Precision detection method and system for construction engineering deflection for building quality appraisal

Abstract

The present disclosure provides a method and system for precise detection of construction engineering deflection for building quality appraisal, which relates to the field of building deflection monitoring. The method includes: monitoring the deflection data, wind load data and vehicle load data of the first target measuring point of the building to be measured; determining the first influence factor of wind load on deflection based on the deflection data, wind load data and vehicle load data; for each monitoring moment, determining the second target measuring point according to the wind load data of each first target measuring point, and determining the second influence factor of deflection based on the deflection data of each second target measuring point; wherein the second influence factor is an influence factor other than wind load and vehicle load; determining the target influence factor of wind load on deflection based on the first influence factor and the second influence factor; and determining the final value of deflection data based on the target influence factor. The present disclosure can remove the influence of wind load in the deflection monitoring process based on the above-mentioned target influence factor, thereby improving monitoring accuracy.

Description

Technical Field

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

[0002] Building quality appraisal refers to the comprehensive inspection, testing, and evaluation of the quality of construction projects. During this process, the deflection of the building project is monitored to ensure that structural deformation during construction is within the design tolerances, ensuring construction safety and that the final form of the structure meets design requirements. The deflection of the building project can reflect the stress state of the structure and assess its stability and durability.

[0003] In related technologies, when monitoring the deflection of construction projects, since construction projects are often exposed to the elements, wind can cause bridges to vibrate and deform, significantly impacting deflection. This is especially true in inclement weather, where prolonged strong winds can significantly increase bridge deflection. Failure to consider the impact of wind loads during monitoring, or failing to monitor deflection under appropriate wind conditions, can result in inaccurate deflection data, impacting the results of building quality assessments. Summary of the Invention

[0004] In order to solve the problem in related technologies that the influence of wind load is not considered during monitoring, or the deflection is not monitored under appropriate wind conditions, which leads to inaccurate deflection data and thus affects the results of building quality appraisal, the present invention provides a precise detection method for construction engineering deflection for building quality appraisal. The technical solution adopted is as follows:

[0005] Determining a first target measuring point of the building to be measured, and monitoring deflection data, wind load data, and vehicle load data of the first target measuring point;

[0006] determining a first influencing factor of wind load on deflection based on the deflection data, the wind load data, and the vehicle load data;

[0007] At each monitoring moment, determining a second target measuring point based on the wind load data of each first target measuring point, and determining a second influencing factor of the deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than the wind load and the vehicle load;

[0008] determining a target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor;

[0009] A final value of the deflection data is determined based on the target influencing factor to achieve quality assessment of the building to be tested.

[0010] Correspondingly, the present invention also provides a construction engineering deflection precision detection system for building quality appraisal, specifically comprising:

[0011] a data monitoring module, configured to determine a first target measuring point of the building to be measured, and monitor deflection data, wind load data, and vehicle load data of the first target measuring point;

[0012] a data processing module, configured to determine a first influencing factor of wind load on deflection based on the deflection data, the wind load data, and the vehicle load data;

[0013] The data processing module is further configured to determine, at each monitoring moment, a second target measuring point based on the wind load data of each first target measuring point, and determine a second influencing factor of the deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than the wind load and the vehicle load;

[0014] 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;

[0015] A quality assessment module is used to determine a final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested.

[0016] The present invention may have some or all of the following beneficial effects:

[0017] In the precise detection method for deflection of construction projects for building quality appraisal provided by the present invention, a first target measuring point of the building to be tested is determined, and the deflection data, wind load data and vehicle load data of the first target measuring point are monitored; a first influencing factor of wind load on deflection is determined based on the deflection data, wind load data and vehicle load data; for each monitoring moment, a second target measuring point is determined according to the wind load data of each first target measuring point, and a second influencing factor of deflection is determined based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than wind load and vehicle load; a target influencing factor of wind load on deflection is determined based on the first influencing factor and the second influencing factor; and a final value of the deflection data is determined based on the target influencing factor to achieve quality appraisal of the building to be tested. The present invention determines a first influence factor of wind load on deflection based on deflection data, wind load data, and vehicle load data monitored at a first target measuring point, determines a second target measuring point based on the wind load data of each first target measuring point, and determines a second influence factor of deflection based on the deflection data of each second target measuring point, thereby eliminating interference with deflection monitoring caused by factors other than wind load and vehicle load. Thus, a target influence factor of wind load on deflection (i.e., the final influence factor of wind load on deflection after removing interference factors) can be determined based on the first and second influence factors, and a final value of the deflection data can be determined based on the target influence factor. By analyzing the degree to which the deflection of each first target measuring point is affected by wind load at each monitoring moment, the influence factor of wind load on deflection is determined and used as the basis for determining the final value of the deflection data, thereby eliminating the influence of wind load during the deflection monitoring process and improving the accuracy of deflection monitoring.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flow chart of a method for accurately detecting deflection of a construction project for building quality appraisal according to an exemplary embodiment of the present disclosure is shown;

[0021] Figure 2 A schematic diagram showing a change in deflection in a precise detection method for construction engineering deflection for construction quality appraisal according to an exemplary embodiment of the present disclosure is shown;

[0022] Figure 3 A schematic diagram of a precise detection system for construction engineering deflection for construction quality appraisal according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of the precise deflection detection method for building quality assessment proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0024] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0025] The specific scheme of the precise detection method for construction engineering deflection for construction quality appraisal provided by the present invention is described in detail below with reference to the accompanying drawings.

[0026] See also Figure 1 , which shows a method flow chart of a method for precise detection of construction engineering deflection for construction quality appraisal provided by an embodiment of the present invention, such as Figure 1 As shown, the precise detection method for construction engineering deflection for construction quality appraisal specifically includes the following steps:

[0027] S110: Determine a first target measuring point of the building to be measured, and monitor deflection data, wind load data, and vehicle load data of the first target measuring point;

[0028] S120: Determine a first influencing factor of wind load on deflection based on the deflection data, wind load data, and vehicle load data;

[0029] S130: For each monitoring moment, determine a second target measuring point based on the wind load data of each first target measuring point, and determine a second influencing factor of deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than wind load and vehicle load;

[0030] S140: Determine a target influence factor of wind load on deflection based on the first influence factor and the second influence factor;

[0031] S150: Determine a final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested.

[0032] The present invention determines a first influence factor of wind load on deflection based on deflection data, wind load data, and vehicle load data monitored at a first target measuring point, determines a second target measuring point based on the wind load data of each first target measuring point, and determines a second influence factor of deflection based on the deflection data of each second target measuring point, thereby eliminating interference with deflection monitoring caused by factors other than wind load and vehicle load. Thus, a target influence factor of wind load on deflection (i.e., the final influence factor of wind load on deflection after removing interference factors) can be determined based on the first and second influence factors, and a final value of the deflection data can be determined based on the target influence factor. By analyzing the degree to which the deflection of each first target measuring point is affected by wind load at each monitoring moment, the influence factor of wind load on deflection is determined and used as the basis for determining the final value of the deflection data, thereby eliminating the influence of wind load during the deflection monitoring process and improving the accuracy of deflection monitoring.

[0033] Below, each step of the above-mentioned construction engineering deflection precision detection method for building quality appraisal is described in detail:

[0034] In step S110 , a first target measuring point of the building to be measured is determined, and deflection data, wind load data, and vehicle load data of the first target measuring point are monitored.

[0035] In the embodiment of the present application, the building to be tested is a building that needs to undergo deflection testing. For example, the building to be tested can be a construction project such as a bridge.

[0036] In the embodiment of the present application, the first target measuring point is a monitoring point selected on the building to be measured for monitoring deflection, wind load, and vehicle load. For example, the first target measuring point can be arranged at a key load-bearing part of the building to be measured.

[0037] For example, if the structure being measured is a bridge, the first target measuring points can be placed at key load-bearing locations on the bridge, such as the mid-span of the main beam, near the support points, and at the cantilever ends. Specifically, measuring points (the first target measuring points) can be placed at regular intervals (5 meters) in the mid-span area. Near the support points, measuring points (the first target measuring points) can be placed at a distance of 0.2L (L is the span) from the support points.

[0038] 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, plate, column, etc.) in a direction perpendicular to the axis, or the linear displacement of the mid-plane of a plate or shell in a direction perpendicular to the mid-plane, under the influence of factors such as force or non-uniform temperature changes. In other words, when a member bends or deforms after being subjected to force, the vertical offset of a certain point relative to the original position. During the construction process of a building project, deflection detection is required in many scenarios. For example, when building a long-span bridge, due to the large span of the structure, it is easy to produce large deflections under the action of its own weight and construction loads. Therefore, precise detection of the deflection is required to ensure that the deformation of the structure during construction is within the design allowable range, to ensure construction safety, and to ensure that the final shape of the structure meets the design requirements.

[0039] In the embodiment of the present application, the wind load is the pressure and suction generated by the air flow on the structure. Specifically, the principle of the formation of the above-mentioned pressure and suction is: the air has a certain kinetic energy when it flows, and when the wind encounters structures such as buildings, 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, the side and other parts of the building, suction will be formed due to phenomena such as the separation and vortex of the air flow. In addition, the factors that determine the size of the wind load include wind speed, wind direction, wind pressure and other factors, among which wind speed is the most important factor determining the size of the wind load. Generally speaking, the greater the wind speed, the greater the wind load, and the wind load is directly proportional to the square of the wind speed.

[0040] In the embodiments of the present application, vehicle load refers to the various forces exerted on structures such as roads and bridges when a vehicle travels on them. It is an important load factor that must be considered in the design of road and bridge projects.

[0041] In the embodiment of the present application, the above-mentioned deflection data, wind load data and vehicle load data are data obtained by monitoring the above-mentioned deflection, wind load and vehicle load at the above-mentioned first target measuring point respectively.

[0042] Specifically, the monitoring of the deflection data, wind load data, and vehicle load data of the first target measuring point can be achieved as follows:

[0043] (1) Monitor the deflection at each first target measuring point to obtain deflection data. Specifically, select appropriate measuring instruments, such as a level, total station, laser displacement sensor, etc., based on factors such as measurement accuracy and on-site environment, and calibrate the instruments before measurement; use the selected instruments to measure the deflection at each first target measuring point, and record the measured data. For example, the monitoring time interval for each first target measuring point can be 15 minutes, and the deflection change example diagram determined based on the recorded deflection data can be as follows: Figure 2 shown.

[0044] (2) Monitor the wind load at each first target measuring point to obtain wind load data. Specifically, in this embodiment of the present application, the wind speed is mainly measured during the monitoring process. The measurement process can be implemented as follows: install the anemometer at each first target measuring point and debug it to ensure that it is firmly connected and has good contact; monitor the wind speed at each first target measuring point and record the measured data. For example, the monitoring time interval for each first target measuring point is 15 minutes.

[0045] (3) Monitor the vehicle load at each first target measuring point to obtain vehicle load data. Specifically, select a suitable sensor based on the load conditions of the bridge, install the selected sensor at the first target measuring point, and debug it. When the vehicle passes through the bridge, start the sensor and record the measured data. For example, the monitoring interval at each first target measuring point can be 15 minutes.

[0046] In step S120 , a first influencing factor of wind load on deflection is determined based on the deflection data, wind load data, and vehicle load data.

[0047] In the embodiment of the present application, the first influencing factor is the initial influencing factor of wind load on deflection after removing the influence of vehicle load.

[0048] In an embodiment of the present application, the above-mentioned determination of the first influencing factor of wind load on deflection based on deflection data, wind load data and vehicle load data can be implemented, for example, as follows: obtaining the specified deflection value corresponding to the building to be measured; determining the first difference between the deflection data corresponding to each first target measuring point at each monitoring moment and the specified deflection value, identifying the monitoring moment when the first difference exceeds the preset threshold as the deflection abnormal moment, and identifying 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 measuring point at each deflection abnormal moment, calculating the correlation coefficient between the deflection data and the corresponding wind load data; 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, wind load data and vehicle load data; determining the first influencing factor based on the correlation coefficient, the abnormal factor and the synchronization coefficient.

[0049] In the embodiment of the present application, the above-mentioned deflection specified value refers to the maximum deformation allowed to occur in the structural design of the above-mentioned building to be tested under normal use conditions, based on factors such as the type of structure, usage function, material properties, and relevant design standards and specifications.

[0050] Specifically, taking the above-mentioned building to be measured as a bridge as an example, the process of determining the first difference between the deflection data corresponding to each first target measuring point at each monitoring moment and the deflection specified value based on the deflection specified value, identifying the monitoring moment when the first difference exceeds the preset threshold as the deflection abnormal moment, and identifying the monitoring moment when the first difference does not exceed the preset threshold as the deflection normal moment is as follows:

[0051] Determine the specified deflection value of the bridge, denoted as D. In the deflection data sequence obtained by monitoring in step S110, determine the difference between the deflection data of each first target measuring point at each monitoring time and the specified deflection value. :

[0052]

[0053] in, Represents the deflection data of each first target measuring point at the i-th monitoring moment. When the value of is greater than 0, it means that the deflection data at the monitoring moment exceeds the above-mentioned deflection specified value, proving that there may be excessive wind load, vehicle overload or other problems at the monitoring moment. That is, in the process of identifying whether the deflection is abnormal, The monitoring moment when the value of is greater than 0 is identified as the abnormal deflection moment. The monitoring moments with a value of less than or equal to 0 are identified as normal deflection moments. Thus, the deflection data of each first target measuring point at all monitoring moments can be divided into two categories: normal and abnormal. There may be a large wind load influence at the moment of abnormal deflection, while the influence of wind load may be small at the moment of normal deflection.

[0054] In an embodiment of the present application, the aforementioned correlation coefficient may be a Pearson correlation coefficient. After identifying the aforementioned abnormal deflection moment, the calculation of the correlation coefficient between the deflection data and the corresponding wind load data for each first target measuring point at each abnormal deflection moment may be specifically implemented as follows: For each type of deflection data for each first target measuring point (i.e., the deflection data at each abnormal deflection moment and at the abnormal deflection moment), the Pearson correlation coefficient (denoted as p) is calculated between the deflection data and the corresponding wind load data. The Pearson correlation coefficient is a statistical indicator used to measure the degree of linear correlation between two variables. Here, it is used to represent the overall impact of wind load on the deflection at the current normal deflection moment or the current abnormal deflection moment. A larger value of the Pearson correlation coefficient indicates a greater correlation between deflection and wind load, i.e., a greater likelihood that the abnormal deflection at the first target measuring point was caused by wind load. It should be noted that the aforementioned determination of the Pearson correlation coefficient may also be performed only for the deflection data at each abnormal deflection moment.

[0055] In the embodiment of the present application, the above abnormal factors are the abnormal factors of the current first target measuring point at each abnormal deflection moment. For example, the abnormal factor of the current first target measuring point at the i-th abnormal deflection moment is For example, the abnormal factor It can be determined by the abnormal degree of the deflection of the current first target measuring point at the i-th abnormal deflection moment and the similarity of the deflection abnormalities of the current first target measuring point at each abnormal deflection moment.

[0056] In the embodiments of the present application, the synchronization coefficient is used to characterize the degree of synchronization between wind load changes and deflection changes. This synchronization coefficient analyzes the response of the deflection to wind load changes at different monitoring times to determine whether the changes between the two are synchronized. The more synchronized the changes, the more likely the deflection anomaly is caused by wind load.

[0057] Exemplarily, the above-mentioned determination of the abnormal factors 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 can be implemented as follows: for each first target measuring point, the deflection abnormal moment is clustered based on the vehicle load data, and the deflection abnormal moment is divided into multiple cluster clusters; wherein, the size of the vehicle load data corresponding to the deflection abnormal moment in the same cluster cluster is the same; for each cluster cluster, the deflection difference sequence is determined based on the first difference corresponding to each deflection data, the second difference between adjacent first differences in the deflection difference sequence is calculated, and the abnormal factors corresponding to each deflection abnormal moment are determined based on the first difference and the second difference; for all deflection abnormal moments in each cluster cluster, the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data are determined, and the synchronization coefficient between the deflection data and the wind load data is determined based on the wind load change curve and the deflection change curve.

[0058] Among them, the above-mentioned determination of the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data for all deflection abnormal moments in each cluster can be achieved illustratively as follows: for each deflection abnormal moment in each cluster, the deflection data and its corresponding wind load data are dimensionalized; curve fitting is performed based on the deflection data and wind load data after dimensional unification to obtain the wind load change curve and the deflection change curve, and they are plotted in the same coordinate system.

[0059] The above-mentioned determination of the synchronization coefficient of the deflection data and the wind load data based on the wind load change curve and the deflection change curve can be implemented, for example, 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 difference 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, determine the target moment with the smallest time interval between the monitoring moments corresponding to the first maximum point in the monitoring moments corresponding to the second maximum point of the deflection change curve, 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 weight of the first maximum point and the target time difference.

[0060] In a specific embodiment, the process of determining the abnormality factor corresponding to each abnormal deflection moment and the synchronization coefficient between the deflection data and the wind load data is described in detail below:

[0061] Because vehicle loads vary at each monitoring moment, the influence of vehicle loads must be eliminated when determining the primary influencing factor of wind load on deflection. This process can be achieved as follows: for each first target measuring point, all moments of abnormal deflection are clustered based on the magnitude of the vehicle load at each moment of abnormal deflection. These moments are then divided into several clusters. Specifically, each cluster contains consistent vehicle load data corresponding to each abnormal deflection moment.

[0062] After dividing the above-mentioned abnormal deflection moments into multiple clusters based on vehicle load data, the embodiment of the present application classifies the abnormal deflection moments based on the difference between the above-mentioned deflection and the specified value. Determine the abnormal factors of each abnormal deflection moment. Specifically, it can be achieved as follows: for each cluster deflection difference sequence (the deflection difference sequence is a sequence consisting of the difference between the deflection data and the deflection specified value at each abnormal deflection moment in the cluster), calculate the deflection difference sequence of each The next one adjacent to it The differences between: , among which, the above Used to indicate The smaller the value, the closer the difference between the two adjacent deflection data is. Calculate all the deflection difference sequences. ; Calculate the abnormal factor at each deflection abnormal moment as follows:

[0063]

[0064] in, Indicates the abnormal factor of the first target measuring point at the moment of abnormal deflection of the i-th point; It represents the difference between the i-th deflection abnormality moment and the specified deflection value, that is, the degree of deflection abnormality; Indicates the total number of abnormal deflection moments corresponding to the first target measuring point; Indicates the overall abnormality degree of all deflection abnormal moments of the first target measuring point. The smaller the value, the more similar the abnormality degrees of each deflection abnormal moment are. In addition, the above formula can also be used to derive: the abnormality degree of the current deflection abnormal moment (the i-th deflection abnormal moment) The smaller it is, the smaller the abnormal factor of the i-th deflection abnormal moment is, that is, The smaller the value of , the smaller the The larger the value of .

[0065] Furthermore, the synchronization coefficient of the above deflection data and wind load data can be determined by the following steps:

[0066] S101: For each deflection abnormality moment in each cluster, determine the wind load sequence corresponding to each deflection abnormality moment, and unify the dimensions of the deflection data and the corresponding wind load data at all deflection abnormality moments. Perform curve fitting based on the unified deflection data and wind load data, and draw the deflection change curve and wind load change curve at each deflection abnormality moment in the same coordinate system.

[0067] S102: Determine the number of all extreme value points in the deflection change curve and the wind load change curve, record the number of extreme value points in the deflection change curve as j1, and record the number of extreme value points in the wind load change curve as j2.

[0068] In the embodiment of the present application, the more consistent the number j1 of extreme points of the above-mentioned deflection change curve and the number j2 of extreme points of the wind load change curve are, the higher the possibility that the deflection changes with the wind load.

[0069] S103: Taking the wind load variation curve as a reference, for each maximum point in the wind load variation curve, determine the average of the absolute values of the differences between the maximum point and its two adjacent data points before and after it, denoted as A, which is used to represent the change speed of the wind load at the maximum point. The larger the A value and the larger the wind load data corresponding to the maximum point, the greater the influence of the wind load at the 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:

[0070]

[0071] Among them, q represents the weight of each maximum point in the wind load variation curve; Indicates the magnitude of the wind load at the maximum point; Indicates the maximum value of wind load; Indicates the relative size of the wind load at the maximum point.

[0072] S4: For the monitoring time corresponding to the i-th maximum point in the wind load variation curve, the minimum time interval between the monitoring times corresponding to all the maximum points in the deflection variation curve is recorded as Specifically, assuming that the monitoring time corresponding to the i-th maximum point in the wind load variation curve is ti, and there are 10 maximum points in the above deflection variation curve, and the corresponding monitoring times are t1 to t10, then the differences between the monitoring times t1 to t10 and ti are determined respectively, and the smallest difference is the above minimum time interval.

[0073] S5: Calculate the synchronization between the deflection and wind load changes at each deflection maximum in the above deflection change curve as follows:

[0074]

[0075] in, It indicates the synchronization of wind load and deflection changes, and d indicates the number of maximum values in the wind load change curve; It represents the weight of the i-th maximum point. The larger the weight is and the smaller the interval with the maximum point closest to it in the deflection change curve is, the more synchronized the changes in deflection and wind load are, and the larger the value of b is.

[0076] After determining the abnormal factors corresponding to each abnormal deflection 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, the embodiment of the present application can further determine the first impact factor based on the correlation coefficient, the abnormal factor, and the synchronization coefficient. For example, the process can be implemented as follows: determine the difference between the number of the first extreme point and the number of the second extreme point; determine the first impact factor based on the number difference, the correlation coefficient, the abnormal factor, and the synchronization coefficient. Specifically, the first impact factor can be determined by the following formula:

[0077]

[0078] Where, represents the first influencing factor of wind load on deflection at the i-th deflection abnormal moment in the cluster (the influencing factor after excluding the influence of vehicle load); represents the abnormal factor at the moment of abnormal deflection i; Indicates the number of extreme value points (i.e., the second extreme value points mentioned above) in the deflection change curve corresponding to the moment when the deflection of the first target measuring point is abnormal; Indicates the number of extreme value points (i.e., the first extreme value points) in the wind load variation curve corresponding to the moment of abnormal deflection of the first target measuring point; Indicates the overall synchronization of the deflection of the first target measuring point at the moment of abnormal deflection with the wind load. The smaller the value of , the more synchronized the changes of the two are; 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 of , the more relevant the abnormal deflection is to the wind load; Indicates the synchronization of wind load and deflection changes. The stronger the overall synchronization of the deflection of the first target measuring point with the wind load, and the more synchronized the changes of deflection and wind load at each monitoring moment, the greater the influence of wind load on deflection, that is, the deflection influence factor The bigger.

[0079] In step S130, for each monitoring moment, a second target measuring point is determined based on the wind load data of each first target measuring point, and a second influencing factor of deflection is determined based on the deflection data of each second target measuring point; wherein the above-mentioned second influencing factor is an influencing factor other than wind load and vehicle load.

[0080] In the embodiment of the present application, the second influencing factor is a factor that affects the deflection in addition to wind load and vehicle load. For example, the second influencing factor may include factors such as structural stiffness differences in different parts of the bridge, constraints, and dynamic characteristics.

[0081] In order to obtain more accurate deflection data, after determining the first influencing factor of wind load on deflection through the above steps, it is also necessary to determine the above second influencing factor, so as to further remove the interference of the above second influencing factor on the basis of removing the interference of vehicle load, and obtain a more accurate target influencing factor of wind load on deflection.

[0082] Exemplarily, the above-mentioned determination of the second target measuring point based on the wind load data of each first target measuring point and determination of the second influencing factor of the deflection based on the deflection data of each second target measuring point can be implemented as follows: for each monitoring moment, the first target measuring point with the same wind load data at the same monitoring moment is determined as the second target measuring point; for each second target measuring point, the deflection difference between the deflection data corresponding to the second target measuring point and all other second target measuring points is calculated, and the second influencing factor is determined based on the deflection difference.

[0083] The process of determining the second impact factor is described in detail below in a specific embodiment:

[0084] Taking a bridge as an example, different parts of the bridge structure have different shapes and different wind blocking and guiding effects. In addition, in a long-span bridge, the airflow between different structural components will affect each other. Therefore, at the same monitoring time, the wind loads on different first target measurement points will also vary. Specifically, the process of determining the second influencing factor of the bridge deflection may include the following steps:

[0085] S201: At any monitoring moment, according to the wind load data sizes of all first target measuring points, first target measuring points with the same wind load data size at the same monitoring moment are selected as second target measuring points, and their number is recorded as m.

[0086] S202: For each second target measuring point, determine the deflection difference between the current second target measuring point and all other second target measuring points as follows:

[0087]

[0088] in, Indicates the deflection difference between the xth second target measuring point and other second target measuring points among all the second target measuring points at the current monitoring moment; Indicates the deflection of the xth second target measuring point at the current monitoring moment; Indicates the deflection of the fth second target measuring point at the current monitoring moment; It represents the deflection difference between the xth second target measuring point and the fth second target measuring point. The smaller the value of , the more consistent the deflection of the second target measuring point is with other second target measuring points, which also proves that the abnormal deflection of the second target measuring point at the current monitoring moment is mainly caused by wind load.

[0089] S203: For each monitoring moment, calculate the corresponding second target measuring point under each first target measuring point through the above steps S201 and S202. , and use it as the second influencing factor for deflection.

[0090] In step S140 , a target impact factor of wind load on deflection is determined based on the first impact factor and the second impact factor.

[0091] In the embodiment of the present application, the target impact factor is the final impact factor of wind load on deflection obtained by correcting the first impact factor according to the second impact factor.

[0092] For example, the above-mentioned determination of the target influence factor of wind load on deflection based on the first influence factor and the second influence factor can be implemented as follows: determine the opposite of the second influence factor, and perform an exponential operation on the opposite of the second influence factor to obtain an intermediate operation result; multiply the intermediate operation result and the first influence factor, and normalize the result of the multiplication operation to obtain the target influence factor. Specifically, the target influence factor can be determined by the following formula:

[0093]

[0094] Where, It represents the target impact factor of wind load on deflection at the i-th abnormal deflection moment of the x-th first target measuring point; It represents the second influencing factor of the deflection at the i-th deflection abnormal moment of the x-th first target measuring point; It represents the first influence factor of wind load on deflection at the moment of abnormal deflection of the i-th deflection of the x-th first target measuring point. The smaller the second influence factor and the larger the first influence factor, the larger the target influence factor of wind load, indicating that the influence of wind load on deflection is greater.

[0095] In step S150 , a final value of the deflection data is determined based on the target impact factor to achieve quality assessment of the building to be tested.

[0096] In an embodiment of the present application, exemplarily, the above-mentioned determination of the final value of the deflection data based on the target influencing factor to achieve quality appraisal of the building to be tested can be achieved as follows: for each first target measuring point, the data weight of the deflection data at the time of normal deflection is set to 1, and the data weight of the deflection data at the time of abnormal deflection is set to the sum of 1 and the target influencing factor; based on the data weight of the deflection data of each first target measuring point at each monitoring moment, the data weight corresponding to each data in the moving average filter window is determined, and the final value of the deflection data is determined by the moving average filter to achieve quality appraisal of the building to be tested.

[0097] In a specific embodiment, the process of determining the final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested is described in detail below:

[0098] S301: Using 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 hit 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 above-mentioned processing of steps S110 to S140 on the deflection of each first target measuring point to obtain the above-mentioned target influencing factor;

[0099] S302: The weight of the deflection data of each first target measuring point at the time of normal deflection is set to 1, and the weight of the deflection data at the time of abnormal deflection is set to ;

[0100] S303: The weight of the deflection data of each first target measuring point at each monitoring moment is used as the weight of each data in the moving average filter window, and the moving average filter is used to process all the deflection data monitored at the first target measuring point to remove the influence of wind load on the deflection data during the deflection detection process. More accurate deflection data of each first target measuring point of the bridge is obtained after removing the influence of wind load, and is used for construction quality appraisal during the construction process of long-span bridges.

[0101] The present invention determines a first influence factor of wind load on deflection based on deflection data, wind load data, and vehicle load data monitored at a first target measuring point, determines a second target measuring point based on the wind load data of each first target measuring point, and determines a second influence factor of deflection based on the deflection data of each second target measuring point, thereby eliminating interference with deflection monitoring caused by factors other than wind load and vehicle load. Thus, a target influence factor of wind load on deflection (i.e., the final influence factor of wind load on deflection after removing interference factors) can be determined based on the first and second influence factors, and a final value of the deflection data can be determined based on the target influence factor. By analyzing the degree to which the deflection of each first target measuring point is affected by wind load at each monitoring moment, the influence factor of wind load on deflection is determined, and the influence factor is used as the weight of each data within a moving average filter window to process all deflection data. This eliminates the influence of wind load during deflection monitoring, thereby improving the accuracy of deflection monitoring.

[0102] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0103] The embodiment of the present disclosure provides a construction engineering deflection precision detection system for construction quality appraisal. Figure 3 As shown, the construction engineering deflection precision detection system 300 for construction quality appraisal may include a data monitoring module 310, a data processing module 320 and a quality appraisal module 330, wherein:

[0104] The data monitoring module 310 is used to determine a first target measuring point of the building to be measured and monitor the deflection data, wind load data and vehicle load data of the first target measuring point;

[0105] The data processing module 320 is configured to determine a first influencing factor of wind load on deflection based on the deflection data, wind load data, and vehicle load data;

[0106] The data processing module 320 is further configured to determine, at each monitoring moment, a second target measuring point based on the wind load data of each first target measuring point, and determine a second influencing factor of deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than wind load and vehicle load;

[0107] 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;

[0108] The quality assessment module 330 is used to determine the final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested.

[0109] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: obtain the specified deflection value corresponding to the building to be tested; determine the first difference between the deflection data corresponding to each first target measuring 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 measuring 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 influencing factor based on the correlation coefficient, the abnormal factor and the synchronization coefficient.

[0110] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: for each first target measuring point, cluster the deflection abnormality moment based on the vehicle load data, and divide the deflection abnormality moment into multiple cluster clusters; wherein, the size of the vehicle load data corresponding to the deflection abnormality moment in the same cluster cluster is the same; for each cluster cluster, determine the deflection difference sequence based on the first difference corresponding to each deflection data, calculate the second difference between adjacent first differences in the deflection difference sequence, and determine the abnormality factor corresponding to each deflection abnormality moment based on the first difference and the second difference; for all deflection abnormality moments in each cluster 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 of the deflection data and the wind load data based on the wind load change curve and the deflection change curve.

[0111] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: unify the dimensions of the deflection data and its corresponding wind load data at each deflection abnormality moment in each cluster; perform curve fitting based on the deflection data and wind load data after dimension unification to obtain the wind load change curve and the deflection change curve, and draw them in the same coordinate system.

[0112] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: determine 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 first maximum point, determine the average value of the difference 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, in the monitoring moment corresponding to the second maximum point of the deflection change curve, determine the target moment with the smallest time interval between the monitoring moments 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 weight of the first maximum point and the target time difference.

[0113] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: determine the difference between the number of first extreme points and the number of second extreme points; determine the first influencing factor based on the number difference, correlation coefficient, anomaly factor and synchronization coefficient.

[0114] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: for each monitoring moment, determine the first target measuring point with the same wind load data size at the same monitoring moment as the second target measuring point; for each second target measuring point, calculate the deflection difference between the deflection data corresponding to the second target measuring point and all other second target measuring points, and determine the second influencing factor based on the deflection difference.

[0115] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: determine the opposite number of the second influencing factor, and perform an exponential operation on the opposite number of the second influencing factor to obtain an intermediate operation result; multiply the intermediate operation result and the first influencing factor, and normalize the result obtained by the multiplication operation to obtain the target influencing factor.

[0116] In an embodiment of the present application, the above-mentioned quality appraisal module is specifically used to: for each first target measuring point, set the data weight of the deflection data at the time of normal deflection to 1, and set the data weight of the deflection data at the time of abnormal deflection to the sum of 1 and the target influencing factor; based on the data weight of the deflection data of each first target measuring point at each monitoring moment, determine the data weight corresponding to each data in the moving average filter window, and determine the final value of the deflection data through the moving average filter to achieve quality appraisal of the building to be tested.

[0117] The embodiment of the present invention can divide the functional modules of the construction engineering deflection precision detection system used for building quality appraisal according to the above-mentioned method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0118] In addition, the specific implementation details of the above-mentioned construction project deflection precision detection system for building quality appraisal have been described in detail in the corresponding position of the construction project deflection precision detection method for building quality appraisal, so they will not be repeated here.

[0119] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A precise detection method for construction engineering deflection for construction quality appraisal, characterized in that: The method comprises: Determining a first target measuring point of the building to be measured, and monitoring deflection data, wind load data, and vehicle load data of the first target measuring point; determining a first influencing factor of wind load on deflection based on the deflection data, the wind load data, and the vehicle load data; At each monitoring moment, determining a second target measuring point based on the wind load data of each first target measuring point, and determining a second influencing factor of the deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing factor other than the wind load and the 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; A final value of the deflection data is determined based on the target influencing factor to achieve quality assessment of the building to be tested.

2. The construction engineering deflection precision detection method for construction quality appraisal according to claim 1 is characterized in that: The determining a first influence factor of wind load on deflection based on the deflection data, the wind load data, and the vehicle load data includes: Obtaining a specified deflection value corresponding to the building to be tested; determining a first difference between the deflection data corresponding to each first target measuring point and the specified deflection value at each monitoring moment, identifying the monitoring moment when the first difference exceeds a preset threshold as an abnormal deflection moment, and identifying the monitoring moment when the first difference does not exceed the preset threshold as a normal deflection moment; For the deflection data of each of the first target measuring points at each of the deflection abnormality moments, calculating a correlation coefficient between the deflection data and the corresponding wind load data; Determining, based on the deflection data, the wind load data, and the vehicle load data, an abnormality factor corresponding to each abnormal deflection moment and a synchronization coefficient between the deflection data and the wind load data; The first influencing factor is determined based on the correlation coefficient, the anomaly factor, and the synchronicity coefficient.

3. The construction engineering deflection precision detection method for construction quality appraisal according to claim 2 is characterized in that: The determining, based on the deflection data, the wind load data, and the vehicle load data, of an abnormality factor corresponding to each abnormal deflection moment and a synchronization coefficient between the deflection data and the wind load data includes: For each of the first target measuring points, clustering the abnormal deflection moments based on the vehicle load data is performed to divide the abnormal deflection moments into a plurality of clusters; wherein the vehicle load data corresponding to the abnormal deflection moments in the same cluster have the same magnitude; For each of the clusters, determining a deflection difference sequence based on the first difference corresponding to each deflection data, calculating a second difference between adjacent first difference values in the deflection difference sequence, and determining an abnormality factor corresponding to each deflection abnormality moment based on the first difference and the second difference; For all the deflection abnormal moments in each of the clusters, the wind load change curve corresponding to the wind load data and the deflection change curve corresponding to the deflection data are determined, and the synchronization coefficient between the deflection data and the wind load data is determined based on the wind load change curve and the deflection change curve.

4. The construction engineering deflection precision detection method for construction quality appraisal according to claim 3 is characterized in that: The determining of the wind load variation curve corresponding to the wind load data and the deflection variation curve corresponding to the deflection data for all the deflection abnormal moments in each of the clusters includes: For each abnormal deflection moment in each cluster, unifying the deflection data and the corresponding wind load data; Curve fitting is performed based on the dimensionally unified deflection data and the wind load data to obtain the wind load variation curve and the deflection variation curve, and the curves are plotted in the same coordinate system.

5. The construction engineering deflection precision detection method for construction quality appraisal according to claim 3 is characterized in that: The determining 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 includes: Determining all first extreme value points of the wind load variation curve and a first maximum value point among the first extreme value points, and all second extreme value points of the deflection variation curve and a second maximum value point among the second extreme value points; For each of the first maximum points, determining an average of differences between the first maximum point and two adjacent data points before and after the first maximum point, and determining a weight of the first maximum point based on the average; For each time instant corresponding to the first maximum point, determine a target time instant with the smallest time interval between the monitoring times corresponding to the second maximum point of the deflection change curve and the monitoring times corresponding to the first maximum point, and calculate a target time difference between the time instant corresponding to the first maximum point and the target time instant; The synchronization coefficient is determined based on the number of the first maximum value points, the weight of the first maximum value points, and the target time difference.

6. The construction engineering deflection precision detection method for construction quality appraisal according to claim 5 is characterized in that: The determining the first influencing factor based on the correlation coefficient, the abnormality factor, and the synchronization coefficient includes: determining a difference between the number of the first extreme value points and the number of the second extreme value points; The first influencing factor is determined based on the number difference, the correlation coefficient, the anomaly factor, and the synchronicity coefficient.

7. The construction engineering deflection precision detection method for construction quality appraisal according to claim 1 is characterized in that: The determining of the second target measuring point according to the wind load data of each of the first target measuring points, and determining the second influencing factor of the deflection based on the deflection data of each of the second target measuring points, includes: For each monitoring moment, determining the first target measuring point with the same wind load data at the same monitoring moment as the second target measuring point; For each second target measuring point, a deflection difference between the second target measuring point and the deflection data corresponding to all other second target measuring points is calculated, and the second influencing factor is determined based on the deflection difference.

8. The construction engineering deflection precision detection method for construction quality appraisal according to claim 1 is characterized in that: The determining the target influence factor of the wind load on the deflection based on the first influence factor and the second influence factor includes: Determining the opposite number of the second impact factor, and performing an exponential operation on the opposite number of the second impact factor to obtain an intermediate operation result; A multiplication operation is performed on the intermediate operation result and the first influencing factor, and a normalization process is performed on the result obtained by the multiplication operation to obtain the target influencing factor.

9. The construction engineering deflection precision detection method for construction quality appraisal according to claim 2 is characterized in that: Determining the final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested includes: For each of the first target measuring points, the data weight of the deflection data at the time when the deflection is normal is set to 1, and the data weight of the deflection data at the time when the deflection is abnormal is set to the sum of 1 and the target influencing factor; The data weight corresponding to each data in the moving average filter window is determined based on the data weight of the deflection data of each first target measuring point at each monitoring moment, and the final value of the deflection data is determined by the moving average filter to achieve quality appraisal of the building to be measured.

10. A precise detection system for construction engineering deflection used for construction quality appraisal, characterized in that: The system comprises: a data monitoring module, configured to determine a first target measuring point of the 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 influencing factor of wind load on deflection based on the deflection data, the wind load data, and the vehicle load data; The data processing module is further configured to determine, at each monitoring moment, a second target measuring point based on the wind load data of each first target measuring point, and determine a second influencing factor of the deflection based on the deflection data of each second target measuring point; wherein the second influencing factor is an influencing 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 is used to determine a final value of the deflection data based on the target influencing factor to achieve quality assessment of the building to be tested.

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