Bridge building prediction device and method based on BIM

Abstract

The invention relates to the technical field of electronic digital data processing, in particular to a BIM-based bridge building prediction device and method. The method comprises the steps that at each monitoring moment, the safety degree of each module at the monitoring moment is determined according to monitoring data of each module of a bridge; for each module, determining time influence parameters of each weather type on the module according to the safety degree of the module at each previous monitoring moment and the weather type at each previous monitoring moment; according to the safety degree of the module at the current monitoring moment and the time influence parameter and the maintenance duration of the future weather type, determining the preliminary prediction safety degree of the module after the future target duration; and according to the difference between the safety degrees of the module and each adjacent module at the current monitoring moment, correcting the preliminary predicted safety degree of the module to obtain the predicted safety degree of the module after the future target duration. According to the invention, the accuracy of bridge safety prediction can be improved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and specifically to a BIM-based bridge construction prediction device and method. Background Technology

[0002] Building Information Modeling (BIM) is increasingly used in bridge engineering. It not only makes bridge design and construction more standardized and precise, but also optimizes construction plans and identifies potential risks. Furthermore, BIM models can be referenced during bridge use for maintenance and safety assessments. Since bridge safety involves a large number of pedestrians, advance preparation is necessary to avoid serious accidents; therefore, reasonable safety predictions for bridge structures are essential.

[0003] Weather forecasts provide information about the weather conditions in a given area, allowing for the implementation of protective measures for bridges in the event of severe weather. However, due to the complex internal structure and frequent use of bridges, coupled with diverse weather conditions, the lifespan of bridge materials may not be as expected. Furthermore, the extent of damage that impending severe weather may cause to bridges cannot be accurately assessed, making it difficult to determine the accuracy of bridge safety predictions and increasing the risk of traffic accidents. Summary of the Invention

[0004] To address the technical problem of inaccurate prediction of bridge construction safety, the present invention aims to provide a BIM-based bridge construction prediction device and method, the specific technical solution of which is as follows:

[0005] In a first aspect, the present invention provides a BIM-based method for predicting bridge construction, the method comprising:

[0006] At each monitoring time, the safety level of each module is determined based on the monitoring data of each module of the bridge at that monitoring time.

[0007] For each module, based on the safety level of the module at each of the previous monitoring times and the weather type at each of the previous monitoring times, determine the time impact parameters of each weather type on the module;

[0008] Based on the safety level of the module at the current monitoring time, and the time impact parameters and duration of future weather types, the preliminary predicted safety level of the module after the future target duration is determined.

[0009] Based on the difference between the security level of the module and each adjacent module at the current monitoring time, the preliminary predicted security level of the module is corrected to obtain the predicted security level of the module after the future target duration.

[0010] According to the BIM-based bridge construction prediction method provided by the present invention, the monitoring data includes displacement data, stress data, strain data, and crack data;

[0011] The step of determining the safety level of each module at the monitoring time based on the monitoring data of each module of the bridge includes:

[0012] For each module of the bridge, the deformation index of the module at the monitoring time is determined based on the displacement data of the module at each monitoring time prior to the monitoring time.

[0013] Based on the building materials of each sampling point of the module, and the stress and strain data of each sampling point of the module at the previous monitoring times, the actual stress-strain data curve of each sampling point of the module is constructed.

[0014] The safety level of the module at the monitoring time is determined based on the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data of each sampling point at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data of each sampling point at the previous monitoring times and the actual stress-strain data curve, and the crack data at the previous monitoring times.

[0015] According to the BIM-based bridge construction prediction method provided by the present invention, determining the deformation index of the module at the monitoring time based on the displacement data of the module at previous monitoring times at the monitoring time includes:

[0016] The total displacement of the module is obtained by adding the displacement of the module relative to its initial position at each of the previous monitoring times and the displacement relative to the previous monitoring time.

[0017] Based on the displacement of the module relative to its initial position at each of the previous monitoring times, a displacement curve is constructed;

[0018] Determine the number of extreme values ​​within each preset time period on the displacement curve;

[0019] The deformation index of the module at the monitoring time is determined based on the difference between the total displacement and the number of extreme values ​​in adjacent preset time periods.

[0020] According to the BIM-based bridge construction prediction method provided by the present invention, the step of determining the time impact parameters of various weather types on the module based on the safety level of the module at each of the previous monitoring times and the weather type at each of the previous monitoring times includes:

[0021] Based on the weather type at each of the previously mentioned monitoring times, adjacent monitoring times with the same weather type are merged to obtain several weather periods;

[0022] The degree of impact of each weather period on the module is determined based on the difference in the safety level of the module at the beginning and end of each weather period;

[0023] For each weather type, the time impact parameter of the weather type on the module is determined based on the duration of each weather period corresponding to the weather type and the degree of impact on the module.

[0024] According to the BIM-based bridge construction prediction method provided by the present invention, the time influence parameters include the slope and intercept of the fitted straight line of the influence degree changing with time.

[0025] The step of determining the time impact parameter of the weather type on the module based on the duration of each weather period corresponding to the weather type and the degree of impact on the module includes:

[0026] Using the duration of each weather period corresponding to the weather type as the horizontal axis value and the degree of influence of each weather period corresponding to the weather type on the module as the vertical axis value, a straight line is fitted to obtain the slope and intercept of the fitted straight line showing the change of the degree of influence of the weather type on the module over time.

[0027] According to the BIM-based bridge construction prediction method provided by the present invention, determining the preliminary predicted safety level of the module after a future target duration based on the module's safety level at the current monitoring time and the time impact parameters and duration of future weather types includes:

[0028] Determine the time impact parameters and duration of each of the target weather types expected to be experienced sequentially within the future target time period on the module;

[0029] Based on the time impact parameters and duration of each target weather type on the module, determine the amount of safety attenuation of each target weather type on the module;

[0030] Based on the safety level at the current detection time, the safety level of the module is reduced by the amount of reduction in safety level of each target weather type, respectively, to obtain the preliminary predicted safety level of the module after the target time.

[0031] According to the BIM-based bridge construction prediction method provided by the present invention, the time influence parameters include the slope and intercept of the fitted straight line of the influence degree changing with time.

[0032] The step of determining the safety attenuation of the module by each target weather type based on the time impact parameters and duration of each target weather type includes:

[0033] For each target weather type, determine a linear formula consisting of the slope and intercept of a fitted straight line that represents the degree of influence of the target weather type on the module over time;

[0034] Substituting the duration of the target weather type into the linear formula yields the amount by which the target weather type reduces the safety level of the module.

[0035] According to the BIM-based bridge construction prediction method provided by the present invention, the step of correcting the preliminary predicted safety level of the module based on the difference between the safety level of the module and each adjacent module at the current monitoring time, to obtain the predicted safety level of the module after the future target time period, includes:

[0036] For each adjacent module of the module, the correlation between the module and the adjacent module is determined based on the difference between the time influence parameters corresponding to each weather type.

[0037] Based on the correlation between the module and each of the adjacent modules, and the difference between the security level at the current monitoring time, a security level correction value is determined;

[0038] Based on the security level correction value, the initial predicted security level of the module is corrected to obtain the predicted security level of the module after the future target duration.

[0039] According to the BIM-based bridge construction prediction method provided by the present invention, after correcting the preliminary predicted safety level of the module based on the difference between the safety level of the module and each adjacent module at the current monitoring time, and obtaining the predicted safety level of the module after the future target time period, the method further includes:

[0040] For each module, if the predicted safety level of the module is less than or equal to the first hyperparameter, then it is determined that the module needs to be repaired; if the predicted safety level of the module is less than or equal to the second hyperparameter, then it is determined that the module and its neighboring areas need to be thoroughly inspected and repaired manually.

[0041] If all the modules of the bridge are less than or equal to the third hyperparameter, then it is determined that the use of the bridge needs to be stopped, and the bridge needs to be inspected and maintained as a whole.

[0042] Wherein, the second hyperparameter is smaller than the third hyperparameter, and the third hyperparameter is smaller than the first hyperparameter.

[0043] Secondly, the present invention provides a BIM-based bridge construction prediction device, the device comprising:

[0044] The current safety level calculation module is used to determine the safety level of each module at each monitoring time based on the monitoring data of each module of the bridge at the monitoring time.

[0045] The weather impact determination module is used to determine the time impact parameters of various weather types on each module based on the safety level of the module at each previous monitoring time and the weather type at each previous monitoring time.

[0046] The preliminary prediction module is used to determine the preliminary predicted safety level of the module after a future target duration based on the safety level of the module at the current monitoring time and the time impact parameters and duration of future weather types.

[0047] The correction prediction module is used to correct the initial predicted security level of the module based on the difference between the security level of the module and each adjacent module at the current monitoring time, so as to obtain the predicted security level of the module after the future target duration.

[0048] The present invention has the following beneficial effects: At each monitoring time, based on the monitoring data of each module of the bridge, the safety level of each module at the monitoring time is determined. For each module, based on the safety level of the module at previous monitoring times and the weather type at previous monitoring times, the time influence parameters of various weather types on the module are determined. Based on the safety level of the module at the current monitoring time, and the time influence parameters and duration of future weather types, the preliminary predicted safety level of the module after a future target duration is determined. This can accurately predict the future safety level of the module based on future weather conditions. Then, based on the difference between the safety level of the module and each adjacent module at the current monitoring time, the preliminary predicted safety level of the module is corrected. By combining the influence between adjacent modules, a more accurate predicted safety level of the module can be obtained. Therefore, the accuracy of bridge construction safety prediction is improved. Attached Figure Description

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A schematic flowchart of a BIM-based bridge construction prediction method provided in one embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of a process for determining the security level at the current monitoring moment, provided by an embodiment of the present invention.

[0052] Figure 3 This is a flowchart illustrating the process of determining the time-related parameters of weather type on a module, as provided in one embodiment of the present invention.

[0053] Figure 4 This is an example of a scatter plot showing the duration of each weather period according to the weather type and its impact on the module, provided as an embodiment of the present invention.

[0054] Figure 5 A structural block diagram of a BIM-based bridge construction prediction device provided in one embodiment of the present invention;

[0055] Figure 6 The diagram below shows a structural block diagram of a BIM-based bridge construction prediction device, which is provided as another embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a BIM-based bridge construction prediction device and method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, 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 pertains.

[0058] The following description, in conjunction with the accompanying drawings, details the specific scheme of a BIM-based bridge construction prediction device and method provided by the present invention.

[0059] Please see Figure 1 The diagram illustrates a flowchart of a BIM-based bridge construction prediction method according to an embodiment of the present invention, including the following steps:

[0060] Step 101: At each monitoring time, determine the safety level of each module based on the monitoring data of each module of the bridge at that monitoring time.

[0061] BIM (Building Information Modeling) is a new tool for architecture, engineering, and civil engineering. By integrating digitized and informational models of buildings, it facilitates sharing and transmission throughout the entire lifecycle of a project, from planning and operation to maintenance. This enables engineering professionals to correctly understand and efficiently respond to various building information, providing a foundation for collaborative work among design teams and all stakeholders, including building and operation units. BIM plays a crucial role in improving productivity, saving costs, and shortening construction periods.

[0062] In the BIM model of a bridge, each structure of the bridge is treated as a module, resulting in several modules of the bridge. The structure refers to any structural unit in the bridge's BIM model, such as piers, abutments, and standard beams.

[0063] In one embodiment, a monitoring period is defined as a preset interval. That is, monitoring data for each module of the bridge is acquired at preset intervals, and the safety level of each module is determined based on the monitoring data. The preset interval can be set according to actual needs. For example, the preset interval can be set to 1 hour.

[0064] In one embodiment, the monitoring data for the module may include displacement data, stress data, strain data, and crack data. Additionally, weather data for each monitoring moment is acquired. In one embodiment, the weather data may include weather type, such as light rain, cloudy, rain, or sunny. In one embodiment, the crack data may include several cracks present in the module, along with the length, width, and depth of each crack.

[0065] In one embodiment, crack data can be acquired using an infrared thermal imager, stress data using an ultrasonic detector, strain data using a strain sensor, displacement data using a GPS system, and weather data obtained from meteorological agencies.

[0066] In one embodiment, the acquired monitoring data and weather data from the module can be transmitted to a database. Dynamo (a visual programming tool that can interact with software such as Revit) reads the database and displays it in the Revit software (a software specifically built for Building Information Modeling, BIM). Based on changes in the data, the model is dynamically updated through Revit's model dynamic update mechanism, making the prediction of bridge construction and changes in bridge construction safety more visual and intuitive.

[0067] In one embodiment, at each monitoring time, for each module of the bridge, the deformation index of the module at the monitoring time is determined based on the displacement data of the module at each of the previous monitoring times. Then, the safety level of the module at the monitoring time is determined based on the deformation index, stress data, strain data, and crack data of the module at the monitoring time.

[0068] Step 102: For each module, based on the module's safety level at previous monitoring times and the weather type at previous monitoring times, determine the time impact parameters of various weather types on the module.

[0069] Among them, the time influence parameter is used to characterize the relationship between the duration of weather type and the degree of influence of weather type on module.

[0070] In one embodiment, several weather periods are obtained based on the weather type at each previous monitoring time. Then, based on the safety level of the module in each weather period, the degree of influence of the weather period on the module is determined. For each weather type, the time influence parameter of the weather type on the module is determined based on the duration of each weather period corresponding to the weather type and the degree of influence on the module.

[0071] Step 103: Based on the module's safety level at the current monitoring time, as well as the time impact parameters and duration of future weather types, determine the module's preliminary predicted safety level after the target duration in the future.

[0072] Specifically, by obtaining weather forecasts, the module acquires information on the various target weather types expected to occur sequentially within the target timeframe, as well as the duration of each target weather type. This information determines the time impact parameters of each target weather type on the module. Based on the module's safety level at the current monitoring time, and the time impact parameters and durations of the various target weather types expected to occur sequentially within the target timeframe, the module's preliminary predicted safety level after the target timeframe is determined. Here, "target weather type" refers to the weather type expected to occur within the target timeframe.

[0073] Step 104: Based on the difference in security level between the module and each adjacent module at the current monitoring time, the initial predicted security level of the module is corrected to obtain the predicted security level of the module after the target time period in the future.

[0074] In one embodiment, a safety level correction value can be determined based on the difference between the safety level of the module and each adjacent module at the current monitoring time. Based on the safety level correction value, the initial predicted safety level of the module is corrected to obtain the predicted safety level of the module after the target time in the future.

[0075] In one embodiment, the target duration can take one or more values.

[0076] In one embodiment, after obtaining the predicted safety level of the module after a future target time, the predicted safety level can be compared with preset hyperparameters, and the treatment plan for the bridge can be determined based on the comparison results.

[0077] In one embodiment, the predicted safety level of a module over time can be read using Dynamo and displayed in the Revit software model. Different colors are assigned according to the safety level values, which can intuitively reflect the overall safety status of the bridge and allow for timely analysis of the bridge based on changes.

[0078] The aforementioned BIM-based bridge construction prediction method determines the safety level of each module at each monitoring time based on the monitoring data of each module. For each module, it determines the time impact parameters of various weather types on the module based on the safety level of the module at previous monitoring times and the weather types at previous monitoring times. Based on the module's safety level at the current monitoring time, the time impact parameters of future weather types, and their duration, it determines the preliminary predicted safety level of the module after a target future duration. This method can accurately predict the module's future safety level based on future weather conditions. Then, it corrects the preliminary predicted safety level of the module based on the differences between the module and its adjacent modules at the current monitoring time. By combining the influence between adjacent modules, it obtains a more accurate predicted safety level for the module, thus improving the accuracy of bridge construction safety prediction.

[0079] In one embodiment, the monitoring data includes displacement data, stress data, strain data, and crack data. See also... Figure 2 Based on the monitoring data of each module of the bridge, the safety level of each module at the monitoring time is determined, including the following steps:

[0080] Step 201: For each module of the bridge, determine the deformation index of the module at the monitoring time based on the displacement data of the module at each previous monitoring time.

[0081] In one embodiment, displacement data from multiple sampling points on the module can be collected, and the displacement data from each sampling point can be summed to obtain the displacement data of the module.

[0082] In one embodiment, the displacement range and frequency of the module are determined based on the displacement data of the module at previous monitoring times, and the deformation index of the module at the monitoring time is determined based on the displacement range and frequency of the module.

[0083] Step 202: Based on the building materials of each sampling point of the module, and the stress and strain data of each sampling point of the module at previous monitoring times, construct the actual stress-strain data curve of each sampling point of the module.

[0084] In one embodiment, stress and strain data from multiple sampling points on the module can be collected. Based on the building material of each sampling point, a standard stress-strain data curve corresponding to that sampling point is determined. Then, based on the standard stress-strain data curve corresponding to the sampling point, the stress and strain data of the sampling point at previous monitoring times are fitted to obtain the actual stress-strain data curve of the sampling point.

[0085] Step 203: Determine the safety level of the module at the monitoring time based on the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data of each sampling point at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data of each sampling point at previous monitoring times and the actual stress-strain data curve, and the crack data at previous monitoring times.

[0086] The Ultimate Tensile Strength (UTS) position is the location corresponding to the highest stress point on the actual stress-strain data curve, representing the material's maximum resistance during the plastic deformation stage.

[0087] In one embodiment, the crack volume of the module can be determined based on the length, width, and depth of each crack on the module in the crack data of the module.

[0088] In one embodiment, the safety level of the module at the monitoring time is negatively correlated with the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data of each sampling point of the module at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data of each sampling point of the module at previous monitoring times and the actual stress-strain data curve, and the crack volume of the module at previous monitoring times.

[0089] In one embodiment, the average distance between the data points corresponding to the stress and strain data at each sampling point of the module at previous monitoring times and the actual stress-strain data curve can be calculated, along with the sum of the products of these distances and the crack volumes at each previous monitoring time. The safety level of the module at the monitoring time is then determined by multiplying the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data at each sampling point at the monitoring time and the tensile strength location, and the sum of these products. The formula is as follows:

[0090]

[0091] Where Y represents the safety level of the module at the monitoring time. C represents the deformation index of the module at the monitoring time. d represents the average distance between the data points corresponding to the stress and strain data at each sampling point on the module and the tensile strength location at the monitoring time. h t This represents the average distance between the data points corresponding to the stress and strain data at each sampling point on the module at the previous monitoring time t and the actual stress-strain data curve. tLet represent the crack volume of the module at the t-th monitoring time. T represents the number of previous monitoring times. exp represents an exponential function with base e.

[0092] In the above embodiments, since the safety level of the bridge module is related to its changes from the initial construction period to the present, deformation, displacement, and the appearance of cracks all indicate structural damage to the module and directly affect its safety level. Therefore, the safety level of the module is calculated by the degree of deformation, displacement, and crack appearance (crack volume). The smaller the deformation, displacement, and crack volume, the higher the safety level of the module. Therefore, based on the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data at each sampling point at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data at each sampling point at previous monitoring times and the actual stress-strain data curve, and the crack data at previous monitoring times, the safety level of the module at the monitoring time can be accurately determined.

[0093] In one embodiment, determining the deformation index of the module at the monitoring time based on the displacement data of the module at each monitoring time prior to the monitoring time includes: adding the displacement of the module relative to the initial position at each monitoring time prior to the monitoring time and the displacement relative to the previous monitoring time to obtain the total displacement of the module; constructing a displacement curve based on the displacement of the module relative to the initial position at each monitoring time prior to the monitoring time; determining the number of extreme values ​​in each preset time period on the displacement curve; and determining the deformation index of the module at the monitoring time based on the difference between the total displacement and the number of extreme values ​​in adjacent preset time periods.

[0094] The duration of the preset time period can be an integer multiple of the preset duration. For example, the preset time period can be 5 times the preset duration, that is, the preset time period contains 5 monitoring moments. That is, if every hour is a monitoring moment (i.e., the preset duration is 1 hour), then the duration of the preset time period can be 5 hours.

[0095] In one embodiment, the difference between the deformation index of the module at the monitoring time and the total displacement, as well as the number of extreme values ​​in adjacent preset time periods, is positively correlated.

[0096] In one embodiment, the differences in the number of extreme values ​​between adjacent preset time periods can be summed to obtain the total difference in the number of extreme values. The deformation index of the module at the monitoring time is then determined by multiplying the total displacement by the total difference in the number of extreme values. The formula is as follows:

[0097]

[0098] Where C represents the deformation index of the module at the monitoring time. h1p h2 represents the displacement of the module relative to its initial position at the p-th monitoring time. p This represents the displacement of the module relative to the previous monitoring time at the p-th monitoring time. P represents the number of previous monitoring times. K1 j+1 This represents the number of extreme values ​​within the (j+1)th preset time interval on the displacement curve. K1 j This represents the number of extreme values ​​within the j-th preset time interval on the displacement curve. J represents the number of groups of adjacent preset time intervals on the displacement curve (i.e., the number of preset time intervals on the displacement curve minus 1). norm[] represents the normalization function.

[0099] In the above embodiments, since bridges have a certain safe deformation range, and the earlier the deformation, the smaller the impact on the future safety prediction of the bridge. The safe deformation range of the bridge will become smaller and smaller, but due to the influence of material life, the deformation of the bridge will be more frequent and the deformation range will be more severe. The displacement range of the module can be reflected by calculating the total displacement of the module, and the frequency of module displacement can be reflected by calculating the difference between the number of extreme values ​​in adjacent preset time periods. Therefore, based on the total displacement and the difference between the number of extreme values ​​in adjacent preset time periods, the deformation index of the module at the monitoring time can be accurately determined.

[0100] In one embodiment, see Figure 3 Based on the module's safety level and weather type at each previous monitoring time, the time impact parameters of various weather types on the module are determined, including the following steps:

[0101] Step 301: Based on the weather type at each previous monitoring time, merge adjacent monitoring times with the same weather type to obtain several weather periods.

[0102] Step 302: Determine the impact of each weather period on the module based on the difference in the module's safety level at the beginning and end of each weather period.

[0103] It's understandable that since weather types are acquired separately at each monitoring time, merging adjacent monitoring times with the same weather type results in several weather periods, each with its own start and end time. Because the security level of the module is calculated at each monitoring time, the security level at the start and end of each weather period can be obtained.

[0104] In one embodiment, the difference in safety level between the beginning and end of each weather period is used to calculate the impact of each weather period on the module. The formula is as follows:

[0105] P = Y′ - Y

[0106] Where P represents the degree of impact of the weather period on the module. Y' represents the safety level of the module at the end of the weather period. Y represents the safety level of the module at the beginning of the weather period.

[0107] Step 303: For each weather type, determine the time impact parameter of the weather type on the module based on the duration of each weather period corresponding to the weather type and the degree of impact on the module.

[0108] In one embodiment, a linear fit is performed based on the duration of each weather period corresponding to the weather type and the degree of influence on the module to obtain the slope and intercept of the fitted line of the degree of influence of the weather type on the module over time, which is used as the time influence parameter of the weather type on the module.

[0109] In the above embodiments, historical data is segmented based on weather changes, and the impact of each weather period on the module's safety level can be determined by combining the changes in the module's safety level during each weather period. Since even for the same weather type, the impact on the module's safety level varies depending on its duration, the temporal impact parameters of the weather type on the module can be accurately determined for each weather type based on the duration of each weather period corresponding to that weather type and its impact on the module. Based on these temporal impact parameters and the duration of the weather type, the initial predicted safety level of the module can be accurately predicted.

[0110] In one embodiment, the time-related impact parameter includes the slope and intercept of a fitted straight line showing the degree of impact changing over time. Based on the duration of each weather period corresponding to the weather type and its degree of impact on the module, the time-related impact parameter of the weather type on the module is determined, including: using the duration of each weather period corresponding to the weather type as the abscissa and the degree of impact of each weather period corresponding to the weather type on the module as the ordinate, performing a straight line fitting to obtain the slope and intercept of the fitted straight line showing the degree of impact of the weather type on the module changing over time.

[0111] In one embodiment, such as Figure 4 As shown, a scatter plot is drawn with the duration of each weather period corresponding to the weather type as the x-axis value and the degree of influence of each weather period corresponding to the weather type on the module as the y-axis value. Then, the least squares method can be used to perform linear fitting on the scatter plot to obtain the fitted line y = kx + b, where the slope of the fitted line is k and the intercept is b.

[0112] In the above embodiments, the duration of each weather period corresponding to the weather type is used as the horizontal axis value, and the degree of influence of each weather period corresponding to the weather type on the module is used as the vertical axis value. By performing linear fitting, the slope and intercept of the fitted line of the degree of influence of the weather type on the module over time can be accurately obtained.

[0113] In one embodiment, the preliminary predicted safety level of the module after a target duration is determined based on the module's safety level at the current monitoring time and the time impact parameters and duration of future weather types. This includes: determining the time impact parameters and duration of each target weather type expected to be experienced sequentially within the target duration on the module; determining the safety level attenuation of each target weather type on the module based on the time impact parameters and duration of each target weather type; and subtracting the safety level attenuation of each target weather type from the safety level at the current monitoring time to obtain the preliminary predicted safety level of the module after the target duration.

[0114] In one embodiment, if only one target weather type is expected to be experienced within the target future duration, the safety attenuation of the target weather type on the module is determined based on the time impact parameter and duration of the target weather type on the module. The preliminary predicted safety level of the module after the target future duration is determined based on the difference between the module's current safety level and the safety attenuation. In another embodiment, if multiple target weather types are expected to be experienced within the target future duration, the safety attenuation of each target weather type on the module is determined based on the time impact parameter and duration of each expected target weather type on the module. The preliminary predicted safety level of the module after the target future duration is obtained by successively subtracting the safety attenuation of each target weather type from the current safety level.

[0115] In the above embodiments, based on the time impact parameters and duration of each target weather type on the module, the amount of safety reduction of each target weather type on the module is determined. Based on the safety level at the current detection time, the amount of safety reduction of each target weather type on the module expected to be experienced within the future target duration is subtracted in turn, so as to accurately obtain the preliminary predicted safety level of the module after the future target duration.

[0116] In one embodiment, the time-related impact parameters include the slope and intercept of a fitted straight line showing the degree of impact changing over time. Based on the time-related impact parameters and duration of each target weather type on the module, the reduction in the safety level of the module by each target weather type is determined. This includes: for each target weather type, determining a linear formula composed of the slope and intercept of a fitted straight line showing the degree of impact of the target weather type on the module changing over time; and substituting the duration of the target weather type into the linear formula to obtain the reduction in the safety level of the target weather type on the module.

[0117] In one embodiment, the initial predicted safety level of the module after the target future time period can be determined according to the following formula:

[0118] R r =Y - (kr + b)

[0119] Where r represents the target duration. R r Y represents the module's initial predicted safety level after r hours. Y represents the module's safety level at the current detection time. k represents the slope of the fitted line showing the impact of the target weather type expected to occur in the next r hours on the module over time. b represents the intercept of the fitted line showing the impact of the target weather type expected to occur in the next r hours on the module over time. kr+b represents the decrease in the module's safety level due to the target weather type.

[0120] It is understood that the above formula for calculating the preliminary forecast of safety level assumes that only one target weather type is expected within the future target duration. If multiple target weather types are expected within the future target duration, the above formula can be transformed into the following form to calculate the preliminary forecast of safety level:

[0121]

[0122] Among them, R r This represents the module's preliminary predicted safety level after r hours. Y represents the module's safety level at the current detection moment. s k represents the expected duration of the s-th target weather type within the next r hours. s denoted by b, represents the slope of the fitted straight line showing the change in the impact of the s-th target weather type on the module over time within the next r hours. s denoted by , represents the intercept of the fitted straight line showing the change in the impact of the s-th target weather type on the module over time within the next r hours. S represents the number of target weather types expected to occur within the next r hours. s r s +b s This represents the decrease in the module's safety level caused by the s-th target weather type expected to occur within the next r hours.

[0123] In the above embodiments, for each target weather type, a linear formula is determined, consisting of the slope and intercept of the fitted line that shows the degree of influence of the target weather type on the module over time. By substituting the duration of the target weather type into the linear formula, the amount of decrease in the safety level of the module due to the target weather type can be accurately obtained.

[0124] In one embodiment, the preliminary predicted safety level of the module is corrected based on the difference between the safety level of the module and each of its neighboring modules at the current monitoring time to obtain the predicted safety level of the module after a target future duration. This includes: determining the correlation between the module and its neighboring modules based on the difference between the time influence parameters of the module and its neighboring modules under various weather types for each neighboring module; determining a safety level correction value based on the correlation between the module and each of its neighboring modules and the difference in safety level at the current monitoring time; and correcting the preliminary predicted safety level of the module based on the safety level correction value to obtain the predicted safety level of the module after a target future duration.

[0125] In one embodiment, the difference between the slopes of the fitted lines corresponding to the module and its neighboring modules under various weather types can be calculated. The correlation between the module and its neighboring modules is determined by summing the absolute values ​​of these differences. The correlation between the module and its neighboring modules is negatively correlated with this summation result.

[0126] In one embodiment, the correlation between any two adjacent modules can be determined according to the following formula:

[0127]

[0128] Among them, L u,v This represents the correlation between any two adjacent modules u and v. u,m k represents the slope of the fitted line showing the change in the influence of the m-th weather type on module u over time. v,m Let represent the slope of the fitted line showing the change in the influence of the m-th weather type on module v over time. M represents the number of weather types. exp represents an exponential function with base e. The sum of the absolute values ​​of the differences between the slopes of the fitted lines corresponding to adjacent modules u and v under various weather types indicates the difference in the impact of all weather types on two adjacent modules. The smaller this value, the smaller the difference in the impact of all weather types on two adjacent modules, and the greater the correlation between the two adjacent modules.

[0129] In one embodiment, the difference between the security level of each adjacent module and the current module at the current monitoring time is used to obtain the security level difference. The corresponding security level difference is weighted by the correlation between each adjacent module and the current module to obtain the weighted result for each adjacent module. The weighted results of each adjacent module are summed to obtain the security level correction value.

[0130] In one embodiment, the predicted safety level of the module after a future target duration can be determined based on the sum of the module's initial predicted safety level and the safety level correction value.

[0131] In one embodiment, the predicted safety level of the module after the target time period can be determined according to the following formula:

[0132]

[0133] Among them, R' r R represents the predicted safety level of the module after r hours. r This indicates the module's initial predicted safety level after r hours. L v Y represents the correlation between the module's v-th neighboring module and this module. v This indicates the security level of the module's v-th adjacent module at the current monitoring time. Y represents the security level of the module at the current monitoring time. v -Y represents the difference in security level between the module and its v-th neighboring module at the current monitoring time. V represents the number of neighboring modules of the module. This indicates a safety level adjustment value.

[0134] In the above embodiments, the initial safety prediction is based on a single module. However, there is a correlation between modules in a bridge structure. When a module with a high degree of danger or significant damage occurs, it has already broken with its adjacent modules. At this point, the more stable state of the adjacent modules due to the connected structure changes, and the corresponding degree of danger should also increase. Therefore, it is necessary to consider the relationship between modules and adjust the safety level of modules with more dangerous adjacent modules. The correlation between modules is related to the connection relationship between modules and the size of the connection area. For example, bridge modules connected by a large area of ​​concrete will have a stronger correlation. The correlation is mainly reflected in the similarity of the danger levels of the modules. The correlation represents the degree of impact of a module becoming dangerous on other modules. If any module itself has a low degree of danger but low disaster resistance and strength, and the adjacent modules have a high degree of danger and the connection is broken, then the danger level of that module should increase relatively. Therefore, if two adjacent modules exhibit similar changes in safety levels under any weather conditions, then the more effective the early warning function of these two modules is for the other module in dangerous situations, the more reliable the predicted safety level can be. Thus, by analyzing the differences in time-related parameters between a module and its adjacent modules under various weather types, the correlation between the modules can be accurately determined. For any given module, the higher the correlation among its adjacent modules, the lower its safety level, and consequently, the lower the module's safety level. Therefore, by weighting the corresponding safety level differences based on the correlations between each adjacent module and the given module, a corrected safety level value can be accurately obtained. This allows for accurate correction of the module's initial predicted safety level, resulting in a more accurate predicted safety level for the module after the target timeframe.

[0135] In one embodiment, after correcting the initial predicted safety level of a module based on the difference between the safety level of the current module and its neighboring modules at the current monitoring time, and obtaining the predicted safety level of the module after a future target duration, the method further includes: for each module, if the predicted safety level of the module is less than or equal to a first hyperparameter, then it is determined that the module needs to be repaired; if the predicted safety level of the module is less than or equal to a second hyperparameter, then it is determined that the module and its neighboring area need to be thoroughly inspected and repaired; for a bridge, if all modules of the bridge are less than or equal to a third hyperparameter, then it is determined that the bridge needs to be taken out of service and undergo overall inspection and maintenance. Wherein, the second hyperparameter is less than the third hyperparameter, and the third hyperparameter is less than the first hyperparameter.

[0136] In one embodiment, the predicted safety level of the module can be normalized first to obtain a normalized predicted safety level, and then the normalized predicted safety level can be compared with the first hyperparameter, the second hyperparameter, and the third hyperparameter to determine the handling method.

[0137] For example: the first hyperparameter can be set to 0.7. The second hyperparameter can be set to 0.4. The third hyperparameter can be set to 0.5.

[0138] In the above embodiments, by setting hyperparameters and comparing the predicted safety level with the hyperparameters, potential safety hazards in the bridge can be identified in a timely and accurate manner. When it is predicted that the bridge will be dangerous at a certain point in the future, the bridge can be shut down in advance, minimizing the possibility of casualties and making bridge maintenance costs more controllable.

[0139] In one embodiment, see Figure 5 The present invention provides a BIM-based bridge construction prediction device 500, the device comprising:

[0140] The current safety level calculation module 501 is used to determine the safety level of each module at each monitoring time based on the monitoring data of each module of the bridge at each monitoring time.

[0141] The weather impact determination module 502 is used to determine the time impact parameters of various weather types on each module based on the safety level of the module at previous monitoring times and the weather type at previous monitoring times.

[0142] The preliminary prediction module 503 is used to determine the preliminary predicted safety level of the module after a target duration in the future, based on the module's safety level at the current monitoring time, as well as the time impact parameters and duration of future weather types.

[0143] The prediction correction module 504 is used to correct the initial predicted safety level of the module based on the difference between the safety level of the module and each adjacent module at the current monitoring time, so as to obtain the predicted safety level of the module after a target time period in the future. In one embodiment, the monitoring data includes displacement data, stress data, strain data, and crack data. The current safety level calculation module 501 is also used to determine the deformation index of the module at the monitoring time based on the displacement data of the module at each previous monitoring time for each module of the bridge; construct the actual stress-strain data curve of each sampling point of the module based on the building materials of each sampling point of the module and the stress and strain data of each sampling point of the module at each previous monitoring time; and determine the safety level of the module at the monitoring time based on the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data of each sampling point at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data of each sampling point at each previous monitoring time and the actual stress-strain data curve, and the crack data of each previous monitoring time.

[0144] In one embodiment, the current safety level calculation module 501 is further configured to add the displacement of the module relative to the initial position at each previous monitoring time and the displacement relative to the previous monitoring time to obtain the total displacement of the module; construct a displacement curve based on the displacement of the module relative to the initial position at each previous monitoring time; determine the number of extreme values ​​in each preset time period on the displacement curve; and determine the deformation index of the module at the monitoring time based on the difference between the total displacement and the number of extreme values ​​in adjacent preset time periods.

[0145] In one embodiment, the weather impact determination module 502 is further configured to merge adjacent monitoring times with the same weather type according to the weather type at each previous monitoring time to obtain several weather periods; determine the degree of impact of each weather period on the module according to the difference between the safety level of the module at the beginning and end of each weather period; and determine the time impact parameter of the weather type on the module for each weather type according to the duration of each weather period corresponding to the weather type and the degree of impact on the module.

[0146] In one embodiment, the time-related impact parameters include the slope and intercept of a fitted straight line showing the change in the degree of impact over time. The weather impact determination module 502 is further configured to perform linear fitting with the duration of each weather period corresponding to the weather type as the abscissa and the degree of impact of each weather period corresponding to the weather type on the module as the ordinate, to obtain the slope and intercept of a fitted straight line showing the change in the degree of impact of the weather type on the module over time.

[0147] In one embodiment, the preliminary prediction module 503 is further configured to determine the time impact parameters and duration of each target weather type expected to be experienced sequentially within the future target duration on the module; determine the safety level attenuation of each target weather type on the module based on the time impact parameters and duration of each target weather type on the module; and subtract the safety level attenuation of each target weather type on the module sequentially from the safety level at the current detection time to obtain the preliminary predicted safety level of the module after the future target duration.

[0148] In one embodiment, the time-related parameters include the slope and intercept of a fitted straight line showing the degree of influence over time. The preliminary prediction module 503 is further configured to, for each target weather type, determine a linear formula consisting of the slope and intercept of a fitted straight line showing the degree of influence of the target weather type on the module over time; and substitute the duration of the target weather type into the linear formula to obtain the amount of attenuation of the target weather type's impact on the module's safety level.

[0149] In one embodiment, the correction prediction module 504 is further configured to, for each adjacent module of the module, determine the correlation between the module and the adjacent modules based on the difference between the time influence parameters corresponding to the module and the adjacent modules under various weather types; determine the safety level correction value based on the difference between the correlation between the module and each adjacent module and the safety level at the current monitoring time; and correct the initial predicted safety level of the module based on the safety level correction value to obtain the predicted safety level of the module after the target time in the future.

[0150] In one embodiment, see Figure 6 The BIM-based bridge construction prediction device 500 also includes:

[0151] The prediction result processing module 505 is used to determine the following for each module: if the predicted safety level of the module is less than or equal to the first hyperparameter, then the module needs to be repaired; if the predicted safety level of the module is less than or equal to the second hyperparameter, then the module and its adjacent area need to be thoroughly inspected and repaired manually. For bridges, if all modules of the bridge are less than or equal to the third hyperparameter, then the bridge needs to be taken out of service and undergo overall inspection and maintenance. The second hyperparameter is less than the third hyperparameter, and the third hyperparameter is less than the first hyperparameter.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A BIM-based method for predicting bridge construction, characterized in that, The method includes: At each monitoring time, the safety level of each module is determined based on the monitoring data of each module of the bridge at that monitoring time. For each module, based on the safety level of the module at each of the previous monitoring times and the weather type at each of the previous monitoring times, determine the time impact parameters of each weather type on the module; Based on the safety level of the module at the current monitoring time, and the time impact parameters and duration of future weather types, the preliminary predicted safety level of the module after the future target duration is determined. Based on the difference between the security level of the module and each adjacent module at the current monitoring time, the preliminary predicted security level of the module is corrected to obtain the predicted security level of the module after the future target duration.

2. The BIM-based bridge construction prediction method according to claim 1, characterized in that, The monitoring data includes displacement data, stress data, strain data, and crack data; The step of determining the safety level of each module at the monitoring time based on the monitoring data of each module of the bridge includes: For each module of the bridge, the deformation index of the module at the monitoring time is determined based on the displacement data of the module at each monitoring time prior to the monitoring time. Based on the building materials of each sampling point of the module, and the stress and strain data of each sampling point of the module at the previous monitoring times, the actual stress-strain data curve of each sampling point of the module is constructed. The safety level of the module at the monitoring time is determined based on the deformation index of the module at the monitoring time, the average distance between the data points corresponding to the stress and strain data of each sampling point at the monitoring time and the tensile strength position, the average distance between the data points corresponding to the stress and strain data of each sampling point at the previous monitoring times and the actual stress-strain data curve, and the crack data at the previous monitoring times.

3. The BIM-based bridge construction prediction method according to claim 2, characterized in that, The step of determining the deformation index of the module at the monitoring time based on the displacement data of the module at previous monitoring times includes: The total displacement of the module is obtained by adding the displacement of the module relative to its initial position at each of the previous monitoring times and the displacement relative to the previous monitoring time. Based on the displacement of the module relative to its initial position at each of the previous monitoring times, a displacement curve is constructed; Determine the number of extreme values ​​within each preset time period on the displacement curve; The deformation index of the module at the monitoring time is determined based on the difference between the total displacement and the number of extreme values ​​in adjacent preset time periods.

4. The BIM-based bridge construction prediction method according to claim 1, characterized in that, The step of determining the time impact parameters of various weather types on the module based on the safety level of the module at each of the previous monitoring times and the weather type at each of the previous monitoring times includes: Based on the weather type at each of the previously mentioned monitoring times, adjacent monitoring times with the same weather type are merged to obtain several weather periods; The degree of impact of each weather period on the module is determined based on the difference in the safety level of the module at the beginning and end of each weather period; For each weather type, the time impact parameter of the weather type on the module is determined based on the duration of each weather period corresponding to the weather type and the degree of impact on the module.

5. The BIM-based bridge construction prediction method according to claim 4, characterized in that, The time-related parameters include the slope and intercept of the fitted straight line showing how the degree of influence changes over time. The step of determining the time impact parameter of the weather type on the module based on the duration of each weather period corresponding to the weather type and the degree of impact on the module includes: Using the duration of each weather period corresponding to the weather type as the horizontal axis value and the degree of influence of each weather period corresponding to the weather type on the module as the vertical axis value, a straight line is fitted to obtain the slope and intercept of the fitted straight line showing the change of the degree of influence of the weather type on the module over time.

6. The BIM-based bridge construction prediction method according to claim 1, characterized in that, The step of determining the preliminary predicted safety level of the module after a future target duration based on the module's safety level at the current monitoring time and the time impact parameters and duration of future weather types includes: Determine the time impact parameters and duration of each of the target weather types expected to be experienced sequentially within the future target time period on the module; Based on the time impact parameters and duration of each target weather type on the module, determine the amount of safety attenuation of each target weather type on the module; Based on the safety level at the current detection time, the safety level of the module is reduced by the amount of reduction in safety level of each target weather type, respectively, to obtain the preliminary predicted safety level of the module after the target time.

7. The BIM-based bridge construction prediction method according to claim 6, characterized in that, The time-related parameters include the slope and intercept of the fitted straight line showing how the degree of influence changes over time. The step of determining the safety attenuation of the module by each target weather type based on the time impact parameters and duration of each target weather type includes: For each target weather type, determine a linear formula consisting of the slope and intercept of a fitted straight line that represents the degree of influence of the target weather type on the module over time; Substituting the duration of the target weather type into the linear formula yields the amount by which the target weather type reduces the safety level of the module.

8. The BIM-based bridge construction prediction method according to claim 1, characterized in that, The step of revising the initial predicted security level of the module based on the difference between the security level of the module and each adjacent module at the current monitoring time, to obtain the predicted security level of the module after the future target duration, includes: For each adjacent module of the module, the correlation between the module and the adjacent module is determined based on the difference between the time influence parameters corresponding to each weather type. Based on the correlation between the module and each of the adjacent modules, and the difference between the security level at the current monitoring time, a security level correction value is determined; Based on the security level correction value, the initial predicted security level of the module is corrected to obtain the predicted security level of the module after the future target duration.

9. The BIM-based bridge construction prediction method according to any one of claims 1 to 8, characterized in that, After correcting the initial predicted security level of the module based on the difference between the security level of the module and each adjacent module at the current monitoring time, and obtaining the predicted security level of the module after the future target duration, the method further includes: For each module, if the predicted safety level of the module is less than or equal to the first hyperparameter, then it is determined that the module needs to be repaired; if the predicted safety level of the module is less than or equal to the second hyperparameter, then it is determined that the module and its neighboring areas need to be thoroughly inspected and repaired manually. If all the modules of the bridge are less than or equal to the third hyperparameter, then it is determined that the use of the bridge needs to be stopped, and the bridge needs to be inspected and maintained as a whole. Wherein, the second hyperparameter is smaller than the third hyperparameter, and the third hyperparameter is smaller than the first hyperparameter.

10. A BIM-based bridge construction prediction device, characterized in that, The device includes: The current safety level calculation module is used to determine the safety level of each module at each monitoring time based on the monitoring data of each module of the bridge at the monitoring time. The weather impact determination module is used to determine the time impact parameters of various weather types on each module based on the safety level of the module at each previous monitoring time and the weather type at each previous monitoring time. The preliminary prediction module is used to determine the preliminary predicted safety level of the module after a future target duration based on the safety level of the module at the current monitoring time and the time impact parameters and duration of future weather types. The correction prediction module is used to correct the initial predicted security level of the module based on the difference between the security level of the module and each adjacent module at the current monitoring time, so as to obtain the predicted security level of the module after the future target duration.

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