A BIM-based bridge construction status monitoring method and system

Abstract

The present invention provides a bridge construction status monitoring method and system based on BIM. First, a physical monitoring data set collected in real time by multiple monitoring nodes during bridge construction is obtained, and the data is dynamically mapped with a pre-built BIM model to generate a monitoring-related data set. Then, a structural difference analysis is performed on the BIM model based on the data set to obtain a structural difference index set of each construction unit. Then, a construction status adjustment strategy set including construction parameter correction instructions and construction resource allocation instructions is generated based on the structural difference index set. Finally, the construction status adjustment strategy set is fed back to a construction control terminal, and the monitoring-related data set of the BIM model is updated according to the execution result, thereby realizing real-time monitoring and dynamic adjustment of the construction status and improving the quality and efficiency of bridge construction.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a bridge construction status monitoring method and system based on BIM. Background Art

[0002] In the field of bridge construction, accurate monitoring of construction status is crucial for ensuring quality, safety, and efficiency. Traditional bridge construction monitoring methods rely primarily on manual inspections. This approach is not only inefficient but also significantly affected by human factors, making it difficult to ensure the accuracy and timeliness of monitoring results. Manual inspections cannot provide real-time monitoring, and sudden and subtle changes may not be detected in a timely manner, which can easily lead to the accumulation of safety hazards.

[0003] With the development of relevant technologies, some automated monitoring equipment has gradually been applied to bridge construction monitoring. These automated monitoring devices can collect some physical data, such as stress and displacement. However, this monitoring data is often isolated and lacks effective integration and analysis. Construction personnel have difficulty in comprehensively and intuitively understanding the actual status of the entire bridge construction from this large amount of scattered data, which hinders their ability to make timely and accurate decisions.

[0004] At the same time, most existing monitoring methods simply monitor construction status and record data, without effectively integrating with the bridge's design model. During construction, if discrepancies with the design plan occur, it's difficult to quickly analyze the problem and develop appropriate adjustment strategies. Furthermore, there's a lack of scientific and systematic methods for allocating construction resources and adjusting construction parameters, and decisions often rely on experience. This leads to low construction efficiency and significant resource waste. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a bridge construction status monitoring method based on BIM, the method comprising:

[0006] Acquire a set of physical monitoring data collected in real time by multiple monitoring nodes during the bridge construction process, and perform dynamic data mapping processing on the physical monitoring data set and a pre-built BIM model to generate a monitoring-related data set of the BIM model;

[0007] Performing structural difference analysis on the BIM model based on the monitoring-related data set to obtain a set of structural difference indicators for each construction unit in the BIM model;

[0008] generating a construction status adjustment strategy set according to the structural difference indicator set, wherein the construction status adjustment strategy set includes a construction parameter correction instruction and a construction resource allocation instruction for the construction unit;

[0009] The construction status adjustment strategy set is fed back to the construction control terminal, and the monitoring associated data set of the BIM model is updated based on the execution result of the construction control terminal.

[0010] In a possible implementation of the first aspect, performing structural difference analysis on the BIM model based on the monitoring-related data set to obtain a set of structural difference indicators for each construction unit in the BIM model includes:

[0011] Extracting an actual geometric data set and an actual physical data set of the construction unit from the monitoring-related data set, wherein the actual geometric data set includes spatial coordinate data, dimensional measurement data, and deformation monitoring data of the construction unit, and the actual physical data set includes stress distribution data, load response data, and material property data of the construction unit;

[0012] Calling a design geometry data set and a design physical data set of the construction unit predefined in the BIM model, wherein the design geometry data set includes design coordinate data, design dimension data, and deformation allowable threshold of the construction unit, and the design physical data set includes a design stress range, design load standard, and material performance standard of the construction unit;

[0013] Comparing the actual geometric data set with the designed geometric data set item by item to generate a geometric difference index set of the construction unit, the geometric difference index set including a coordinate offset, a dimensional error value, and a deformation limit value;

[0014] Dynamically matching the actual physical data set with the designed physical data set to generate a physical difference index set of the construction unit, the physical difference index set including a stress excess ratio, a load response deviation value, and a material property deviation;

[0015] The geometric difference index set and the physical difference index set are fused to obtain the structural difference index set of the construction unit.

[0016] In a possible implementation of the first aspect, the comparing the actual geometric data set with the designed geometric data set item by item to generate a geometric difference index set for the construction unit includes:

[0017] Extracting the spatial coordinate data of the construction unit from the actual geometric data set, and performing a three-dimensional coordinate offset calculation on the spatial coordinate data and the design coordinate data to obtain the coordinate offset;

[0018] Extracting the dimension measurement data from the actual geometric data set, and performing difference calculation between the dimension measurement data and the design dimension data to obtain the dimension error value;

[0019] Extracting the deformation monitoring data from the actual geometric data set, performing interval comparison processing on the deformation monitoring data and the deformation allowable threshold value, and determining a deformation over-limit value in the deformation monitoring data that exceeds the deformation allowable threshold value;

[0020] The coordinate offset, the dimensional error value, and the deformation excess value are respectively subjected to standardized parameter space conversion processing to generate the geometric difference index set.

[0021] In a possible implementation of the first aspect, dynamically matching the actual physical data set with the designed physical data set to generate the physical difference indicator set of the construction unit includes:

[0022] extracting the stress distribution data from the actual physical data set, comparing the maximum stress value in the stress distribution data with the design stress range, and calculating the stress excess ratio at which the maximum stress value exceeds the design stress range;

[0023] Extracting the load response data from the actual physical data set, and performing dynamic difference calculation between the load response data and the design load standard to obtain the load response deviation value;

[0024] Extracting the material property data from the actual physical data set, and calculating the deviation between the material property data and the material property standard to obtain the material property deviation;

[0025] The stress excess ratio, the load response deviation value, and the material property deviation are respectively subjected to standardized parameter space conversion processing to generate the physical difference index set.

[0026] In a possible implementation of the first aspect, generating a construction status adjustment strategy set according to the structural difference indicator set includes:

[0027] Extracting the geometric difference index set and the physical difference index set from the structural difference index set, and inputting the geometric difference index set into a pre-trained construction parameter optimization model to generate a construction parameter correction instruction for the construction unit, wherein the construction parameter correction instruction includes a construction equipment adjustment parameter, a construction process optimization sequence, and a construction accuracy calibration parameter;

[0028] Inputting the physical difference indicator set into a predefined resource allocation rule library to match and obtain construction resource allocation instructions corresponding to the stress excess ratio, the load response deviation value, and the material property deviation, wherein the construction resource allocation instructions include material replenishment type, support structure reinforcement plan, and construction progress adjustment strategy;

[0029] The construction parameter correction instruction and the construction resource allocation instruction are subjected to strategy integration processing to generate the construction status adjustment strategy set.

[0030] In a possible implementation of the first aspect, inputting the geometric difference indicator set into a pre-trained construction parameter optimization model to generate a construction parameter correction instruction for the construction unit includes:

[0031] Invoking a geometric error analysis module in the construction parameter optimization model to determine position calibration parameters of the construction equipment based on the coordinate offset, and determining machining accuracy adjustment parameters of the construction equipment based on the dimensional error value;

[0032] Invoking a deformation control module in the construction parameter optimization model to generate a construction process optimization sequence based on the deformation overlimit value, wherein the construction process optimization sequence includes priority adjustment of construction steps, correction of construction interval time, and a temporary support structure addition plan;

[0033] The position calibration parameters, the machining accuracy adjustment parameters and the construction process optimization sequence are subjected to instruction coding processing to generate the construction parameter correction instruction.

[0034] In a possible implementation of the first aspect, inputting the physical difference indicator set into a predefined resource allocation rule library to match and obtain a construction resource allocation instruction corresponding to the stress exceedance ratio, the load response deviation value, and the material property deviation includes:

[0035] Matching a material supplement type from the resource allocation rule library according to the stress excess ratio, wherein when the stress excess ratio exceeds a first threshold, a high-strength material supplement instruction is matched, and when the stress excess ratio is lower than the first threshold, a local reinforcement material supplement instruction is matched;

[0036] Matching a support structure reinforcement scheme from the resource allocation rule library according to the load response deviation value, wherein when the load response deviation value is a positive deviation, matching a lateral support addition instruction, and when the load response deviation value is a negative deviation, matching a longitudinal support reinforcement instruction;

[0037] Matching a construction progress adjustment strategy from the resource allocation rule library based on the material performance deviation, wherein when the material performance deviation exceeds a second threshold, matching a construction progress delay instruction and associating it with a material re-inspection process; and when the material performance deviation is lower than the second threshold, matching a construction progress segmentation optimization instruction;

[0038] The material replenishment type, the support structure reinforcement plan and the construction progress adjustment strategy are packaged and processed to generate the construction resource deployment instruction.

[0039] In a possible implementation of the first aspect, feeding back the construction status adjustment strategy set to a construction control terminal, and updating the monitoring-related data set of the BIM model based on an execution result of the construction control terminal, includes:

[0040] Sending the construction parameter correction instruction to the construction equipment control terminal, and collecting in real time a set of equipment status data after the construction equipment control terminal executes the construction parameter correction instruction;

[0041] Sending the construction resource allocation instruction to the resource scheduling terminal, and collecting in real time a resource distribution data set after the resource scheduling terminal executes the construction resource allocation instruction;

[0042] Dynamically fusing the device status data set with the resource distribution data set to generate a post-execution monitoring data set;

[0043] Incremental updating is performed on the post-execution monitoring data set and the monitoring-related data set of the BIM model to obtain an updated monitoring-related data set.

[0044] In a possible implementation of the first aspect, dynamically fusing the device status data set with the resource distribution data set to generate a post-execution monitoring data set includes:

[0045] Extracting position calibration execution data, machining accuracy adjustment data, and construction process execution progress data of the construction equipment from the equipment status data set;

[0046] Extracting material supplement location data, support structure reinforcement status data, and construction progress adjustment node data from the resource distribution data set;

[0047] Comparing the position calibration execution data with the design coordinate data of the BIM model in real time to generate a position calibration feedback indicator;

[0048] Performing correlation analysis on the machining accuracy adjustment data and the dimensional error value to generate a machining accuracy feedback index;

[0049] Matching the construction process execution progress data with the construction process optimization sequence to generate a process execution feedback indicator;

[0050] Overlaying the material replenishment location data with the material distribution map of the BIM model to generate a material replenishment feedback indicator;

[0051] Performing a secondary check on the support structure reinforcement status data and the design load standard to generate a support reinforcement feedback index;

[0052] Dynamically aligning the construction progress adjustment node data with the construction plan timeline of the BIM model to generate progress adjustment feedback indicators;

[0053] The position calibration feedback index, the processing accuracy feedback index, the process execution feedback index, the material replenishment feedback index, the support reinforcement feedback index and the progress adjustment feedback index are integrated to generate the post-execution monitoring data set.

[0054] For example, in a possible implementation of the first aspect, obtaining a set of physical monitoring data collected in real time by multiple monitoring nodes during bridge construction, and performing dynamic data mapping processing on the physical monitoring data set and a pre-built BIM model to generate a set of monitoring-related data for the BIM model includes:

[0055] Deploy multiple monitoring nodes at the bridge construction site, each of which includes a 3D laser scanner, a stress sensor, an inclination sensor, and a displacement meter;

[0056] The three-dimensional laser scanner is used to collect a surface deformation data set of the construction unit in real time, the stress sensor is used to collect a real-time stress data set of the construction unit, the tilt sensor is used to collect a tilt angle data set of the construction unit, and the displacement meter is used to collect a displacement change data set of the construction unit;

[0057] Performing time stamp alignment processing on the surface deformation data set, the real-time stress data set, the tilt angle data set, and the displacement change data set to generate the physical monitoring data set;

[0058] Perform spatial coordinate association mapping on each data item in the physical monitoring data set and the design parameters of the corresponding construction unit in the BIM model to generate the monitoring association data set, wherein the monitoring association data set includes real-time monitoring data dynamically bound to the design coordinates, design stress and design deformation threshold of the BIM model.

[0059] On the other hand, an embodiment of the present invention also provides a BIM-based bridge construction status monitoring system, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0060] Based on the above aspects, the embodiment of the present invention realizes the deep integration of actual construction physical data and virtual BIM model by dynamically mapping the physical monitoring data set collected in real time by multiple monitoring nodes during the bridge construction process with the pre-built BIM model, which can timely and accurately reflect the actual situation during the construction process. Based on this monitoring-related data set, the BIM model is subjected to structural difference analysis and processing, and the structural difference index set of each construction unit is accurately obtained, which can effectively discover the deviation from the expected structure during the construction process. Based on these structural difference index sets, a construction status adjustment strategy set is generated, which includes construction parameter correction instructions and construction resource allocation instructions. The construction status is optimized and adjusted from two key aspects: construction parameters and resource allocation, ensuring that the construction process is more scientific and reasonable. The construction status adjustment strategy set is fed back to the construction control terminal, and the monitoring-related data set of the BIM model is updated according to its execution results, so that the bridge construction can be dynamically optimized in real time according to the actual situation, improve construction quality and efficiency, reduce construction risks, and reduce construction costs, thereby realizing intelligent, precise and efficient management of the bridge construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the execution flow of the BIM-based bridge construction status monitoring method provided by an embodiment of the present invention.

[0062] Figure 2 Schematic diagram of exemplary hardware and software components of a BIM-based bridge construction status monitoring system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 The figure is a flow chart of a bridge construction status monitoring method based on BIM provided by an embodiment of the present invention. The bridge construction status monitoring method based on BIM is introduced in detail below.

[0064] Step S110: obtaining a physical monitoring data set collected in real time by multiple monitoring nodes during the bridge construction process, and performing dynamic data mapping processing on the physical monitoring data set and the pre-built BIM model to generate a monitoring-related data set of the BIM model.

[0065] In the actual scenario of bridge construction status monitoring, a series of orderly operations are required to obtain comprehensive and accurate information reflecting the bridge construction status. First, multiple monitoring nodes are rationally deployed at key locations on the bridge construction site. Each monitoring node is equipped with a variety of data collection devices that can monitor the status of the bridge construction unit from different aspects.

[0066] Step S111: deploy multiple monitoring nodes at the bridge construction site, wherein each monitoring node includes a three-dimensional laser scanner, a stress sensor, an inclination sensor, and a displacement meter.

[0067] At the bridge construction site, monitoring nodes are scientifically and rationally deployed based on the structural characteristics, construction technology, and monitoring needs of the bridge. For example, for large bridges, piers, beams, and other parts are key areas that bear loads and undergo deformation, and monitoring nodes should be deployed as a priority. The entire bridge is divided into multiple construction units, and each construction unit is equipped with one or more monitoring nodes based on its size and complexity. Assuming that the bridge is divided into n construction units, and each construction unit is equipped with an average of m monitoring nodes, the total number of deployed monitoring nodes is n×m. Each monitoring node is equipped with a 3D laser scanner to obtain surface geometry information of the construction unit, a stress sensor to monitor the stress borne by the construction unit, an inclination sensor to detect changes in the inclination angle of the construction unit, and a displacement meter to measure the displacement of the construction unit.

[0068] Step S112: The surface deformation data set of the construction unit is collected in real time by the three-dimensional laser scanner, the real-time stress data set of the construction unit is collected by the stress sensor, the inclination angle data set of the construction unit is collected by the inclination sensor, and the displacement change data set of the construction unit is collected by the displacement meter.

[0069] After the monitoring nodes are deployed, each device begins collecting data in real time. The 3D laser scanner scans the surface of the construction unit at set intervals, acquiring a set of 3D coordinate data with each scan. Assuming the first scan is performed at time t1, the resulting 3D coordinate set of surface points is P1 = {p11, p12, ..., p1x}, where p1i represents the 3D coordinates of the i-th point in the first scan. A second scan is performed at time t2, yielding the coordinate set P2 = {p21, p22, ..., p2x}. By calculating the coordinate difference of corresponding points, i.e., di = p2i - p1i (i = 1, 2, ..., x), we can obtain the surface deformation data set D = {d1, d2, ..., dx}.

[0070] The stress sensor uses its internal sensitive elements to sense the stress changes experienced by the construction unit in real time and converts the stress signals into electrical signals for output. Assuming the electrical signal data set collected by the stress sensor over a period of time is E = {e1, e2, ..., ey}, using the pre-established stress-to-electrical signal conversion function f(E), this electrical signal data can be converted into a real-time stress data set S = {s1, s2, ..., sy}, where si = f(ei) (i = 1, 2, ..., y).

[0071] The inclination sensor determines the tilt angle of a construction unit by detecting the components of gravity acceleration along different coordinate axes. At each sampling moment, the inclination sensor outputs a tilt angle value. Assuming z sampling times over a period of time, the resulting tilt angle data set is A = {a1, a2, ..., az}, where ai represents the tilt angle at the i-th sampling time.

[0072] The displacement meter measures the relative displacement between itself and the construction unit to obtain the displacement change of the construction unit. Assume that the displacement data set collected by the displacement meter over a period of time is L = {l1, l2, ..., lw}, where li represents the displacement value at the i-th sampling moment.

[0073] Step S113: performing time stamp alignment processing on the surface deformation data set, the real-time stress data set, the tilt angle data set, and the displacement change data set to generate the physical monitoring data set.

[0074] Since the data collection frequency and collection time of each device may vary, to ensure data consistency and accuracy, it is necessary to perform timestamp alignment on the collected surface deformation data sets, real-time stress data sets, tilt angle data sets, and displacement change data sets. Specifically, using a unified time base as a reference, the data in each data set is sorted in chronological order and the data is matched at the same time point. For example, suppose the time series corresponding to the data in the surface deformation data set D is TD = {tD1, tD2, ..., tDx}, the time series corresponding to the data in the real-time stress data set S is TS = {tS1, tS2, ..., tSy}, the time series corresponding to the data in the tilt angle data set A is TA = {tA1, tA2, ..., tAz}, and the time series corresponding to the data in the displacement change data set L is TL = {tL1, tL2, ..., tLw}. By comparing these time series, the data at the same time point is found and combined to form a new data set. If there is no corresponding data in a data set at a certain time point, interpolation or other appropriate methods can be used to supplement it. After the timestamp alignment process, the generated physical monitoring data set M contains data on surface deformation, real-time stress, tilt angle, and displacement changes at the same time point.

[0075] Step S114: Perform spatial coordinate association mapping on each data item in the physical monitoring data set and the design parameters of the corresponding construction unit in the BIM model to generate the monitoring association data set, wherein the monitoring association data set includes real-time monitoring data dynamically bound to the design coordinates, design stress and design deformation threshold of the BIM model.

[0076] After obtaining the physical monitoring data set, each data item needs to be spatially mapped to the design parameters of the corresponding construction unit in the pre-built BIM model. The BIM model contains detailed design information for each bridge construction unit, such as design coordinates, design stress, and design deformation threshold. First, the corresponding design parameters are found in the BIM model based on the spatial location of the construction unit corresponding to each data item in the physical monitoring data set. For example, for a data item di in the surface deformation data set, its corresponding three-dimensional coordinate information is used to find the design coordinates, design deformation threshold, and other parameters of the construction unit corresponding to that coordinate location in the BIM model. Then, the data items in the physical monitoring data set are associated with the corresponding design parameters, forming a one-to-one correspondence. This generates a monitoring-associated data set N, in which each data item is dynamically bound to parameters such as the design coordinates, design stress, and design deformation threshold in the BIM model, enabling real-time reflection of the discrepancies between the actual status of the construction unit and the design requirements.

[0077] Step S120: performing structural difference analysis on the BIM model based on the monitoring-related data set to obtain a set of structural difference indicators for each construction unit in the BIM model.

[0078] Once the monitoring-related data set is obtained, further analysis is needed to identify discrepancies between the actual and designed states of each construction unit in the BIM model. By deeply mining and analyzing the monitoring-related data set, the geometric and physical differences between each construction unit can be quantified, providing a basis for the subsequent development of construction status adjustment strategies.

[0079] Step S121: Extracting the actual geometric data set and actual physical data set of the construction unit from the monitoring-related data set, wherein the actual geometric data set includes the spatial coordinate data, dimensional measurement data and deformation monitoring data of the construction unit, and the actual physical data set includes the stress distribution data, load response data and material performance data of the construction unit.

[0080] The monitoring-related data set includes real-time monitoring data associated with BIM model design parameters. To conduct structural variance analysis, it is necessary to extract the actual geometric data set and the actual physical data set of the construction unit. The actual geometric data set reflects the spatial geometric characteristics of the construction unit. Spatial coordinate data can be obtained by processing surface deformation data collected by a 3D laser scanner, representing the actual position of the construction unit in space. Dimensional measurement data can be obtained by analyzing and calculating the 3D laser scanning data, reflecting the actual dimensions of the construction unit. Deformation monitoring data is directly derived from the surface deformation data collected by the 3D laser scanner and reflects the shape changes of the construction unit. The actual physical data set reflects the physical performance characteristics of the construction unit. Stress distribution data, collected by stress sensors, describes the stress distribution within the construction unit. Load response data, obtained by analyzing the load borne by the construction unit and the corresponding stress and displacement data, reflects the response of the construction unit under load. Material performance data, obtained by testing and analyzing the materials used in the construction unit, reflects the actual material performance parameters.

[0081] Step S122: Call the design geometry data set and design physical data set of the construction unit predefined in the BIM model, wherein the design geometry data set includes the design coordinate data, design dimension data and deformation allowable threshold of the construction unit, and the design physical data set includes the design stress range, design load standard and material performance standard of the construction unit.

[0082] When conducting structural difference analysis, it is necessary to compare the actual data with the design data. Therefore, it is necessary to call the design geometry data set and design physical data set of the construction unit predefined in the BIM model. The design geometry data set is the ideal geometric characteristics of the construction unit determined during the bridge design phase. The design coordinate data specifies the ideal position of the construction unit in space; the design dimension data clarifies the standard size of the construction unit; and the deformation allowable threshold limits the maximum deformation range allowed for the construction unit under normal use. The design physical data set is the ideal physical performance standard of the construction unit. The design stress range specifies the reasonable range of stress that the construction unit is subjected to under normal working conditions; the design load standard clarifies the design load size that the construction unit should be able to withstand; and the material performance standard specifies the performance parameters that the materials used in the construction unit should have.

[0083] Step S123: performing item-by-item comparison processing on the actual geometric data set and the designed geometric data set to generate a geometric difference index set of the construction unit, wherein the geometric difference index set includes a coordinate offset, a dimensional error value, and a deformation limit value.

[0084] In order to accurately evaluate the difference between the geometric status of the construction unit and the design requirements, it is necessary to compare the actual geometric data set with the designed geometric data set item by item. The details are as follows:

[0085] Step S1231: extracting the spatial coordinate data of the construction unit from the actual geometric data set, and performing a three-dimensional coordinate offset calculation on the spatial coordinate data and the design coordinate data to obtain the coordinate offset.

[0086] Extract the spatial coordinate data of the construction units from the actual geometric data set and compare them with the design coordinate data. Assuming the actual spatial coordinate data is Pactual = {pactual1, pactual2, ..., pactualn} and the design coordinate data is Pdesign = {pdesign1, pdesign2, ..., pdesignn}, by calculating the difference between the corresponding coordinate points, that is, Δpi = pactualali - pdesigni (i = 1, 2, ..., n), we can obtain the coordinate offset set ΔP = {Δp1, Δp2, ..., Δpn}.

[0087] Step S1232: extracting the dimension measurement data from the actual geometric data set, and performing difference calculation between the dimension measurement data and the design dimension data to obtain the dimension error value.

[0088] Extract dimensional measurement data from the actual geometric data set and compare it with the design dimensional data. Assuming the actual dimensional measurement data is Dactual = {dactual1, dactual2, ..., dactualm} and the design dimensional data is Ddesign = {ddesign1, ddesign2, ..., ddesignm}, by calculating the difference, that is, Δdi = dactuali - ddesigni (i = 1, 2, ..., m), we can obtain the dimensional error value set ΔD = {Δd1, Δd2, ..., Δdm}.

[0089] Step S1233: extracting the deformation monitoring data from the actual geometric data set, performing interval comparison processing on the deformation monitoring data and the deformation allowable threshold, and determining the deformation limit value in the deformation monitoring data that exceeds the deformation allowable threshold.

[0090] Deformation monitoring data is extracted from the actual geometric data set and compared with the deformation tolerance threshold. Assuming the deformation monitoring data is Factual = {factual1, factual2, ..., factual1} and the deformation tolerance threshold is Fthreshold, for each deformation monitoring data item factuali, if factuali > Fthreshold, the data item is considered to have exceeded the deformation tolerance threshold and is considered an excess deformation value. Through screening, the set of excess deformation values Fexceed = {fexceed1, fexceed2, ..., fexceedl} is obtained, where l ≤ k.

[0091] Step S1234: performing standardized parameter space conversion processing on the coordinate offset, the dimensional error value, and the deformation limit value to generate the geometric difference index set.

[0092] To facilitate comprehensive analysis and comparison of different types of geometric difference indices, coordinate offsets, dimensional errors, and deformation excess values need to be transformed into standardized parameter spaces. Normalization eliminates dimensional differences between different indices and brings them into the same scale. Assuming the coordinate offset set is ΔP, the dimensional error set is ΔD, and the deformation excess value set is Fexceed, these are normalized using conventional normalization functions g1(ΔP), g2(ΔD), and g3(Fexceed) in related art. This yields the standardized coordinate offset set ΔP'=g1(ΔP), the standardized dimensional error set ΔD'=g2(ΔD), and the standardized deformation excess value set Fexceed'=g3(Fexceed). These standardized sets are combined to generate the geometric difference index set G={ΔP', ΔD', Fexceed'}.

[0093] Step S124: Dynamically matching the actual physical data set with the designed physical data set to generate a physical difference index set of the construction unit, wherein the physical difference index set includes a stress excess ratio, a load response deviation value, and a material property deviation.

[0094] In order to evaluate the difference between the physical performance of the construction unit and the design requirements, it is necessary to dynamically match the actual physical data set with the designed physical data set. The details are as follows:

[0095] Step S1241: extracting the stress distribution data from the actual physical data set, comparing the maximum stress value in the stress distribution data with the design stress range, and calculating the stress excess ratio of the maximum stress value exceeding the design stress range.

[0096] Extract stress distribution data from an actual physical data set and find the maximum stress value. Assume the stress distribution data is Sactual = {sactual1, sactual2, ..., sactualp}, and the maximum stress value is smax = max(Sactual). The design stress range is [Smin, Smax]. If smax > Smax, the maximum stress value is considered to exceed the design stress range. The stress excess ratio, Rstress = (smax - Smax) / Smax (when smax > Smax), is calculated. When smax ≤ Smax, Rstress = 0.

[0097] Step S1242: extracting the load response data from the actual physical data set, and performing dynamic difference calculation between the load response data and the design load standard to obtain the load response deviation value.

[0098] Load response data is extracted from the actual physical data set and compared with the design load standard. Assuming the load response data is Lactual = {lactual1, laactual2, ..., laactualq} and the design load standard is Ldesign, by calculating the difference, that is, Δli = laactuali - Ldesign (i = 1, 2, ..., q), we can obtain the load response deviation value set ΔL = {Δl1, Δl2, ..., Δlq}.

[0099] Step S1243: extracting the material property data from the actual physical data set, and calculating the deviation between the material property data and the material property standard to obtain the material property deviation.

[0100] Extract material performance data from the actual physical data set and compare it with the material performance standard. Assuming the material performance data is Mactual = {mactual1, mactual2, ..., mactualr} and the material performance standard is Mstandard, calculate the absolute value of the difference between each material performance data item and the material performance standard and then find the average, i.e., the material performance deviation Rmaterial = (1 / r) × Σ|mactuali - Mstandard| (i = 1, 2, ..., r).

[0101] Step S1244: performing standardized parameter space conversion processing on the stress excess ratio, the load response deviation value, and the material property deviation, respectively, to generate the physical difference index set.

[0102] To facilitate comprehensive analysis and comparison of different types of physical difference indices, the stress excess ratio, load response deviation, and material property deviation need to be transformed into standardized parameter spaces. Assuming the stress excess ratio is Rstress, the set of load response deviations is ΔL, and the material property deviation is Rmaterial, these are standardized using conventional normalization functions h1(Rstress), h2(ΔL), and h3(Rmaterial). This yields the standardized stress excess ratio Rstress'=h1(Rstress), the standardized set of load response deviations ΔL'=h2(ΔL), and the standardized material property deviation Rmaterial'=h3(Rmaterial). These standardized sets are combined to generate the physical difference index set P={Rstress', ΔL', Rmaterial'}.

[0103] Step S125: fusing the geometric difference index set with the physical difference index set to obtain a structural difference index set of the construction unit.

[0104] To fully reflect the structural differences among construction units, it is necessary to fuse the geometric and physical difference indicator sets. This can be achieved by using a weighted concatenation method, assigning different weights to the geometric and physical difference indicator sets and then concatenating them. Assume that the geometric difference indicator set is G and the physical difference indicator set is P. The weight of the geometric difference indicator set is wG, and the weight of the physical difference indicator set is wP, with wG + wP = 1. Through weighted concatenation, the resulting structural difference indicator set C = (wG × G; wP × P), where the ";" represents the concatenation operation.

[0105] Step S130: generating a construction status adjustment strategy set according to the structural difference indicator set, wherein the construction status adjustment strategy set includes a construction parameter correction instruction and a construction resource allocation instruction for the construction unit.

[0106] After obtaining the set of structural difference indicators of the construction units, it is necessary to generate a corresponding set of construction status adjustment strategies based on these difference indicators to ensure that the bridge construction can proceed smoothly according to the design requirements.

[0107] Step S131: Extract the geometric difference index set and the physical difference index set from the structural difference index set, and input the geometric difference index set into the pre-trained construction parameter optimization model to generate construction parameter correction instructions for the construction unit, wherein the construction parameter correction instructions include construction equipment adjustment parameters, construction process optimization sequence and construction accuracy calibration parameters.

[0108] A set of geometric and physical difference indicators are separated from the set of structural difference indicators. The geometric difference indicators are then fed into a pre-trained construction parameter optimization model, which is trained using a large amount of historical construction data and corresponding optimal construction parameters. The construction parameter optimization model contains multiple modules that generate corresponding construction parameter correction instructions based on the input geometric difference indicators. Specifically, the construction parameter optimization model includes a geometric error analysis module and a deformation control module.

[0109] Step S1311: calling the geometric error analysis module in the construction parameter optimization model, determining the position calibration parameters of the construction equipment based on the coordinate offset, and determining the processing accuracy adjustment parameters of the construction equipment based on the dimensional error value.

[0110] The geometric error analysis module analyzes and processes the coordinate offsets and dimensional error values in the input geometric difference index set. For coordinate offsets, the model calculates the required position adjustment of the construction equipment to eliminate the coordinate offset, i.e., the position calibration parameters, based on pre-defined algorithms and rules. For example, assuming the coordinate offset is represented by the vector ΔP, the model, based on the characteristics of the coordinate system and the operation of the construction equipment, uses a series of transformations and calculations to determine the distance the construction equipment needs to move along each coordinate axis. These distance values constitute the position calibration parameter set Pcal = {pcalx, pcaly, pcalz}, where pcalx, pcaly, and pcalz represent the position adjustment along the x, y, and z axes, respectively.

[0111] For dimensional error values, the geometric error analysis module determines the machining accuracy adjustment parameters for the construction equipment based on the magnitude and direction of the dimensional error. If the dimensional error value is positive, indicating that the actual size is larger than the designed size, the model calculates the adjustment required to reduce the machining accuracy of the construction equipment. If the dimensional error value is negative, indicating that the actual size is smaller than the designed size, the model calculates the adjustment required to improve the machining accuracy of the construction equipment. Assuming the set of dimensional error values is ΔD, by analyzing and calculating each dimensional error value, the machining accuracy adjustment parameter set Aadj={aadj1, aadj2, ..., aadjm} is obtained, where aadji represents the machining accuracy adjustment corresponding to the i-th dimensional error value.

[0112] Step S1312: calling the deformation control module in the construction parameter optimization model to generate a construction process optimization sequence according to the deformation limit value, wherein the construction process optimization sequence includes priority adjustment of construction steps, correction of construction interval time and temporary support structure addition plan.

[0113] The Deformation Control module generates an optimized sequence of construction steps based on the deformation excess values in the input geometric difference index set. First, the module analyzes the causes and impact of the deformation excesses and adjusts the priority of the construction steps based on this information. For example, if a construction step is likely to further exacerbate the deformation excess, the priority of that step will be lowered; conversely, if a step can help improve the deformation situation, its priority will be increased.

[0114] At the same time, the deformation control module adjusts the construction interval based on the deformation limit. If the deformation limit is severe, the interval between certain construction steps may need to be extended to allow the structure sufficient time for stress release and stabilization. If the deformation situation is relatively mild, the construction interval can be appropriately shortened to improve construction efficiency.

[0115] In addition, for situations where deformation exceeds the limit, the deformation control module will generate a plan for adding temporary support structures. This plan will determine the type, quantity, and layout of the temporary support structures based on the location, size, and direction of the deformation. For example, if the deformation of a certain area is mainly caused by excessive lateral force, it may be recommended to add lateral support structures; if the vertical deformation is too large, it may be recommended to add vertical support structures. The final generated construction process optimization sequence Sopt={sopt1, sopt2,..., soptn}, where sopti represents an optimization adjustment item in the construction process, such as construction step priority adjustment, construction interval time correction, or the specific content of the temporary support structure addition plan.

[0116] Step S1313: performing instruction encoding processing on the position calibration parameters, the machining accuracy adjustment parameters and the construction process optimization sequence to generate the construction parameter correction instruction.

[0117] In order to facilitate accurate understanding and execution by construction equipment and construction personnel, the position calibration parameters, machining accuracy adjustment parameters and construction process optimization sequence need to be subjected to instruction encoding processing. The instruction encoding process converts these parameters and sequences into the conventional encoding format of the relevant technology, and each code corresponds to a specific operation instruction. For example, for the position calibration parameter set Pcal, each position adjustment amount will be encoded into a binary or decimal code according to the set rules; similar encoding processing is also performed for the machining accuracy adjustment parameter set Aadj and the construction process optimization sequence Sopt. Finally, these codes are combined together to generate the construction parameter correction instruction Iparam, which can be sent to the construction equipment control terminal and the construction personnel's operation terminal through the data transmission system.

[0118] Step S132: Input the physical difference indicator set into a predefined resource allocation rule library, and match it to obtain construction resource allocation instructions corresponding to the stress excess ratio, the load response deviation value and the material performance deviation. The construction resource allocation instructions include material replenishment type, support structure reinforcement plan and construction progress adjustment strategy.

[0119] The predefined resource allocation rule library contains numerous rules and strategies developed based on bridge construction experience and design requirements. By inputting a set of physical difference indicators into the resource allocation rule library, appropriate construction resource allocation instructions can be automatically matched based on stress exceedance ratios, load response deviation values, and material property deviations.

[0120] Step S1321: Match the material supplement type from the resource allocation rule library according to the stress excess ratio, wherein when the stress excess ratio exceeds a first threshold, match the high-strength material supplement instruction, and when the stress excess ratio is lower than the first threshold, match the local reinforcement material supplement instruction.

[0121] The resource allocation rule library sets a first threshold for the stress excess ratio. When the stress excess ratio exceeds this threshold, it indicates that the stress on the structure exceeds the design tolerance by a significant amount, and additional high-strength materials are needed to improve the structure's load-bearing capacity. In this case, a high-strength material replenishment instruction can be matched from the resource allocation rule library. This instruction specifies the type, quantity, and specifications of the required high-strength material. For example, the high-strength material could be high-strength steel or high-performance concrete.

[0122] When the stress excess ratio is below the first threshold, the stress excess is relatively minor and only local reinforcement may be required. In this case, a local reinforcement material supplementary instruction can be matched. This reinforcement material supplementary instruction specifies the type of material required for local reinforcement, such as steel plate or carbon fiber cloth, as well as the location and quantity of these materials.

[0123] Step S1322: Match the support structure reinforcement plan from the resource allocation rule library according to the load response deviation value, wherein when the load response deviation value is a positive deviation, match the lateral support addition instruction, and when the load response deviation value is a negative deviation, match the longitudinal support reinforcement instruction.

[0124] The load response deviation value reflects the difference between the construction unit's response to actual loads and the design load standard. A positive load response deviation indicates that the actual load response exceeds the design load standard, possibly due to excessive lateral forces. In this case, the resource allocation rule library will match the lateral support addition instruction, which details the type, quantity, and layout of the required lateral supports to enhance the structure's lateral load-bearing capacity.

[0125] If the load response deviation value is negative, it indicates that the actual load response is less than the design load standard, which may indicate a problem with the longitudinal force. In this case, a longitudinal support enhancement instruction can be matched. This longitudinal support enhancement instruction will specify specific measures to strengthen the longitudinal support structure, such as increasing the number of longitudinal supports or replacing higher-strength longitudinal support materials.

[0126] Step S1323: Match the construction progress adjustment strategy from the resource allocation rule library according to the material performance deviation, wherein, when the material performance deviation exceeds the second threshold, match the construction progress delay instruction and associate it with the material re-inspection process; when the material performance deviation is lower than the second threshold, match the construction progress segmentation optimization instruction.

[0127] The resource allocation rule library sets a second threshold for material performance deviation. When the deviation exceeds this threshold, it indicates that the actual material performance deviates significantly from the design standard, potentially impacting construction quality and structural safety. In this case, a construction progress delay order can be matched and linked to the material re-inspection process. The construction progress delay order will suspend or slow the current construction progress to allow sufficient time for re-inspection and re-evaluation of the materials. The material re-inspection process will clearly define the re-inspection items, methods, and standards.

[0128] When the material property deviation falls below the second threshold, it indicates that while the material properties exhibit some deviation, they are still within an acceptable range. At this point, a construction progress segment optimization instruction can be matched. This instruction adjusts and optimizes the construction progress segment by segment based on the actual material properties to improve construction efficiency and quality.

[0129] Step S1324: performing strategy packaging processing on the material replenishment type, the support structure reinforcement plan, and the construction progress adjustment strategy to generate the construction resource deployment instruction.

[0130] To facilitate the deployment and management of construction resources, it's necessary to encapsulate the material replenishment type, support structure reinforcement plan, and construction schedule adjustment strategy into a policy. This policy encapsulation integrates this information into a unified instruction format, encompassing detailed information on all aspects of construction resource deployment. For example, the specific details of the material replenishment type, support structure reinforcement plan, and construction schedule adjustment strategy are encoded and combined to generate a construction resource deployment instruction (Ires). This construction resource deployment instruction can be sent to the appropriate resource management department and construction team through the resource scheduling system.

[0131] Step S133: performing strategy integration processing on the construction parameter correction instruction and the construction resource allocation instruction to generate the construction status adjustment strategy set.

[0132] In order to form a complete construction status adjustment plan, it is necessary to strategically integrate the construction parameter correction instructions and the construction resource allocation instructions. The strategic integration process will consider the relationship and synergy between construction parameter correction and resource allocation to ensure the rationality and effectiveness of the adjustment strategy. For example, the correction of construction parameters may affect the demand and allocation method of resources, and the allocation of resources will also affect the execution effect of construction parameters. Through strategic integration, the construction parameter correction instructions Iparam and the construction resource allocation instructions Ires are comprehensively analyzed and optimized to generate a construction status adjustment strategy set Sstrategy={Iparam, Ires}. This construction status adjustment strategy set contains comprehensive adjustment strategies for construction units.

[0133] Step S140: Feedback the construction status adjustment strategy set to the construction control terminal, and update the monitoring-related data set of the BIM model based on the execution result of the construction control terminal.

[0134] After generating a set of construction status adjustment strategies, they need to be fed back to the construction control terminal so that construction personnel and equipment can make adjustments based on the strategies. Simultaneously, based on the execution results of the construction control terminal, the monitoring-related data set of the BIM model is updated to reflect changes in the construction status in real time.

[0135] Step S141: sending the construction parameter correction instruction to the construction equipment control terminal, and collecting in real time a set of equipment status data after the construction equipment control terminal executes the construction parameter correction instruction.

[0136] The construction parameter correction instruction Iparam is sent to the construction equipment control terminal via the data transmission system. Based on the instructions, the construction equipment control terminal will make corresponding adjustments to the construction equipment, such as adjusting the equipment's position, processing accuracy, and construction process. As the construction equipment executes the instructions, it collects real-time equipment status data, including information such as the equipment's location, operating speed, and processing accuracy. For example, sensors installed on the equipment can capture data such as the equipment's position coordinates, motor speed, and tool wear in real time. This collected data is organized and aggregated to generate a device status data set Ddevice = {ddevice1, ddevice2, ..., ddevicen}, where ddevicei represents a specific device status parameter.

[0137] Step S142: sending the construction resource allocation instruction to the resource scheduling terminal, and collecting in real time a resource distribution data set after the resource scheduling terminal executes the construction resource allocation instruction.

[0138] The construction resource allocation instruction Ires is sent to the resource scheduling terminal. Based on the instructions, the resource scheduling terminal allocates and manages construction resources, such as replenishing materials, reinforcing support structures, and adjusting the construction schedule. As the resource scheduling terminal executes the instructions, it collects real-time resource distribution data, including information such as the storage location of materials, the installation status of support structures, and the completion status of the construction schedule. For example, the inventory management system obtains information on the storage quantity and location of materials, on-site monitoring equipment obtains data on the reinforcement status of support structures, and the progress management system obtains the actual completion status of the construction schedule. This collected data is organized and summarized to generate a resource distribution data set Dresource = {dresource1, dresource2, ..., dresourcem}, where dresourcei represents a distribution parameter of a resource.

[0139] Step S143: Dynamically merge the device status data set and the resource distribution data set to generate a post-execution monitoring data set.

[0140] In order to fully understand the execution effect of the construction status adjustment strategy, it is necessary to dynamically integrate the equipment status data set and the resource distribution data set. The details are as follows:

[0141] Step S1431: extracting the position calibration execution data, processing accuracy adjustment data and construction process execution progress data of the construction equipment from the equipment status data set.

[0142] Data related to the position calibration, machining accuracy adjustment, and construction process execution progress of construction equipment is filtered from the device status data set Ddevice. For example, the data filtering algorithm extracts the actual movement distance and direction of the equipment during the position calibration process as position calibration execution data; the actual machining error data after the equipment's machining accuracy adjustment is extracted as machining accuracy adjustment data; and the actual completion time and progress percentage of the construction process are extracted as construction process execution progress data. These extracted data are respectively combined into the position calibration execution data set Dcal = {dcal1, dcal2, ..., dcalp}, the machining accuracy adjustment data set Dadj = {dadj1, dadj2, ..., dadjq}, and the construction process execution progress data set Dproc = {dproc1, dproc2, ..., dprocr}.

[0143] Step S1432: extracting material replenishment location data, support structure reinforcement status data, and construction progress adjustment node data from the resource distribution data set.

[0144] Data related to material replenishment locations, support structure reinforcement status, and construction schedule adjustment nodes are filtered from the resource distribution data set Dresource. For example, through a data filtering algorithm, the actual storage location and quantity of replenishment materials are extracted as material replenishment location data; the actual load-bearing capacity and stability data after support structure reinforcement are extracted as support structure reinforcement status data; and the actual start and end times after construction schedule adjustment are extracted as construction schedule adjustment node data. These extracted data are respectively organized into the material replenishment location data set Dmat = {dmat1, dmat2, ..., dmats}, the support structure reinforcement status data set Dsup = {dsup1, dsup2, ..., dsupt}, and the construction schedule adjustment node data set Dtime = {dtime1, dtime2, ..., dtimeu}.

[0145] Step S1433: performing real-time comparison between the position calibration execution data and the design coordinate data of the BIM model to generate a position calibration feedback indicator.

[0146] The position calibration execution data set Dcal is compared and analyzed with the design coordinate data in the BIM model. The performance of the position calibration is evaluated by calculating the difference between the position calibration execution data and the design coordinate data. For example, for each position calibration execution data item dcali, the coordinate difference Δdi = dcali - pdesigni (where pdesigni is the corresponding point in the design coordinate data) is calculated. These differences are statistically analyzed to generate the position calibration feedback index Fcal, which reflects the accuracy and degree of deviation of the position calibration.

[0147] Step S1434: performing correlation analysis on the processing accuracy adjustment data and the dimensional error value to generate a processing accuracy feedback index.

[0148] Perform a correlation analysis between the precision adjustment data set Dadj and the previously calculated dimensional error value set ΔD. By comparing the relationship between the adjusted data and the dimensional error values, the effectiveness of the precision adjustment can be evaluated. For example, the correlation coefficient between the adjusted data and the dimensional error values can be calculated, or the reduction ratio of the dimensional error after the adjustment can be calculated. Based on these analysis results, a precision feedback index Fadj is generated, which reflects the effectiveness and degree of improvement of the precision adjustment.

[0149] Step S1435: Match the construction process execution progress data with the construction process optimization sequence to generate a process execution feedback indicator.

[0150] A progress matching analysis is performed between the construction process execution progress data set Dproc and the construction process optimization sequence Sopt. The actual execution progress of the construction process is compared with the progress requirements specified in the optimization sequence to evaluate the execution effectiveness of the construction process. For example, the difference between the actual completion time and the time node specified in the optimization sequence is calculated, or the difference between the actual completion progress and the progress percentage specified in the optimization sequence is calculated. Based on these analysis results, the process execution feedback indicator Fproc is generated, which can reflect the timeliness and accuracy of the construction process execution.

[0151] Step S1436: Overlaying the material replenishment position data with the material distribution map of the BIM model to generate a material replenishment feedback indicator.

[0152] The material replenishment location data set Dmat is overlaid with the material distribution map in the BIM model. The accuracy and rationality of material replenishment are evaluated by comparing the actual material replenishment locations with the locations specified in the material distribution map. For example, the distance difference between the material replenishment location and the corresponding location in the material distribution map can be calculated, or the degree of overlap between the material replenishment location and the specified location in the material distribution map can be calculated. Based on these analysis results, a material replenishment feedback index Fmat is generated, which can reflect the effectiveness and quality of material replenishment.

[0153] Step S1437: performing a secondary check on the support structure reinforcement status data and the design load standard to generate a support reinforcement feedback index.

[0154] The support structure reinforcement status data set, Dsup, is rechecked against the design load standards in the BIM model. The effectiveness of the support structure reinforcement is evaluated by analyzing whether the actual load-bearing capacity and stability of the reinforced support structure meet the design load standards. For example, the ratio of the actual load-bearing capacity of the reinforced support structure to the design load standard can be calculated, or the deformation of the support structure under the design load can be evaluated. Based on these analysis results, the support reinforcement feedback index, Fsup, is generated to reflect the effectiveness and safety of the support structure reinforcement.

[0155] Step S1438: Dynamically align the construction progress adjustment node data with the construction plan timeline of the BIM model to generate a progress adjustment feedback indicator.

[0156] Dynamically align the construction progress adjustment node data set Dtime with the construction plan timeline in the BIM model. Compare the actual time nodes after the construction progress adjustment with the time nodes specified in the construction plan timeline to evaluate the effectiveness of the construction progress adjustment. For example, the time difference between the actual time node and the planned time node is calculated, or the degree of overlap between the actual time node and the planned time node is calculated. Based on these analysis results, the progress adjustment feedback indicator Ftime is generated to reflect the rationality and effectiveness of the construction progress adjustment.

[0157] Step S1439: Integrate the position calibration feedback index, the processing accuracy feedback index, the process execution feedback index, the material replenishment feedback index, the support reinforcement feedback index and the progress adjustment feedback index to generate the post-execution monitoring data set.

[0158] The position calibration feedback indicator Fcal, machining accuracy feedback indicator Fadj, process execution feedback indicator Fproc, material replenishment feedback indicator Fmat, support reinforcement feedback indicator Fsup, and progress adjustment feedback indicator Ftime are integrated. A weighted concatenation method can be used to assign different weights to each feedback indicator and then concatenate them. Assume that the weight of the position calibration feedback indicator is wcal, the weight of the machining accuracy feedback indicator is wadj, the weight of the process execution feedback indicator is wproc, the weight of the material replenishment feedback indicator is wmat, the weight of the support reinforcement feedback indicator is wsup, and the weight of the progress adjustment feedback indicator is wtime, and that wcal + wadj + wproc + wmat + wsup + wtime = 1. Through weighted concatenation, the post-execution monitoring data set Dpost = (wcal × Fcal; wadj × Fadj; wproc × Fproc; wmat × Fmat; wsup × Fsup; wtime × Ftime) is obtained.

[0159] Step S144: performing incremental update processing on the post-execution monitoring data set and the monitoring-related data set of the BIM model to obtain an updated monitoring-related data set.

[0160] After obtaining the post-execution monitoring data set, it needs to be incrementally updated with the BIM model's existing monitoring-related data set to ensure that the BIM model accurately reflects the latest status of the bridge construction in real time. Incremental updating only updates the data that has changed, rather than replacing the entire monitoring-related data set. This improves data update efficiency and reduces data processing workload.

[0161] First, the post-implementation monitoring data set and the BIM model's monitoring-related data set need to be checked for consistency in data structure and data type. This ensures that the data items in both data sets have the same meaning and format, allowing for accurate data comparison and updates. For example, if the position calibration feedback indicator in the post-implementation monitoring data set is expressed as coordinate difference, then the corresponding position data in the BIM model's monitoring-related data set should also be able to be effectively compared and updated.

[0162] Next, perform a data comparison operation. Compare each data item in the post-execution monitoring data set with the corresponding data item in the monitoring-related data set of the BIM model one by one. The purpose of the comparison is to find out the data items that have changed, which may be caused by the execution of the construction status adjustment strategy. For example, by comparing the position calibration feedback indicators in the post-execution monitoring data set with the corresponding construction unit position data in the BIM model, it is determined whether the position has changed; by comparing the processing accuracy feedback indicators with the dimension-related data in the model, it is determined whether the processing accuracy has been adjusted, etc.

[0163] For data changes discovered during the comparison process, corresponding update operations are performed. Update operations are handled differently depending on the specific data type and the changes. For example, if the data is numerical, such as stress values or displacement values, the old values in the BIM model monitoring data set are directly replaced with the new values in the post-execution monitoring data set. If the data is status-based, such as the completion status of the construction process or the reinforcement status of the supporting structure, it is updated according to the new status information.

[0164] During the update process, data relevance and consistency must also be considered. Certain data items may be interrelated. For example, a change in the position of a construction unit may affect the stress distribution and load response of surrounding units. Therefore, when updating a data item, the associated data items must be adjusted and updated accordingly to ensure the consistency and accuracy of the entire monitoring data set.

[0165] At the same time, in order to facilitate the traceability and management of the data update process, it is necessary to record the detailed information of each update, including the update time, updated data items, update reasons, etc. This recorded information can be stored in a dedicated data log to facilitate subsequent query and analysis.

[0166] After the incremental update process described above, an updated monitoring-related data set is obtained. This new monitoring-related data set contains the latest status information of the bridge construction after the construction status adjustment strategy is implemented, providing an accurate data foundation for subsequent structural difference analysis and construction status adjustment.

[0167] In summary, the entire BIM-based bridge construction condition monitoring method forms a closed-loop monitoring and adjustment system through a series of orderly steps, from data collection and analysis to strategy generation and feedback updates. By continuously acquiring real-time data, analyzing structural differences, generating adjustment strategies, and updating monitoring data, problems in the bridge construction process can be promptly identified and effective measures can be taken to adjust them, ensuring that bridge construction proceeds smoothly according to design requirements and improving construction quality and safety.

[0168] In addition, regarding the construction and training of the pre-trained construction parameter optimization model, its necessary modules, layers, and connections are as follows:

[0169] The construction parameter optimization model primarily consists of an input layer, a geometric error analysis module, a deformation control module, and an output layer. The input layer receives a set of geometric difference indicators and passes them on to subsequent processing modules. The geometric error analysis module and the deformation control module are the core processing components of the model, analyzing and processing different types of geometric difference indicators, respectively, to generate corresponding construction parameter correction information. The output layer integrates this correction information and outputs it as construction parameter correction instructions.

[0170] At the hierarchical level, the input layer directly connects to the geometric error analysis module and the deformation control module, passing input data to each module. Some information exchange may occur between the geometric error analysis module and the deformation control module to ensure the rationality and consistency of the generated construction parameter correction information. Finally, the output results of these two modules are aggregated and sent to the output layer, which then generates the final instructions.

[0171] The specific steps for training the construction parameter optimization model are as follows:

[0172] Step S210: Collect training sample data. The training sample data is historical bridge construction data, including geometric difference index data and corresponding optimal construction parameter data during the construction process. This data can come from multiple different bridge construction projects to ensure data diversity and representativeness.

[0173] Step S220: Clean and organize the training sample data to remove noise and outliers. At the same time, standardize the training sample data to bring different types of geometric difference indicators and construction parameter data into the same scale range, allowing the model to better learn and process this data.

[0174] Step S230: Retrieve the model architecture and initial parameters of the construction parameter optimization model to initialize the construction parameter optimization model. The model architecture of the construction parameter optimization model should be reasonably designed based on the characteristics of the data and the requirements of the task to ensure that the construction parameter optimization model can effectively process the input data and generate accurate output results.

[0175] Step S240: The preprocessed training sample data is divided into a training set and a validation set. The construction parameter optimization model is trained using the training set. During the training process, the parameters of the construction parameter optimization model are continuously adjusted to minimize the error between the output of the construction parameter optimization model and the actual optimal construction parameter data. Optimization algorithms such as gradient descent can be used to update the model parameters.

[0176] Step S250: Evaluate the trained construction parameter optimization model using the validation set to assess its performance and accuracy. Evaluation metrics may include error rate, accuracy, etc. If the performance of the construction parameter optimization model does not meet the requirements, return to step S230 to adjust the model architecture or parameters and retrain.

[0177] Step S260: When the performance of the construction parameter optimization model reaches a satisfactory level, it is deployed to the actual bridge construction status monitoring system to generate construction parameter correction instructions in real time.

[0178] Throughout the entire data collection process, especially when collecting physical monitoring data, attention must be paid to protecting privacy-sensitive data. Since this scenario primarily collects data on the physical status of bridge construction units, it generally does not involve sensitive information such as personal privacy. However, if device identification information or other potentially sensitive data may be involved during data collection, encryption technology is necessary to encrypt this data. For example, symmetric encryption algorithms can be used to encrypt data, ensuring that only authorized devices and personnel can decrypt and access it. Furthermore, secure transmission protocols, such as SSL / TLS, should be used during data transmission to ensure data security and prevent theft or tampering. Data storage should be stored on secure servers with strict access control, ensuring that only authorized personnel can access and process the data. These technical measures can effectively protect privacy-sensitive data and prevent data leaks.

[0179] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a BIM-based bridge construction status monitoring system 100 that can implement the concepts of the present application, as provided in some embodiments of the present application. For example, a processor 120 can be used in the BIM-based bridge construction status monitoring system 100 to perform the functions described in the present application.

[0180] The BIM-based bridge construction status monitoring system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the BIM-based bridge construction status monitoring method of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0181] For example, the BIM-based bridge construction status monitoring system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in various forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the BIM-based bridge construction status monitoring system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented based on these program instructions. The BIM-based bridge construction status monitoring system 100 also includes an I / O interface 150 between the computer and other input and output devices.

[0182] For ease of explanation, only one processor is described in the BIM-based bridge construction status monitoring system 100. However, it should be noted that the BIM-based bridge construction status monitoring system 100 in this application may also include multiple processors, so the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the BIM-based bridge construction status monitoring system 100 performs step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor performs step A and the second processor performs step B, or the first processor and the second processor perform steps A and B together.

[0183] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned BIM-based bridge construction status monitoring method is implemented.

[0184] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A bridge construction status monitoring method based on BIM, characterized in that: The method comprises: Acquire a set of physical monitoring data collected in real time by multiple monitoring nodes during the bridge construction process, and perform dynamic data mapping processing on the physical monitoring data set and a pre-built BIM model to generate a monitoring-related data set of the BIM model; Performing structural difference analysis on the BIM model based on the monitoring-related data set to obtain a set of structural difference indicators for each construction unit in the BIM model; generating a construction status adjustment strategy set according to the structural difference indicator set, wherein the construction status adjustment strategy set includes a construction parameter correction instruction and a construction resource allocation instruction for the construction unit; Feedback the construction status adjustment strategy set to the construction control terminal, and update the monitoring-related data set of the BIM model based on the execution result of the construction control terminal; The performing structural difference analysis on the BIM model based on the monitoring-related data set to obtain a set of structural difference indicators for each construction unit in the BIM model includes: Extracting an actual geometric data set and an actual physical data set of the construction unit from the monitoring-related data set, wherein the actual geometric data set includes spatial coordinate data, dimensional measurement data, and deformation monitoring data of the construction unit, and the actual physical data set includes stress distribution data, load response data, and material property data of the construction unit; Calling a design geometry data set and a design physical data set of the construction unit predefined in the BIM model, wherein the design geometry data set includes design coordinate data, design dimension data, and deformation allowable threshold of the construction unit, and the design physical data set includes a design stress range, design load standard, and material performance standard of the construction unit; Comparing the actual geometric data set with the designed geometric data set item by item to generate a geometric difference index set of the construction unit, the geometric difference index set including a coordinate offset, a dimensional error value, and a deformation limit value; Dynamically matching the actual physical data set with the designed physical data set to generate a physical difference index set of the construction unit, the physical difference index set including a stress excess ratio, a load response deviation value, and a material property deviation; Fusing the geometric difference index set with the physical difference index set to obtain a structural difference index set of the construction unit; Generating a construction status adjustment strategy set according to the structural difference indicator set includes: Extracting the geometric difference index set and the physical difference index set from the structural difference index set, and inputting the geometric difference index set into a pre-trained construction parameter optimization model to generate a construction parameter correction instruction for the construction unit, wherein the construction parameter correction instruction includes a construction equipment adjustment parameter, a construction process optimization sequence, and a construction accuracy calibration parameter; Inputting the physical difference indicator set into a predefined resource allocation rule library to match and obtain construction resource allocation instructions corresponding to the stress excess ratio, the load response deviation value, and the material property deviation, wherein the construction resource allocation instructions include material replenishment type, support structure reinforcement plan, and construction progress adjustment strategy; The construction parameter correction instruction and the construction resource allocation instruction are subjected to strategy integration processing to generate the construction status adjustment strategy set.

2. The bridge construction status monitoring method based on BIM according to claim 1 is characterized in that: The step of comparing the actual geometric data set with the designed geometric data set item by item to generate a geometric difference index set of the construction unit includes: Extracting the spatial coordinate data of the construction unit from the actual geometric data set, and performing a three-dimensional coordinate offset calculation on the spatial coordinate data and the design coordinate data to obtain the coordinate offset; Extracting the dimension measurement data from the actual geometric data set, and performing difference calculation between the dimension measurement data and the design dimension data to obtain the dimension error value; Extracting the deformation monitoring data from the actual geometric data set, performing interval comparison processing on the deformation monitoring data and the deformation allowable threshold value, and determining a deformation over-limit value in the deformation monitoring data that exceeds the deformation allowable threshold value; The coordinate offset, the dimensional error value, and the deformation excess value are respectively subjected to standardized parameter space conversion processing to generate the geometric difference index set.

3. The bridge construction status monitoring method based on BIM according to claim 1 is characterized in that: The dynamically matching the actual physical data set with the designed physical data set to generate a physical difference index set of the construction unit includes: extracting the stress distribution data from the actual physical data set, comparing the maximum stress value in the stress distribution data with the design stress range, and calculating the stress excess ratio at which the maximum stress value exceeds the design stress range; Extracting the load response data from the actual physical data set, and performing dynamic difference calculation between the load response data and the design load standard to obtain the load response deviation value; Extracting the material property data from the actual physical data set, and calculating the deviation between the material property data and the material property standard to obtain the material property deviation; The stress excess ratio, the load response deviation value, and the material property deviation are respectively subjected to standardized parameter space conversion processing to generate the physical difference index set.

4. The bridge construction status monitoring method based on BIM according to claim 1 is characterized in that: Inputting the geometric difference index set into a pre-trained construction parameter optimization model to generate a construction parameter correction instruction for the construction unit includes: Invoking a geometric error analysis module in the construction parameter optimization model to determine position calibration parameters of the construction equipment based on the coordinate offset, and determining machining accuracy adjustment parameters of the construction equipment based on the dimensional error value; Invoking a deformation control module in the construction parameter optimization model to generate a construction process optimization sequence based on the deformation overlimit value, wherein the construction process optimization sequence includes priority adjustment of construction steps, correction of construction interval time, and a temporary support structure addition plan; The position calibration parameters, the machining accuracy adjustment parameters and the construction process optimization sequence are subjected to instruction coding processing to generate the construction parameter correction instruction.

5. The bridge construction status monitoring method based on BIM according to claim 1 is characterized in that: Inputting the physical difference indicator set into a predefined resource allocation rule library to match and obtain construction resource allocation instructions corresponding to the stress excess ratio, the load response deviation value, and the material property deviation degree includes: Matching a material supplement type from the resource allocation rule library according to the stress excess ratio, wherein when the stress excess ratio exceeds a first threshold, a high-strength material supplement instruction is matched, and when the stress excess ratio is lower than the first threshold, a local reinforcement material supplement instruction is matched; Matching a support structure reinforcement scheme from the resource allocation rule library according to the load response deviation value, wherein when the load response deviation value is a positive deviation, matching a lateral support addition instruction, and when the load response deviation value is a negative deviation, matching a longitudinal support reinforcement instruction; Matching a construction progress adjustment strategy from the resource allocation rule library based on the material performance deviation, wherein when the material performance deviation exceeds a second threshold, matching a construction progress delay instruction and associating it with a material re-inspection process; and when the material performance deviation is lower than the second threshold, matching a construction progress segmentation optimization instruction; The material replenishment type, the support structure reinforcement plan and the construction progress adjustment strategy are packaged to generate the construction resource deployment instruction.

6. The bridge construction status monitoring method based on BIM according to claim 1 is characterized in that: Feeding back the construction status adjustment strategy set to the construction control terminal, and updating the monitoring-related data set of the BIM model based on the execution result of the construction control terminal, includes: Sending the construction parameter correction instruction to the construction equipment control terminal, and collecting in real time a set of equipment status data after the construction equipment control terminal executes the construction parameter correction instruction; Sending the construction resource allocation instruction to the resource scheduling terminal, and collecting in real time a resource distribution data set after the resource scheduling terminal executes the construction resource allocation instruction; Dynamically fusing the device status data set with the resource distribution data set to generate a post-execution monitoring data set; Incremental updating is performed on the post-execution monitoring data set and the monitoring-related data set of the BIM model to obtain an updated monitoring-related data set.

7. The bridge construction status monitoring method based on BIM according to claim 6 is characterized in that: The dynamically fusing the device status data set with the resource distribution data set to generate a post-execution monitoring data set includes: Extracting position calibration execution data, machining accuracy adjustment data, and construction process execution progress data of the construction equipment from the equipment status data set; Extracting material supplement location data, support structure reinforcement status data, and construction progress adjustment node data from the resource distribution data set; Comparing the position calibration execution data with the design coordinate data of the BIM model in real time to generate a position calibration feedback indicator; Performing correlation analysis on the machining accuracy adjustment data and the dimensional error value to generate a machining accuracy feedback index; Matching the construction process execution progress data with the construction process optimization sequence to generate a process execution feedback indicator; Overlaying the material replenishment location data with the material distribution map of the BIM model to generate a material replenishment feedback indicator; Performing a secondary check on the support structure reinforcement status data and the design load standard to generate a support reinforcement feedback index; Dynamically aligning the construction progress adjustment node data with the construction plan timeline of the BIM model to generate progress adjustment feedback indicators; The position calibration feedback index, the processing accuracy feedback index, the process execution feedback index, the material replenishment feedback index, the support reinforcement feedback index and the progress adjustment feedback index are integrated to generate the post-execution monitoring data set.

8. A BIM-based bridge construction status monitoring system, characterized by: The method comprises a processor and a memory, wherein the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the BIM-based bridge construction status monitoring method described in any one of claims 1 to 7.

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