A method and system for collaborative design of concrete member steel bar identification and misalignment connection

Abstract

The application discloses a kind of concrete member reinforcing bar identification and misplacement connection collaborative design method and system.The method is collected by high-precision three-dimensional laser scanning acquisition exposed reinforcing area point cloud data, obtains accurate three-dimensional geometric parameters of existing reinforcing bar after registration, denoising and geometric reconstruction;Based on this, a multi-objective optimization model is constructed with the core target of maximum bearing capacity, taking into account the uniformity and symmetry of distribution, and the optimal arrangement scheme of new reinforcing bar is automatically solved by using two-stage optimization strategy, and the digital results that can be directly constructed are output after performance checking.The system includes four modules of data acquisition, point cloud processing and reconstruction, collaborative optimization design, scheme output and checking.The application realizes the full-process automation from reinforcing bar identification to optimization design, and significantly improves the design quality and efficiency of reinforcement engineering.

Description

Technical Field

[0001] This invention relates to the field of civil engineering, and in particular to a method and system for collaborative design of steel reinforcement identification and misaligned connection in concrete components. Background Technology

[0002] In the field of civil engineering, one of the core requirements of structural reinforcement, renovation and repair projects is to insert new steel bars into existing concrete components (such as beams and columns) and use post-cast concrete or special grouting materials to achieve a "misaligned connection" between the new steel bars and the original steel bars inside the component, ultimately forming an overall system that shares the load. The reliability of this connection technology fundamentally depends on the degree of positional matching between the new steel bars and the existing steel bars in three-dimensional space, as well as the effectiveness of their collaborative transfer of mechanical loads, which is directly related to the safety performance and service life of the reinforced structure.

[0003] In current engineering practice, the identification and positioning of reinforcing bars inside existing concrete components mainly relies on three traditional technical methods, all of which have significant limitations: First, the location of existing reinforcing bars is verified through design drawings; however, due to factors such as construction deviations and material aging, the consistency between the drawings and the actual site conditions is generally low, making it difficult to serve as a basis for accurate design. Second, the method of obtaining reinforcing bar information by partial chiseling and probing can directly observe local reinforcing bars, but it can cause irreversible structural damage to existing components and can only obtain information on discrete points of reinforcing bars, failing to fully reconstruct the direction, distribution density, and global positional relationship of the reinforcing bars in three-dimensional space. Third, the use of traditional electromagnetic radar scanning technology to detect reinforcing bars is susceptible to electromagnetic interference in densely reinforced areas and changes in the thickness of the concrete cover, resulting in limited identification resolution and accuracy (usually only able to locate the approximate position of the reinforcing bars, unable to accurately measure key parameters such as diameter and degree of corrosion), far from meeting the sub-millimeter accuracy design requirements for misaligned connections.

[0004] After obtaining information on existing reinforcing steel, the layout design of new reinforcing steel often relies on the engineer's personal experience, employing a manual arrangement method based on the principle of "avoidance." This process is characterized by significant arbitrariness and subjectivity, lacking a systematic quantitative analysis and optimization of the overall load-bearing performance of the component. Specifically, this manifests as: the inability to accurately assess key indicators such as the load-bearing capacity, crack control, and durability of the new and old reinforcing steel working together; and insufficient consideration of multiple constraints during the design process, such as the minimum clear distance between new and old reinforcing steel, the thickness of the concrete cover, and the uniformity of steel distribution. This often results in the design scheme being unfeasible on-site, or, although feasible, posing potential safety hazards.

[0005] In recent years, the development of 3D laser scanning and visual reconstruction technologies has provided new avenues for acquiring structural surface geometric information. Existing technologies, such as Chinese Patent ZL202110678931.7, disclose an automated inspection method for concrete components based on 3D laser scanning, capable of automatically detecting the dimensions and positions of component surfaces, protruding reinforcing bars, or grouting sleeves. However, these existing technologies primarily focus on the quality inspection and acceptance of components, with their technical endpoint being the acquisition and reporting of the component's geometric parameters. They have not yet addressed how to directly and automatically use the precise information of identified internal reinforcing bars in existing structural reinforcement scenarios to drive the collaborative optimization design of newly added reinforcing bars. Even if point cloud data of exposed rebar areas is obtained through scanning, how to automatically extract the precise geometric parameters of the reinforcing bars (such as diameter, axis, and corrosion status) and, based on this, generate an optimal arrangement scheme for newly added reinforcing bars that meets multiple engineering objectives (such as maximizing bearing capacity and uniformity of arrangement) and strict regulatory constraints remains an unresolved technological gap.

[0006] Furthermore, in the existing technological process, the two key stages of rebar identification and collaborative design are independent of each other, creating a significant data gap. The process from detection to design requires cumbersome manual data conversion and processing, which is not only inefficient but also introduces new errors during the conversion. This severe separation between the "detection" and "design" stages hinders the establishment of an automated closed-loop workflow from rebar identification to optimized design, seriously restricting further improvements in project quality and efficiency. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method and system for collaborative design of steel reinforcement identification and misaligned connection in concrete components, which realizes an integrated closed loop from accurate steel reinforcement identification to collaborative optimization design, and significantly improves the accuracy, efficiency and reliability of reinforcement design for concrete components.

[0008] The technical solution adopted by this invention to solve its technical problem is:

[0009] A collaborative design method for identifying and misaligning reinforcement in concrete members is provided, comprising the following steps:

[0010] S1: Three-dimensional point cloud data of exposed rebar areas of concrete components are collected using a high-precision laser scanner. Differentiated scanning strategies are adopted based on the distribution characteristics of exposed rebar during the collection process, and auxiliary processing measures are taken to address on-site interference factors.

[0011] S2: First, the point cloud features are extracted and a coarse registration algorithm is used to achieve preliminary spatial alignment of each batch of point clouds. Then, a fine registration algorithm is used to further eliminate local errors to improve coordinate accuracy. Subsequently, the point cloud is denoised to obtain high-quality point cloud data that can be used for geometric fitting.

[0012] S3: The preprocessed point cloud is filtered to obtain a set of candidate rebar points. A clustering algorithm is used to obtain independent rebar point clusters. Linear fitting and cross-sectional circle fitting are performed on each rebar point cluster to extract the rebar geometric parameters. Then, the corrosion rate is calculated based on the rebar shape deviation and reflection signal characteristics. The effective cross-sectional area of ​​the rebar is corrected based on the corrosion rate. Finally, a structured data package containing the three-dimensional geometric parameters of the rebar is generated.

[0013] S4: Using the existing steel reinforcement parameters in the structured data package as fixed constraints, input the component type, component geometry, and new steel reinforcement configuration parameters to construct a multi-objective optimization model: if the component is a beam, the core objective is to maximize the flexural bearing capacity; if the component is a column, the core objective is to maximize the axial bearing capacity, while also taking into account the uniformity of steel reinforcement distribution and cross-sectional symmetry. The multi-objective optimization model must satisfy the constraints of concrete cover thickness, minimum net spacing of steel reinforcement, and steel reinforcement boundary position.

[0014] S5: A two-stage optimization strategy is adopted to solve the multi-objective optimization model, and multiple Pareto optimal candidate schemes are obtained. The bearing capacity and steel bar anchorage length of each candidate scheme are checked respectively, and the optimal scheme that passes the check and the corresponding digital results are output.

[0015] Preferably, in step S1, the high-precision laser scanner uses a structured light or blue light laser scanner, and its single-point ranging accuracy should be ≤0.05mm, and the point cloud resolution should be controlled at ≤0.3mm. The differentiated scanning strategy specifically includes: for areas with concentrated exposed rebar distribution, controlling the scanning distance to improve the point cloud resolution, and collecting point cloud data from at least three different angles for each area with concentrated exposed rebar; for the entire concrete component, widening the scanning distance to achieve overall area coverage; and the auxiliary measures for handling on-site interference factors are: if there is reflection or shadow, interference can be eliminated by adjusting the angle of the light source or by applying matte stickers to the surface of the concrete component.

[0016] Preferably, in step S2, the coarse registration algorithm adopts the RANSAC algorithm, and the fine registration algorithm adopts the point-to-plane ICP algorithm; the denoising process includes voxel filtering and statistical outlier removal. Voxel filtering is used to reduce redundant data in the point cloud and retain geometric features, and statistical outlier removal is used to remove abnormal discrete points in the point cloud; after preprocessing, it also includes local normal estimation of the processed point cloud, and surface reconstruction method is used to complete the small hole regions formed by occlusion or reflection.

[0017] Preferably, in step S3, the specific method for screening the candidate point set of reinforcing bars is as follows: extract points with significant linear characteristics by calculating the local principal curvature ratio of the points, and take the points with curvature ratio greater than 3 as the initial candidate point set; perform secondary screening by combining the reflection intensity information of the points, and exclude points with reflection values ​​lower than a set threshold; apply the DBSCAN clustering algorithm to the candidate point set, merge small clusters that are less than 2 mm apart, and remove isolated clusters with a volume of less than 0.5 cm³.

[0018] Preferably, in step S3, the corrosion rate is calculated as follows: by statistically analyzing the non-circularity deviation between the fitted circle and the actual point cluster, a corrosion area is determined when the deviation is greater than 5%; by analyzing the attenuation rate of the reflected signal intensity, when the attenuation rate makes the reflection coefficient... The corrosion zone is considered as such. The corrosion rate of the reinforcing steel is calculated by considering the non-roundness deviation and the attenuation rate of the reflected signal intensity, and the effective cross-sectional area of ​​the reinforcing steel is corrected according to the following formula:

[0019]

[0020] In the formula, This represents the original cross-sectional area of ​​the reinforcing steel. This is the corrected effective cross-sectional area. This represents the corrosion rate.

[0021] Preferably, in step S4, the formula for calculating the flexural bearing capacity of the beam member is:

[0022]

[0023] in, It is the flexural bearing capacity of the beam. The coefficients are the equivalent rectangular stress diagram coefficients for concrete. Design compressive strength for concrete. The width of the beam section. The height of the pressure zone. It is the vertical distance from the edge of the concrete in the compression zone to the point where the resultant force of the steel reinforcement in the tension zone is applied. It is the vertical distance from the resultant point of the longitudinal reinforcing bars in the tension zone to the tension edge of the section. For the total area of ​​newly added steel bars in the tension zone, The design yield strength of the newly added reinforcing steel; and , , The height of the beam section;

[0024] The formula for calculating the axial bearing capacity of column members is:

[0025]

[0026] in, It refers to the axial bearing capacity of the column. It is the concrete strength reduction factor. It is the design compressive strength of concrete. It is the cross-sectional area of ​​the concrete, and , It is the total cross-sectional area of ​​the old steel bars. It is the steel reinforcement strength reduction factor. The design yield strength of the newly added reinforcing steel. It is the total area of ​​the newly added steel bars;

[0027] The uniformity of the steel reinforcement distribution is determined by the formula: Quantitative evaluation; where, This is the score for the uniformity of rebar distribution; a higher value indicates a more uniform distribution. N is the number of newly added rebars. The spacing between adjacent reinforcing bars. The average spacing between adjacent reinforcing bars;

[0028] The symmetry of the cross section is determined by the formula. Quantitative assessment, including This is a score based on symmetry, where N is the number of newly added steel bars. These are the coordinates of the newly added reinforcing bars. For the width and height of the component.

[0029] Preferably, in step S4, the constraints that the optimization process needs to satisfy are defined by the following formula:

[0030] Protective layer thickness constraints: ,in, Minimum protective layer thickness, For the increased diameter of the reinforcing bars;

[0031] Minimum spacing constraints for reinforcing bars: ,in This is the minimum clear spacing between reinforcing bars. The diameter of the other reinforcing bar;

[0032] Boundary constraints: ;

[0033] The formula for calculating the comprehensive fitness function of a beam member is:

[0034]

[0035] The formula for calculating the comprehensive fitness function of column members is:

[0036]

[0037] in , , Let be the weight coefficients for each objective, and , , These are the design load-bearing capacity requirements for beams and columns, respectively.

[0038] Preferably, in step S5, the formula for checking the anchorage length of the reinforcing bar is:

[0039]

[0040] in The final anchorage length to be adopted. This refers to the shape coefficient of the reinforcing steel bars; This is the anchorage condition coefficient. The design value for the yield strength of the newly added reinforcing steel; This represents the design value for the bond strength between the steel reinforcement and concrete. The diameter of the newly added reinforcing bar; To add a safety length;

[0041] The bearing capacity verification requirements are met. ≥ or ≥ ,in / To design load effects.

[0042] Preferably, in step S5, the output digital result includes at least one of the following:

[0043] Construction drawings should indicate the precise coordinates, diameter, number, and relative position of all newly added reinforcing bars within the cross-section, as well as their positional relationship with the existing reinforcing bars.

[0044] A lightweight 3D BIM model that includes information on both new and old steel reinforcement.

[0045] The design parameter report includes the bearing capacity, crack width, uniformity score, symmetry score, and anchorage length verification results for each scheme.

[0046] The bill of materials includes the type, diameter, single length, total length, and total weight of any new reinforcing bars.

[0047] A collaborative design system for reinforcing bar identification and misaligned connection in concrete components, used to implement the collaborative design method for reinforcing bar identification and misaligned connection in concrete components as described above, comprising:

[0048] The data acquisition module is configured to acquire three-dimensional point cloud data of exposed rebar areas of concrete components using a high-precision laser scanner, and adopt a differentiated scanning strategy based on the distribution characteristics of exposed rebar, while taking auxiliary processing measures for on-site interference factors.

[0049] The point cloud processing and reconstruction module is configured to perform multi-level registration and preprocessing on the acquired 3D point cloud data. This includes using a coarse registration algorithm to achieve preliminary spatial alignment of point clouds in each batch, using a fine registration algorithm to eliminate local errors, and performing noise reduction on the point clouds to obtain high-quality point cloud data. The preprocessed point clouds are then filtered, clustered, geometrically fitted, and corroded to generate a structured data package containing the 3D geometric parameters of the reinforcing bars.

[0050] The collaborative optimization design module is configured to use the existing steel reinforcement parameters in the structured data package as fixed constraints, receive component type, component geometry and new steel reinforcement configuration parameters, construct a multi-objective optimization model, and use a two-stage optimization strategy to solve the multi-objective optimization model to obtain multiple Pareto optimal candidate solutions.

[0051] The scheme output and verification module is configured to verify the bearing capacity and rebar anchorage length of each candidate scheme, and output the optimal scheme that passes the verification and the corresponding digital results.

[0052] The beneficial effects of this invention are:

[0053] This invention provides a method and system for collaborative design of rebar identification and misaligned connection in concrete components. Through high-precision laser scanning and intelligent point cloud processing technology, it achieves accurate identification and three-dimensional reconstruction of existing rebar in concrete components. Based on this, a multi-objective optimization model is constructed, automatically generating new rebar arrangement schemes that meet the requirements of bearing capacity, uniformity, symmetry, and code constraints. This effectively solves the problems of reliance on manual experience, data gaps, and strong design subjectivity in traditional methods, significantly improving the accuracy, efficiency, and reliability of misaligned connection design. It provides an integrated, digital closed-loop solution for structural reinforcement engineering from detection to design. Attached Figure Description

[0054] Figure 1 This is a flowchart of a collaborative design method for steel reinforcement identification and misaligned connection in concrete components according to the present invention.

[0055] Figure 2 This is an operating system diagram of a collaborative design method for steel reinforcement identification and misaligned connection in concrete components according to the present invention.

[0056] Figure 3 The distribution optimization results of the four newly added 25mm diameter steel bars are presented for this invention.

[0057] Figure 4 The distribution optimization results of the three newly added 25mm diameter steel bars are presented for this invention.

[0058] Figure 5 The distribution optimization results of the four newly added 20mm diameter steel bars are presented for this invention.

[0059] In the diagram: green dots represent newly added reinforcing bars.

[0060] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] like Figure 1-5 As shown, the present invention provides a method for collaborative design of reinforcement identification and misaligned connection in concrete components, which specifically includes the following steps:

[0063] S1: A high-precision laser scanner is used to collect 3D point clouds of exposed rebar in structural members, providing the original dataset for 3D reconstruction and engineering design and construction.

[0064] When implementing this invention, it is preferable to use a structured light or blue laser scanner, whose single-point ranging accuracy should be ≤0.05mm and point cloud resolution should be controlled at ≤0.3mm, so as to ensure that the diameter of the steel bar and the corrosion characteristics can be accurately captured.

[0065] The scanning adopts a "global coverage + local high precision" strategy: for areas with concentrated exposed rebar, the scanning distance is controlled at 0.2–0.4m, the point cloud resolution is ≤0.1mm, and each area needs to be sampled from at least three different angles (such as front, 30° to the left, and 30° to the right) to ensure that the circumferential features of the rebar are completely recorded; for the entire component, a scanning distance of 0.5–0.8m is used, and the point cloud resolution is relaxed to 0.2–0.3mm, which is only used as a global positioning reference; if there is reflection or shadow on site, the angle of the light source needs to be adjusted or a matte sticker needs to be applied to the concrete surface to reduce interference.

[0066] S2: Perform multi-level registration and preprocessing on the original point cloud to obtain a high-quality dataset suitable for geometric fitting. To ensure accurate alignment of point clouds from different scanning batches, key features are first extracted using point cloud features (such as FPFH, SHOT, etc.), and coarse registration is performed using RANSAC to initially align the point clouds of each batch in space, with the coarse registration residual controlled within 1mm. Based on the coarse registration, a point-to-plane ICP algorithm is used for fine registration, with the iteration convergence condition set at a residual of less than 0.3mm or no more than 100 iterations. Multi-scale iterative optimization is used to further eliminate local errors. After fine registration is completed, an error report is output, including the RMS value and maximum deviation, to ensure that the global coordinate accuracy meets the requirements of subsequent fitting.

[0067] After registration, the point cloud is denoised. First, voxel filtering is performed, with the voxel side length controlled between 0.1 and 0.2 mm to reduce redundant points and maintain the integrity of geometric features. Then, statistical outlier removal is performed, with the number of neighborhood points K=50, the standard deviation threshold set to 1.5, and the removal ratio not exceeding 5% to ensure that the main steel structure is not lost. The downsampled point cloud files are renumbered, and the index information is retained for traceability, providing a high-quality dataset for subsequent fitting.

[0068] Local normals were estimated for the preprocessed point cloud, and the normal direction was calculated using the PCA method. The neighborhood search radius was set to 1.0 mm to provide a reference for subsequent cylinder fitting. For small hole regions (diameter <5 mm) caused by occlusion or reflection, the Poisson surface reconstruction method was used to complete them. The completed mesh was marked as a "virtual point" and was only used to assist fitting. It was not included in the effective cross section of the reinforcing steel to ensure the fitting accuracy and completeness.

[0069] S3: Transform the preprocessed point cloud data into an accurate geometric model, which will then serve as the direct input for the optimization algorithm in subsequent steps.

[0070] For the preprocessed point cloud, the system employs a multi-level filtering strategy. First, points with significant linear characteristics are extracted by calculating the local principal curvature ratio, and points with a curvature ratio greater than 3 are selected as candidate points. Then, the system performs a secondary filtering based on the point reflection intensity information. Since the reflection value of rebar points is significantly higher than that of the concrete surface, points with low reflection values ​​are excluded, thus generating a more accurate set of candidate rebar points. Finally, the DBSCAN clustering algorithm is applied to the candidate point set to merge small clusters less than 2 mm apart and remove isolated clusters with a volume less than 0.5 cm³, ultimately obtaining independent rebar point clusters.

[0071] For each individual rebar cluster, a high-precision fitting is performed to extract its core geometric parameters. For each rebar cluster, a weighted least-squares linear fitting is performed, with weights set based on the local density distribution of the points. The fitting residual must be controlled within ≤0.3mm; if it exceeds the threshold, the system will adjust the cluster boundaries and refit. The fitting results will output the starting coordinates, direction vector, and effective length of the rebar axis, providing a geometric basis for cross-section fitting. Subsequently, the system cuts the point cloud every 10mm along the fitted rebar axis to form a cross-sectional point set, and performs circular fitting on each cross-section, with the residual controlled within ≤0.2mm. The system records the diameter sequence of each cross-section and calculates their average diameter and standard deviation, retaining three decimal places, providing data for subsequent effective cross-sectional area calculation.

[0072] After the cross-sectional diameter is fitted, the system corrects for the corrosion of the reinforcing bars to ensure that the data provided to S4 is an accurate effective cross-sectional area. The system calculates the corrosion rate and corrects the effective cross-sectional area of ​​the reinforcing bars by statistically analyzing the non-circularity deviation between the fitted circle and the actual point cluster (>5% is considered corrosion) and the attenuation rate of the reflected signal intensity (when R<0.7 is considered a corrosion zone). The corrected effective area is calculated using the following formula:

[0073]

[0074] in This represents the original cross-sectional area of ​​the reinforcing steel. The corrected effective area. The value represents the corrosion rate; the corrected result will be directly used in subsequent load-bearing capacity and crack calculations.

[0075] The results of all previous steps are integrated into a structured data package that can be directly used for subsequent design and optimization. This data package is organized in a multi-level, multi-dimensional manner. First, a unique ID is assigned to each rebar, and its precise geometric parameters in three-dimensional space are provided, including the starting coordinates (x, y, z) of the axis and the direction vector (…). The data packet also contains cross-sectional feature data, such as the average diameter calculated through multiple circle fitting operations. ) and its standard deviation ( The corrected effective cross-sectional area (Aeff) is directly used as the core input of the newly added rebar location optimization system in S4.

[0076] S4: Using the precise geometric information of the identified existing rebar as the basic input, the system generates an optimal arrangement scheme for new rebar that meets multiple engineering objectives. The system receives the precise coordinates, diameter, direction vector, and corrected effective cross-sectional area of ​​each existing rebar from step S3. Simultaneously, the user needs to input the component type ("beam" or "column"), geometric dimensions (width b, height h), and the configuration (quantity and diameter) of the new rebar. The two-dimensional coordinates of all new rebars (…) The variables are defined as variables in the optimization algorithm, forming the solution space. The position and diameter of the old reinforcing bars are used as fixed constraints in the calculation.

[0077] This system transforms the problem of optimizing the location of newly added reinforcing bars into a multi-objective continuous variable optimization problem. Its core lies in dynamically adjusting the main objective function according to the component type.

[0078] If the component type is "beam": the system prioritizes maximizing the flexural capacity (Mu); the calculation formula is:

[0079]

[0080] in It is the flexural bearing capacity of the beam. This is a coefficient for the equivalent rectangular stress diagram of concrete, taken as 1.0. It is the design compressive strength of concrete. It is the beam cross-section width. It is the height of the pressure zone. , It is the vertical distance from the edge of the concrete in the compression zone to the point of resultant force of the steel reinforcement in the tension zone when the member is subjected to bending. ; It is the vertical distance from the resultant point of the longitudinal reinforcing bars in the tension zone to the tension edge of the cross section; It is the total area of ​​newly added reinforcing bars in the tension zone. It is the design yield strength of the newly added steel reinforcement.

[0081] If the component type is "column": the system prioritizes maximizing the axial bearing capacity (Nu); the simplified calculation formula is:

[0082]

[0083] in, It refers to the axial bearing capacity of the column. This is the concrete strength reduction factor, typically around 0.85. It is the design compressive strength of concrete. It is the cross-sectional area of ​​the concrete. , It is the total cross-sectional area of ​​the old steel bars. This is the steel reinforcement strength reduction factor, with a value of 0.9. The design yield strength of the newly added reinforcing steel. It is the total area of ​​the newly added steel bars.

[0084] Besides the core objective, the other two objectives apply to both beams and columns:

[0085] Reinforcement uniformity: Measured by minimizing the standard deviation of the spacing between adjacent newly added reinforcement bars to ensure uniform distribution of reinforcement within the cross-section, facilitating construction; the calculation formula is:

[0086]

[0087] in, This is the score for the uniformity of rebar distribution; a higher value indicates a more uniform distribution. N is the number of newly added rebars. The spacing between adjacent reinforcing bars, and ≥5mm. This represents the average spacing between adjacent reinforcing bars.

[0088] Symmetry score This is achieved by minimizing the lateral deviation of the newly added reinforcing bars relative to the centerline of the structural member, which is particularly important for avoiding eccentric compression of the column; the calculation formula is:

[0089]

[0090] in This is a score based on symmetry, where N is the number of newly added steel bars. , These are the coordinates of the newly added reinforcing bars. , For the width and height of the component.

[0091] In addition, the following constraints must be satisfied during the optimization process:

[0092] Protective layer thickness constraint: The distance from the center point of the newly added rebar to the edge of the concrete must be greater than the minimum protective layer thickness. .in, The typical value is 25.0 mm.

[0093]

[0094]

[0095] Minimum spacing constraint for reinforcing bars: The clear distance between any two reinforcing bars must be greater than the minimum spacing required by the specification; Reinforcing bar spacing:

[0096]

[0097] in, The typical value is 25.00 mm.

[0098] Boundary constraints: All new reinforcement bars must be located inside the beam section.

[0099]

[0100] The final comprehensive fitness function is as follows:

[0101] Beam components:

[0102] Column components:

[0103] in , , The weighting coefficients for each objective are set to a default value of 100%. =0.6, =0.25, =0.15; , These are the design bearing capacity requirements for beams and columns, used for normalization; the weighting coefficients can be flexibly adjusted according to project requirements, but must meet the following conditions. + + =1.

[0104] S5: Transform the rebar location data generated by the optimization algorithm into a usable engineering solution, and perform comprehensive performance verification and output.

[0105] This invention employs a heuristic two-stage optimization strategy. The first stage involves the layout of critical reinforcement bars: based on component type and load-bearing capacity contribution analysis, the system prioritizes determining the initial optimal region for critical new reinforcement bars (such as corner reinforcement bars in the tension zone of beams and corner reinforcement bars in columns) that have the greatest impact on load-bearing capacity. Subsequently, global optimization algorithms, such as differential evolution algorithms, are used to solve for the approximate optimal coordinates of these reinforcement bars within the critical regions. The second stage involves global position refinement: using the critical reinforcement bar coordinates obtained in the first stage as fixed constraints, the optimization algorithm is run again to globally optimize the positions of all remaining new reinforcement bars, maximizing the comprehensive fitness function while satisfying all constraints. This strategy effectively reduces the complexity of the problem by performing a "primary to secondary, step-by-step fixed" search in the solution space.

[0106] After optimization, the system selects 3 to 5 optimal candidate solutions located on the Pareto front from the algorithm's iteration history. These solutions represent optimal trade-offs between multiple objectives (bearing capacity, uniformity, and symmetry), with each solution outperforming others in at least one objective while being slightly inferior in others. For example, solution A may have the highest bearing capacity, solution B may have the most uniform reinforcement distribution, and solution C may achieve the best balance between bearing capacity and symmetry. This provides designers with the possibility of making choices based on different project priorities.

[0107] The system performs a comprehensive performance verification on each candidate scheme, including the ultimate limit state of load capacity and the normal service limit state.

[0108] Bearing capacity verification: Calculate the flexural bearing capacity based on the component type selected in step S4. or axial bearing capacity and require to meet ≥ or ≥ ,in / To design load effects.

[0109] Anchorage length verification: Calculate the basic anchorage length of each newly added rebar according to the specifications. The calculation formula is compared with the actual available length to ensure the reliability of the connection. It is based on the fundamental principles of the "Code for Design of Concrete Structures" GB50010, and modified to consider the special working conditions of the interface between new and old concrete.

[0110]

[0111] in The final anchorage length to be adopted. This is the shape factor of the reinforcing bar (e.g., 0.16 for plain round bars and 0.14 for ribbed bars). The anchorage condition coefficient is calculated by reducing factors such as the interface between new and old concrete and construction measures (such as the performance of rebar adhesive). The value range is usually 0.8 to 1.2, and needs to be determined according to the specific working conditions. The design value for the yield strength of the newly added reinforcing steel; This is the design value for the bond strength between the steel reinforcement and concrete (or the anchoring adhesive). This is a core variable and must be accurately determined based on field tests or the properties of the selected materials (such as the certification value of the anchoring adhesive). The diameter of the newly added reinforcing bar; To provide additional safety length to account for uncertainties such as on-site positioning errors and localized concrete defects, it is recommended to have a length of not less than 50mm.

[0112] All candidate solutions that pass the performance verification will be sorted according to their comprehensive fitness function scores. The solution with the highest score will be the primary recommended solution, and the rest will be considered as backup solutions. The output format includes:

[0113] Construction drawings (CAD / DXF format): Provide detailed drawings of components, clearly marking the precise coordinates (x, y), diameter, number, and relative positional relationship of all newly added reinforcing bars within the cross section, to guide on-site layout and drilling.

[0114] 3D BIM Model (IFC Format): Generates a lightweight 3D BIM model containing information on new and old steel reinforcement, facilitating visual handover and clash detection.

[0115] Design Parameter Report: This report details all key performance indicators and constraint conditions for each scheme, including final bearing capacity, crack width, uniformity score, symmetry score, anchorage length verification results, etc.

[0116] Material list: Automatically compiles and generates a material list for newly added steel bars, including the type of steel bar, diameter, single bar length, total length and total weight, for procurement and cost accounting.

[0117] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

[0118] In the description of this invention, it should be understood that the terms "upper", "lower", "upper end", "lower end", "upper surface", "lower surface", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0119] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

Claims

1. A collaborative design method for steel reinforcement identification and misaligned connection in concrete components, characterized in that, Includes the following steps: S1: Three-dimensional point cloud data of exposed rebar areas of concrete components are collected using a high-precision laser scanner. Differentiated scanning strategies are adopted based on the distribution characteristics of exposed rebar during the collection process, and auxiliary processing measures are taken to address on-site interference factors. S2: First, the point cloud features are extracted and a coarse registration algorithm is used to achieve preliminary spatial alignment of each batch of point clouds. Then, a fine registration algorithm is used to further eliminate local errors to improve coordinate accuracy. Subsequently, the point cloud is denoised to obtain high-quality point cloud data that can be used for geometric fitting. S3: The preprocessed point cloud is filtered to obtain a set of candidate rebar points. A clustering algorithm is used to obtain independent rebar point clusters. Linear fitting and cross-sectional circle fitting are performed on each rebar point cluster to extract the rebar geometric parameters. Then, the corrosion rate is calculated based on the rebar shape deviation and reflection signal characteristics. The effective cross-sectional area of ​​the rebar is corrected based on the corrosion rate. Finally, a structured data package containing the three-dimensional geometric parameters of the rebar is generated. S4: Using the existing steel reinforcement parameters in the structured data package as fixed constraints, input the component type, component geometry, and new steel reinforcement configuration parameters to construct a multi-objective optimization model: if the component is a beam, the core objective is to maximize the flexural bearing capacity; if the component is a column, the core objective is to maximize the axial bearing capacity, while also taking into account the uniformity of steel reinforcement distribution and cross-sectional symmetry. The multi-objective optimization model must satisfy the constraints of concrete cover thickness, minimum net spacing of steel reinforcement, and steel reinforcement boundary position. S5: A two-stage optimization strategy is adopted to solve the multi-objective optimization model, and multiple Pareto optimal candidate schemes are obtained. The bearing capacity and steel bar anchorage length of each candidate scheme are checked respectively, and the optimal scheme that passes the check and the corresponding digital results are output.

2. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S1, the high-precision laser scanner uses a structured light or blue light laser scanner, with a single-point ranging accuracy of ≤0.05mm and a point cloud resolution controlled at ≤0.3mm. The differentiated scanning strategy specifically involves: controlling the scanning distance to improve the point cloud resolution for areas with concentrated exposed rebar, and collecting point cloud data from at least three different angles for each area with concentrated exposed rebar; and relaxing the scanning distance for the entire concrete component to achieve overall area coverage. The auxiliary measures for handling on-site interference factors are: if there is reflection or shadow, the interference can be eliminated by adjusting the angle of the light source or by applying matte stickers to the surface of the concrete component.

3. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S2, the coarse registration algorithm uses the RANSAC algorithm, and the fine registration algorithm uses the point-to-plane ICP algorithm. The denoising process includes voxel filtering and statistical outlier removal. Voxel filtering is used to reduce redundant data in the point cloud and retain geometric features, while statistical outlier removal is used to remove abnormal discrete points in the point cloud. After preprocessing, the process also includes local normal estimation of the processed point cloud and surface reconstruction to fill in small hole regions formed by occlusion or reflection.

4. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S3, the specific method for screening the candidate point set of reinforcing bars is as follows: extract points with significant linear characteristics by calculating the local principal curvature ratio of the points, and take the points with curvature ratio greater than 3 as the initial candidate point set; combine the reflection intensity information of the points for secondary screening, and exclude points with reflection values ​​lower than the set threshold. The DBSCAN clustering algorithm is applied to the candidate point set to merge small clusters that are less than 2 mm apart and remove isolated clusters with a volume of less than 0.5 cm³.

5. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S3, the corrosion rate is calculated as follows: by statistically analyzing the non-circularity deviation between the fitted circle and the actual point cluster, a corrosion area is determined when the deviation is greater than 5%; by analyzing the attenuation rate of the reflected signal intensity, when the attenuation rate makes the reflection coefficient... The corrosion zone is considered as such. The corrosion rate of the reinforcing steel is calculated by considering the non-roundness deviation and the attenuation rate of the reflected signal intensity, and the effective cross-sectional area of ​​the reinforcing steel is corrected according to the following formula: In the formula, This represents the original cross-sectional area of ​​the reinforcing steel. This is the corrected effective cross-sectional area. This represents the corrosion rate.

6. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S4, the formula for calculating the flexural bearing capacity of the beam member is: in, It refers to the beam's bending capacity. The coefficients are the equivalent rectangular stress diagram coefficients for concrete. Design compressive strength for concrete. The width of the beam section. The height of the pressure zone. It is the vertical distance from the edge of the concrete in the compression zone to the point where the resultant force of the steel reinforcement in the tension zone is applied. It is the vertical distance from the resultant point of the longitudinal reinforcing bars in the tension zone to the tension edge of the section. For the total area of ​​newly added steel bars in the tension zone, The design yield strength of the newly added reinforcing steel; and , , The height of the beam section; The formula for calculating the axial bearing capacity of a column member is: in, It refers to the axial bearing capacity of the column. It is the concrete strength reduction factor. It is the design compressive strength of concrete. It is the cross-sectional area of ​​the concrete, and , It is the total cross-sectional area of ​​the old steel bars. It is the steel reinforcement strength reduction factor. The design yield strength of the newly added reinforcing steel. It is the total area of ​​the newly added steel bars; The uniformity of the steel reinforcement distribution is determined by the formula: Quantitative assessment; where, This is the score for the uniformity of rebar distribution; a higher value indicates a more uniform distribution. N is the number of newly added rebars. The spacing between adjacent reinforcing bars. The average spacing between adjacent reinforcing bars; The symmetry of the cross section is determined by the formula. Quantitative assessment, including This is a score based on symmetry, where N is the number of newly added steel bars. These are the coordinates of the newly added reinforcing bars. For the width and height of the component.

7. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 6, characterized in that: In step S4, the constraints that the optimization process must satisfy are defined by the following formula: Protective layer thickness constraints: ,in, Minimum protective layer thickness, For the increased diameter of the reinforcing bars; Minimum spacing constraints for reinforcing bars: ,in This is the minimum clear spacing between reinforcing bars. The diameter of the other reinforcing bar; The coordinates represent the coordinates of another newly added reinforcing bar; Boundary constraints: ; The formula for calculating the comprehensive fitness function of a beam member is: The formula for calculating the comprehensive fitness function of column members is: in , , Let be the weight coefficients for each objective, and , , These are the design load-bearing capacity requirements for beams and columns, respectively.

8. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S5, the formula for checking the anchorage length of the reinforcing bar is: in The final anchorage length to be adopted. This refers to the shape coefficient of the reinforcing steel bars; This is the anchorage condition coefficient. The design value for the yield strength of the newly added reinforcing steel; This represents the design value for the bond strength between the steel reinforcement and concrete. The diameter of the newly added reinforcing bar; To add a safety length; The bearing capacity verification requirements are met. ≥ or ≥ ,in / To design load effects.

9. The method for collaborative design of reinforcement identification and misaligned connection in concrete components as described in claim 1, characterized in that: In step S5, the output digital result includes at least one of the following: Construction drawings should indicate the precise coordinates, diameter, number, and relative position of all newly added reinforcing bars within the cross-section, as well as their positional relationship with the existing reinforcing bars. A lightweight 3D BIM model that includes information on both new and old steel reinforcement. The design parameter report includes the bearing capacity, crack width, uniformity score, symmetry score, and anchorage length verification results for each scheme. The bill of materials includes the type, diameter, single length, total length, and total weight of any new reinforcing bars.

10. A collaborative design system for reinforcing bar identification and misaligned connection in concrete components, used to implement the collaborative design method for reinforcing bar identification and misaligned connection in concrete components as described in any one of claims 1-9, characterized in that, include: The data acquisition module is configured to acquire three-dimensional point cloud data of exposed rebar areas of concrete components using a high-precision laser scanner, and adopt a differentiated scanning strategy based on the distribution characteristics of exposed rebar, while taking auxiliary processing measures for on-site interference factors. The point cloud processing and reconstruction module is configured to perform multi-level registration and preprocessing on the acquired 3D point cloud data. This includes using a coarse registration algorithm to achieve preliminary spatial alignment of point clouds in each batch, using a fine registration algorithm to eliminate local errors, and performing noise reduction on the point clouds to obtain high-quality point cloud data. The preprocessed point clouds are then filtered, clustered, geometrically fitted, and corroded to generate a structured data package containing the 3D geometric parameters of the reinforcing bars. The collaborative optimization design module is configured to use the existing steel reinforcement parameters in the structured data package as fixed constraints, receive component type, component geometry and new steel reinforcement configuration parameters, construct a multi-objective optimization model, and use a two-stage optimization strategy to solve the multi-objective optimization model to obtain multiple Pareto optimal candidate solutions. The scheme output and verification module is configured to verify the bearing capacity and rebar anchorage length of each candidate scheme, and output the optimal scheme that passes the verification and the corresponding digital results.

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