Bridge structure deflection monitoring method used in bridge construction

Abstract

The present invention relates to the technical field of bridge deflection monitoring, and specifically to a bridge structure deflection monitoring method applied in the bridge construction process. By comparing the similarity of construction data of monitored and to-be-monitored bridge sections, combined with the numerical characteristics of the construction data, the reference weight of each monitored bridge is quantified to reflect its reference value in determining the monitoring points of the to-be-monitored bridge sections. The predicted points are reasonably determined on the to-be-monitored bridge sections using the location distribution information of the monitored bridge sections’ measuring points. The change fluctuations and time series characteristics of the deflection time series data of the monitored bridge sections’ measuring points are further analyzed, and combined with the reference weights, the predicted monitoring sensitivity of the predicted points of the to-be-monitored bridge sections’ predicted points are calculated to determine their contribution to the monitoring of the bridge deflection. The optimal monitoring points of the to-be-monitored bridge sections are determined by comprehensively considering the change trend of the deflection time series data, the construction similarity, and the predicted monitoring sensitivity, thereby improving the accuracy and reliability of the monitoring results.

Description

Technical Field

[0001] The invention relates to the technical field of bridge deflection monitoring, and in particular to a bridge structure deflection monitoring method applied in a bridge construction process. Background Art

[0002] During the bridge construction process, monitoring the deflection of the bridge structure is an important part of ensuring construction safety and quality. The deflection of the bridge structure refers to the vertical displacement of the mid-span or specific cross-section structural elements (such as beams, arches, cables, etc.) perpendicular to the bridge axis under the action of load. It is an important indicator to measure the bearing capacity and structural performance of the bridge, and directly reflects the status and safety performance of the bridge. Therefore, monitoring the deflection of the bridge is of great significance for evaluating the health of the bridge, preventing structural damage and ensuring traffic safety.

[0003] With the development of sensor technology and data analysis technology, when monitoring the deflection of a bridge, a dedicated monitoring device, such as a deflection detector, is usually used, which is installed at the bottom of the bridge. The displacement sensor on the monitoring device can measure the deflection of the bridge in real time. However, when installing the monitoring equipment in the prior art, measuring points are usually set in advance, such as installing the monitoring equipment at fixed intervals. However, due to the different stages of bridge construction, the stress and deformation conditions of the bridge will also change. Therefore, in the actual construction process, if the pre-set measuring points are still used to monitor the deflection of the bridge, the selection of the monitoring points may be improper, thereby affecting the final monitoring results. Summary of the invention

[0004] In order to solve the problem that the stress and deformation conditions of the bridge will change during the different stages of bridge construction, if the pre-set measuring points are still used to monitor the bridge deflection during the actual construction process, the technical problem that the monitoring points are improperly selected may affect the final monitoring results. The purpose of the present invention is to provide a bridge structure deflection monitoring method applied to the bridge construction process, and the technical scheme adopted is as follows:

[0005] Obtain the time series data of the deflection of each measuring point in the monitored bridge section, wherein the monitored bridge section is the bridge section where the deflection monitoring equipment has been installed; obtain various construction data of each bridge section during the bridge construction process;

[0006] Analyze the similarity between the construction data of the monitored bridge sections and the bridge sections to be monitored, and determine the construction similarity between each monitored bridge section and the bridge section to be monitored; combine the construction similarity of each monitored bridge section with the numerical characteristics of its own construction data to determine the reference weight of each monitored bridge section;

[0007] According to the position distribution of the measuring points on all monitored bridge sections, the predicted position of each measuring point on the bridge section to be monitored is determined; the fluctuation changes between the deflection time series data of the measuring points on the monitored bridge sections and the time series characteristics between the fluctuation changes are analyzed, and the predicted monitoring sensitivity of the predicted point position on the bridge section to be monitored is determined in combination with the reference weight of the monitored bridge sections;

[0008] The monitoring points are selected from all the predicted points of the bridge section to be monitored based on the changing trend of the deflection time series data of the measuring points in each monitored bridge section, the corresponding construction similarity, and the prediction sensitivity of the corresponding predicted points in the bridge section to be monitored.

[0009] Furthermore, the construction data at least includes the length of the bridge section, the location of the bridge section, the duration of bridge construction, the number of deflection monitoring devices on the monitored bridge section, and the installation location.

[0010] Furthermore, the method for obtaining the construction similarity includes:

[0011] For any two bridge segments, the absolute value of the difference between the lengths of the bridge segments is taken as the length difference factor;

[0012] The Euclidean distance between each monitored bridge segment and the starting point of the bridge segment to be monitored is used as the distance factor;

[0013] The product of the length difference factor and the distance factor of each monitored bridge section and the bridge section to be monitored is used as the construction process difference degree between each monitored bridge section and the bridge section to be monitored;

[0014] Based on the difference in bridge segment length between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored, as well as the difference in bridge construction time, determine the difference in the previous segment construction between each monitored bridge segment and the bridge segment to be monitored;

[0015] The product of the construction process difference between each monitored bridge segment and the bridge segment to be monitored and the difference in the previous segment construction is negatively correlated and normalized to obtain the value, which is used as the construction similarity between each monitored bridge segment and the bridge segment to be monitored.

[0016] Furthermore, the method for obtaining the difference of the front-end construction includes:

[0017] The absolute value of the difference in bridge construction time between the previous bridge section of each monitored bridge section and the previous bridge section of the bridge section to be monitored is used as the difference factor of the previous construction time between each monitored bridge section and the bridge section to be monitored;

[0018] The product of the length difference factor between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored and the difference factor of the front-end construction time between each monitored bridge segment and the bridge segment to be monitored is taken as the front-end construction difference degree between each monitored bridge segment and the bridge segment to be monitored.

[0019] Furthermore, the method for obtaining the reference weight includes:

[0020] In each monitored bridge section, the ratio of the number of deflection monitoring devices to the length of the bridge section is taken as the measurement point density;

[0021] The value obtained by normalizing the product of the construction similarity corresponding to each monitored bridge section and the density of the measuring points is used as the reference weight of each monitored bridge section.

[0022] Furthermore, the method for obtaining the predicted point includes:

[0023] In each monitored bridge segment, the ratio of the Euclidean distance between each measuring point and the starting point of the monitored bridge segment to the bridge segment length of the monitored bridge segment is used as the position characteristic value of each measuring point;

[0024] The product of the position characteristic value corresponding to each measuring point in all monitored bridge sections and the bridge section length of the bridge section to be monitored is used as the position parameter of each predicted point;

[0025] On the bridge section to be monitored, starting from the starting point of the bridge section, the length corresponding to the position parameters of each predicted point is measured, and the position of the end point of the length is marked as the predicted point.

[0026] Furthermore, the method for obtaining the predicted monitoring sensitivity includes:

[0027] For any measuring point on any monitored bridge segment, the extreme value point in the deflection time series data of the measuring point is obtained, and the data between two adjacent minimum value points is taken as a transportation data segment;

[0028] On the monitored bridge section, all measuring points are sorted according to the bridge construction direction to obtain a measuring point sequence;

[0029] In the measuring point sequence, based on the time series characteristics between the transport data segments in the deflection time series data of all measuring points, the transport data segments are classified to determine the target data segments, and based on the numerical characteristics and time length of the target data segments contained in the deflection time series data of each measuring point, the deformation sensitivity of each measuring point is determined;

[0030] For any predicted point on the bridge section to be monitored, the corresponding measuring point of the predicted point on all monitored bridge sections is used as a comparison point;

[0031] The deformation sensitivity of the comparison point is weighted by using the reference weights of the monitored bridge sections to which all the comparison points of the prediction point belong, so as to obtain the prediction monitoring sensitivity of the prediction point.

[0032] Furthermore, in the measuring point sequence, based on the time series characteristics between the transport data segments in the deflection time series data of all measuring points, the transport data segments are classified to determine the target data segments, and based on the numerical characteristics and time length of the target data segments contained in the deflection time series data of each measuring point, the deformation sensitivity of each measuring point is determined, including:

[0033] In the measuring point sequence, each transport data segment in the deflection time series data of the first measuring point is used as a data segment to be measured in sequence according to the time sequence;

[0034] For each data segment to be measured, in the deflection time series data of the second measuring point, the first unclassified transportation data segment located after the data segment to be measured in time series is used as the first comparison data segment; in the deflection time series data of the third measuring point, the first unclassified transportation data segment located after the first comparison data segment in time series is used as the second comparison data segment; in the deflection time series data of the fourth measuring point, the first unclassified transportation data segment located after the second comparison data segment in time series is used as the third comparison data segment;

[0035] By analogy, the deflection time series data of all measuring points are traversed to obtain all comparison data segments of each data segment to be measured;

[0036] Classify each transport data segment and all corresponding comparison data segments in the deflection time series data of the first measuring point into one category, and when the total number of transport data segments in a category is equal to the total number of measuring points in the measuring point sequence, the transport data segments in this category are regarded as a type of target data segments;

[0037] For any measuring point, the average of the absolute values ​​of the slopes of all data points in each target data segment in the deflection time series data of the measuring point is calculated as the deflection change factor;

[0038] Based on the deflection change factor and time length of each target data segment in the deflection time series data of the measuring point, the sensitivity adjustment factor corresponding to each target data segment is determined, and the deflection change factor is negatively correlated with the sensitivity adjustment factor, and the time length is positively correlated with the deflection change factor;

[0039] In the deflection time series data of the measuring point, the maximum deflection value in the target data segment is weighted using the sensitivity adjustment factors in all target data segments, so as to obtain the deformation sensitivity of the measuring point.

[0040] Furthermore, the method for acquiring the monitoring points includes:

[0041] Analyze the change trend of the deflection time series data of each measuring point in each monitored bridge section to obtain the material accumulation parameters of each measuring point;

[0042] For any predicted point, the material accumulation parameters of the comparison points are weighted by using the construction similarity corresponding to the monitored bridge sections of all the comparison points of the predicted point, so as to predict the material accumulation index of the predicted point;

[0043] The product of the material accumulation index of each prediction point and the prediction monitoring sensitivity is normalized to obtain the value as the key monitoring index of each prediction point;

[0044] Among all the predicted points on the bridge section to be monitored, the predicted points whose monitoring key index is greater than the preset key threshold are used as monitoring points of the bridge section to be monitored.

[0045] Furthermore, the analysis of the change trend of the deflection time series data at all measuring points in each monitored bridge section to obtain the material accumulation parameters of each measuring point includes:

[0046] For any measuring point on each monitored bridge segment, the mean of the deflection values ​​in each transport data segment in the deflection time series data of the measuring point is calculated as the mean deflection value of each transport data segment;

[0047] In the deflection time series data of the measuring point, the deflection mean of the transport data segment is weighted by utilizing the difference between the deflection mean values ​​of adjacent transport data segments, thereby calculating the material accumulation parameter of the measuring point.

[0048] The present invention has the following beneficial effects:

[0049] During the construction of a bridge, the entire bridge is usually segmented based on special structures such as bridge fulcrums, so that different construction methods are adopted for different segments. For the completed bridge segments, monitoring equipment needs to be installed to monitor the changes in deflection data in real time. In the present invention, it is necessary to optimize the setting of the measuring points on the bridge segment to be monitored. Specifically, firstly, the deflection time series data at each measuring point in the monitored bridge segment and the construction data of each bridge segment during the bridge construction process are obtained to provide a rich data source for the subsequent analysis process. By comparing the similarity between the construction data of the monitored bridge segment and the bridge segment to be monitored, the construction similarity between the two is obtained. At the same time, the construction similarity is combined with the numerical characteristics of the construction data of the monitored bridge segment itself, and the reference weight of each monitored bridge segment can be quantified. The reference weight reflects the reference value of each monitored bridge segment in the subsequent determination process of the monitoring points in the bridge segment to be monitored. Then, the position distribution information of the measuring points on the monitored bridge segment is used to reasonably determine the predicted point position on the bridge segment to be monitored, which preliminarily avoids the problem of improper selection of monitoring points that may be caused by pre-setting measuring points. Furthermore, by analyzing the fluctuations and time series characteristics of the deflection time series data of the measuring points on the monitored bridge segment, and combining with the reference weights, the predicted monitoring sensitivity of the predicted points on the bridge segment to be monitored can be calculated. This step can determine the contribution of each predicted point to the monitored bridge deflection, and provide key information for the subsequent determination of the monitoring points. Finally, by comprehensively considering multiple factors, including the changing trend of the deflection time series data of the measuring points in the monitored bridge segment, the construction similarity, and the predicted monitoring sensitivity of the predicted points in the bridge segment to be monitored, the optimal monitoring point can be determined among all the predicted points in the bridge segment to be monitored. The present invention ensures that the selection of monitoring points is both reasonable and effective, and can accurately reflect the changes in the deflection of the bridge, thereby improving the accuracy and reliability of the monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0051] Figure 1 A method flow chart of a bridge deflection monitoring method applied in a bridge construction process provided by an embodiment of the present invention;

[0052] Figure 2 A method flow chart of a method for obtaining construction similarity provided by an embodiment of the present invention;

[0053] Figure 3 A method flow chart of a method for obtaining predicted monitoring sensitivity provided by an embodiment of the present invention;

[0054] Figure 4 A method flow chart of a method for determining monitoring points provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a bridge structure deflection monitoring method applied in the bridge construction process proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0056] 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.

[0057] A specific scheme of a bridge structure deflection monitoring method applied in a bridge construction process provided by the present invention is described in detail below with reference to the accompanying drawings.

[0058] See also Figure 1 , which shows a method flow chart of a bridge structure deflection monitoring method applied in a bridge construction process provided by an embodiment of the present invention, the method comprising the following steps:

[0059] Step S1: Obtain the deflection time series data of each measuring point in the monitored bridge section, wherein the monitored bridge section is a bridge section on which the deflection monitoring equipment has been installed; obtain various construction data of each bridge section during the bridge construction process.

[0060] Deformation measurement during bridge construction is a necessary part of the bridge construction process, which can provide effective support for bridge construction safety and scientific decision-making in a timely manner. Among them, deflection monitoring is an important detection item for vertical displacement measurement of bridge construction. In order to objectively reflect the actual deformation or deformation trend of the bridge, it is necessary to select reasonable operation methods and monitoring requirements to ensure the quality of deformation measurement data, so the reasonable layout of monitoring points is a key project to ensure the quality of measurement data.

[0061] The construction process of bridges usually divides the bridge into sections based on special structural parts such as bridge fulcrums. There are differences in the construction methods and construction equipment used in different stages. In the bridge design stage, the initial monitoring points will be selected in combination with the bridge design structure. The setting of these initial monitoring points is only affected by the bridge structure. However, in the actual construction process, due to the combined effects of the environment, equipment, and bridge structure, the initial monitoring points may not meet the construction process. The demand for monitoring quality of deflection data at different locations of the bridge structure, therefore, needs to be further analyzed in combination with the data generated during the actual construction process.

[0062] In order to ensure the construction quality and structural safety, during the bridge construction process, deflection monitoring is gradually implemented at different stages of bridge construction. Specifically, deflection monitoring equipment will be installed at the bottom of the built bridge segment. It can be evenly distributed along the center line of the bridge at certain intervals, and priority will be given to the key parts of the bridge and the positions susceptible to stress concentration, such as the mid-span, the quarter points, etc. The deflection monitoring equipment can specifically use a deflection sensor or a displacement monitor. In the embodiment of the present invention, the deflection time series data of each measuring point in the monitored bridge segment where the monitoring equipment has been installed is obtained, the horizontal axis is time, the vertical axis is the deflection value, and the deflection values ​​are connected using the least squares method, so that the deflection time series data is expressed in the form of a time series curve. At the same time, it is also necessary to obtain various construction data of each bridge segment during the bridge construction process (at least including the length of the bridge segment, the location information of the bridge segment, the bridge construction time, the number of monitoring equipment on each monitored bridge segment, and the installation position, etc.). Based on these data, the measuring point positions on the bridge segment to be monitored can be arranged more reasonably and accurately in the subsequent process.

[0063] It should be noted that the length of the deflection time series data is set from the initial stage of bridge construction to the completion of the bridge section to be monitored; the method for obtaining various construction data can be directly obtained through construction logs and other methods; before setting the deflection monitoring points, it is generally necessary to lay out working base points and reference points to facilitate the subsequent deflection monitoring. The reference points are generally established in stable and reliable positions, such as on the solid foundations or piers at both ends of the bridge, and the number of reference points is not less than two, which are used to form a stable monitoring network; the working base points are generally set on the bearing platform, and the specific layout can be adjusted according to the implementation scenario, which is not limited here; in the embodiment of the present invention, since the essence is to adjust the measurement point setting on the bridge section to be monitored based on the measurement point setting on the monitored bridge section (the measurement point setting has been completed), the application scenario should not be the initial stage of bridge construction, and there should be several monitored bridge sections, which can be specifically set to 3.

[0064] Step S2: Analyze the similarity between the construction data of the monitored bridge segment and the bridge segment to be monitored, and determine the construction similarity between each monitored bridge segment and the bridge segment to be monitored; combine the construction similarity of each monitored bridge segment with the numerical characteristics of its own construction data to determine the reference weight of each monitored bridge segment.

[0065] The main purpose of the embodiment of the present invention is to optimize the layout of measuring points on the bridge segment to be monitored based on the position distribution of measuring points on the monitored bridge segment, so as to make it more reasonable. Therefore, in order to more accurately layout the measuring points on the bridge segment to be monitored, the reference value of the layout of measuring points on each monitored bridge segment can be analyzed. Specifically, since the bridge construction process involves many factors, such as construction methods, construction requirements, etc., these factors may differ significantly between different bridge segments; therefore, by comparing the construction data of the monitored bridge segment and the bridge segment to be monitored, the similarity between them can be identified, and the construction similarity between the two bridge segments can be quantified, and the construction similarity corresponding to each monitored bridge segment can be further combined with the numerical characteristics of its own construction data to obtain the reference weight of each monitored bridge segment, so that the reference value of the monitored bridge segment to the bridge segment to be monitored can be more accurately determined. This helps to more accurately layout the monitoring points on the bridge segment to be monitored in the subsequent steps.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining construction similarity includes:

[0067] See also Figure 2 , which shows a method flow chart of a method for obtaining construction similarity in one embodiment of the present invention, the method comprising the following steps:

[0068] Step S201: Determine the difference in construction process between each monitored bridge segment and the bridge segment to be monitored based on the difference in bridge segment length between the monitored bridge segment and the bridge segment to be monitored, as well as the relationship between the bridge segment positions.

[0069] During the bridge design and construction process, the bearing capacity of a bridge segment can usually be reflected by the length of the bridge segment. As the length of the bridge segment changes, the construction materials, methods, etc. will also change accordingly to maintain sufficient strength and rigidity of the bridge segment.

[0070] Therefore, firstly, the absolute value of the difference between the bridge segment lengths of each monitored bridge segment and the bridge segment to be monitored is taken as the length difference factor between the two. The larger the length difference factor, the lower the consistency of the bridge lengths of the two bridge segments, and the construction methods of the two may be greatly different.

[0071] On the other hand, bridge sections at different locations may have different natural conditions due to their different construction times, and their different locations will also play different roles in the overall bridge. Therefore, the positional relationship between bridge sections can be used as an indicator to evaluate their similarity.

[0072] Therefore, the Euclidean distance between each monitored bridge segment and the starting point of the bridge segment to be monitored is taken as the distance factor. The smaller the distance factor is, the closer the two segments are, and the more similar the environmental conditions of the two segments during construction are. At the same time, the more consistent the roles of the two segments in the overall bridge are, so the construction technology may also be more consistent.

[0073] Finally, the product of the length difference factor and the distance factor between each monitored bridge segment and the bridge segment to be monitored is taken as the construction process difference between each monitored bridge segment and the bridge segment to be monitored. Based on the above analysis, it can be seen that the greater the construction process difference, the higher the difference in construction technology between the two bridge segments, and the reference value of the monitored bridge segment for the layout of measuring points on the bridge segment to be monitored will be reduced.

[0074] Step S202: Based on the difference in bridge segment length between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored, and the difference in bridge construction time, determine the difference in the previous construction between each monitored bridge segment and the bridge segment to be monitored.

[0075] When building a bridge, the construction process of the bridge is often continuous and mutually influential. The construction conditions of the previous section often have a direct or indirect impact on the subsequent construction. Therefore, when evaluating the construction similarity of two bridge sections, it can also be quantified by the similarity of the construction conditions between the previous bridge sections.

[0076] Since most of the construction equipment, vehicles, etc. mainly stay in the previous section during construction, this means that the construction duration of the previous section will directly affect the allocation of construction resources and time for subsequent sections. Therefore, the absolute value of the difference in bridge construction duration between the previous bridge section of each monitored bridge section and the previous bridge section of the bridge section to be monitored is used as the difference factor of the front section construction duration between each monitored bridge section and the bridge section to be monitored. The larger the difference factor of the front section construction duration, the greater the difference in the efficiency of the front section bridge construction between the monitored bridge section and the bridge section to be monitored. Under its influence, the lower the construction similarity between the monitored bridge section and the bridge section to be monitored.

[0077] Given that the construction of bridges often needs to be adjusted according to the characteristics of the segments, if there is a large difference in length between the previous bridge segment of the monitored bridge segment and the previous bridge segment of the bridge segment to be monitored, the construction method may need to be adjusted accordingly, which will also affect the construction situation. Therefore, based on the calculation method of the length difference factor in step S201, the absolute value of the difference in bridge segment length between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored is also calculated as the length difference factor. At this time, the larger the length difference factor, the lower the consistency of the bridge lengths of the two bridge segments, and the construction methods of the two may be greatly different, and the more inconsistent the impact on the subsequent bridge segments.

[0078] Finally, the product of the length difference factor between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored and the difference factor of the front-end construction time between each monitored bridge segment and the bridge segment to be monitored is taken as the front-end construction difference between each monitored bridge segment and the bridge segment to be monitored. The larger the front-end construction difference, the lower the construction similarity between the previous bridge segment of the monitored bridge segment and the previous bridge segment of the bridge segment to be monitored. Therefore, under the premise that the construction conditions of the bridge segments have a continuous impact, the construction similarity between the monitored bridge segments and the bridge segments to be monitored will also be lower.

[0079] Step S203: Determine the construction similarity between each monitored bridge segment and the bridge segment to be monitored by comprehensively considering the construction process difference and the previous construction difference between each monitored bridge segment and the bridge segment to be monitored.

[0080] Based on the analysis in the previous steps, it can be seen that the construction process difference and the previous construction difference between the monitored bridge segment and the bridge segment to be monitored are negatively correlated with the construction similarity of the two. Therefore, the product of the construction process difference and the previous construction difference between each monitored bridge segment and the bridge segment to be monitored is negatively correlated and normalized, thereby realizing logical relationship correction, and the value after negative correlation mapping and normalization is used as the construction similarity between each monitored bridge segment and the bridge segment to be monitored. At this time, the greater the construction similarity, the more consistent the construction conditions between the two bridge segments.

[0081] It should be noted that the negative correlation mapping and normalization method here can be used Function, where It represents an exponential function with the natural constant e as the base, and x represents the independent variable.

[0082] Based on the above process, the construction similarity between each monitored bridge section and the bridge section to be monitored can be obtained. Given that the higher the construction similarity of the monitored bridge section, the more consistent its construction conditions are with the bridge section to be monitored, so the position of the deflection measuring point on it is more instructive for the setting of the measuring points on the bridge section to be monitored, and has a higher reference value; and the numerical characteristics of certain construction data of each monitored bridge section itself can characterize the distribution of its deflection measuring points, so the two are combined to jointly determine the reference weight of each monitored bridge section, providing a basis for the subsequent setting of deflection measuring points on the bridge section to be monitored.

[0083] Preferably, in one embodiment of the present invention, the method for obtaining the reference weight includes:

[0084] When the monitoring density of a monitored bridge section is large, it means that the monitoring importance of the bridge section is high. Therefore, when using it as a basis to set the measuring points on the bridge to be monitored, its reference value should also be improved. Therefore, the density of measuring points is used as an indicator for calculating the reference weight.

[0085] The density of measuring points can be specifically characterized by the ratio of the number of measuring points to the length of the bridge section. Therefore, in each monitored bridge section, the ratio of the number of deflection monitoring devices to the length of the bridge section is taken as the measuring point density. The larger the measuring point density, the more deflection monitoring devices there are and the denser the installation locations on the bridge section.

[0086] Finally, the product of the construction similarity and the measurement point density corresponding to each monitored bridge segment is normalized to obtain the value as the reference weight of each monitored bridge segment. The larger the reference weight is, the higher the reference value of the monitored bridge segment to the setting of the measurement points on the bridge segment to be monitored. Among them, normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0087] Step S3: Determine the predicted position of each measuring point on the bridge segment to be monitored based on the position distribution of all the measuring points on the monitored bridge segments; analyze the fluctuation changes between the deflection time series data of the measuring points on the monitored bridge segments and the time series characteristics between the fluctuation changes, and determine the predicted monitoring sensitivity of the predicted point position on the bridge segment to be monitored in combination with the reference weight of the monitored bridge segments.

[0088] Since the deflection monitoring points on the monitored bridge segments have been laid out, the predicted points can be determined on the bridge segments to be monitored based on the position distribution of the points on the monitored bridge segments. The predicted points are the key positions for subsequent deflection monitoring. In order to determine the best monitoring points among all the predicted points, the fluctuations and changes between the deflection time series data of the points on the monitored bridge segments and the time series characteristics between the fluctuations can be analyzed. These characteristics reflect the fluctuations and changes in the deflection data of the points on the monitored bridge segments in different time periods. Then, combined with the reference weight of the monitored bridge segments (the reference weight reflects the similarity between the monitored bridge segments and the bridge segments to be monitored in terms of construction methods, material use, structural form, etc.), the predicted monitoring sensitivity of the predicted points on the bridge segments to be monitored is determined. The predicted sensitivity reflects the difficulty or importance of the predicted points that may be monitored when the bridge segments are deformed in the future, providing a basis for the subsequent screening of the best monitoring points.

[0089] Preferably, in one embodiment of the present invention, the method for obtaining the predicted point position includes:

[0090] In each monitored bridge section, the ratio of the Euclidean distance between each measuring point and the starting point of the monitored bridge section to the bridge section length of the monitored bridge section is used as the position characteristic value of each measuring point. The position characteristic value of each measuring point is used to describe the relative position of each measuring point in the monitored bridge section to which it belongs.

[0091] Then, the product of the position characteristic value corresponding to each measuring point in all monitored bridge sections and the bridge section length of the bridge section to be monitored is used as the position parameter of each predicted point. The position parameter is used to convert the position information of the measuring point in the monitored bridge section to the bridge section to be monitored.

[0092] Finally, the specific predicted point position needs to be determined on the bridge segment to be monitored. The length corresponding to the position parameter of each predicted point position can be measured from the starting point of the bridge segment on the bridge segment to be monitored, and the position of the end point of the length is marked as the predicted point position. At this point, the relative position of each measuring point in each monitored bridge segment on the monitored bridge segment to which it belongs can be projected onto the bridge segment to be monitored through this method.

[0093] In order to make the best prediction point finally obtained to better realize the deflection monitoring of the bridge, its sensitivity to the monitoring deflection can be evaluated. In view of the fact that the corresponding measuring point of each prediction point in the monitored bridge segment has been obtained in the previous steps, the prediction monitoring sensitivity of the prediction point can be obtained by analyzing the deflection time series data of the measuring point on the monitored bridge segment. When analyzing the sensitivity of deflection monitoring, the fluctuation change of the deflection time series data can more intuitively reflect the deflection change of the bridge. At the same time, the reference weight of the monitored bridge segment represents the reference value of the monitored bridge segment. Therefore, in an embodiment of the present invention, the fluctuation change of the deflection time series data of the measuring points on the monitored bridge segment and the time series characteristics between the fluctuation changes are analyzed, and combined with the reference weight of the monitored bridge segment, the prediction monitoring sensitivity of the prediction point on the bridge segment to be monitored is determined.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining the predicted monitoring sensitivity includes:

[0095] See also Figure 3 , which shows a method flow chart of a method for obtaining predicted monitoring sensitivity in one embodiment of the present invention, the method comprising the following steps:

[0096] Step S301: based on the local fluctuation in the deflection time series data of each measuring point, the deflection time series data is divided to obtain transportation data segments.

[0097] During the bridge construction process, when the construction materials are transported through the completed bridge sections, the deflection of each measuring point on the bridge section will change during the transportation process. Therefore, based on the local fluctuation in the deflection time series data of each measuring point, the deflection time series data is divided to obtain the transportation data segment.

[0098] In the deflection time series data, all extreme value points are obtained, and the data between two adjacent minimum value points in the time series is regarded as a transportation data segment.

[0099] Step S302: In each monitored bridge segment, the time series characteristics between the transport data segments in the deflection time series data of all measuring points are analyzed, the transport data segments are classified, and the target data segments in the deflection time series data of each measuring point are determined.

[0100] Since the transportation weight of construction materials may be different during the transportation process, the impact of different transportation events on deflection monitoring will also be different. Therefore, in order to improve the accuracy of subsequent sensitivity quantification, the sensitivity of different measuring points to monitoring the same transportation event can be evaluated.

[0101] First, on each monitored bridge section, all measuring points are sorted according to the direction of bridge construction to obtain a measuring point sequence (the order of the measuring points in the measuring point sequence represents the order of the measuring points that vehicles pass when transporting construction materials).

[0102] Then, in the measuring point sequence, each transport data segment in the deflection time series data of the first measuring point is used as the data segment to be measured in sequence.

[0103] For each data segment to be measured, in the deflection time series data of the second measuring point, the first unclassified transport data segment that is located after the data segment to be measured in time series is used as the first comparison data segment; in the deflection time series data of the third measuring point, the first unclassified transport data segment that is located after the first comparison data segment in time series is used as the second comparison data segment; in the deflection time series data of the fourth measuring point, the first unclassified transport data segment that is located after the second comparison data segment in time series is used as the third comparison data segment.

[0104] By analogy, after traversing the deflection time series data of all measuring points, all comparison data segments of each data segment to be measured can be obtained.

[0105] Since the comparison data segments corresponding to the data segments to be measured in each measuring point can be regarded as representing the same transportation event with a greater probability, each transportation data segment and all corresponding comparison data segments in the deflection time series data of the first measuring point are classified into one category.

[0106] At this point, the transportation data segments in the deflection time series data of each measuring point have been classified. It should be noted that there are some unclassified transportation data segments. It is believed that the fluctuations of such transportation data segments may be the deflection changes caused by the transportation vehicles passing through other measuring points, which will not be discussed here. At the same time, if the number of transportation data segments in a certain category is not equal to the total number of measuring points in the measuring point sequence, it is believed that it cannot represent a complete transportation process, which will not be discussed here either. Only when the total number of transportation data segments in a certain category is equal to the total number of measuring points in the measuring point sequence, the transportation data segments in this category are regarded as a type of target data segments.

[0107] The above classification process is illustrated by an example: for example, there are three measuring points on a monitored bridge section, which are recorded as measuring point a, measuring point b, and measuring point c; after step S301, there are several transport data segments in the deflection time series data of each measuring point, which are recorded as a1, a2, a3, b1, b2, b3, b4, c1, c2, c3, c4, c5. First, for the first transport data segment a1 in the time series of the deflection time series data of measuring point a (the starting and ending points are 0 and 3 respectively), in the deflection time series data of measuring point b, determine the first unclassified transport data segment after time 3, if it is b2 (the starting and ending points are 5 and 9 respectively), then b2 is used as a1. A comparison data segment is identified, which is recorded as the first comparison data segment. At this time, b2 has been classified. Then, in the deflection time series data of measuring point c, the first unclassified transport data segment after time 9 is determined. If it is c2, then c2 is used as a comparison data segment of a1, which is recorded as the second comparison data segment. At this time, c2 has been classified. The first traversal is completed. Then, the second transport data segments a2 and a3 in the time series of the deflection time series data of measuring point a are sequentially subjected to the same operation. After the operation is completed, a1, a2 and a3 each obtain all the corresponding comparison data segments, and a1 and all the corresponding comparison data segments can reflect the deflection monitoring situation corresponding to the same transport event. The same is true for a2 and a3. Therefore, each transport data segment (a1, a2, a3) in the deflection time series data of the first measuring point and its corresponding comparison data segment are classified into one category.

[0108] Step S303: Determine the deformation sensitivity of each measuring point based on the deflection change and time length of the target data segment contained in the deflection time series data of each measuring point.

[0109] In the actual transportation process, the speed of the transport vehicle at different locations is different, and the dynamic loads generated are also different: the moving speed of the vehicle affects its impact force and vibration frequency on the bridge. When the operating frequency of the vehicle is close to the natural frequency of the bridge, the deflection is more likely to increase. However, it is difficult to match the vehicle speed with the natural frequency of the bridge: for example, the natural frequency of a simply supported beam may be around 10Hz, while the natural frequency of a suspension bridge or a cable-stayed bridge may be lower than 1Hz. To take an extreme example, if the natural frequency of a bridge is 1Hz, theoretically, the vehicle needs to travel at a speed of one complete bridge span per second to reach the resonance condition. Such a speed is unrealistic for ordinary vehicles; and the speed of the transport vehicle is usually relatively low. Therefore, in the embodiment of the present invention, it is believed that the transport speed is positively correlated with the impact of deflection monitoring. Therefore, in order to improve the accuracy of sensitivity measurement, it is necessary to eliminate the influence of the transport speed.

[0110] The transport speed can be analyzed by the length of the target data segment and the change of the deflection value therein.

[0111] First, since the increase in transportation speed may lead to more drastic changes in the deflection value, for any measuring point, the average of the absolute values ​​of the slopes of all data points in each target data segment in the deflection time series data of the measuring point is calculated as the deflection change factor; the larger the deflection change factor, the more drastic the change in the deflection value, which implies that the faster the transportation speed, the greater the impact on deflection monitoring.

[0112] Then, based on the deflection change factor and time length of each target data segment in the deflection time series data of the measuring point, the sensitive adjustment factor corresponding to each target data segment is determined. Since the longer the time length, the slower the transportation speed, the smaller the impact on deflection monitoring.

[0113] Therefore, in this embodiment of the present invention, the sum of the deflection change factor and the preset parameter is used as the denominator, and the time length is used as the numerator, so as to obtain the sensitivity adjustment factor corresponding to each target data segment. The smaller the sensitivity adjustment factor, the faster the transportation speed, that is, the higher the degree of influence of the transportation speed in the transportation process on the deflection monitoring, and the data fluctuation reflected in the deflection time series data is more likely to be caused by the transportation speed; conversely, the larger the sensitivity adjustment factor, the smaller the degree of influence of the transportation speed in the transportation process on the deflection detection, which means that the deflection value in the deflection time series data can better represent the real bridge deformation. It should be noted that the role of the preset parameter is mainly to prevent the denominator from being 0, and the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0114] Finally, considering that the maximum value in the deflection time series data can reflect the sensitivity of different measuring points on the bridge to deflection data monitoring to a certain extent; at the same time, in order to reduce the impact of transportation speed, in the deflection time series data of each measuring point, the maximum value in the target data segment is weighted by the sensitivity adjustment factor in all target data segments, so as to obtain the deformation sensitivity of the measuring point. The specific process includes: in the deflection time series data of each measuring point, the product of the sensitivity adjustment factor corresponding to each target data segment and the maximum deflection value in each target data segment is used as the deformation sensitivity factor. The larger the deformation sensitivity factor, the higher the possibility that the maximum deflection value in the target data segment represents the real bridge deformation. The cumulative value of the deformation sensitivity factors of all target data segments in the deflection time series data of each measuring point is used as the deformation sensitivity of each measuring point. The larger the deformation sensitivity, the more accurately the deflection time series data at the measuring point can represent the deformation of the bridge, that is, the setting of the measuring point is more accurate and reasonable.

[0115] Step S304: Determine the predicted monitoring sensitivity of each predicted point on the bridge section to be monitored based on the deformation sensitivity of all measuring points, the reference weight of the monitored bridge section to which the measuring points belong, and the correspondence between the measuring points and the predicted points.

[0116] For any predicted point on the bridge section to be monitored, the corresponding measuring point of the predicted point on all monitored bridge sections is used as a comparison point.

[0117] The deformation sensitivity of the comparison points is then used to evaluate the sensitivity of the predicted points to deflection monitoring.

[0118] Since each monitored bridge segment has a reference weight, which represents the reference value of the monitored bridge segment to the monitoring point setting on the bridge segment to be monitored, the deformation sensitivity of the comparison point is weighted using the reference weight of the monitored bridge segment to which all the comparison points of the predicted point belong, thereby obtaining the predicted monitoring sensitivity of the predicted point. The weighting process may include: for each predicted point, multiplying the deformation sensitivity of each comparison point of the predicted point by the reference weight of the monitored bridge segment to which each comparison point belongs, to obtain a prediction factor. At this time, the larger the prediction factor, the higher the sensitivity of the predicted point to deflection monitoring. Finally, the product of all the prediction factors corresponding to the predicted point is used as the predicted sensitivity of the predicted point. The larger the prediction sensitivity, the more accurately the deflection time series data of the predicted point can represent the deformation of the bridge, and the possibility of using the predicted point as the final monitoring point should be increased.

[0119] Step S4: Comprehensively consider the change trend of the deflection time series data of the measuring points in each monitored bridge section, the corresponding construction similarity, and the prediction sensitivity of the corresponding prediction points in the bridge section to be monitored, and select the monitoring points from all the prediction points in the bridge section to be monitored.

[0120] When constructing a new bridge section, in order to ensure construction efficiency, bridge construction materials are mainly piled up on the built bridge section near the part of the bridge section to be built. Therefore, for the accumulation area, the weight of the material itself will still have an impact on the deformation of the bridge. With the continuous accumulation of construction materials, the deflection of the measuring points in the accumulation area is also increasing. Therefore, in order to more accurately monitor the deflection of the bridge and facilitate timely discovery of potential problems of the bridge, when setting the measuring points, it is also necessary to consider the impact of the material accumulation area on the deformation of the bridge.

[0121] The deflection time series data records the deformation of the bridge at different times, so by analyzing its changing trend, the material accumulation of the measuring point can be characterized to a certain extent. At the same time, the similarity of construction between bridge sections can also be used as an indicator to predict the material accumulation on the bridge section to be monitored. Therefore, the changing trend of the deflection time series data of the measuring point in each monitored bridge section, the construction similarity with the bridge section to be monitored, and the prediction sensitivity of the predicted point in the bridge section to be monitored are comprehensively considered to determine the monitoring point among all the predicted points in the bridge section to be monitored.

[0122] Preferably, in one embodiment of the present invention, the process of determining the monitoring point includes:

[0123] See also Figure 4 , which shows a flow chart of a process for determining a monitoring point in one embodiment of the present invention, the process includes the following steps:

[0124] Step S401: Analyze the variation trend of the deflection time series data at each measuring point in each monitored bridge section to obtain the material accumulation parameters of each measuring point.

[0125] For any measuring point on each monitored bridge segment, the mean deflection value in each transport data segment at the measuring point is calculated as the mean deflection value of each transport data segment. The mean deflection value represents the average level of all deflection values ​​in the target data segment and can smooth out some random fluctuations.

[0126] In the deflection time series data of the measuring point, the deflection mean of the transport data segment is weighted by using the difference between the deflection mean values ​​of adjacent transport data segments, so as to obtain the material accumulation parameter of the measuring point. The formula model of the material accumulation parameter includes:

[0127]

[0128] in, Indicates the material accumulation parameters at each measuring point; represents the mean deflection value of the i-th transport data segment in the deflection time series data of each measuring point; It represents the mean deflection value of the i-1th transport data segment in the deflection time series data of each measuring point; Indicates the total number of transport data segments in the deflection time series data of each measuring point; Represents the normalization function.

[0129] In the formula model of material accumulation parameters, the difference between the mean deflections of adjacent transport data segments is calculated, specifically, the difference between the mean deflection of the latter transport data segment in the time series and the mean deflection of the previous transport data segment in the time series. If the difference is positive and the larger it is, the more the mean deflection is increasing. In this case, the material accumulation degree of the measuring point in this transport data segment may be increasing. On the contrary, if the difference is negative and the smaller it is, the more the mean deflection is decreasing. In this case, the material accumulation degree of the measuring point in this transport data segment may be decreasing. Therefore, the use of The difference is normalized to obtain the stacking weight The product of the stacking weight and the mean deflection of the latter transport data segment in the time series between two adjacent transport data segments is taken as the stacking factor , the larger the accumulation factor, the more materials may be accumulated at this measuring point in a certain transport data segment. Finally, the accumulation factors calculated from all adjacent transport data segments at this measuring point are combined, and the accumulated value is used as the material accumulation parameter of this measuring point. The larger the material accumulation parameter, the higher the possibility of material accumulation at this measuring point during the bridge construction process. Therefore, the setting of this measuring point can more timely discover potential problems of the bridge.

[0130] Step S402: For any predicted point, the material accumulation parameters of the comparison point are weighted using the construction similarities corresponding to the monitored bridge sections to which all the comparison points of the predicted point belong, so as to predict the material accumulation index of the predicted point.

[0131] For each predicted point, the material accumulation parameter of each comparison point of the predicted point is multiplied by the construction similarity corresponding to the monitored bridge section to which each comparison point belongs, to obtain the material accumulation degree value. At this time, the larger the material accumulation degree value is, the more likely it is that the predicted point will accumulate materials when the next bridge section is constructed in the future; finally, the product of all material accumulation degree values ​​corresponding to the predicted point is used as the material accumulation index of the predicted point. The larger the material accumulation index is, the more likely it is that the predicted point will be in the material accumulation area when the next bridge section is constructed in the future. Therefore, in order to more accurately monitor the deflection of the bridge and promptly discover potential problems of the bridge, the possibility of the predicted point as the final monitoring point should also increase accordingly.

[0132] Step S403: Determine the key monitoring index of each prediction point by comprehensively considering the material accumulation index and the prediction monitoring sensitivity of each prediction point, and select the monitoring point from all prediction points based on the key monitoring index.

[0133] Based on the analysis in the above process, it can be seen that the greater the prediction sensitivity of the prediction point, the more accurately the deflection time series data of the prediction point can characterize the deformation of the bridge, and the possibility of using the prediction point as the final monitoring point should be increased. At the same time, the larger the material accumulation index, the higher the possibility that the prediction point will be in the material accumulation area when the next bridge section is constructed in the future, and the possibility of using the prediction point as the final monitoring point should also increase accordingly.

[0134] Therefore, the product of the material accumulation index and the prediction monitoring sensitivity of each prediction point is normalized to obtain the value as the key monitoring index of each prediction point. At this time, the larger the key monitoring index of the prediction point, the higher the possibility of it being the final monitoring point. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0135] Finally, among all the predicted points on the bridge section to be monitored, the predicted points whose monitoring key index is greater than the preset key threshold are used as the monitoring points of the bridge section to be monitored.

[0136] It should be noted that the preset key threshold is 0.5, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0137] After determining the monitoring points on the bridge section to be monitored, deflection monitoring equipment is deployed at these monitoring points to sample the deflection data of the bridge. The data is transmitted to the processing center, and the deflection value is compared with the safety threshold. If it is greater than or equal to the safety threshold, an early warning is required to achieve deflection monitoring of the bridge structure.

[0138] It should be noted that the safety threshold can be set to 1 / 600 of the span of each bridge segment. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0139] In summary, during the construction of a bridge, the entire bridge is usually segmented based on special structures such as bridge fulcrums, so that different construction methods are adopted for different segments, and for the completed bridge segments, monitoring equipment needs to be installed to monitor the changes in deflection data in real time. In an embodiment of the present invention, it is necessary to optimize the setting of the measuring points on the bridge segment to be monitored. Specifically, first obtain the deflection time series data at each measuring point in the monitored bridge segment, as well as the construction data of each bridge segment during the bridge construction process, to provide a rich data source for the subsequent analysis process. By comparing the similarity between the construction data of the monitored bridge segment and the bridge segment to be monitored, the construction similarity between the two is obtained. At the same time, the construction similarity is combined with the numerical characteristics of the construction data of the monitored bridge segment itself, and the reference weight of each monitored bridge segment can be quantified. The reference weight reflects the reference value of each monitored bridge segment in the subsequent determination of the monitoring point in the bridge segment to be monitored. Next, the predicted points are reasonably determined on the bridge segment to be monitored using the location distribution information of the measuring points on the monitored bridge segment, which preliminarily avoids the problem of improper selection of monitoring points that may be caused by pre-setting measuring points. Further, by analyzing the change fluctuation and time series characteristics of the deflection time series data of the measuring points on the monitored bridge segment, and combining the reference weights, the predicted monitoring sensitivity of the predicted points on the bridge segment to be monitored can be calculated. This step can determine the contribution of each predicted point to the monitoring bridge deflection, and provide key information for the subsequent determination of the monitoring points. Finally, by comprehensively considering multiple factors, including the change trend of the deflection time series data of the measuring points in the monitored bridge segment, the construction similarity and the predicted monitoring sensitivity of the predicted points in the bridge segment to be monitored, the optimal monitoring point can be determined among all the predicted points of the bridge segment to be monitored. The embodiment of the present invention ensures that the selection of monitoring points is both reasonable and effective, and can accurately reflect the change of the deflection of the bridge, thereby improving the accuracy and reliability of the monitoring results.

[0140] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0141] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A bridge structure deflection monitoring method used in a bridge construction process, characterized in that: The method comprises: Obtain the time series data of the deflection of each measuring point in the monitored bridge section, wherein the monitored bridge section is the bridge section where the deflection monitoring equipment has been installed; obtain various construction data of each bridge section during the bridge construction process; Analyze the similarity between the construction data of the monitored bridge sections and the bridge sections to be monitored, and determine the construction similarity between each monitored bridge section and the bridge section to be monitored; combine the construction similarity of each monitored bridge section with the numerical characteristics of its own construction data to determine the reference weight of each monitored bridge section; According to the position distribution of the measuring points on all monitored bridge sections, the predicted position of each measuring point on the bridge section to be monitored is determined; the fluctuation changes between the deflection time series data of the measuring points on the monitored bridge sections and the time series characteristics between the fluctuation changes are analyzed, and the predicted monitoring sensitivity of the predicted point position on the bridge section to be monitored is determined in combination with the reference weight of the monitored bridge sections; The monitoring points are selected from all the predicted points of the bridge section to be monitored based on the changing trend of the deflection time series data of the measuring points in each monitored bridge section, the corresponding construction similarity, and the prediction sensitivity of the corresponding predicted points in the bridge section to be monitored.

2. A bridge structure deflection monitoring method used in a bridge construction process according to claim 1, characterized in that: The construction data at least includes the length of the bridge section, the location of the bridge section, the duration of bridge construction, the number of deflection monitoring devices on the monitored bridge section, and the installation location.

3. A bridge structure deflection monitoring method used in a bridge construction process according to claim 2, characterized in that: The method for obtaining the construction similarity includes: For any two bridge segments, the absolute value of the difference between the lengths of the bridge segments is taken as the length difference factor; The Euclidean distance between each monitored bridge segment and the starting point of the bridge segment to be monitored is used as the distance factor; The product of the length difference factor and the distance factor of each monitored bridge section and the bridge section to be monitored is used as the construction process difference degree between each monitored bridge section and the bridge section to be monitored; Based on the difference in bridge segment length between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored, as well as the difference in bridge construction time, determine the difference in the previous segment construction between each monitored bridge segment and the bridge segment to be monitored; The product of the construction process difference between each monitored bridge segment and the bridge segment to be monitored and the difference in the previous segment construction is negatively correlated and normalized to obtain the value, which is used as the construction similarity between each monitored bridge segment and the bridge segment to be monitored.

4. A bridge structure deflection monitoring method used in a bridge construction process according to claim 3, characterized in that: The method for obtaining the front-end construction difference includes: The absolute value of the difference in bridge construction time between the previous bridge section of each monitored bridge section and the previous bridge section of the bridge section to be monitored is used as the difference factor of the previous construction time between each monitored bridge section and the bridge section to be monitored; The product of the length difference factor between the previous bridge segment of each monitored bridge segment and the previous bridge segment of the bridge segment to be monitored and the difference factor of the front-end construction time between each monitored bridge segment and the bridge segment to be monitored is taken as the front-end construction difference degree between each monitored bridge segment and the bridge segment to be monitored.

5. The method for monitoring the deflection of a bridge structure during bridge construction according to claim 2, characterized in that: The method for obtaining the reference weight includes: In each monitored bridge section, the ratio of the number of deflection monitoring devices to the length of the bridge section is taken as the measurement point density; The value obtained by normalizing the product of the construction similarity corresponding to each monitored bridge section and the density of the measuring points is used as the reference weight of each monitored bridge section.

6. The method for monitoring the deflection of a bridge structure during bridge construction according to claim 2, characterized in that: The method for obtaining the predicted point position includes: In each monitored bridge segment, the ratio of the Euclidean distance between each measuring point and the starting point of the monitored bridge segment to the bridge segment length of the monitored bridge segment is used as the position characteristic value of each measuring point; The product of the position characteristic value corresponding to each measuring point in all monitored bridge sections and the bridge section length of the bridge section to be monitored is used as the position parameter of each predicted point; On the bridge section to be monitored, starting from the starting point of the bridge section, the length corresponding to the position parameters of each predicted point is measured, and the position of the end point of the length is marked as the predicted point.

7. The bridge structure deflection monitoring method used in the bridge construction process according to claim 1 is characterized in that: The method for obtaining the prediction monitoring sensitivity includes: For any measuring point on any monitored bridge segment, the extreme value point in the deflection time series data of the measuring point is obtained, and the data between two adjacent minimum value points is taken as a transportation data segment; On the monitored bridge section, all measuring points are sorted according to the bridge construction direction to obtain a measuring point sequence; In the measuring point sequence, based on the time series characteristics between the transport data segments in the deflection time series data of all measuring points, the transport data segments are classified to determine the target data segments, and based on the numerical characteristics and time length of the target data segments contained in the deflection time series data of each measuring point, the deformation sensitivity of each measuring point is determined; For any predicted point on the bridge section to be monitored, the corresponding measuring point of the predicted point on all monitored bridge sections is used as a comparison point; The deformation sensitivity of the comparison point is weighted by using the reference weights of the monitored bridge sections to which all the comparison points of the prediction point belong, so as to obtain the prediction monitoring sensitivity of the prediction point.

8. The bridge structure deflection monitoring method used in the bridge construction process according to claim 7 is characterized in that: In the measuring point sequence, based on the time series characteristics between the transport data segments in the deflection time series data of all measuring points, the transport data segments are classified to determine the target data segments, and based on the numerical characteristics and time length of the target data segments contained in the deflection time series data of each measuring point, the deformation sensitivity of each measuring point is determined, including: In the measuring point sequence, each transport data segment in the deflection time series data of the first measuring point is used as a data segment to be measured in sequence according to the time sequence; For each data segment to be measured, in the deflection time series data of the second measuring point, the first unclassified transportation data segment located after the data segment to be measured in time series is used as the first comparison data segment; in the deflection time series data of the third measuring point, the first unclassified transportation data segment located after the first comparison data segment in time series is used as the second comparison data segment; in the deflection time series data of the fourth measuring point, the first unclassified transportation data segment located after the second comparison data segment in time series is used as the third comparison data segment; By analogy, the deflection time series data of all measuring points are traversed to obtain all comparison data segments of each data segment to be measured; Classify each transport data segment and all corresponding comparison data segments in the deflection time series data of the first measuring point into one category, and when the total number of transport data segments in a category is equal to the total number of measuring points in the measuring point sequence, the transport data segments in this category are regarded as a type of target data segments; For any measuring point, the average of the absolute values ​​of the slopes of all data points in each target data segment in the deflection time series data of the measuring point is calculated as the deflection change factor; Based on the deflection change factor and time length of each target data segment in the deflection time series data of the measuring point, the sensitivity adjustment factor corresponding to each target data segment is determined, and the deflection change factor is negatively correlated with the sensitivity adjustment factor, and the time length is positively correlated with the deflection change factor; In the deflection time series data of the measuring point, the maximum deflection value in the target data segment is weighted using the sensitivity adjustment factors in all target data segments, so as to obtain the deformation sensitivity of the measuring point.

9. The bridge structure deflection monitoring method used in the bridge construction process according to claim 7 is characterized in that: The method for acquiring the monitoring point comprises: Analyze the change trend of the deflection time series data of each measuring point in each monitored bridge section to obtain the material accumulation parameters of each measuring point; For any predicted point, the material accumulation parameters of the comparison points are weighted by using the construction similarity corresponding to the monitored bridge sections of all the comparison points of the predicted point, so as to predict the material accumulation index of the predicted point; The product of the material accumulation index of each prediction point and the prediction monitoring sensitivity is normalized to obtain the value as the key monitoring index of each prediction point; Among all the predicted points on the bridge section to be monitored, the predicted points whose monitoring key index is greater than the preset key threshold are used as monitoring points of the bridge section to be monitored.

10. A bridge structure deflection monitoring method used in a bridge construction process according to claim 9, characterized in that: The analysis of the change trend of the deflection time series data at all measuring points in each monitored bridge section to obtain the material accumulation parameters of each measuring point includes: For any measuring point on each monitored bridge segment, the mean of the deflection values ​​in each transport data segment in the deflection time series data of the measuring point is calculated as the mean deflection value of each transport data segment; In the deflection time series data of the measuring point, the deflection mean of the transport data segment is weighted by utilizing the difference between the deflection mean values ​​of adjacent transport data segments, thereby calculating the material accumulation parameter of the measuring point.

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