Municipal road and bridge intelligent design method based on BIM

Abstract

The invention discloses a BIM-based municipal road and bridge intelligent design method, and particularly relates to the technical field of data analysis. By measuring bridge segment position information, splicing errors are calculated, and rotation angle error features are extracted; a concrete shrinkage sensor and a temperature and humidity monitoring system are combined, material deformation data are analyzed, humidity shrinkage characteristics are extracted, a BIM-Internet of Things error prediction model is constructed, an error accumulation effect is analyzed, segment assembly parameters in the BIM model are subjected to self-adaptive correction, construction equipment positioning parameters are adjusted based on the corrected BIM model, and the construction equipment positioning accuracy is improved. Secondary measurement is carried out through laser scanning, errors before and after adjustment are compared, and the construction precision is optimized; according to the method, dynamic monitoring, feedback and closed-loop optimization control of construction errors are achieved, the precision of bridge assembly is remarkably improved, the influence of error accumulation on the stability and the bearing capacity of the axis of the bridge is reduced, and the construction quality and the long-term durability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to an intelligent design method for municipal road and bridge based on BIM. Background Art

[0002] With the acceleration of the urbanization process, the construction scale of municipal roads and bridges is constantly expanding, and the requirements for design accuracy, construction efficiency and later maintenance are also increasing day by day. As an integrated, visual and collaborative design method, BIM (Building Information Modeling) technology has gradually been applied to the field of municipal infrastructure construction. BIM technology can provide functions such as three-dimensional modeling, data integration, intelligent analysis, construction simulation, and operation and maintenance management, improving the full life cycle management efficiency of municipal roads and bridges.

[0003] The existing technologies have the following deficiencies: In the construction of elevated interchanges, due to the difficulty of real-time feedback of the error accumulation of the combined process of precast segment splicing and cantilever casting by the BIM model, the misalignment of the bridge splicing is caused, affecting the structural stability. The current BIM design defaults to a relatively high construction accuracy. However, in the actual construction process, due to beam segment deformation, concrete shrinkage and construction errors, the cumulative deviation may exceed the safety threshold, resulting in the deviation of the bridge axis and affecting the bearing capacity and durability. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent design method for municipal road and bridge based on BIM to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An intelligent design method for municipal road and bridge based on BIM, including: Collect the actual position information of the bridge precast segments and cantilever casting structures through a laser scanner and a total station, calculate the segment splicing error, and extract the rotation angle error characteristics; Collect the material deformation data by using a concrete shrinkage sensor and a temperature and humidity monitoring system, and extract the humidity shrinkage amount characteristics in the material deformation data; Based on the rotation angle error characteristics and the humidity shrinkage amount characteristics, use the BIM-Internet of Things integration platform to construct an error prediction model, analyze the error accumulation effect, and correct the segment assembly parameters in the BIM model according to the analysis results; According to the corrected BIM model, adjust the construction equipment, and perform secondary measurement by using laser scanning. Compare the errors before and after adjustment, store the construction error data in the BIM database, and optimize the next construction adjustment parameters in combination with the historical error trend.

[0006] Preferably, the ICP algorithm is used to calculate the segment splicing error. The point cloud data obtained by the laser scanner and total station is matched with the theoretical coordinates in the BIM model, and the three-dimensional displacement deviation and rotation angle error of the segment are calculated.

[0007] Preferably, after analyzing the extracted rotation angle error features, a rotation angle error abnormal factor is generated. The method for obtaining the rotation angle error abnormal factor is as follows: To reflect the importance of each axial error, a weight factor is introduced to calculate the overall influence degree of the rotation angle error. The expression is: ; where: is the weight factor of each rotation axis, D represents the overall influence degree of the rotation angle error, and the rotation angle error abnormal factor is calculated. The expression is: ; where: λ is the empirical threshold.

[0008] Preferably, after analyzing the humidity shrinkage amount feature, a humidity shrinkage drift factor is generated. The specific obtaining method includes: collecting the humidity shrinkage data sequence of concrete: ; establishing an ARIMA model: training the model and calculating the predicted value , the expression is: ; where, is the model parameter, is the error term, setting the historical humidity shrinkage mean and the predicted drift value , calculating the humidity shrinkage drift factor. The expression is: ; where, is the historical standard deviation of humidity shrinkage, is the humidity shrinkage drift factor.

[0009] Preferably, the BIM-Internet of Things integration platform is used to construct an error prediction model to calculate the influence amount of the total assembly error. The calculation expression is: ; in the formula, is the influence amount of the total assembly error, is the influence amount of the rotation angle error, calculated from the rotation angle error abnormal factor RAEAF; is the length of a single segment of the bridge, is the equivalent rotation error angle during the assembly process; is the influence amount of the humidity shrinkage error, calculated from the humidity shrinkage drift factor HSDF; is the humidity shrinkage rate at time t, are the weight coefficients of the rotation angle error abnormal factor and the humidity shrinkage drift factor, and are all greater than 0; During the assembly of bridge segments, errors will gradually accumulate among n segments. After summing them up, the cumulative errors at the n segments are obtained. If the cumulative errors exceed the design tolerance, the assembly parameters in the BIM model need to be corrected.

[0010] Preferably, based on the error analysis results, correct the assembly parameters in the BIM model: where: is the original assembly parameter in the BIM model, is the corrected assembly parameter, is the cumulative error of the current segment; If , it is necessary to optimize the segment docking method: where: is the original BIM design angle, is the adjusted assembly angle; If , adopt the lead compensation strategy: where: is the segment length of the original BIM design, is the adjusted segment length.

[0011] Preferably, when the error cumulative effect calculated by the error prediction model exceeds the safety threshold, the system automatically generates adjustment instructions to optimize the crane hoisting positioning, the size of the cantilever casting formwork, and the height of the pier bearing, and updates the construction parameters in real time through the BIM model.

[0012] Preferably, the secondary measurement uses a laser scanner to obtain the point cloud data of the assembled segments, and uses the Hausdorff distance calculation method to compare the errors before and after adjustment, and calculate the construction error correction rate.

[0013] Preferably, the error data is combined with a deep learning model for trend analysis. When the predicted error exceeds the set threshold, the compensation amount of the assembly parameters for the next construction is automatically adjusted to ensure that the error accumulation is within the safe range, forming a dynamic adaptive optimization closed loop of the BIM model.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention: 1. The present invention realizes the intelligent monitoring, prediction, and correction of errors by constructing a BIM-Internet of Things integration platform. The segment assembly errors are collected through a laser scanner and a total station, and the ICP algorithm is used to match the BIM model to extract the rotational angle error features. Combining with the concrete shrinkage sensor and the temperature and humidity monitoring system, the material deformation data is analyzed to extract the humidity shrinkage amount features, and the rotational angle error abnormal factor (RAEAF) and the humidity shrinkage drift factor (HSDF) are calculated for the analysis of the error cumulative effect. Based on the error prediction model, the total influence amount of the assembly error is calculated, and when the error exceeds the safety threshold, the assembly parameters in the BIM model are automatically adjusted to optimize the construction accuracy.

[0015] 2. The implementation of the present invention can realize the dynamic monitoring, adjustment, and optimization of construction errors, improve the bridge assembly accuracy, reduce the structural offset caused by error accumulation, and enhance the bearing capacity and durability of the bridge. Through the real-time correction of the BIM model, construction equipment (such as crane hoisting positioning, cantilever casting formwork, bridge pier bearings) can adapt to error adjustment to ensure that the assembly accuracy of each segment meets the design requirements. At the same time, the construction error data is stored in the BIM database, and trend analysis is carried out in combination with deep learning models (such as LSTM) to form a closed-loop optimization system for error prediction and adjustment, realizing the intelligent control of the entire construction process. This method can reduce the construction rework rate, shorten the construction period, improve the construction quality, and has wide application value in the field of intelligent construction and digital management of municipal bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0017] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment, please refer to Figure 1 As shown, the intelligent design method for municipal road and bridge based on BIM in this embodiment includes: Collect the actual position information of the precast segments and cantilever casting structures of the bridge through a laser scanner and a total station, calculate the segment splicing error, and extract the characteristics of the rotation angle error; Collect the material deformation data using concrete shrinkage sensors and a temperature and humidity monitoring system, and extract the characteristics of the humidity shrinkage amount in the material deformation data; Based on the characteristics of the rotation angle error and the humidity shrinkage amount, use the BIM-IoT integration platform to construct an error prediction model, analyze the error accumulation effect, and correct the segment assembly parameters in the BIM model according to the analysis results; According to the corrected BIM model, adjust the construction equipment, and perform secondary measurement using laser scanning. Compare the errors before and after adjustment, store the construction error data in the BIM database, and optimize the next construction adjustment parameters in combination with the historical error trend.

[0020] In the intelligent design of municipal road and bridge based on BIM, to ensure the accuracy of precast segment splicing and cantilever casting structures, it is necessary to perform high-precision measurement on the actual position information of the construction site and calculate the splicing error. As the core measurement tools, the laser scanner and the total station can be used to obtain the spatial position information of the bridge segments and extract the characteristics of the rotation angle error.

[0021] Before and after the precast segment is hoisted, perform 360° laser scanning on the bridge splicing area to obtain point cloud data and construct a three-dimensional coordinate model (X, Y, Z). Use the ICP (Iterative Closest Point) algorithm to match the measured point cloud with the BIM design model and calculate the spatial deviation before and after segment assembly.

[0022] By setting multiple reference measurement points, use the principle of triangulation to obtain the accurate spatial coordinates (X, Y, Z) of the segment in the construction state. Combine with an inclination sensor to obtain the attitude information of the beam segment, including parameters such as offset and inclination angle.

[0023] Set the ideal positions before and after precast segment splicing and compare them with the actual positions measured by scanning to calculate the position offsets ΔX, ΔY, ΔZ. Use the Hausdorff distance measurement method to evaluate the point cloud data deviation, identify the maximum error points, and perform error fitting analysis.

[0024] Measure the reference control points (P1, P2) at both ends of the segment through a total station, calculate the angular deviation from the theoretical reference line of the BIM model, and obtain the planar rotation error (Δθ). Use quaternions or Euler angles (Yaw, Pitch, Roll) to calculate the spatial attitude error and obtain the rotation errors of the segment around the three axes (ΔθX, ΔθY, ΔθZ).

[0025] Based on the comprehensively calculated rotational angle errors, construct an error feature vector: E = [ΔX, ΔY, ΔZ, ΔθX, ΔθY, ΔθZ]; where: ΔX, ΔY, and ΔZ represent displacement deviations; ΔθX, ΔθY, and ΔθZ represent rotational angle errors about the three axes.

[0026] After analyzing the extracted rotational angle error features, generate a rotational angle error abnormal factor. The method for obtaining the rotational angle error abnormal factor is as follows: To reflect the importance of the errors in each axis, introduce a weight factor and calculate the overall influence degree of the rotational angle error. The expression is: ; where: are the weight factors for each rotational axis and can be determined based on engineering experience or historical data. For example: = 0.3 (lateral tilt has a greater impact on the splicing accuracy); = 0.4 (longitudinal slope affects the structural stress); = 0.3 (horizontal offset affects the assembly accuracy), and D represents the overall influence degree of the rotational angle error. Calculate the rotational angle error abnormal factor. The expression is: ; where: λ is an empirical threshold representing the acceptable error range. For example, based on historical construction data, set λ = 1.5, indicating that when the error exceeds 1.5 times the standard deviation, it may affect the splicing accuracy. RAEAF is the rotational angle error abnormal factor. If RAEAF > 1, the rotational angle error may exceed the safe range and the construction plan needs to be adjusted.

[0027] During the construction of municipal road bridges, the moisture shrinkage of concrete is one of the key factors affecting the structural splicing accuracy and long-term stability. Moisture shrinkage refers to the volume reduction phenomenon that occurs when the moisture in concrete evaporates or the environmental humidity decreases, which may lead to segment splicing misalignment, crack generation, and a decline in long-term durability. Therefore, using concrete shrinkage sensors and a temperature and humidity monitoring system to collect material deformation data and extract the moisture shrinkage amount characteristics is crucial for construction adjustment and structural optimization.

[0028] Embed concrete shrinkage sensors (such as LVDT displacement sensors, fiber Bragg grating (FBG) sensors) inside precast segments and in-situ cast structures to measure the length shrinkage of concrete over time. Collect shrinkage data at different depths of concrete (such as 3 mm at the surface layer and 50 mm at the deep layer) to obtain the layered shrinkage characteristics. Record the relationship between the shrinkage strain ε and time t, usually in μm / m (microstrain).

[0029] Arrange temperature and humidity sensors at the construction site to measure the external air humidity RHenv and the internal humidity of concrete RHint. Use a wireless data acquisition system (such as an IoT Internet of Things platform) to transmit the humidity change data in real-time.

[0030] Humidity shrinkage Deformation mainly caused by the change of internal humidity of concrete, and its characteristics can be extracted by the following methods; Humidity shrinkage rate Defined as the shrinkage of unit length of concrete, and the calculation formula is: ; Where: is the initial length of concrete (mm); is the length of concrete at time t (mm); is the humidity shrinkage rate, in μm / m.

[0031] By performing non-linear regression fitting on the data at different time points, a humidity shrinkage trend model is established: ; Where: is the initial humidity shrinkage value, A is the maximum possible humidity shrinkage, B is the humidity shrinkage rate coefficient, which is related to material composition and environmental conditions. Set the humidity shrinkage threshold, and when the humidity shrinkage exceeds the threshold, adjust the construction process, such as extending the curing time, using waterproof coatings, etc.

[0032] After analyzing the characteristics of humidity shrinkage, a humidity shrinkage drift factor is generated. The specific acquisition methods include: collecting the humidity shrinkage data sequence of concrete: ; Perform a stationarity test to determine whether differential operation is required. Establish an ARIMA(p, d, q) model: p: the order of the autoregressive term (AR), indicating how the shrinkage value depends on the past shrinkage trend. d: the order of differencing, used to eliminate the data trend and make the time series stationary. q: the order of the moving average (MA), indicating the degree to which the data is affected by past error terms. Train the model and calculate the predicted value , the expression is: ; Where, is the model parameter, is the error term. Set the historical humidity shrinkage mean and the predicted drift value , calculate the humidity shrinkage drift factor, and the expression is: ; Where, is the historical standard deviation of humidity shrinkage, is the humidity shrinkage drift factor.

[0033] The present invention uses a BIM-Internet of Things integration platform to construct an error prediction model for calculating the influence amount of the total assembly error, and the calculation expression is: ; In the formula, is the influence amount of the total assembly error, is the influence amount of the rotation angle error (mm), which is calculated by the rotation angle error abnormal factor RAEAF, and the expression is: ; is the length of a single segment of the bridge (mm). is the equivalent rotational error angle (rad) during the assembly process, and the calculation formula is: ; where, ΔθX, ΔθY, and ΔθZ are the rotational errors (rad) about the X, Y, and Z axes; is the influence amount of the humidity shrinkage error (mm), which is calculated by the humidity shrinkage drift factor HSDF, and the expression is: ; is the humidity shrinkage rate (μm / m) at time t; are the weight coefficients of the rotational angle error abnormal factor and the humidity shrinkage drift factor (which can be optimized according to experimental experience or machine learning), and are all greater than 0.

[0034] During the assembly of bridge segments, errors will gradually accumulate among n segments. After summing them up, the cumulative error at n segments is obtained. If the cumulative error exceeds the design tolerance, the assembly parameters in the BIM model need to be corrected.

[0035] Based on the error analysis results, correct the assembly parameters in the BIM model: ; where: is the original assembly parameter (mm) in the BIM model, is the corrected assembly parameter (mm), is the cumulative error (mm) of the current segment.

[0036] If , it indicates that the rotational angle error has a greater impact, and the segment docking method needs to be optimized: ; where: is the original BIM design angle (rad), is the adjusted assembly angle (rad).

[0037] If , it indicates that the humidity shrinkage error has a greater impact, and an advanced compensation strategy can be adopted: ; where: is the original segment length (mm) designed by BIM, is the adjusted segment length (mm).

[0038] Feed the corrected assembly parameter back to the BIM-IoT integration platform to form a closed-loop control system for real-time error monitoring, model adjustment, and construction optimization. Through this closed-loop control, errors can be adjusted in real time during the construction process to avoid cumulative structural safety problems and improve the construction accuracy of municipal bridges.

[0039] In the construction of municipal bridges, error control is a key link to ensure the assembly accuracy of segments. Based on the revised BIM model, construction equipment needs to be adjusted, and a laser scanner is used for secondary measurement. The errors before and after adjustment are compared, and the construction error data is stored in the BIM database to optimize the subsequent construction adjustment parameters.

[0040] According to the revised BIM assembly parameters , adjust the hoisting accuracy of the crane; if the humidity shrinkage error of the current segment is large, then adjust the formwork size; during the assembly process of bridge segments, the bearing height error may accumulate, so it needs to be corrected.

[0041] After adjusting the construction equipment, use a laser scanner to perform secondary measurement on the assembled segments to collect high-precision point cloud data: ; where: is the point cloud data set of the measured segment, is the three-dimensional coordinate (mm) of the i-th point, and N is the total number of measurement points.

[0042] Perform error analysis through the theoretical coordinates and measured coordinates in the BIM model: ; is the average error (mm) after construction, is the BIM theoretical coordinate point, is the actual measured coordinate point. If the construction error exceeds the tolerance range, the construction equipment parameters need to be further adjusted.

[0043] The construction error data is stored as a time series database, recording the error parameters of each segment; the data is stored in the BIM database and linked with the Internet of Things sensor data (temperature and humidity, displacement, stress, etc.) to form a complete construction error database.

[0044] Use historical error data to train machine learning models (such as LSTM or SVR) to predict the error trend of future segments: ; where: is the predicted error (mm) at future time t, and f is the prediction model (such as LSTM or SVR) based on historical error data. Optimize the construction adjustment parameters of the next segment according to the predicted error: ; where: is the revised assembly parameter (mm) of the next segment, is the original BIM model parameter (mm), is the predicted error (mm). Use the BIM-Internet of Things integration platform to achieve dynamic monitoring, adjustment and optimization of construction errors.

[0045] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0047] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0048] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. The BIM-based intelligent design method for municipal roads and bridges is characterized by: include: The actual position information of the bridge precast segments and cantilever casting structures is collected through laser scanners and total stations, the segment splicing errors are calculated, and the rotation angle error characteristics are extracted; Use concrete shrinkage sensors and temperature and humidity monitoring systems to collect material deformation data and extract humidity shrinkage characteristics from material deformation data; Based on the characteristics of rotation angle error and humidity shrinkage, the BIM-IoT integrated platform was used to build an error prediction model, analyze the error accumulation effect, and modify the segment assembly parameters in the BIM model according to the analysis results. According to the revised BIM model, the construction equipment is adjusted, and laser scanning is used for secondary measurement. The errors before and after the adjustment are compared, and the construction error data is stored in the BIM database. The historical error trends are combined to optimize the next construction adjustment parameters.

2. The BIM-based municipal road and bridge intelligent design method according to claim 1 is characterized by: The segment splicing error is calculated using the ICP algorithm, which matches the point cloud data obtained by the laser scanner and the total station with the theoretical coordinates in the BIM model, and calculates the segment three-dimensional displacement deviation and rotation angle error.

3. The BIM-based municipal road and bridge intelligent design method according to claim 1 is characterized in that: After analyzing the extracted rotation angle error features, the rotation angle error anomaly factor is generated. The method for obtaining the rotation angle error anomaly factor is as follows: In order to reflect the importance of each axial error, a weight factor is introduced to calculate the overall influence of the rotation angle error. The expression is: ;in: is the weight factor of each rotation axis, D represents the overall influence of the rotation angle error, and the rotation angle error abnormality factor is calculated. The expression is: ; Where: λ is the empirical threshold.

4. The BIM-based municipal road and bridge intelligent design method according to claim 3 is characterized by: After analyzing the characteristics of humidity shrinkage, a humidity shrinkage drift factor is generated. The specific acquisition method includes: collecting the humidity shrinkage data sequence of concrete: ;Build ARIMA model: train the model and calculate the predicted value , the expression is: ;in, are model parameters, As the error term, set the historical humidity shrinkage mean and predicted drift value , calculate the humidity shrinkage drift factor, the expression is: ;in, is the historical standard deviation of humidity shrinkage, is the humidity shrinkage drift factor.

5. The BIM-based municipal road and bridge intelligent design method according to claim 4 is characterized in that: The error prediction model is constructed using the BIM-IoT integration platform to calculate the total error impact of assembly. The calculation expression is: ; In the formula, is the total assembly error influence, is the influence of the rotation angle error, which is calculated by the rotation angle error anomaly factor RAEAF; is the length of a single bridge segment, is the equivalent rotation error angle during assembly; is the humidity shrinkage error influence, which is calculated by the humidity shrinkage drift factor HSDF; is the humidity shrinkage at time t, is the weight coefficient of the rotation angle error anomaly factor and the humidity shrinkage drift factor, and All are greater than 0; When assembling bridge segments, errors will gradually accumulate among n segments. The cumulative errors at n segments are summed up. If the cumulative errors exceed the design tolerance, the assembly parameters in the BIM model need to be corrected.

6. The BIM-based municipal road and bridge intelligent design method according to claim 5 is characterized by: Based on the error analysis results, the assembly parameters in the BIM model are modified: ;in: It is the original assembly parameter in the BIM model. is the corrected assembly parameter, is the accumulated error of the current segment; like , it is necessary to optimize the segment docking method: ;in: For the original BIM design perspective, is the assembly angle after adjustment; like , using the advance compensation strategy: ;in: Segment length designed for the original BIM, is the adjusted segment length.

7. The BIM-based municipal road and bridge intelligent design method according to claim 6 is characterized by: When the cumulative error effect calculated by the error prediction model exceeds the safety threshold, the system automatically generates adjustment instructions to optimize the crane lifting positioning, cantilever casting formwork size and pier support height, and updates the construction parameters in real time through the BIM model.

8. The BIM-based municipal road and bridge intelligent design method according to claim 7 is characterized by: The secondary measurement uses a laser scanner to obtain point cloud data of the assembled segment, and uses the Hausdorff distance calculation method to compare the errors before and after adjustment to calculate the construction error correction rate.

9. The BIM-based municipal road and bridge intelligent design method according to claim 8 is characterized by: The error data is combined with a deep learning model for trend analysis. When the predicted error exceeds a set threshold, the assembly parameter compensation amount for the next construction step is automatically adjusted to ensure that the error accumulation is within a safe range, forming a dynamic adaptive optimization closed loop of the BIM model.

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