A method, system, device and medium for predicting steel bar rust expansion force

Abstract

The application discloses a steel bar rust expansion force prediction method, system, device and medium, and belongs to the technical field of civil engineering durability evaluation. The method comprises the following steps: acquiring the steel bar radius, concrete cover thickness and rust layer thickness of a non-prestressed reinforced concrete member; acquiring the average porosity in the concrete cover range through micro image analysis, and acquiring the phase composition of the rust product and the volume fraction of each phase through micro component analysis; calculating the equivalent elastic modulus of the rust layer by adopting the volume weighted mixing law according to the phase composition and the volume fraction; calculating the void correction coefficient according to the average porosity and the cover thickness; and substituting the parameters into a rust expansion force prediction model to obtain the predicted rust expansion force. The rust expansion force prediction method solves the problem that the existing model has a systematic high prediction value due to neglecting the non-uniformity of concrete and the multiphase nature of the rust product, and improves the accuracy of rust expansion force prediction and the reliability of structure durability evaluation.

Description

Technical Field

[0001] This application relates to the field of civil engineering durability assessment technology, and in particular to a method, system, equipment and medium for predicting the rust expansion force of reinforcing steel. Background Technology

[0002] With the development of durability assessment and digital operation and maintenance technologies for civil engineering structures, the accuracy of predicting steel reinforcement rust expansion force, as the core driving force inducing cracking of the concrete cover, has gradually become a focus of attention for structural safety early warning and remaining service life prediction. In existing technologies, engineering projects generally use a classic model based on the theory of thick-walled cylinders in elasticity to predict rust expansion force. This involves setting macroscopic geometric parameters such as the steel reinforcement radius and cover thickness, combined with empirical mechanical parameters of the concrete and rust layer, to calculate the internal expansion stress, thus meeting the basic requirements of conventional durability testing.

[0003] In practical applications where reinforced concrete components are in long-term service, existing methods either fail to capture the absorption and dissipation of expansion stress by micro-pores and micro-cracks inside the concrete due to the homogenization assumption, or fail to reflect the differences in mechanical properties of multiphase compositions such as goethite and fibrous iron due to the single modulus assumption of corrosion products. This leads to the problem that existing models have systematically high predicted values ​​because they ignore the non-homogeneity of concrete and the multiphase nature of corrosion products. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, system, equipment and medium for predicting the rust expansion force of steel bars, which can solve the problem that the predicted values ​​are systematically too high due to the neglect of the non-homogeneity of concrete and the multiphase nature of rust products in existing models.

[0005] This application mainly includes the following aspects: In a first aspect, embodiments of this application provide a method for predicting the rust expansion force of reinforcing bars, the method comprising the following steps: Obtain the radius of the reinforcing bars, the thickness of the concrete cover, and the thickness of the corrosion layer in non-prestressed reinforced concrete members; The average porosity within the concrete protective layer is obtained through microscopic image analysis, and the phase composition and volume fraction of the corrosion products on the steel reinforcement surface are obtained through microscopic composition analysis. Based on the phase composition and volume fraction of each phase, the equivalent elastic modulus of the corrosion layer is calculated using the volume-weighted mixing law. Calculate the porosity correction coefficient based on the average porosity and the protective layer thickness; The predicted rust expansion force is calculated by substituting the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model.

[0006] In one possible implementation, the step of calculating the equivalent elastic modulus of the corrosion layer using a volume-weighted mixing law based on the phase composition and the volume fraction of each phase includes: Using a volume-weighted mixing law, based on the volume fraction of each phase obtained from the microscopic composition analysis and the pre-calibrated elastic modulus of each phase, the volume fraction of each phase is multiplied by the elastic modulus of the phase and then summed to obtain the equivalent elastic modulus of the corrosion layer.

[0007] By adopting the above technical solution, the mechanical contribution of each phase is weighted and summed using the volume weighted mixing law, which realizes the dynamic calculation of the equivalent elastic modulus of the corrosion layer and reflects the heterogeneous mechanical properties of the corrosion products.

[0008] In one possible implementation, calculating the porosity correction coefficient based on the average porosity and the protective layer thickness includes: Calculate the target ratio of average porosity to protective layer thickness; Multiplying the target ratio by the empirical coefficient calibrated in the experiment yields the attenuation term; The result obtained based on the attenuation term is defined as the void correction coefficient.

[0009] By adopting the above technical solution, the effect of concrete micropores on the dissipation of expansion stress was quantified by calculating the target ratio of average porosity to protective layer thickness and constructing an attenuation term, thus realizing the correction of the homogenization assumption of the classical model.

[0010] In one possible implementation, the calibrated empirical coefficients include: Multiple concrete specimens with different porosities and different protective layer thicknesses were prepared, and accelerated corrosion tests were conducted on each specimen until the protective layer cracked. Record the thickness of the rust layer when each group of specimens cracks, and combine the porosity, protective layer thickness and rust product phase composition of the corresponding specimens to calculate the empirical coefficient value that matches each specimen. The empirical coefficient values ​​of all specimens are statistically analyzed, and the intervals in which the values ​​are concentrated are determined as the range of values ​​for the empirical coefficients.

[0011] By adopting the above technical solution, accelerated corrosion tests and inversion calculations of multiple sets of specimens were conducted to determine the numerical concentration range of the empirical coefficients, ensuring the physical rationality and universality of the void correction coefficients under different working conditions.

[0012] In one possible implementation, the step of substituting the rebar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into a preset rust expansion force prediction model to calculate the predicted rust expansion force includes: Calculate the expansion stress term and geometric transfer term of the corrosion layer separately; The predicted rust expansion force is defined by multiplying the void correction coefficient, the rust layer expansion stress term, and the geometric transfer term together.

[0013] By adopting the above technical solution, the calculation of rust expansion force is decomposed into the expansion stress term of the rust layer and the geometric transfer term, and a void correction coefficient is introduced for coupled calculation. This clarifies the transfer mechanism of each physical parameter in the rust expansion force generation process and improves the interpretability of the prediction model.

[0014] In one possible implementation, calculating the expansion stress term of the rust layer includes: The Poisson's ratio of the rust layer is pre-calibrated based on the phase composition of the rust products; The product of the equivalent elastic modulus and the thickness of the corrosion layer is calculated. Based on the product, the Poisson's ratio of the corrosion layer, and the radius of the reinforcing bar, the result is denoted as the expansion stress term of the corrosion layer.

[0015] By employing the above technical solution, the contribution of the corrosion layer's own material properties to the internal expansion effect is quantified by calculating the expansion stress term based on the pre-calibrated Poisson's ratio and equivalent elastic modulus.

[0016] In one possible implementation, calculating the geometry transfer term includes: Calculate the square of the sum of the rebar radius and the cover thickness; The numerator is the sum of the squared value and the square of the rebar radius, and the denominator is the difference between the squared value and the square of the rebar radius. The ratio of the numerator to the denominator is then calculated and denoted as the geometric transitive term.

[0017] By employing the above technical solution, the geometric amplification effect of rust expansion force in the concrete protective layer is characterized by constructing the ratio of the square difference and sum of the rebar radius and the protective layer thickness, thus restoring the stress transmission law in thick-walled cylindrical structures.

[0018] Secondly, embodiments of this application also provide a steel bar rust expansion force prediction system, the steel bar rust expansion force prediction system comprising: The parameter acquisition module is used to obtain the steel reinforcement radius, concrete cover thickness, and corrosion layer thickness of non-prestressed reinforced concrete members. The microscopic testing module is used to obtain the average porosity within the concrete protective layer through microscopic image analysis, and to obtain the phase composition and volume fraction of the corrosion products on the steel reinforcement surface through microscopic composition analysis. The modulus calculation module is used to calculate the equivalent elastic modulus of the corrosion layer based on the phase composition and the volume fraction of each phase using the volume weighted mixing law. The correction coefficient calculation module is used to calculate the porosity correction coefficient based on the average porosity and the protective layer thickness. The rust expansion force prediction module is used to substitute the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model to calculate the predicted rust expansion force.

[0019] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus. The machine-readable instructions are executed by the processor to perform the steps of the steel reinforcement rust expansion force prediction method described in the first aspect or any possible implementation of the first aspect.

[0020] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps for predicting the rust expansion force of reinforcing bars as described in the first aspect or any possible implementation of the first aspect.

[0021] The steel reinforcement rust expansion force prediction method, system, equipment, and medium provided in this application can calculate the equivalent elastic modulus and porosity correction coefficient of the rust layer based on the average porosity of the concrete cover obtained from microscopic image analysis and the phase composition of the rust products obtained from microscopic composition analysis. These values ​​are then substituted into a preset rust expansion force prediction model to obtain the predicted rust expansion force. By introducing microscopic measured parameters to correct the prediction model, the problem of systematically overestimating predicted values ​​due to neglecting the non-homogeneity of concrete and the multiphase nature of rust products in existing models is solved, thereby improving the accuracy of rust expansion force prediction and the reliability of structural durability assessment. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart of a method for predicting the rust expansion force of reinforcing bars provided in an embodiment of this application is shown; Figure 2 A flowchart of a method for predicting the rust expansion force of reinforcing bars according to another embodiment of this application is shown; Figure 3 This illustration shows a comparison diagram of the prediction effects of different models provided in an embodiment of this application; Figure 4This paper illustrates a functional block diagram of a steel reinforcement rust expansion force prediction system provided in an embodiment of this application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0024] Explanation of key component symbols: In the diagram: 410 - Parameter acquisition module; 420 - Microscopic testing module; 430 - Modulus calculation module; 440 - Correction coefficient calculation module; 450 - Rust expansion force prediction module; 500 - Electronic equipment; 510 - Processor; 520 - Memory; 530 - Bus. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0026] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] To enable those skilled in the art to use the content of this application, and in conjunction with the specific application scenario of "durability assessment of reinforced concrete structures," the following implementation methods are provided. For those skilled in the art, the general principles defined herein can be applied to damage monitoring scenarios for other geotechnical or composite material structures without departing from the spirit and scope of this application.

[0028] The methods, systems, devices, and media described in the embodiments of this application can be applied to any scenario requiring a quantitative assessment of the corrosion state of reinforcing bars. The embodiments of this application do not limit the specific type of component or service environment; any rust expansion force prediction scheme based on microscopic measurements provided in the embodiments of this application is within the scope of protection of this application.

[0029] It is worth noting that, prior to this application, the mainstream steel reinforcement rust expansion force prediction schemes in the prior art generally adopted the classic thick-walled cylindrical model based on macroscopic geometric parameters, treating the concrete protective layer as a uniform and continuous medium and the corrosion products as a single homogeneous material. This lack of consideration for microscopic non-uniformity and multiphase composition resulted in systematically high prediction values, which could not reflect the true critical cracking state of the component and could not support the needs of refined operation and maintenance and life prediction.

[0030] Based on this, the embodiments of this application provide a method, system, device and medium for predicting the rust expansion force of steel bars. The average porosity of the concrete cover and the phase composition of the corrosion products are obtained through microscopic image analysis and microscopic component analysis. The rust expansion force prediction model is corrected by combining the volume weighted mixing law and the porosity correction coefficient, and the predicted rust expansion force is calculated. This solves the problem of systematically high predicted values ​​caused by neglecting non-uniformity and multiphase nature in existing models.

[0031] To facilitate understanding of this application, the technical solutions provided in this application will be described in detail below with reference to specific embodiments.

[0032] Please see Figure 1 , Figure 1 A flowchart illustrating a method for predicting the rust expansion force of reinforcing bars, provided in an embodiment of this application.

[0033] like Figure 1 As shown, the method for predicting the rust expansion force of reinforcing bars provided in this application includes steps S101 to S105.

[0034] S101. Obtain the radius of the reinforcing bars, the thickness of the concrete cover, and the thickness of the rust layer of the non-prestressed reinforced concrete member.

[0035] Here, the rebar radius, concrete cover thickness, and corrosion layer thickness are fundamental parameters characterizing the geometric features of the component. Specifically, the rebar radius is the reference dimension defining the inner boundary of corrosion expansion; the concrete cover thickness is the key distance determining the distribution of restraint stress; and the corrosion layer thickness is a geometric measure characterizing the cumulative amount of corrosion products.

[0036] The specific methods for obtaining the above parameters can be flexibly configured according to the actual detection conditions.

[0037] For example, the reinforcing bars can be located and their dimensions measured using non-destructive testing methods, or the aforementioned geometric parameters can be obtained through physical measurement methods after the specimen has broken.

[0038] It should be understood that the above-described detection scenarios and measurement methods are merely specific implementation examples of this application, and not the only limitation on the acquisition method. The non-prestressed reinforced concrete components cover various types of concrete structures, and any equivalent means capable of obtaining the above geometric parameters falls within the protection scope of this application.

[0039] S102. Obtain the average porosity within the concrete protective layer range through microscopic image analysis, and obtain the phase composition and volume fraction of the corrosion products on the steel reinforcement surface through microscopic composition analysis.

[0040] Here, microscopic image analysis is used to obtain information on the microscopic pore structure of the concrete protective layer, and the obtained average porosity reflects the overall development degree of micropores inside the concrete. Microscopic composition analysis is used to obtain information on the chemical composition of corrosion products, and the obtained phase composition and volume fraction quantify the material composition of the corrosion layer.

[0041] S103. Based on the phase composition and volume fraction of each phase, the equivalent elastic modulus of the corrosion layer is calculated using the volume weighted mixing law.

[0042] Here, the volume-weighted mixing law is a calculation method used to solve the macroscopic equivalent properties of multiphase composite materials. By converting the phase information obtained from microscopic composition analysis into macroscopic mechanical parameters, the equivalent elastic modulus reflecting the overall stiffness characteristics of the corrosion layer can be obtained.

[0043] This solves the problem of mechanical parameter distortion caused by traditional models treating corrosion products as a single homogeneous material, making the model input more closely match the true physical state of the material.

[0044] S104. Calculate the porosity correction coefficient based on the average porosity and the thickness of the protective layer.

[0045] Here, the porosity correction factor is a dimensionless physical correction factor used to quantify the absorption effect of concrete micropores on expansion stress. The porosity correction factor establishes the correlation between micropore characteristics and macroscopic mechanical response, and is used to adaptively reduce theoretical expansion stress.

[0046] This can correct the stress overestimation problem caused by neglecting the internal microcracks and pore structure of concrete in traditional thick-walled cylindrical models, and improve the model's accuracy in reproducing real constraints.

[0047] S105. Substitute the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model to calculate the predicted rust expansion force.

[0048] Here, the rust expansion force prediction model is constructed based on the theoretical framework of elasticity mechanics. By introducing the equivalent elastic modulus and void correction coefficient obtained in the aforementioned steps into the model calculation, the theoretical expansion stress is coupled and corrected.

[0049] In this way, the predicted rust expansion force can simultaneously reflect the mechanical contribution of the multiphase composition of rust products and the attenuation effect of the concrete micropores on the force, thereby improving the consistency between the predicted value and the actual critical cracking state of the component.

[0050] In this embodiment, the steel reinforcement radius, concrete cover thickness, and corrosion layer thickness of the non-prestressed reinforced concrete member are obtained; the average porosity within the concrete cover area is obtained through microscopic image analysis, and the phase composition and volume fraction of each phase of the corrosion products are obtained through microscopic composition analysis; based on the phase composition and volume fraction, the equivalent elastic modulus of the corrosion layer is calculated using a volume-weighted mixing law; a porosity correction coefficient is calculated based on the average porosity and cover thickness; and the parameters are substituted into the corrosion expansion force prediction model to calculate the predicted corrosion expansion force. This solves the problem of systematically overestimating predicted values ​​in existing models due to neglecting the non-homogeneity of concrete and the multiphase nature of corrosion products, thus improving the accuracy of corrosion expansion force prediction and the reliability of structural durability assessment.

[0051] Please see Figure 2 , Figure 2 A flowchart of a method for predicting the rust expansion force of reinforcing bars, provided for another embodiment of this application.

[0052] Based on the above S101 to S105, this application also provides a specific implementation process, including steps S201 to S205.

[0053] S201. Obtain the radius of the reinforcing bars, the thickness of the concrete cover, and the thickness of the corrosion layer for non-prestressed reinforced concrete members, and further obtain the verification benchmark parameters.

[0054] Here, S201 is a further explanation and expansion of step S101 above. Since the accuracy of the rust expansion force prediction results needs to be verified by benchmarking against the actual cracking behavior of the component, in addition to obtaining the necessary geometric parameters of the model, it is also necessary to simultaneously establish the material strength benchmark of the component before executing the core prediction process, so as to build a complete analysis and verification system.

[0055] For example, in addition to the three model inputs mentioned in S101—reinforcing bar radius, concrete cover thickness, and rust layer thickness—auxiliary parameters such as the macroscopic dimensions of the component, concrete strength grade, and splitting tensile strength can also be obtained. These auxiliary parameters are not involved in the calculation of rust expansion force and are only used for subsequent verification of the reasonableness of the prediction results.

[0056] Furthermore, the parameter acquisition path can be adapted to the scenario in which the component is located. For existing service structures, theoretical geometric dimensions can be obtained by consulting the original design drawings, or material strength can be determined by core sampling combined with laboratory tests. For accelerated corrosion test specimens prepared in the laboratory, design parameters can be recorded simultaneously during the forming stage, or measured values ​​can be obtained through physical measurement methods after the specimen breaks.

[0057] It should be noted that the auxiliary parameters are not necessary inputs for calculating and predicting rust expansion force in this application, but are only provided as a reference for verifying model accuracy or constructing experimental environments. The core improvement of this application lies in correcting the prediction model through microscopic parameters. The setting of macroscopic benchmark parameters only serves to verify the rationality of the final output results and avoid magnitude deviations between the predicted values ​​and the actual stress state.

[0058] In one optional implementation, the radius of the reinforcing bar can be set to 0.008m, the thickness of the concrete protective layer can be set to 0.067m, the splitting tensile strength can be set to 2.5MPa, and the thickness of the corrosion layer can be set to 0.0001m.

[0059] The above parameters are typical values ​​for test calibration conditions. The predicted rust expansion force calculated based on this set of benchmark parameters can be directly compared with the measured value of the splitting tensile strength of concrete under the same conditions to quickly verify the predictive effectiveness of the model.

[0060] S202. Obtain the average porosity within the concrete protective layer range through microscopic image analysis, and obtain the phase composition and volume fraction of the corrosion products on the steel reinforcement surface through microscopic composition analysis.

[0061] This step is a further explanation and quantification of step S102 above. After obtaining the macroscopic baseline parameters by performing S101, it is necessary to go further to the microscopic scale to quantify the pore characteristics of the concrete protective layer and the material composition of the corrosion products.

[0062] For example, X-ray computed tomography (X-CT) technology can be used to perform non-destructive scanning of the concrete protective layer area, and the average porosity can be obtained through three-dimensional image reconstruction and stereoscopic statistical analysis; X-ray diffraction (XRD) technology can be used to identify the phases of corrosion products on the surface of steel bars, and the diffraction pattern can be refined using the Rietveld full-spectrum fitting algorithm, thereby quantitatively determining the phase composition and volume fraction.

[0063] It should be noted that the aforementioned microscopic testing methods and quantitative analysis algorithms are merely specific implementation examples of this application. Those skilled in the art will understand that any equivalent microscopic analysis method capable of obtaining the average porosity and phase volume fraction should be considered to fall within the protection scope of this application.

[0064] In one optional implementation, the average porosity can be set to 1.5%; the volume fractions of goethite, tetragonal ferrihydrite and ferrihydrite can be set to 0.60, 0.25 and 0.15, respectively.

[0065] Thus, based on the aforementioned microscopic quantitative data, the equivalent elastic modulus calculated subsequently can truly reflect the multiphase composition characteristics of the corrosion layer, avoiding the errors caused by the traditional model's single homogenization of corrosion products.

[0066] S203. Based on the phase composition and volume fraction of each phase, the equivalent elastic modulus of the corrosion layer is calculated using the volume weighted mixing law.

[0067] This step is a further explanation and specific calculation of step S103 above. Based on S202, the composition information of the multiphase mixture needs to be converted into macroscopic equivalent mechanical parameters to solve the problem of mechanical parameter distortion caused by the traditional model treating the corrosion products as a single homogeneous material.

[0068] The step of calculating the equivalent elastic modulus of the corrosion layer using the volume weighted mixing law based on the phase composition and volume fraction of each phase includes: using the volume weighted mixing law, based on the volume fraction of each phase obtained from microscopic composition analysis and the pre-calibrated elastic modulus of each phase, multiplying the volume fraction of each phase by the elastic modulus of the phase and summing the results to obtain the equivalent elastic modulus of the corrosion layer.

[0069] The volume-weighted mixing law is based on the rule of mixtures in composite mechanics. It obtains the equivalent elastic modulus that can characterize the macroscopic behavior of multiphase mixtures by quantifying the weighted sum of the contributions of each component to the overall stiffness.

[0070] Specifically, the formula for calculating the equivalent elastic modulus is as follows:

[0071] in, This is the equivalent elastic modulus of the corrosion layer, in GPa. The volume fraction of the i-th phase is obtained from microscopic composition analysis; is the pre-defined elastic modulus of the i-th phase; n is the total number of phase types contained in the corrosion products.

[0072] It should be noted that the elastic modulus of each of the above phases These are pre-calibrated parameters, not the calculation targets of this application. These parameters characterize the intrinsic mechanical properties of a single phase, and their values ​​are relatively stable and unaffected by the mixing state of the corrosion layer.

[0073] For example, the elastic moduli of goethite, tetragonal lepidocrocite, and lepidocrocite can be in the order of 85 GPa, 70 GPa, and 75 GPa, respectively. In practical applications, the elastic modulus of a specific phase can be directly determined by nanoindentation testing, or a general calibration value can be directly retrieved from relevant literature or industry standards, without the need for repeated calibration each time the rust swelling force is predicted.

[0074] It should be understood that the above parameter magnitudes and acquisition methods are merely examples. This application does not limit the specific values ​​and calibration methods of the elastic modulus of each phase, and any method for calculating the equivalent modulus of a multiphase mixture based on the volume weighting concept falls within the scope of protection.

[0075] S204. Calculate the porosity correction coefficient based on the average porosity and the thickness of the protective layer.

[0076] This step is a further explanation and specific calculation of step S104 above. Based on obtaining the protective layer thickness in S201 and the average porosity in S202, it is necessary to quantify the absorption effect of concrete micropores on expansion stress in order to correct the stress overestimation problem caused by neglecting the internal microporous structure of concrete in the traditional thick-walled cylindrical model.

[0077] The calculation of the void correction coefficient based on the average porosity and the protective layer thickness can be achieved through the following steps: obtaining the target ratio of the average porosity to the protective layer thickness; multiplying the target ratio by the empirical coefficient calibrated by the experiment to obtain the attenuation term; and defining the result based on the attenuation term as the void correction coefficient.

[0078] It should be noted that the coefficients used to calculate the attenuation term are empirical coefficients calibrated by experiments, and not the calculation targets of this application. These coefficients characterize the inherent discrete properties of concrete materials under specific mix proportions and curing conditions, and their values ​​are relatively stable and unaffected by fluctuations in single measurement data.

[0079] For example, the empirical coefficient can be calibrated by preparing multiple sets of concrete specimens with different porosities and protective layer thicknesses, conducting accelerated corrosion tests on each specimen until the protective layer cracks; recording the thickness of the corrosion layer at the time of cracking of each set of specimens, and combining the porosity, protective layer thickness and phase composition of corrosion products of the corresponding specimens to calculate the coefficient value matching each specimen; statistically analyzing the coefficient values ​​of all specimens, and determining the range of values ​​where the values ​​are concentrated as the range of the empirical coefficient.

[0080] In practical engineering applications, it is unnecessary to repeat the above calibration process for each prediction. The calibrated empirical coefficient range can be directly retrieved for calculation. Through extensive testing, the range of this empirical coefficient can usually be set to 0.1-0.2; in typical application scenarios with high computational accuracy requirements, the empirical coefficient value can be fixed at 0.15.

[0081] Specifically, the formula for calculating the porosity correction factor is expressed as follows:

[0082] in, This is the void correction factor, with a value ranging from 0 to 1; The average porosity is obtained from microscopic image analysis; C is the thickness of the concrete protective layer; and k is the empirical coefficient determined by the experiment.

[0083] It should be understood that the above calibration methods, value ranges, and calculation formulas are all specific implementation examples of this application. This application does not limit the specific values ​​of the empirical coefficients or the calibration methods. Any method that introduces empirical coefficients to reduce theoretical stress based on the ratio of average porosity to protective layer thickness falls within the scope of protection.

[0084] S205. Substitute the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model to calculate the predicted rust expansion force.

[0085] This step is a further explanation and specific calculation of step S105 above. Based on obtaining all input parameters in S201 to S204, it is necessary to realize the coupling calculation of multi-source parameters through a mechanical model, and transform the microscopic structural features into macroscopic mechanical responses, so as to solve the prediction distortion problem caused by the single parameter in traditional models.

[0086] The step of substituting the rebar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into a preset rust expansion force prediction model to calculate the predicted rust expansion force can be achieved through the following steps: Calculate the expansion stress term and geometric transfer term of the rust layer separately; multiply the void correction coefficient, the expansion stress term of the rust layer, and the geometric transfer term together, and the result is defined as the predicted rust expansion force.

[0087] Traditional thick-walled cylindrical models typically only consider macroscopic geometric dimensions and single material parameters, making it difficult to reflect the effects of microscopic non-uniformity and multiphase composition. This application corrects the expansion stress term of the traditional model by introducing the obtained equivalent elastic modulus and void correction coefficient, while retaining its geometric transfer term for the concrete confinement effect, thereby achieving high-precision prediction.

[0088] Furthermore, the calculation of the expansion stress term of the rust layer is explained.

[0089] The expansion stress term of the rust layer is used to characterize the internal driving force generated by the expansion of rust products. Based on the theory of composite material mechanics, this stress term is related to the equivalent elastic modulus of the rust layer, the thickness of the rust layer, and the Poisson's ratio of the material itself.

[0090] It should be noted that the Poisson's ratio of the aforementioned rust layer is a constitutive parameter characterizing the material's lateral deformation capacity. It is a pre-calibrated general parameter and not the calculation target of this application.

[0091] In one alternative implementation, for steel corrosion products commonly found in civil engineering, the Poisson's ratio can be taken as a fixed empirical value of 0.3, which is unaffected by fluctuations in microscopic porosity. In actual calculations, this general empirical value can be directly used, eliminating the need for repeated measurements on each component under test.

[0092] Specifically, the formula for calculating the expansion stress term of the rust layer is as follows:

[0093] in, This refers to the expansion stress term of the rust layer; The equivalent elastic modulus obtained by the volume-weighted mixing law; This refers to the thickness of the rust layer; The Poisson's ratio of the pre-defined rust layer; Where is the radius of the reinforcing bar.

[0094] Furthermore, the calculation of the geometric transit term is explained.

[0095] The geometric transfer term is used to characterize the constraint effect of the concrete cover on corrosion expansion. Based on the theory of thick-walled cylinders in elastic mechanics, the geometric transfer is determined by the geometric relationship between the rebar radius and the cover thickness.

[0096] Specifically, the formula for calculating the geometric transitive term is as follows:

[0097] in, R is the geometric transfer term; C is the radius of the reinforcing bar; and C is the thickness of the concrete cover.

[0098] Furthermore, by integrating various factors, the predicted rust expansion force was obtained.

[0099] void correction coefficient 1. Expansion stress of the rust layer and geometric transitive terms Multiplying these three factors together yields the final predicted rust expansion force. ; Specifically, calculate and predict rust expansion force. The formula is as follows:

[0100] in, To predict rust expansion force.

[0101] The results indicate that the magnitude of the rust expansion force depends on the coupling effect of the expansion capacity of the rust products themselves, the constraint amplification effect of the concrete ring, and the force dissipation capacity of the micropores.

[0102] It should be understood that the above formulas, parameter magnitudes, and calculation logic are all specific implementation examples of this application. This application does not limit the specific value of Poisson's ratio, the means of obtaining the equivalent elastic modulus, or the specific form of the model. Any prediction method that modifies the classical thick-walled cylinder theory based on the equivalent elastic modulus and the void correction coefficient falls within the scope of protection.

[0103] To further verify the accuracy of the steel reinforcement rust expansion force prediction method provided in this application, this application also provides a verification method based on critical state.

[0104] In one alternative implementation, several non-prestressed reinforced concrete specimens produced in the same batch are selected for accelerated corrosion tests to verify the accuracy of the method for predicting steel reinforcement rust expansion force.

[0105] Taking one set of typical specimens as an example, the steel reinforcement radius was 0.008m, the concrete cover thickness was 0.067m, and the measured tensile strength of the concrete splitting tensile strength was 2.5MPa. Microscopic tests showed that the average porosity of the concrete cover was 1.5%, the phase composition of the corrosion products was set as 60% goethite, 25% tetragonal fibrous ore and 15% fibrous ore, with corresponding elastic moduli of 85GPa, 70GPa and 75GPa, respectively, the Poisson's ratio of the corrosion layer was 0.3, and the measured thickness of the corrosion layer was 0.0001m.

[0106] Based on the above parameters, the predicted rust expansion force of the steel bars in the above embodiment of this application is calculated to be 7.58 MPa; in contrast, the traditional predicted rust expansion force calculated using the traditional homogeneous thick-walled cylindrical model is 9.85 MPa.

[0107] To facilitate verification by benchmarking against the splitting tensile strength of concrete, the predicted rust expansion force needs to be converted into the equivalent circumferential tensile stress on the inner surface of the concrete. This application adopts the following simplified conversion relationship, which has been experimentally calibrated:

[0108] in, This represents the equivalent circumferential tensile stress on the inner surface of the concrete. To predict rust expansion force, C is the thickness of the concrete cover, R is the radius of the reinforcing steel, and K is the stress conversion coefficient determined through inversion analysis of a large amount of experimental data. K is a pre-calibrated empirical parameter that can be obtained by inverting the measured circumferential stress values ​​and predicted rust expansion force values ​​of multiple sets of accelerated corrosion specimens.

[0109] Please see Figure 3 , Figure 3 This is a schematic diagram comparing the prediction effects of different models provided in an embodiment of this application.

[0110] like Figure 3 The diagram shows a comparison of the prediction results provided in this application. Experimental results show that the predicted rust expansion force obtained by the method of this application can fall well within the measured critical stress range, while the predicted value of the traditional model shows a systematic tendency to be too high.

[0111] The first appearance of a longitudinal crack penetrating the protective layer thickness on the specimen surface is defined as the critical state of crack initiation. Experimental verification showed that when five specimens reached this critical state under accelerated corrosion, the ratio of the equivalent circumferential tensile stress to the concrete tensile strength, calculated according to the model of this application, ranged from 98% to 103%. In this embodiment, the calculated equivalent circumferential tensile stress was 2.45 MPa, which is 98% of the measured splitting tensile strength. At this point, a critical crack was observed on the specimen surface, proving the accuracy of the prediction. In contrast, the circumferential tensile stress predicted by traditional models far exceeds the concrete tensile strength before the crack appears, contradicting the experimental fact that the crack did not appear earlier, thus highlighting the rationality of the prediction results of the model of this application.

[0112] It should be understood that the above values ​​are merely typical examples of working conditions for ease of understanding and are not intended to limit the scope of application of this application. In practical engineering applications, technicians can adaptively adjust the input parameters for calculation based on the actual geometric dimensions, material ratios, and microscopic test results of the component under test. Verification with multiple sets of specimens with different porosities, protective layer thicknesses, and phase compositions has shown that the method of this application can improve the systematic overestimation problem of traditional models under various working conditions.

[0113] It should also be understood that the above verification approach and example data are only specific implementation examples of this application, and any model verification and effect verification based on the core concept of this application shall fall within the protection scope of this application.

[0114] Please see Figure 4 , Figure 4 This is a functional block diagram of a steel reinforcement rust expansion force prediction system provided in an embodiment of this application.

[0115] like Figure 4As shown, the steel reinforcement rust expansion force prediction system provided in this application embodiment includes: a parameter acquisition module 410, a microscopic testing module 420, a modulus calculation module 430, a correction coefficient calculation module 440, and a rust expansion force prediction module 450.

[0116] The parameter acquisition module 410 is used to acquire the steel reinforcement radius, concrete cover thickness and corrosion layer thickness of non-prestressed reinforced concrete members.

[0117] Here, the rebar radius, concrete cover thickness, and corrosion layer thickness are fundamental parameters characterizing the geometric features of the component. Specifically, the rebar radius is the reference dimension defining the inner boundary of corrosion expansion; the concrete cover thickness is the key distance determining the distribution of restraint stress; and the corrosion layer thickness is a geometric measure characterizing the cumulative amount of corrosion products.

[0118] The specific implementation method, technical details and technical effects of the parameter acquisition module 410 can be referred to the relevant description of S101 in the method embodiment, and will not be repeated here.

[0119] The microscopic testing module 420 is used to obtain the average porosity within the concrete protective layer through microscopic image analysis, and to obtain the phase composition and volume fraction of the corrosion products on the steel reinforcement surface through microscopic composition analysis.

[0120] Here, microscopic image analysis is used to obtain information on the microscopic pore structure of the concrete protective layer, and the obtained average porosity reflects the overall development degree of micropores inside the concrete. Microscopic composition analysis is used to obtain information on the chemical composition of corrosion products, and the obtained phase composition and volume fraction quantify the material composition of the corrosion layer.

[0121] The specific implementation method, technical details and technical effects of the microscopic testing module 420 can be referred to the relevant description of S102 in the method embodiment, and will not be repeated here.

[0122] The modulus calculation module 430 is used to calculate the equivalent elastic modulus of the corrosion layer based on the phase composition and volume fraction of each phase using the volume weighted mixing law.

[0123] Here, the volume-weighted mixing law is a calculation method used to solve the macroscopic equivalent properties of multiphase composite materials. By converting the phase information obtained from microscopic composition analysis into macroscopic mechanical parameters, the equivalent elastic modulus reflecting the overall stiffness characteristics of the corrosion layer can be obtained.

[0124] The specific implementation method, technical details and technical effects of the modulus calculation module 430 can be referred to the relevant description of S103 in the method embodiment, and will not be repeated here.

[0125] The correction coefficient calculation module 440 is used to calculate the void correction coefficient based on the average porosity and the thickness of the protective layer.

[0126] Here, the porosity correction factor is a dimensionless physical correction factor used to quantify the absorption effect of concrete micropores on expansion stress. This factor establishes the correlation between micropore characteristics and macroscopic mechanical response, and is used to adaptively reduce the theoretical expansion stress.

[0127] The specific implementation method, technical details and technical effects of the correction coefficient calculation module 440 can be referred to the relevant description of S104 in the method embodiment, and will not be repeated here.

[0128] The rust expansion force prediction module 450 is used to substitute the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness and void correction coefficient into the rust expansion force prediction model to calculate the predicted rust expansion force.

[0129] Here, the rust expansion force prediction model is constructed based on the theoretical framework of elasticity mechanics. By introducing the equivalent elastic modulus and void correction coefficient obtained in the aforementioned steps into the model calculation, the theoretical expansion stress is coupled and corrected.

[0130] The specific implementation method, technical details and technical effects of the rust expansion force prediction module 450 can be referred to the relevant description of S105 in the method embodiment, and will not be repeated here.

[0131] In one possible implementation, such as Figure 4 As shown, the modulus calculation module 430 is used to calculate the equivalent elastic modulus of the corrosion layer based on the phase composition and the volume fraction of each phase using a volume-weighted mixing law, including: Using a volume-weighted mixing law, based on the volume fraction of each phase obtained from the microscopic composition analysis and the pre-calibrated elastic modulus of each phase, the volume fraction of each phase is multiplied by the elastic modulus of the phase and then summed to obtain the equivalent elastic modulus of the corrosion layer.

[0132] In one possible implementation, such as Figure 4 As shown, the correction coefficient calculation module 440 is used to calculate the porosity correction coefficient based on the average porosity and the protective layer thickness, including: Calculate the target ratio of average porosity to protective layer thickness; Multiplying the target ratio by the empirical coefficient calibrated in the experiment yields the attenuation term; The result obtained based on the attenuation term is defined as the void correction coefficient.

[0133] Furthermore, the calibrated empirical coefficients include: Multiple concrete specimens with different porosities and different protective layer thicknesses were prepared, and accelerated corrosion tests were conducted on each specimen until the protective layer cracked. Record the thickness of the rust layer when each group of specimens cracks, and combine the porosity, protective layer thickness and rust product phase composition of the corresponding specimens to calculate the empirical coefficient value that matches each specimen. The empirical coefficient values ​​of all specimens are statistically analyzed, and the intervals in which the values ​​are concentrated are determined as the range of values ​​for the empirical coefficients.

[0134] In one possible implementation, such as Figure 4 As shown, the rust expansion force prediction module 450 is used to substitute the rebar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into a preset rust expansion force prediction model to calculate the predicted rust expansion force, including: Calculate the expansion stress term and geometric transfer term of the corrosion layer separately; The predicted rust expansion force is defined by multiplying the void correction coefficient, the rust layer expansion stress term, and the geometric transfer term together.

[0135] Furthermore, the calculation of the expansion stress term of the corrosion layer includes: The Poisson's ratio of the rust layer is pre-calibrated based on the phase composition of the rust products; The product of the equivalent elastic modulus and the thickness of the corrosion layer is calculated. Based on the product, the Poisson's ratio of the corrosion layer, and the radius of the reinforcing bar, the result is denoted as the expansion stress term of the corrosion layer.

[0136] Furthermore, calculating the geometric transfer term includes: Calculate the square of the sum of the rebar radius and the cover thickness; The numerator is the sum of the squared value and the square of the rebar radius, and the denominator is the difference between the squared value and the square of the rebar radius. The ratio of the numerator to the denominator is then calculated and denoted as the geometric transitive term.

[0137] Based on the same application concept, see [link / reference] Figure 5 The diagram shown is a structural schematic of an electronic device 500 provided in an embodiment of this application. It includes a processor 510, a memory 520, and a bus 530. The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate through the bus 530. When the machine-readable instructions are executed by the processor 510, they perform the steps of the steel reinforcement rust expansion force prediction method as described in any of the above embodiments.

[0138] Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the steel reinforcement rust expansion force prediction method provided in the above embodiments.

[0139] Specifically, the storage medium can be a general-purpose storage medium, such as a portable disk or hard disk. When the computer program on the storage medium is run, it can execute the above-mentioned method for predicting the rust expansion force of reinforcing bars.

[0140] In this embodiment, the computer program, when run by the processor, can also execute other machine-readable instructions to perform other methods as described in the embodiments. For details on the specific execution steps and principles, please refer to the description of the embodiments, which will not be repeated here.

[0141] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0144] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0146] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for predicting the rust expansion force of reinforcing bars, characterized in that, The method for predicting the rust expansion force of reinforcing bars includes the following steps: Obtain the radius of the reinforcing bars, the thickness of the concrete cover, and the thickness of the corrosion layer in non-prestressed reinforced concrete members; The average porosity within the concrete protective layer is obtained through microscopic image analysis, and the phase composition and volume fraction of the corrosion products on the steel reinforcement surface are obtained through microscopic composition analysis. Based on the phase composition and volume fraction of each phase, the equivalent elastic modulus of the corrosion layer is calculated using the volume-weighted mixing law. Calculate the porosity correction coefficient based on the average porosity and the protective layer thickness; The predicted rust expansion force is calculated by substituting the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model.

2. The method for predicting the rust expansion force of reinforcing bars according to claim 1, characterized in that, The calculation of the equivalent elastic modulus of the corrosion layer using the volume-weighted mixing law based on the phase composition and volume fraction of each phase includes: Using a volume-weighted mixing law, based on the volume fraction of each phase obtained from the microscopic composition analysis and the pre-calibrated elastic modulus of each phase, the volume fraction of each phase is multiplied by the elastic modulus of the phase and then summed to obtain the equivalent elastic modulus of the corrosion layer.

3. The method for predicting the rust expansion force of reinforcing bars according to claim 1, characterized in that, The step of calculating the porosity correction coefficient based on the average porosity and the protective layer thickness includes: Calculate the target ratio of average porosity to protective layer thickness; Multiplying the target ratio by the empirical coefficient calibrated in the experiment yields the attenuation term; The result obtained based on the attenuation term is defined as the void correction coefficient.

4. The method for predicting the rust expansion force of reinforcing bars according to claim 3, characterized in that, The empirical coefficients for calibration include: Multiple concrete specimens with different porosities and different protective layer thicknesses were prepared, and accelerated corrosion tests were conducted on each specimen until the protective layer cracked. Record the thickness of the rust layer when each group of specimens cracks, and combine the porosity, protective layer thickness and rust product phase composition of the corresponding specimens to calculate the empirical coefficient value that matches each specimen. The empirical coefficient values ​​of all specimens are statistically analyzed, and the intervals in which the values ​​are concentrated are determined as the range of values ​​for the empirical coefficients.

5. The method for predicting the rust expansion force of reinforcing bars according to claim 1, characterized in that, The step of substituting the rebar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into a preset rust expansion force prediction model to calculate the predicted rust expansion force includes: Calculate the expansion stress term and geometric transfer term of the corrosion layer separately; The predicted rust expansion force is defined by multiplying the void correction coefficient, the rust layer expansion stress term, and the geometric transfer term together.

6. The method for predicting the rust expansion force of reinforcing bars according to claim 5, characterized in that, The calculation of the expansion stress term of the corrosion layer includes: The Poisson's ratio of the rust layer is pre-calibrated based on the phase composition of the rust products; The product of the equivalent elastic modulus and the thickness of the corrosion layer is calculated. Based on the product, the Poisson's ratio of the corrosion layer, and the radius of the reinforcing bar, the result is denoted as the expansion stress term of the corrosion layer.

7. The method for predicting the rust expansion force of reinforcing bars according to claim 5, characterized in that, Calculating the geometric transfer term includes: Calculate the square of the sum of the rebar radius and the cover thickness; The numerator is the sum of the squared value and the square of the rebar radius, and the denominator is the difference between the squared value and the square of the rebar radius. The ratio of the numerator to the denominator is then calculated and denoted as the geometric transitive term.

8. A system for predicting the rust expansion force of reinforcing bars, characterized in that, include: The parameter acquisition module is used to obtain the steel reinforcement radius, concrete cover thickness, and corrosion layer thickness of non-prestressed reinforced concrete members. The microscopic testing module is used to obtain the average porosity within the concrete protective layer through microscopic image analysis, and to obtain the phase composition and volume fraction of the corrosion products on the steel reinforcement surface through microscopic composition analysis. The modulus calculation module is used to calculate the equivalent elastic modulus of the corrosion layer based on the phase composition and the volume fraction of each phase using the volume weighted mixing law. The correction coefficient calculation module is used to calculate the porosity correction coefficient based on the average porosity and the protective layer thickness. The rust expansion force prediction module is used to substitute the steel bar radius, protective layer thickness, equivalent elastic modulus, rust layer thickness, and void correction coefficient into the rust expansion force prediction model to calculate the predicted rust expansion force.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the steel reinforcement rust expansion force prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the steel reinforcement rust expansion force prediction method as described in any one of claims 1 to 7.

Images