Corrosion reinforced concrete beam performance evaluation method and system based on digital twinning

Abstract

The invention provides a method and system for evaluating the performance of a corroded reinforced concrete beam based on digital twinning, and relates to the technical field of structural performance evaluation.The method comprises the steps that first information and second information are obtained, the first information comprises in-situ three-dimensional point cloud data and steel bar arrangement information, and the second information comprises in-situ three-dimensional point cloud data; the second information comprises surface corrosion crack width, steel bar weight loss distribution data and static mechanical response monitoring and detection data; speculating the weight loss of the internal reinforcing steel bar according to the actually measured surface corrosion crack width, and generating a corrosion digital twinborn body; establishing a finite element model of the corroded reinforced concrete beam with the cracks, and optimizing the finite element model to generate a mechanical digital twinborn body; and evaluating the mechanical property of the corroded reinforced concrete beam based on the mechanical digital twin to obtain a mechanical property evaluation result of the corroded reinforced concrete beam. The method has the advantages that corrosion mechanism modeling is fine, parameters can be shared, and structural performance prediction is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structural performance evaluation, in particular to a corrosion reinforced concrete beam performance evaluation method and system based on digital twinning. BACKGROUND

[0002] Reinforced concrete structures are widely used in building and bridge engineering. With the increase of service life and due to environmental exposure and material aging, the passivation film on the surface of the steel bars in the reinforced concrete structure is easily damaged due to carbonation of concrete or invasion of chloride ions, thereby causing corrosion of the steel bars inside the structure, inducing cracking or spalling of the concrete protective layer, degradation of the interfacial bond, and reduction of the bearing capacity, which seriously affects the durability and safety of the structure. Existing structural performance evaluation techniques mainly rely on periodic inspection data or simplified mechanical models, and cannot accurately reflect the corrosion crack development process and its influence on the structural performance. Surface cracking of the reinforced concrete structure is a direct result of the expansion of internal corrosion products, so the apparent corrosion crack width can be used as a basis for inferring the corrosion state of the steel bars inside the reinforced concrete structure. Some methods attempt to infer the corrosion degree based on the corrosion surface crack width and evaluate the performance of the corroded reinforced concrete structure by means of nonlinear finite element analysis, but there are generally problems such as dependence on experience for parameter setting, simplification of corrosion influence, and uncertainty of the model. In addition, existing evaluation methods cannot effectively integrate test data and numerical simulation information, and lack a complete modeling framework that considers the linkage mechanism of corrosion process evolution and mechanical response, resulting in limited prediction accuracy and applicability of the performance of the reinforced concrete beam structure throughout its life cycle.

[0003] Therefore, there is an urgent need for a corrosion reinforced concrete beam performance evaluation method and system based on digital twinning to solve the above technical problems. SUMMARY

[0004] The purpose of the present application is to provide a corrosion reinforced concrete beam performance evaluation method and system based on digital twinning to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a corrosion reinforced concrete beam performance evaluation method based on digital twinning, comprising:

[0006] obtaining first information and second information, the first information including in-situ three-dimensional point cloud data and steel bar arrangement information of the corroded reinforced concrete beam, and the second information being steel weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test, and static mechanical response monitoring and detection data collected through a bending load test;

[0007] speculating the weight loss of the steel bars inside the corroded reinforced concrete beam based on the first information and the second information, and generating a corrosion digital twin.

[0008] establish a finite element model of the corroded reinforced concrete beam with cracks based on the first information, the second information and the corrosion digital twin, and optimize the finite element model to generate a mechanics digital twin;

[0009] evaluate the mechanical performance of the corroded reinforced concrete beam based on the mechanics digital twin to obtain a mechanical performance evaluation result of the corroded reinforced concrete beam.

[0010] In a second aspect, the application further provides a performance evaluation system for a corroded reinforced concrete beam based on digital twinning, comprising:

[0011] an acquisition unit configured to acquire first information and second information, wherein the first information comprises in-situ three-dimensional point cloud data and reinforcement arrangement information of the corroded reinforced concrete beam, and the second information comprises steel weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test, and static mechanical response monitoring and detection data collected through a bending load test;

[0012] a construction unit configured to infer the weight loss of internal reinforcement of the corroded reinforced concrete beam based on the first information and the second information, and generate a corrosion digital twin;

[0013] an optimization unit configured to establish a finite element model of the corroded reinforced concrete beam with cracks based on the first information, the second information and the corrosion digital twin, and optimize the finite element model to generate a mechanics digital twin;

[0014] an evaluation unit configured to evaluate the mechanical performance of the corroded reinforced concrete beam based on the mechanics digital twin to obtain a mechanical performance evaluation result of the corroded reinforced concrete beam.

[0015] The application has the following beneficial effects:

[0016] The application integrates electrochemical accelerated corrosion test, bending load test and nonlinear finite element simulation, comprehensively collects in-situ three-dimensional point cloud, static mechanical response monitoring and detection data and surface corrosion crack width distribution, and constructs a corrosion and mechanics sequence digital twin. Through the construction of a finite element analysis of the corrosion process based on a semi-elliptical corrosion layer model, the mapping relationship between the corrosion rate and the surface corrosion crack width is deduced, and then the multi-objective particle swarm optimization algorithm is combined to dynamically update the structural mechanics parameters, thereby realizing the accurate evaluation of the performance of the corroded reinforced concrete beam with cracks. Compared with the prior art, the application has the advantages of fine corrosion mechanism modeling, shareable parameters and more accurate performance prediction, and significantly improves the accuracy of the performance evaluation of the structure in the corrosion environment.

[0017] Other features and advantages of the present application will be set forth in the following description, and in part will be apparent from the description, or can be learned by practice of the application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0019] Figure 1 A flowchart of the performance evaluation method for the corroded reinforced concrete beam based on digital twinning described in the embodiments of the present application;

[0020] Figure 2 A structure diagram of the performance evaluation system for the corroded reinforced concrete beam based on digital twinning described in the embodiments of the present application.

[0021] In the figure: 701, acquisition unit; 702, construction unit; 703, optimization unit; 704, evaluation unit. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art without creative labor on the basis of the embodiments in the present application belong to the scope of protection of the present application.

[0023] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0024] Embodiment 1:

[0025] The embodiment provides a performance evaluation method for a corroded reinforced concrete beam based on digital twinning.

[0026] Referring to Figure 1 , the method comprises steps S1, S2, S3 and S4.

[0027] In step S1, first information and second information are acquired, wherein the first information comprises in-situ three-dimensional point cloud data of the corroded reinforced concrete beam and steel bar arrangement information, and the second information comprises steel bar weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test and static mechanical response monitoring and detection data collected through a bending load test;

[0028] It can be understood that in this step, the point cloud data of the corroded reinforced concrete beam is first acquired through three-dimensional laser scanning of the structure in-situ, and the point cloud data can accurately reflect the geometric shape and local damage characteristics of the beam body and is the core spatial basis for constructing a subsequent three-dimensional geometric model; in combination with nondestructive testing means, such as a steel bar scanner, the actual arrangement of the steel bar in the concrete and the thickness of the protective layer are accurately acquired to form a multi-modal information fusion data set at the geometric level, which is collectively referred to as “first information”. The “second information” is acquired by means of a multi-source synchronous test scheme, that is, electrochemical accelerated corrosion is performed on the steel bar, after the corrosion target is reached, the surface corrosion crack width distribution data is collected, and after chemical rust removal, the mass loss of different steel bar segments is determined to acquire the actual weight loss distribution of the steel bar. A bending load test is performed, and the deflection, strain and crack width and other static mechanical response monitoring and detection data are acquired by means of displacement meters, strain gauges and crack detection devices arranged at key positions of the beam body. These high-dimensional, multi-source test data not only provide objective quantitative basis for the damage state of the structure, but also constitute the input basis for subsequent model construction and digital twinning body establishment.

[0029] This step provides a multi-modal fusion data source with corrosion spatial distribution characteristics and physical response characteristics for subsequent digital twinning body construction, realizes real restoration of the actual structure state of the reinforced concrete beam, and significantly improves the accuracy and applicability of model construction.

[0030] In this step, step S1 comprises steps S11, S12 and S13.

[0031] In step S11, a corroded reinforced concrete beam is prepared, an electrochemical accelerated corrosion test is performed on the corroded reinforced concrete beam, the corrosion crack width distribution on the surface of the beam is recorded after the test is completed, and the weight loss of each steel bar segment is measured to obtain the weight loss distribution data of the steel bar;

[0032] It can be understood that in this step, first of all, the reinforced concrete beam specimen is prepared by standard proportioning and pre-set reinforcement arrangement scheme, and the surface of the reinforcement is pretreated to destroy the passivation film on the surface. Then the electrochemical accelerated corrosion method is introduced, usually in the form of constant current or constant potential, and the anode current is applied by an external power source in a simulated chloride salt corrosion environment to cause electrochemical oxidation of the reinforcement and accelerate the generation of corrosion products. Compared with natural corrosion, this method can induce crack propagation and mass loss within a controlled period, significantly improving experimental efficiency and corrosion controllability. During the corrosion development process, the crack detection system (such as high-definition imaging combined with digital crack width recognition technology) records the width of the corrosion cracks on the surface of the beam, captures the crack propagation path, width change and its time evolution. After the corrosion test is completed, the beam is cut to expose the reinforcement, and the mass loss of the reinforcement is measured by the segment-by-segment weighing method, three-dimensional optical measurement or chemical immersion method, thereby obtaining the corrosion degree of each reinforcement segment and forming a high spatial resolution distribution of the weight loss of the reinforcement.

[0033] Step S12, bending load test is performed on the corroded reinforced concrete beam, and static mechanical response monitoring and detection data of the corroded reinforced concrete beam during loading are collected, including deflection, strain and apparent damage data of the corroded reinforced concrete beam;

[0034] It can be understood that this step carries out a bending load test on the reinforced concrete beam that has undergone electrochemical accelerated corrosion treatment, aiming to evaluate the mechanical response characteristics of the structure after corrosion under bending conditions. The loading method generally uses four-point bending to produce a pure bending region in the middle span of the beam to significantly amplify the effect of corrosion on bending performance. During the loading process, multi-modal monitoring means are used to obtain static mechanical response monitoring and detection data: deflection data are recorded by laser displacement meters or linear displacement sensors to measure the deformation of the beam at the mid-span; strain data are measured by strain gauges arranged on the upper surface, side surface and inside the reinforcement of the beam; apparent damage is obtained by combining image recognition methods and visual crack tracking systems to obtain crack distribution, development path and cracking width. To improve the temporal accuracy and spatial distribution coverage of the data, the response data collection should be synchronized with the loading history to form a complete "loading-response" mapping data set.

[0035] This step provides a real and fine-grained structure response data basis for subsequent construction of a finite element model reflecting the actual stress-damage evolution process. Compared with the traditional monitoring method of a single parameter, this step realizes the fusion collection of multiple types of response data, which comprehensively reflects the influence of corrosion on stiffness, crack propagation and bonding performance. Ultimately, these test data will serve as a calibration standard for the behavior of the real structure in the digital twin, significantly improving the prediction accuracy and mechanical response consistency of the model.

[0036] Step S13, three-dimensional laser scanning of the corroded reinforced concrete beam to obtain in-situ three-dimensional point cloud, and using non-destructive testing equipment to detect the thickness of the reinforced concrete cover and the actual arrangement of the steel bars to obtain the geometric data of the corroded reinforced concrete beam.

[0037] It can be understood that this step is to comprehensively characterize the geometric state and steel bar configuration of the corroded reinforced concrete beam. First, a high-precision three-dimensional laser scanner (such as a laser radar or a structured light scanning system) is used to perform in-situ three-dimensional laser measurement on the surface of the beam body to obtain complete three-dimensional point cloud data. The point cloud data can truly reflect the geometric characteristics of the beam body surface, including the crack topology, local erosion or volume change caused by corrosion, and has the advantages of non-contact, high resolution, real-time imaging, etc., and is the basis for subsequent geometric twin modeling.

[0038] Subsequently, non-destructive testing equipment (such as a steel bar scanner) is used to scan the inside of the concrete to extract information such as the thickness of the steel bar cover, the spacing, position and orientation of the steel bars. These detection devices can penetrate the concrete, identify the steel bar layout path and depth, and effectively distinguish complex situations such as steel bar intersection or overlapping arrangement.

[0039] Step S2, based on the first information and the second information, inferring the weight loss of the internal steel bars of the corroded reinforced concrete beam, and generating a corrosion digital twin;

[0040] It can be understood that this step converts the invisible corrosion process into a structure analyzable internal damage variable through modeling-simulation-inversion, and the corrosion digital twin constructed not only has the mapping ability of surface crack response, but also can realize the prediction of internal corrosion evolution path across time scale, providing accurate structure input basis for subsequent mechanical property evaluation and maintenance strategy. In this step, step S2 includes step S21, step S22, step S23, step S24 and step S25.

[0041] Step S21, generating a geometric twin model of the in-situ reinforced concrete beam based on the in-situ three-dimensional point cloud in the geometric data of the corroded reinforced concrete beam and the actual arrangement of the steel bars;

[0042] It can be understood that this step constructs a three-dimensional geometric twin body that highly fits the real physical structure, which not only provides geometric basis and boundary precision for subsequent corrosion modeling, but also greatly improves the credibility of structure simulation and damage mapping. In this step, step S21 includes step S211, step S212 and step S213.

[0043] Step S211, according to the in-situ three-dimensional point cloud data of the corroded reinforced concrete beam, performing multi-scale geometric registration processing to obtain a three-dimensional structure point cloud model after spatial registration;

[0044] It can be understood that this step extracts a local geometric descriptor from the original point cloud based on a fast point feature histogram (Fast Point Feature Histogram), and preliminarily performs spatial rigid registration on the point clouds of multiple views through random sample consensus matching (RANSAC). This process is mainly used to eliminate the initial attitude offset generated in the scanning process and obtain a rough overlapping model.

[0045] Then, in order to improve the local accuracy, an iterative closest point algorithm (ICP) is applied for fine registration. At each scale, the algorithm updates the rigid transformation parameters by minimizing the Euclidean distance error between the two point clouds, thereby gradually aligning the detailed features. In the multi-scale framework, the registration of the overall structure is first quickly completed with sparse point clouds, and then high-density point clouds are gradually introduced to refine the edge profile, concave-convex changes and micro-crack structure. This layer-by-layer optimization strategy maintains global stability while strengthening the alignment effect of local details, avoiding the problem that ICP is easy to fall into local optimization.

[0046] In addition, in order to suppress the high-frequency noise introduced by the scanning device, the application performs preprocessing on the point cloud through statistical outlier rejection method and bilateral filtering method, so that the final structural point cloud model has high signal-to-noise ratio and shape stability.

[0047] Step S212, based on the three-dimensional structural point cloud model after spatial registration, slice and surface fitting processing is performed to obtain the cross-sectional geometric parameters of the corroded reinforced concrete beam;

[0048] It can be understood that this step first uses a region growing algorithm based on normal vector estimation to perform initial segmentation on the point cloud. This method can effectively identify the main planar structure (such as the top and bottom surfaces, side surfaces) of the concrete beam by analyzing the angle and distance relationship between the local point cloud normal vectors, and grouping points with similar geometric direction and spatial density into the same region.

[0049] Subsequently, point cloud slicing processing is performed along the length direction of the beam, and a plane fitting algorithm (such as RANSAC algorithm or least squares method) is applied in each slice to obtain the surface of the beam at different positions, and the intersection line of the surfaces is the contour line.

[0050] Step S213, based on the cross-sectional geometric parameters of the corroded reinforced concrete beam and the steel bar arrangement information fusion processing, a geometric twin model of the in-situ reinforced concrete beam is obtained;

[0051] It can be understood that this step first constructs an initial positioning coordinate set of the steel bar according to the thickness of the steel bar protection layer and the set of steel bar center position points obtained by a non-destructive testing means (such as a steel bar scanner), in combination with the beam body cross-section information provided by the three-dimensional point cloud model. Then, based on the cross-sectional geometric parameters of the reinforced concrete beam and the actual arrangement information of the steel bar, a geometric twin model that truly reflects the structural relationship between the concrete and the steel bar is obtained.

[0052] Step S22, modeling processing is performed according to the geometric twin model of the in-situ reinforced concrete beam, to obtain a corrosion expansion process finite element model;

[0053] It can be understood that this step first uses the high-precision restored geometric information of the steel bar and the concrete in the geometric twin model to construct a stress-strain field caused by the corrosion product expansion of the steel bar, and uses a diffuse crack model to simulate the crack behavior of the concrete. The model can capture the nonlinear evolution process of the concrete material from micro-damage initiation to macro-crack propagation.

[0054] Using the constitutive model, in combination with the geometric twin model and the radial expansion displacement distribution function, the quantitative relationship between the radial expansion displacement and the surface corrosion crack width is obtained through nonlinear finite element simulation, and finally the corresponding steel bar weight loss distribution is obtained. The distribution function accurately characterizes the distribution of corrosion products along the radial direction of the steel bar and its driving effect on the crack propagation of the concrete.

[0055]

[0056] In the formula: u θ The radial expansion load distribution function of the corroded reinforced concrete is represented, R is the original radius of the steel bar, u1 is the maximum corrosion layer thickness, u2 is the minimum corrosion layer thickness, and the shape of the semi-ellipse is determined by the ratio of u2 / u1.

[0057] Step S23, based on the preset continuous damage crack constitutive model suitable for simulating the crack evolution of the concrete, the preset corrosion radial expansion displacement distribution function and the corrosion expansion process finite element model, the mapping relationship between the radial displacement of the steel bar corrosion expansion and the surface crack width of the reinforced concrete beam is obtained;

[0058] It can be understood that this step captures the local stress concentration and crack opening width response caused by the corrosion product expansion through nonlinear finite element simulation, and obtains the mapping function between the corrosion expansion displacement and the crack width. The mapping relationship not only reflects the positive correlation between the corrosion expansion displacement and the crack propagation, but also reflects the influence of non-uniform corrosion on the crack morphology, ensuring the accuracy and applicability of the model.

[0059] Step S24, according to the volume distribution mechanism of the corrosion product, a relationship function between the radial displacement and the corrosion rate of the steel bar is obtained;

[0060] It can be understood that the total volume of the general corrosion product in this step can be divided into three parts: the first part, the volume consumed to fill the porous zone; the second part, the volume consumed to occupy the corrosion part of the steel bar; and the third part, the volume that generates swelling pressure to the surrounding. The total volume distribution is represented by equation (2). Given the volume swelling ratio of the corrosion product, the relationship between the total volume of the corrosion product and the volume of the corrosion part of the steel bar can be obtained, which is represented by equation (3). The volume that generates swelling pressure to the surrounding is determined by the corrosion layer model, which is represented by equation (4). Substituting equation (3) and equation (4) into equation (2) can obtain the relationship between the radial displacement of the corroded reinforced concrete beam and the corrosion rate of the steel bar.

[0061] V all = 2πRδ + V e + V s (2)

[0062] V all = β·V s (3)

[0063]

[0064] wherein V all is the total volume of the corrosion product, δ is the thickness of the first part of the porous zone, V e is the third part of the volume that generates swelling pressure to the surrounding, V s is the second part of the volume of the corrosion of the steel bar, β is the volume swelling ratio of the corrosion product, R is the original radius of the steel bar, u1 is the maximum corrosion layer thickness, u2 is the minimum corrosion layer thickness, the shape of the semi-ellipse is determined by the ratio of u2 / u1, and η is the relationship function of the radial displacement and the corrosion rate of the steel bar.

[0065] Moreover, this step further considers that the iron rust may be deposited and seeped out and lost in the hydrostatic pressure, and this part of the volume of the iron rust does not generate swelling pressure to the surrounding. The corrected relationship between the radial displacement and the corrosion rate of the steel bar is represented by equation (6).

[0066]

[0067] k = -c1·ln(w) + c2 (8)

[0068] wherein η c is the corrected relationship function of the radial displacement and the corrosion rate of the steel bar, is the correction coefficient, which is obtained by fitting experimental data, and η a is the average corrosion rate and I corr is the corrosion current density.where k is the corrosion loss reduction factor, which is inversely proportional to the corrosion crack width, r is the regional scaling factor, which considers the regional variability of the corrosion loss, d is the thickness of the porous zone in the first part, b is the volume expansion ratio of the corrosion product, R is the original radius of the steel bar, u1 is the maximum corrosion layer thickness, u2 is the minimum corrosion layer thickness, the shape of the semi-ellipse is determined by the ratio of u2 / u1, c1 and c2 are undetermined coefficients, and w represents the corrosion crack width.

[0069] Step S25, determining the internal steel corrosion distribution data of the corroded reinforced concrete according to the mapping relationship, the surface corrosion crack width distribution, and the relationship function of the radial displacement and the steel corrosion rate, and forming a corrosion digital twin.

[0070] It can be understood that this step accurately determines the corrosion degree and corrosion distribution of the internal steel of the corroded reinforced concrete beam through inverse calculation. Specifically, a method of combining nonlinear numerical simulation and experimental data is adopted to inversely deduce the corresponding relationship between the surface crack width and the internal corrosion degree, and then a digital twin model capable of dynamically reflecting the corrosion state of the steel is constructed. Through iterative optimization and parameter calibration, a complete and high-precision corrosion digital twin is finally formed. The technical effect of this step lies in realizing the mapping of the corrosion state and improving the accuracy and reliability of the corrosion state prediction, thereby providing key data support for subsequent structure performance evaluation and maintenance decision-making.

[0071] Step S3, establishing a finite element model of the corrosion crack reinforced concrete beam based on the first information, the second information, and the corrosion digital twin, and optimizing the finite element model to generate a mechanical digital twin;

[0072] It can be understood that this step realizes the fusion of corrosion damage and mechanical properties, and provides a scientific, dynamic, and high-precision mechanical digital model for the structure health assessment and life prediction of the reinforced concrete beam. In this step, step S3 includes step S31, step S32, step S33, and step S34.

[0073] Step S31, performing finite element modeling based on the internal steel corrosion distribution data of the corroded reinforced concrete to obtain a reinforced concrete beam finite element model reflecting the influence of corrosion cracks;

[0074] It can be understood that this step accurately maps the corrosion zone characteristics such as corrosion-induced steel section loss, local corrosion cracks, degradation of steel and concrete interface bonding performance, and damage of concrete protective layer, to the material properties and geometric shapes of the finite element model, ensuring that the model truly reflects the structural weakening and non-uniform damage distribution of the corrosion zone. Through this modeling method, the finite element model can finely depict the influence of corrosion on the overall mechanical properties of the reinforced concrete beam. The technical effect of this step is to build a finite element model that truly and dynamically reflects corrosion damage, laying a solid foundation for subsequent digital twin establishment and structural performance evaluation.

[0075] Step S32, according to the finite element model, determining its optimization objective function, parameters to be optimized and optimization algorithm, obtaining an optimization scheme;

[0076] It can be understood that in this step, the optimization objective function is set as the root mean square error function of the deflection, strain and crack width test measurement values and numerical model calculation values of the given measurement points under certain load conditions, and the objective function value is minimized; the parameters to be optimized are determined through parameter sensitivity analysis, and the parameters with higher sensitivity are selected as the parameters to be optimized; the optimization algorithm adopts a multi-objective particle swarm optimization algorithm for multi-objective optimization to avoid the subjectivity of function combination weight values.

[0077] Through this process, a scientific and reasonable optimization scheme for the finite element model is developed. The technical effect of this step is to ensure that the subsequent model parameter adjustment can effectively improve the simulation accuracy and prediction reliability, thereby providing theoretical and algorithmic support for the accurate construction of the digital twin.

[0078] Step S33, based on the optimization scheme, combining PYTHON programming and finite element analysis software for joint processing, automatically adjusting material parameter input, starting finite element calculation in the background and calling results, iteratively executing the optimization algorithm, completing mechanical parameter updating under normal use and ultimate limit state, and constructing a mechanical digital twin of the reinforced concrete beam;

[0079] It can be understood that this step adopts Python programming language and finite element analysis software (such as ABAQUS, DI ANA, etc.) to realize deep integration, realizes dynamic input adjustment of material parameters and batch calculation of finite element model through writing automatic script. The specific process includes: the Python script automatically modifies the key material parameters in the finite element model, then the background calls the finite element software to execute the calculation, obtains the output results such as deflection, strain and crack width; then the calculation results are fed back to the optimization algorithm module, the parameters are updated based on the error index, and the iterative optimization is realized. This process continues until the mechanical response of the model under the normal use state and the limit state of bearing capacity and the experimental measurement data reach the preset convergence standard. Through the joint processing, efficient multi-iteration optimization is realized, the accuracy of the model is greatly improved, and finally the mechanical digital twin that accurately reflects the actual structure state is constructed. The technical effect of this step is to automatically and efficiently complete the parameter identification of the finite element model, significantly improve the accuracy and real-time performance of the structure performance prediction, and promote the practical application of the digital twin technology in the structure health monitoring.

[0080] Step S34, based on the preset test point data not participating in the model updating and the preset response data of the uncorroded reinforced concrete beam, verifying the mechanical digital twin to obtain the verified mechanical digital twin.

[0081] It can be understood that the structural responses such as deflection, strain and crack width predicted by the digital twin are compared with the above-mentioned measured data not participating in the updating to verify the mechanical digital twin. Through this verification step, the generalization ability of the digital twin and its applicability under different working conditions and corrosion degrees can be tested, ensuring that the model is not only effective within the data range participating in the model updating, but also has good extrapolation ability. The technical effect of this step is to improve the reliability and practicality of the mechanical digital twin, to provide a solid foundation for subsequent structure performance prediction and health state evaluation, and to ensure the popularization and application of the digital twin technology in engineering practice.

[0082] Step S4, based on the mechanical digital twin, evaluating the mechanical performance of the corroded reinforced concrete beam to obtain the mechanical performance evaluation result of the corroded reinforced concrete beam.

[0083] It can be understood that based on the constructed and verified mechanical digital twin, the mechanical performance of the corroded reinforced concrete beam is comprehensively evaluated by using a multi-index comprehensive evaluation method. Specifically, the digital twin is used to predict the structural degradation parameters such as the degradation of the steel bar bonding strength and the reduction of the cross-sectional area in the future preset time period, and then the structural performance is predicted. Through the indexes such as the post-cracking stiffness, crack width development and ultimate bearing capacity, the comprehensive evaluation of the usable limit state and the bearing capacity limit state of the structure is realized. This step can accurately reflect the current state and future trend of the structure, effectively support the structure maintenance decision and safety warning, and significantly improve the accuracy and scientificity of the whole life cycle performance evaluation of the corroded reinforced concrete beam with cracks.

[0084] Embodiment 2:

[0085] As shown in Figure 2 The embodiment provides a performance evaluation system for a corroded reinforced concrete beam based on a digital twin, which is shown in Figure 2 The system comprises an acquisition unit 701, a construction unit 702, an optimization unit 703 and an evaluation unit 704.

[0086] The acquisition unit 701 is configured to acquire first information and second information. The first information comprises in-situ three-dimensional point cloud data and steel bar arrangement information of the corroded reinforced concrete beam. The second information comprises steel bar weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test, and static mechanical response monitoring and detection data collected through a bending load test.

[0087] The construction unit 702 is configured to infer the weight loss of the internal steel bar of the corroded reinforced concrete beam based on the first information and the second information, and generate a corrosion digital twin.

[0088] The optimization unit 703 is configured to establish a finite element model of the corroded reinforced concrete beam with cracks based on the first information, the second information and the corrosion digital twin, and optimize the finite element model to generate a mechanical digital twin.

[0089] The evaluation unit 704 is configured to evaluate the mechanical performance of the corroded reinforced concrete beam based on the mechanical digital twin, and obtain a mechanical performance evaluation result of the corroded reinforced concrete beam.

[0090] It should be noted that as for the system in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0091] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0092] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0092] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0092] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0092] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0092] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in

Claims

1. A method for performance evaluation of corroded reinforced concrete beams based on digital twinning, characterized by, The method comprises the following steps: Obtaining first information and second information, wherein the first information comprises in-situ three-dimensional point cloud data of a corroded reinforced concrete beam and steel bar arrangement information, and the second information comprises steel bar weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test and static mechanical response monitoring and detection data collected through a bending load test; Speculating the weight loss of internal steel bars of the corroded reinforced concrete beam based on the first information and the second information, and generating a corrosion digital twin; Establishing a finite element model of the corroded reinforced concrete beam with cracks based on the first information, the second information and the corrosion digital twin, and optimizing the finite element model to generate a mechanical digital twin; Evaluating the mechanical performance of the corroded reinforced concrete beam based on the mechanical digital twin, and obtaining a mechanical performance evaluation result of the corroded reinforced concrete beam.

2. The digital-twin-based method for performance evaluation of corroded reinforced concrete beams according to claim 1, characterized in that Obtaining first information and second information, comprising: Preparing a corroded reinforced concrete beam, performing an electrochemical accelerated corrosion test on the corroded reinforced concrete beam, recording the corrosion crack width distribution on the surface of the beam after the test is completed, and measuring the weight loss of each steel bar segment to obtain the weight loss distribution data of the steel bars; Performing a bending load test on the corroded reinforced concrete beam, collecting static mechanical response monitoring and detection data of the corroded reinforced concrete beam during the loading process, and the static mechanical response monitoring and detection data comprises deflection, strain and apparent damage data of the corroded reinforced concrete beam; Performing three-dimensional laser scanning on the corroded reinforced concrete beam to obtain in-situ three-dimensional point cloud, and using non-destructive testing equipment to detect the steel bar cover thickness and actual arrangement of the steel bars of the reinforced concrete to obtain geometric data of the corroded reinforced concrete beam.

3. The digital-twin-based method for performance evaluation of corroded reinforced concrete beams according to claim 1, characterized in that Speculating the weight loss of internal steel bars of the corroded reinforced concrete beam based on the first information and the second information, comprising: Generating a geometric twin model of the in-situ reinforced concrete beam based on the in-situ three-dimensional point cloud and the actual arrangement of the steel bars in the geometric data of the corroded reinforced concrete beam; Modeling according to the geometric twin model of the in-situ reinforced concrete beam to obtain a finite element model of the corrosion expansion process; Analyzing based on a preset continuous damage crack constitutive model suitable for simulating the evolution of concrete cracks, a preset corrosion radial expansion displacement distribution function and the finite element model of the corrosion expansion process to obtain a mapping relationship between the radial displacement of the steel bar corrosion expansion and the surface crack width of the reinforced concrete beam; Obtaining a relationship function between the radial displacement and the steel bar corrosion rate according to the volume distribution mechanism of the corrosion product; Determining the internal steel bar corrosion distribution of the corroded reinforced concrete according to the mapping relationship, the surface corrosion crack width distribution and the relationship function between the radial displacement and the steel bar corrosion rate to form a corrosion digital twin.

4. The digital-twin-based method for performance evaluation of corroded reinforced concrete beams according to claim 3, characterized in that Generating a geometric twin model of the in-situ reinforced concrete beam based on the in-situ three-dimensional point cloud and the actual arrangement of the steel bars in the geometric data of the corroded reinforced concrete beam, comprising: Performing multi-scale geometric registration processing according to the in-situ three-dimensional point cloud data of the corroded reinforced concrete beam to obtain a three-dimensional structure point cloud model after spatial registration; Based on the three-dimensional structure point cloud model after spatial registration, slicing and surface fitting processing are performed to obtain the cross-sectional geometric parameters of the corroded reinforced concrete beam; Based on the cross-sectional geometric parameters and the steel bar arrangement information fusion processing of the corroded reinforced concrete beam, a geometric twin model of the in-situ reinforced concrete beam is obtained.

5. The digital-twin-based method for performance evaluation of corroded reinforced concrete beams according to claim 3, characterized in that Based on the first information, the second information and the corrosion digital twin, a finite element model of the corroded reinforced concrete beam with cracks is established, and the mechanical digital twin is generated by optimizing the finite element model, including: Based on the internal steel corrosion distribution data of the corroded reinforced concrete, finite element modeling processing is performed to obtain a reinforced concrete beam finite element model reflecting the influence of corrosion with cracks; According to the finite element model, the optimization objective function, the parameters to be optimized and the optimization algorithm are determined to obtain an optimization scheme; Based on the optimization scheme, combined with PYTHON programming and finite element analysis software, the optimization algorithm is iteratively executed by automatically adjusting the material parameter input, starting the finite element calculation in the background and calling the results, completing the mechanical parameter updating under the normal use and the ultimate state of bearing capacity, and constructing the mechanical digital twin of the reinforced concrete beam; Based on the preset test point data not participating in the model updating and the preset response data of the uncorroded reinforced concrete beam, the mechanical digital twin is verified to obtain the verified mechanical digital twin.

6. A digital-twin-based system for performance evaluation of corroded reinforced concrete beams, characterized by, It includes: An acquisition unit is configured to acquire first information and second information, wherein the first information includes in-situ three-dimensional point cloud data of a corroded reinforced concrete beam and steel bar arrangement information, and the second information includes steel weight loss distribution data and surface corrosion crack width distribution data collected through an electrochemical accelerated corrosion test, and static mechanical response monitoring and detection data collected through a bending load test; A construction unit is configured to infer the weight loss of internal steel bars of the corroded reinforced concrete beam based on the first information and the second information, and generate a corrosion digital twin; An optimization unit is configured to establish a finite element model of a corroded reinforced concrete beam with cracks based on the first information, the second information and the corrosion digital twin, and generate a mechanical digital twin by optimizing the finite element model; An evaluation unit is configured to evaluate the mechanical performance of the corroded reinforced concrete beam based on the mechanical digital twin, and obtain a mechanical performance evaluation result of the corroded reinforced concrete beam.

7. The digital-twin-based system for performance evaluation of corroded reinforced concrete beams according to claim 6, characterized in that, The acquisition unit includes: A first acquisition subunit is configured to prepare a corroded reinforced concrete beam, perform an electrochemical accelerated corrosion test on the corroded reinforced concrete beam, record the corrosion crack width distribution on the surface of the beam after the test is completed, and measure the weight loss of each steel segment to obtain the weight loss distribution data of the steel bars; A second acquisition subunit is configured to perform a bending load test on the corroded reinforced concrete beam, collect static mechanical response monitoring and detection data of the corroded reinforced concrete beam during the loading process, and the static mechanical response monitoring and detection data includes deflection, strain and apparent damage data of the corroded reinforced concrete beam. The third acquisition subunit is configured to perform three-dimensional laser scanning on the corroded reinforced concrete beam to obtain an in-situ three-dimensional point cloud, and to detect the thickness of the reinforced concrete protective layer and the actual arrangement of the steel bars by using a non-destructive testing device to obtain geometric data of the corroded reinforced concrete beam.

8. The digital-twin-based system for performance evaluation of corroded reinforced concrete beams according to claim 6, wherein, The generation unit comprises: The first generation subunit is configured to generate a geometric twin model of the in-situ reinforced concrete beam based on the in-situ three-dimensional point cloud and the actual arrangement of the steel bars in the geometric data of the corroded reinforced concrete beam. The second generation subunit is configured to perform modeling processing according to the geometric twin model of the in-situ reinforced concrete beam to obtain a finite element model of the corrosion expansion process. The third generation subunit is configured to perform analysis based on a preset continuous damage crack constitutive model suitable for simulating concrete crack evolution, a preset corrosion radial expansion displacement distribution function, and the finite element model of the corrosion expansion process to obtain a mapping relationship between the steel bar corrosion radial displacement and the surface crack width of the reinforced concrete beam. The fourth generation subunit is configured to obtain a relationship function between the radial displacement and the steel bar corrosion rate according to a corrosion product volume distribution mechanism. The fifth generation subunit is configured to determine the internal steel bar corrosion distribution of the corroded reinforced concrete according to the mapping relationship, the surface corrosion crack width distribution, and the relationship function between the radial displacement and the steel bar corrosion rate to form a corrosion digital twin.

9. The digital-twin-based system for performance evaluation of corroded reinforced concrete beams according to claim 8, characterized in that, The first generation subunit comprises: The first processing subunit is configured to perform multi-scale geometric registration processing according to the in-situ three-dimensional point cloud data of the corroded reinforced concrete beam to obtain a three-dimensional structure point cloud model after spatial registration. The second processing subunit is configured to perform slicing and surface fitting processing based on the three-dimensional structure point cloud model after spatial registration to obtain the cross-sectional geometric parameters of the corroded reinforced concrete beam. The third processing subunit is configured to perform fusion processing based on the cross-sectional geometric parameters of the corroded reinforced concrete beam and the steel bar arrangement information to obtain the geometric twin model of the in-situ reinforced concrete beam.

10. The digital-twin-based system for performance evaluation of corroded reinforced concrete beams according to claim 8, wherein, The optimization unit comprises: The first optimization subunit is configured to perform finite element modeling processing based on the internal steel bar corrosion distribution data of the corroded reinforced concrete to obtain a reinforced concrete beam finite element model reflecting the influence of corrosion cracks. The second optimization subunit is configured to determine an optimization objective function, to-be-optimized parameters, and an optimization algorithm according to the finite element model to obtain an optimization scheme. The third optimization subunit is configured to perform joint processing based on the optimization scheme, combining PYTHON programming and finite element analysis software, to automatically adjust material parameter input, start background finite element calculation, and call results, iteratively execute the optimization algorithm, complete mechanical parameter updating under normal use and ultimate load bearing capacity, and construct a mechanical digital twin of the reinforced concrete beam. The fourth optimization subunit is configured to verify the mechanical digital twin based on preset test point data not participating in model updating and preset response data of the uncorroded reinforced concrete beam to obtain a verified mechanical digital twin.

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