflection variation curve, and the corresponding monitoring moments are t1 to t10. Then, determine the differences between the monitoring moments t1 to t10 and ti respectively, and the smallest difference is the above-mentioned minimum time interval.

[0044] S5: Calculate the synchronization of the deflection and the wind load variation at each maximum deflection in the above-mentioned deflection variation curve as follows: where represents the synchronization of the wind load and the deflection variation, d represents the number of maximum values in the wind load variation curve; represents the weight of the i-th maximum point. The greater the weight and the smaller the interval from the maximum point closest in time in the deflection variation curve, the more synchronized the deflection and the wind load variation, and the greater the value of b.

[0045] After determining the anomaly factor corresponding to each deflection anomaly moment and the synchronization coefficient of the deflection data and the wind load data based on the deflection data, the wind load data, and the vehicle load data, the embodiment of the present application can further determine the first influence factor based on the correlation coefficient, the anomaly factor, and the synchronization coefficient. Exemplarily, this process can be implemented as follows: determine the difference in the number between the number of the first extreme points and the number of the second extreme points; determine the first influence factor based on the number difference, the correlation coefficient, the anomaly factor, and the synchronization coefficient. Specifically, the first influence factor can be determined by the following formula: In the formula, represents the first influence factor of wind load on deflection at the i-th deflection anomaly moment in the clustering cluster (the influence factor after excluding the influence of vehicle load); represents the anomaly factor at the i-th deflection anomaly moment; represents the number of extreme points (i.e., the above-mentioned second extreme points) in the deflection change curve corresponding to the deflection anomaly moment of the first target measuring point; represents the number of extreme points (i.e., the above-mentioned first extreme points) in the wind load change curve corresponding to the deflection anomaly moment of the first target measuring point; represents the overall synchronization of the deflection of the first target measuring point changing with the wind load at the deflection anomaly moment, The smaller the value, the more synchronous the changes of the two; represents the Pearson correlation coefficient between the deflection anomaly data of the first target measuring point and its corresponding wind load data, The larger the value, the more correlated the abnormal deflection and the wind load; represents the synchronization of the wind load and the deflection change. The stronger the overall synchronization of the deflection of the first target measuring point changing with the wind load, and the more synchronous the changes of the deflection and the wind load at each monitoring moment, it proves that the influence of the wind load on the deflection is greater, that is, the deflection influence factor is larger.

[0046] In step S130, for each monitoring moment, the second target measuring point is determined according to the wind load data of each first target measuring point, and the second influence factor of the deflection is determined based on the deflection data of each second target measuring point; wherein, the above-mentioned second influence factor is the influence factor other than the wind load and the vehicle load.

[0047] In the embodiments of the present application, the above-mentioned second influence factor is a factor that will affect the deflection in addition to the wind load and the vehicle load. Exemplarily, the second influence factor may include factors such as the structural stiffness difference, restraint conditions, and dynamic characteristics of different parts of the bridge.

[0048] In order to obtain more accurate deflection data, after determining the first influence factor of the wind load on the deflection through the above steps, it is also necessary to determine the above-mentioned second influence factor, so as to further remove the interference of the above-mentioned second influence factor on the basis of removing the interference of the vehicle load, and obtain a more accurate target influence factor of the wind load on the deflection.

[0049] Exemplarily, the determination of the second target measurement point based on the wind load data of each first target measurement point and the determination of the second influence factor of the deflection based on the deflection data of each second target measurement point can be achieved as follows: For each monitoring moment, the first target measurement points with the same wind load data size at the same monitoring moment are determined as the second target measurement points; for each second target measurement point, the deflection difference between the deflection data corresponding to this second target measurement point and all other second target measurement points is calculated, and the second influence factor is determined based on the deflection difference.

[0050] Next, in a specific embodiment, the process of determining the second influence factor is described in detail as follows: Taking the building to be measured as a bridge as an example, the shapes of different parts of the bridge structure are diverse, and their hindering and guiding effects on the wind are different. Moreover, in a long-span bridge, the airflows between different structural components will affect each other. Therefore, at the same monitoring moment, the wind loads received by different first target measurement points will also vary. Specifically, the process of determining the second influence factor of the bridge deflection may include the following steps: S201: For any monitoring moment, according to the magnitudes of the wind load data of all first target measurement points, the first target measurement points with the same wind load data size at the same monitoring moment are taken as the second target measurement points, and the number thereof is denoted as m.

[0051] S202: For each second target measurement point, determine the deflection difference between the current second target measurement point and all other second target measurement points as follows: wherein, represents the deflection difference between the x-th second target measurement point and other second target measurement points among all second target measurement points at the current monitoring moment; represents the deflection of the x-th second target measurement point at the current monitoring moment; represents the deflection of the f-th second target measurement point at the current monitoring moment; represents the deflection difference between the x-th second target measurement point and the f-th second target measurement point, The smaller the value of

[0052] S203: For each monitoring moment, through the above steps S201 and S202, calculate the corresponding to the second target measurement points under each first target measurement point, and use it as the second influence factor for the deflection.

[0053] In step S140, the target influence factor of the wind load on the deflection is determined based on the first influence factor and the second influence factor.

[0054] In the embodiment of the present application, the above-mentioned target influence factor is the final influence factor of the wind load on the deflection obtained by correcting the first influence factor according to the second influence factor.

[0055] Exemplarily, the above-mentioned determination of the target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor can be achieved as follows: Determine the opposite number of the second influence factor, perform an exponential operation on the opposite number of the second influence factor to obtain an intermediate operation result; perform a multiplication operation on the intermediate operation result and the first influence factor, and perform a normalization process on the result obtained by the multiplication operation to obtain the target influence factor. Specifically, the target influence factor can be determined by the following formula: In the formula, represents the target influence factor of the wind load on the deflection at the i-th deflection abnormal moment of the x-th first target measurement point; represents the second influence factor of the deflection at the i-th deflection abnormal moment of the x-th first target measurement point; represents the first influence factor of the wind load on the deflection at the i-th deflection abnormal moment of the x-th first target measurement point; the smaller the second influence factor and the larger the first influence factor, the larger the target influence factor of the wind load, indicating that the influence of the wind load on the deflection is greater.

[0056] In step S150, based on the target influence factor, determine the final value of the deflection data to achieve the quality appraisal of the building to be measured.

[0057] In the embodiment of the present application, exemplarily, the above-mentioned determination of the final value of the deflection data based on the target influence factor to achieve the quality appraisal of the building to be measured can be achieved as follows: For each first target measurement point, set the data weight of the deflection data at the normal deflection moment to 1, and set the data weight of the deflection data at the abnormal deflection moment to the sum value of 1 and the target influence factor; based on the data weights of the deflection data at each monitoring moment of each first target measurement point, determine the data weight corresponding to each data within the moving average filter window, and determine the final value of the deflection data through the moving average filter to achieve the quality appraisal of the building to be measured.

[0058] Next, in a specific embodiment, the process of the above-mentioned determination of the final value of the deflection data based on the target influence factor to achieve the quality appraisal of the building to be measured will be described in detail: S301: Use a laser deflection measuring instrument. Install it at an appropriate distance from the bridge. Adjust the height and angle of the transmitter so that the laser beam can accurately shoot at the position of each first target measuring point on the bridge, obtain the deflection data of each first target measuring point at each monitoring moment, and perform the processing of steps S110 to S140 on the deflection of each first target measuring point to obtain the above-mentioned target influence factor; S302: Set the weight of the deflection data at the normal moment of the deflection of each first target measuring point to 1, and set the weight of the deflection data at the abnormal moment of the deflection to ; S303: Use the weight of the deflection data of each first target measuring point at each monitoring moment as the weight of each data within the window of the moving average filter. Use the moving average filter to process all the deflection data monitored at the first target measuring points, remove the influence of wind load on the deflection data during the deflection detection process, obtain more accurate deflection data of each first target measuring point on the bridge after removing the influence of wind load, and use it for the construction quality appraisal during the construction process of long-span bridges.

[0059] The present invention determines the first influence factor of wind load on deflection based on the deflection data, wind load data, and vehicle load data monitored at the first target measuring points, determines the second target measuring points based on the wind load data of each first target measuring point, and determines the second influence factor of deflection based on the deflection data of each second target measuring point, so as to exclude the interference of other influencing factors other than wind load and vehicle load on deflection monitoring. Thus, the target influence factor of wind load on deflection (that is, the final influence factor of wind load on deflection after removing the interference factors) can be determined based on the first influence factor and the second influence factor, and the final value of the deflection data can be determined based on the target influence factor. By analyzing the degree of influence of the deflection of each first target measuring point at each monitoring moment by wind load, the influence factor of wind load on deflection is determined and used as the weight of each data within the window of the moving average filter to process all the deflection data, so that the influence of wind load during the deflection monitoring process can be removed, and further the accuracy of deflection monitoring can be improved.

[0060] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of the method. To implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that the present invention can be implemented in the form of hardware or a combination of hardware and computer software in combination with the units and algorithm steps of each example described in the embodiments disclosed herein. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of the present invention.

[0061] An embodiment of the present disclosure provides a precise building engineering deflection detection system for building quality appraisal. Refer to Figure 3 As shown, the precise building engineering deflection detection system 300 for building quality appraisal may include a data monitoring module 310, a data processing module 320, and a quality appraisal module 330, where: The data monitoring module 310 is configured to determine a first target measurement point of the building to be measured, and monitor the deflection data, wind load data, and vehicle load data of the first target measurement point; The data processing module 320 is configured to determine a first influence factor of the wind load on the deflection based on the deflection data, wind load data, and vehicle load data; The data processing module 320 is further configured to, for each monitoring moment, determine a second target measurement point according to the wind load data of each first target measurement point, and determine a second influence factor of the deflection based on the deflection data of each second target measurement point; wherein, the second influence factor is an influence factor other than the wind load and the vehicle load; The data processing module 320 is further configured to determine a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; The quality appraisal module 330 is configured to determine the final value of the deflection data based on the target influence factor, so as to realize the quality appraisal of the building to be measured.

[0062] In an embodiment of the present application, the above data processing module is specifically configured to: obtain the specified deflection value corresponding to the building to be measured; determine a first difference between the deflection data corresponding to each first target measurement point at each monitoring moment and the specified deflection value, identify the monitoring moment when the first difference exceeds the preset threshold as the deflection abnormal moment, and identify the monitoring moment when the first difference does not exceed the preset threshold as the deflection normal moment; calculate the correlation coefficient between the deflection data and the corresponding wind load data for the deflection data of each first target measurement point at each deflection abnormal moment; determine the abnormal factor corresponding to each deflection abnormal moment and the synchronization coefficient between the deflection data and the wind load data based on the deflection data, wind load data, and vehicle load data; determine the first influence factor based on the correlation coefficient, abnormal factor, and synchronization coefficient.

[0063] In an embodiment of the present application, the above data processing module is specifically configured to: for each first target measurement point, perform clustering processing on the deflection abnormal moments based on the vehicle load data, and divide the deflection abnormal moments into multiple clustering clusters; wherein, the magnitudes of the vehicle load data corresponding to the deflection abnormal moments in the same clustering cluster are the same; for each clustering cluster, determine a deflection difference sequence based on the first differences corresponding to the respective deflection data, calculate the second differences between adjacent first differences in the deflection difference sequence, and determine the abnormal factors corresponding to each deflection abnormal moment based on the first differences and the second differences; for all the deflection abnormal moments in each clustering cluster, determine the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data, and determine the synchronization coefficient between the deflection data and the wind load data based on the wind load change curve and the deflection change curve.

[0064] In an embodiment of the present application, the above data processing module is specifically configured to: for each deflection abnormal moment in each clustering cluster, perform dimension unification on the deflection data and the corresponding wind load data; perform curve fitting on the dimension-unified deflection data and wind load data to obtain the wind load change curve and the deflection change curve, and plot them in the same coordinate system.

[0065] In an embodiment of the present application, the above data processing module is specifically configured to: determine all the first extreme points of the wind load change curve and the first maximum point among the first extreme points, and all the second extreme points of the deflection change curve and the second maximum point among the second extreme points; for each first maximum point, determine the average value of the differences between the first maximum point and the two adjacent data points before and after it, and determine the weight of the first maximum point based on the average value; for the corresponding moment of each first maximum point, among the monitoring moments corresponding to the second maximum points of the deflection change curve, determine the target moment with the smallest time interval from the monitoring moment corresponding to the first maximum point, and calculate the target time difference between the corresponding moment of the first maximum point and the target moment; determine the synchronization coefficient based on the number of first maximum points, the weights of the first maximum points, and the target time difference.

[0066] In an embodiment of the present application, the above data processing module is specifically configured to: determine the difference in the number between the first extreme points and the second extreme points; determine the first influence factor based on the difference in number, the correlation coefficient, the abnormal factor, and the synchronization coefficient.

[0067] In an embodiment of the present application, the above data processing module is specifically configured to: for each monitoring moment, determine the first target measurement points with the same magnitude of wind load data at the same monitoring moment as the second target measurement points; for each second target measurement point, calculate the deflection differences between the second target measurement point and the deflection data corresponding to all other second target measurement points, and determine the second influence factor based on the deflection differences.

[0068] In the embodiment of the present application, the above data processing module is specifically configured to: determine the opposite number of the second influence factor, perform an exponential operation on the opposite number of the second influence factor to obtain an intermediate operation result; perform a multiplication operation on the intermediate operation result and the first influence factor, and perform a normalization process on the result obtained by the multiplication operation to obtain a target influence factor.

[0069] In the embodiment of the present application, the above quality identification module is specifically configured to: for each first target measurement point, set the data weight of the deflection data at the normal deflection time to 1, and set the data weight of the deflection data at the abnormal deflection time to the sum of 1 and the target influence factor; determine the data weight corresponding to each data in the moving average filter window based on the data weights of the deflection data of each first target measurement point at each monitoring time, and determine the final value of the deflection data through the moving average filter to achieve the quality identification of the building to be measured.

[0070] Embodiments of the present invention can divide the functional modules of the building engineering deflection precision detection system for building quality identification according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the embodiments of the present invention is illustrative, and is only a logical function division. There may be other division methods in actual implementation.

[0071] In addition, the specific implementation details of the above building engineering deflection precision detection system for building quality identification have been described in detail at the corresponding positions of the building engineering deflection precision detection method for building quality identification, so they will not be repeated here.

[0072] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A precise detection method for the deflection of building engineering used for building quality appraisal, characterized in that, The method includes: Determine a first target measurement point of the building to be measured, and monitor the deflection data, wind load data, and vehicle load data of the first target measurement point; Determine a first influence factor of the wind load on the deflection based on the deflection data, the wind load data, and the vehicle load data; For each monitoring moment, determine a second target measurement point according to the wind load data of each first target measurement point, and determine a second influence factor of the deflection based on the deflection data of each second target measurement point; wherein, the second influence factor is an influence factor other than the wind load and the vehicle load; Determine a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; Determine the final value of the deflection data based on the target influence factor to achieve the quality appraisal of the building to be measured.

2. The precise detection method for the deflection of a construction project used for building quality appraisal according to claim 1, characterized in that, The determining the first influence factor of the wind load on the deflection based on the deflection data, the wind load data, and the vehicle load data includes: Obtain the specified deflection value corresponding to the building to be measured; Determine a first difference between the deflection data corresponding to each first target measurement point at each monitoring moment and the specified deflection value, identify the monitoring moments with the first difference exceeding a preset threshold as deflection abnormal moments, and identify the monitoring moments with the first difference not exceeding the preset threshold as deflection normal moments; For the deflection data of each first target measurement point at each deflection abnormal moment, calculate the correlation coefficient between the deflection data and the corresponding wind load data; Determine an abnormal factor corresponding to each deflection abnormal moment and a synchronization coefficient between the deflection data and the wind load data based on the deflection data, the wind load data, and the vehicle load data; Determine the first influence factor based on the correlation coefficient, the abnormal factor, and the synchronization coefficient.

3. The method for precise detection of the deflection of a construction project for building quality appraisal according to claim 2, characterized in that, The determining the abnormal factor corresponding to each deflection abnormal moment and the synchronization coefficient between the deflection data and the wind load data based on the deflection data, the wind load data, and the vehicle load data includes: For each first target measurement point, perform clustering processing on the deflection abnormal moments based on the vehicle load data, and divide the deflection abnormal moments into multiple clustering clusters; wherein, the magnitudes of the vehicle load data corresponding to the deflection abnormal moments in the same clustering cluster are the same; For each clustering cluster, determine a deflection difference sequence based on the first difference corresponding to each deflection data, calculate a second difference between adjacent first differences in the deflection difference sequence, and determine an abnormal factor corresponding to each deflection abnormal moment based on the first difference and the second difference; For all the deflection abnormal moments in each clustering cluster, determine a wind load change curve corresponding to the wind load data and a deflection change curve corresponding to the deflection data, and determine the synchronization coefficient between the deflection data and the wind load data based on the wind load change curve and the deflection change curve.

4. The method for precise detection of the deflection of a construction project for building quality appraisal according to claim 3, characterized in that, For all the deflection anomaly times in each of the clusters, determining the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data includes: For each of the deflection anomaly times in each of the clusters, unifying the dimensions of the deflection data and the corresponding wind load data; Performing curve fitting based on the dimension-unified deflection data and wind load data to obtain the wind load change curve and the deflection change curve, and plotting them in the same coordinate system.

5. The method for precise detection of the deflection of a construction project for building quality appraisal according to claim 3, characterized in that, Based on the wind load change curve and the deflection change curve, determining the synchronization coefficient between the deflection data and the wind load data includes: Determining all the first extreme points of the wind load change curve and the first maximum point among the first extreme points, all the second extreme points of the deflection change curve and the second maximum point among the second extreme points; For each of the first maximum points, determining the average value of the differences between the first maximum point and the two adjacent data points before and after it, and determining the weight of the first maximum point based on the average value; For the corresponding time of each of the first maximum points, among the monitoring times corresponding to the second maximum points of the deflection change curve, determining the target time with the smallest time interval from the monitoring time corresponding to the first maximum point, and calculating the target time difference between the corresponding time of the first maximum point and the target time; Determining the synchronization coefficient based on the number of the first maximum points, the weights of the first maximum points, and the target time difference.

6. The precise detection method for the deflection of a construction project used for building quality appraisal according to claim 5, characterized in that, Based on the correlation coefficient, the anomaly factor, and the synchronization coefficient, determining the first influence factor includes: Determining the difference in the number between the number of the first extreme points and the number of the second extreme points; Determining the first influence factor based on the number difference, the correlation coefficient, the anomaly factor, and the synchronization coefficient.

7. The method for precise detection of deflection of a construction project for building quality appraisal according to claim 1, characterized in that, According to the wind load data of each of the first target measuring points, determining the second target measuring points, and based on the deflection data of each of the second target measuring points, determining the second influence factor of the deflection includes: For each of the monitoring times, determining the first target measuring points with the same wind load data size at the same monitoring time as the second target measuring points; For each of the second target measuring points, calculating the deflection differences between the second target measuring point and the deflection data corresponding to all the other second target measuring points, and determining the second influence factor based on the deflection differences.

8. The method for precise detection of building engineering deflection for building quality appraisal according to claim 1, wherein Based on the first influence factor and the second influence factor, determining the target influence factor of the wind load on the deflection includes: Determining the opposite number of the second influence factor, and performing an exponential operation on the opposite number of the second influence factor to obtain an intermediate operation result; Performing a multiplication operation on the intermediate operation result and the first influence factor, and performing a normalization process on the result obtained from the multiplication operation to obtain the target influence factor.

9. The precise detection method for the deflection of a construction project used for building quality appraisal according to claim 2, wherein Determining a final value of the deflection data based on the target influence factor to achieve quality assessment of the building under test, including: For each of the first target measuring points, setting the data weight of the deflection data at the normal deflection time to 1, and setting the data weight of the deflection data at the abnormal deflection time to the sum of 1 and the target influence factor; Determining the data weight corresponding to each data within the moving average filter window based on the data weights of the deflection data of each of the first target measuring points at each monitoring time, and determining the final value of the deflection data through the moving average filter to achieve quality assessment of the building under test.

10. A precise deflection detection system for building engineering used for building quality appraisal, characterized in that, The system includes: A data monitoring module, configured to determine first target measuring points of a building under test, and monitor deflection data, wind load data, and vehicle load data of the first target measuring points; A data processing module, configured to determine a first influence factor of the wind load on the deflection based on the deflection data, the wind load data, and the vehicle load data; The data processing module is further configured to, for each monitoring time, determine second target measuring points according to the wind load data of each of the first target measuring points, and determine a second influence factor of the deflection based on the deflection data of each of the second target measuring points; wherein, the second influence factor is an influence factor other than the wind load and the vehicle load; The data processing module is further configured to determine a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor; A quality assessment module, configured to determine a final value of the deflection data based on the target influence factor to achieve quality assessment of the building under test.

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