CN114386301A - Finite element simulation and parameter analysis method for residual mechanical property of corrosion steel bar - Google Patents
Finite element simulation and parameter analysis method for residual mechanical property of corrosion steel bar Download PDFInfo
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Abstract
The invention provides a finite element simulation and parameter analysis method for residual mechanical property of a corrosion steel bar, which comprises the following steps: extracting the cross section area of the steel bar along the length direction of the steel bar by using the existing corrosion steel bar sample, and performing statistical analysis to obtain a probability distribution model of the steel bar; regenerating rusted steel bar sectional area data according to the probability distribution model, assigning the newly generated sectional area data to the circular table unit through finite element software, and completing the assembly of a new model in the finite element software; according to the Monte Carlo method, the command stream file of the finite element software is rewritten through python, a large number of corrosion steel bar samples are generated to carry out uniaxial tension numerical test, and finally the residual mechanical property of the corrosion steel bar is obtained. By adopting the technical scheme of the invention, the problems of long time consumption of a steel bar corrosion test, large discreteness of test results and insufficient test samples are effectively solved, the time is effectively saved by a finite element simulation method, and compared with actual test results, the simulation results are accurate and effective.
Description
Technical Field
The invention belongs to the technical field of material corrosion, and particularly relates to a finite element simulation and parameter analysis method for residual mechanical property of a rusted steel bar.
Background
Reinforced Concrete (RC) structures are one of the most common structural forms in life, and such structures are often exposed to air for a long time and are affected by corrosive substances in the external environment for a long time, so that the reinforcing steel bars in concrete are easily corroded. The steel bar corrosion can cause the loss of the section of the steel bar, the mechanical property of the steel bar is reduced, the generated corrosion product can cause the cracking of the surface of the concrete to aggravate the corrosion of the steel bar, in addition, the occurrence of the corrosion reduces the bonding strength of the steel bar and the concrete, and under the combined action of the three reasons, the structure finally fails or even collapses. In order to prevent further deterioration of the corrosion of the reinforcing bars in the RC structure, the structure must be repaired and reinforced. To develop an appropriate repair strategy, the residual strength of the corroded steel bars must be accurately evaluated.
Because the corrosion reinforcing steel bar has complicated geometry, carry out axial tensile test to it, the test result discreteness is big, needs a large amount of corrosion reinforcing steel bar samples to carry out statistical research. However, the corrosion reinforcing steel bars in actual engineering are difficult to obtain, a large number of corrosion reinforcing steel bar samples cannot be obtained due to long time consumption of salt spray and electrochemical accelerated corrosion tests under laboratory conditions, and the past researches show that considerable divergence is caused by the influence of corrosion on the mechanical property of the reinforcing steel bars, particularly the ductility degradation rule of the reinforcing steel bars. The method adopts a numerical simulation method to establish a three-dimensional finite element model of the rusted steel bar, can solve the problems caused by test samples, test conditions and the like, is more convenient for observing and researching the local effect in the tensile failure process of the rusted steel bar, but has two common finite element modeling methods at present because the surface of the rusted steel bar has complex morphological characteristics and is difficult to model. Firstly, steel bar pitting corrosion pit is simplified into a hemispherical shape, a semi-elliptical shape, a conical shape and a cylindrical shape, the depth, the length and the width of the corrosion pit and the influence of the quantity of the corrosion pit on the mechanical property of steel are quantitatively researched, but because the difference between the ideal corrosion pit shape and the naturally corroded steel bar shape is large, the obtained conclusion is not convincing. And secondly, the optical surface measurement technology is adopted to represent the three-dimensional appearance of the corroded steel bar, the point cloud data obtained by three-dimensional scanning is processed and converted into a three-dimensional model which can be used for finite element software to carry out numerical value tensile test, although the method can accurately reflect the corrosion appearance characteristics of the steel bar, the problem that the number of samples is limited still cannot be solved according to the real corroded steel bar in the modeling process, a large amount of time is used for the modeling process, and the advantage that the numerical value simulation test is convenient and fast to calculate cannot be fully played. Therefore, how to obtain more corrosion steel bar samples by using limited steel bar corrosion data and carry out parameter analysis to study the change rule of the mechanical property is very important.
Disclosure of Invention
Aiming at the technical problems, the invention discloses a finite element simulation and parameter analysis method for the residual mechanical property of a corrosion steel bar, which is characterized by comprising the following steps of:
step S1, obtaining the area data of the residual cross section of the steel bar under different corrosion rates through the existing corrosion steel bar test piece, and performing probability distribution fitting on the obtained data to obtain probability distribution models of the residual cross section of the steel bar with different corrosion rates;
step S2, steel bar section data which accord with the probability distribution model in the step S1 are generated randomly through mathematical software, the data are assigned to a finite element circular truncated cone model, a new equivalent corrosion steel bar model is combined, and MATLAB or python software can be adopted as the mathematical software;
and step S3, editing the command stream file of the finite element software through python according to the Monte Carlo method to generate a large number of corrosion steel bar models, performing batch tensile numerical tests in the finite element software, and extracting the calculation results to obtain the final residual mechanical properties of the steel bars under the corresponding corrosion rates.
As a further preferable mode, step S1 includes: the method comprises the steps of carrying out 3D scanning on a corrosion steel bar test piece by adopting a 3D scanner to obtain a 3D point cloud file of the test piece, further converting to obtain a three-dimensional entity model, segmenting the steel bar model along the length direction of the steel bar at intervals not larger than 2mm through three-dimensional analysis software to obtain cross section residual area data of the corrosion steel bar, wherein the cross section residual area refers to a steel bar part which is not corroded in the corrosion steel bar, and carrying out probability distribution fitting on the obtained data through mathematical software to obtain probability distribution models of residual sectional areas of the steel bars with different corrosion rates.
As a further preferable scheme, the method for fitting the probability distribution model of the residual section area of the steel bar under different corrosion rates comprises the following steps:
normalizing the sectional area of each section obtained in the step S1 to define the pitting section coefficient R of the rusted steel barA，
Wherein A isaveThe average area of the whole rusted steel bar is A '(x), and the A' (x) is the residual sectional area of any position of the steel bar along the axial direction.
The method comprises the steps of utilizing a mathematical analysis tool to analyze and fit normalized sectional area data, specifically, taking python to perform data fitting as an example, reading sectional area data of each steel bar, utilizing a fitter expansion packet of python to output five distribution models with the best data fitting effect and corresponding fitting errors, and adopting different probability distribution functions to have certain difference with original data, and adopting statistical indexes such as residual sum of squares, AIC, BIC and the like to quantitatively describe the difference, wherein the optimal condition is that the smaller the value of the error is, the better the fitting degree is represented. Returning parameters corresponding to the optimal fitting distribution through a fit _ best () function to obtain a corrosion steel pitting section coefficient RAParameter values corresponding to the accorded optimal distribution model; because each steel bar has different corrosion rates, the operation of the step S1 is repeated to perform distribution fitting on all steel bar models, each steel bar has different corrosion rates, and after each steel bar is fitted, the parameter values corresponding to the corrosion rates and the corresponding probability distribution functions are obtained, so that the distribution model is not subjected to distribution fitting under the corresponding corrosion ratesAnd drawing a parameter scatter diagram corresponding to the distribution model under different corrosion rates and carrying out regression analysis to obtain a regression equation of the distribution model parameters related to the corrosion rates under the same parameter value. For example, in a lognormal distribution model, the function expression of the lognormal distribution model includes two parameters, namely μ and σ, and the two parameters are used together to control the corresponding function shapes of the distribution model under different corrosion rates. Different corrosion rates correspond to different mu and sigma, and the distribution condition of the residual area of the reinforcing steel bar under different corrosion rates is determined by finding the relation between the corrosion rates and the distribution model parameters (mu and sigma).
As a further preferable scheme, the method for combining the new equivalent corrosion steel bar model comprises the following steps:
the new equivalent corrosion steel bar model is a geometric body formed by splicing a series of circular truncated cone units and can be established in general finite element software, the length of the model is determined according to the length of the scale distance of an extensometer in a tensile test, a group of data meeting the distribution model is regenerated through mathematical software according to a probability distribution model obtained by fitting in the step S1, the number of the data is determined by the segmentation precision of the solid steel bar model and the length of the model together, and if the length of the model is L mm and the segmentation precision is S mm, the number of the newly generated data is (L/S + 1).
Because the corrosion reinforcing bar of simulation is the gauge length section of reinforcing bar, according to part 1 of metal material tensile test: room temperature test method (GB/T228.1-2010), the break in the rebar should be within gauge length in the tensile test. And the fracture section of the steel bar after corrosion is generally the minimum section of the steel bar. Therefore, when finite element modeling is carried out, the minimum section is placed in the middle of the model, and the sectional area of the steel bar of the rust pit section (near the minimum section) has certain continuity, and 10mm is uniformly taken in the following for facilitating batch modeling in consideration of the real width of the rust pit in the actual rusted steel bar, and the sectional area distribution of the rusted steel bar is arranged according to the size sequence within the range of 10 mm. Therefore, assuming that a core rust pit having a width of 10mm exists at the middle position of each rusted steel bar, the smallest (10/S +1) data among the generated random cross-sectional data is rearranged to simulate the rust pit in the rusted steel bar, wherein the smallest cross-sectional dataThe data acquisition device is arranged in the middle of the array, and the other 10/S data are sequentially arranged on two sides of the minimum section from small to large; the areas outside the rust pit are still arranged at two sides of the rust pit in a random distribution mode, and after the sequence of the areas is combined, the generated section data is the pitting section coefficient R of the steel barATherefore, all data still need to be multiplied by the average cross section area A of the corrosion reinforcing steel baraveAs the cross-sectional area of the new model; and in the area outside the corrosion section, simulating the clamping section of the steel bar by adopting an even corrosion model obtained by calculating the average corrosion rate.
As a further preferred scheme, according to the monte carlo method, the batch tensile numerical test is carried out on the newly generated corrosion steel bar model in finite element software, and the method comprises the following steps:
establishing the model by using general finite element software, taking a force-displacement curve of the non-rusted steel bar as an input structure of the model, dividing grids, applying axial load to a clamping end of the model, performing static analysis, extracting load and displacement results corresponding to each increment step, completing a numerical tensile test of a single steel bar, and deriving a corresponding command stream file;
according to the Monte Carlo method, command stream files are modified in batches through python, the modeling operation is firstly copied for not less than 50 times, then each modeling operation is modified, the randomly generated corrosion steel bar section data are assigned pairwise and replaced to the circular truncated cone unit in each steel bar sample according to the combination rule of the step S2, so that a large number of corrosion steel bar samples are generated and are reintroduced into finite element software to perform a batch numerical tensile test, and finally the F-D curve of each steel bar is obtained, and the average value of the F-D curves is taken as the final residual mechanical property of the steel bar under the corrosion rate.
As a further preferable scheme, the residual mechanical property of the steel bar under corresponding parameters can be obtained by modifying the parameters of the probability distribution model, the constitutive relation of the steel bar which is not corroded, the corrosion rate, the diameter of the steel bar and the length of the steel bar according to the steps, and the influence of different parameters on the mechanical property of the steel bar can be researched.
Compared with the prior art, the invention has the beneficial effects that:
firstly, by adopting the technical scheme of the invention, the problems of long corrosion test time and large discreteness of test results can be effectively solved, the time can be effectively saved by a finite element simulation method, and compared with actual test results, the simulation results are accurate and effective.
Secondly, by adopting the technical scheme of the invention, the problem that the number of actual corrosion steel bar samples is limited can be solved, the corrosion appearance characteristics of the original test piece can be retained to the maximum extent by establishing a random probability distribution model, a large number of corrosion steel bar samples can be obtained by recombination, the influence rule of different factors such as corrosion rate, steel bar diameter and steel bar mark on the mechanical property of the steel bar can be obtained by changing parameters, and the residual mechanical property of the actual engineering steel bar can be predicted.
Thirdly, by adopting the technical scheme of the invention, the influence of different corrosion methods on the mechanical properties of the steel bar can be transversely compared, and the study of the existing scholars shows that the shape characteristics of the corrosion steel bar obtained under the natural condition are different from those of the corrosion steel bar obtained under the laboratory condition by electrifying acceleration and a salt spray dry-wet circulation method, and a new idea for judging the difference of the different corrosion methods can be provided from the aspect of the mechanical properties by changing different probability distribution functions met by the sectional area of the corrosion steel bar.
Fourthly, the technical scheme of the invention, which utilizes python to modify the command stream file of the finite element software, realizes the batch tensile numerical test of a large number of corrosion steel bar models, effectively saves the manual modeling operation time and greatly improves the solving efficiency by adopting the Monte Carlo method.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram of a finite element model of the present invention, wherein (a) is a partially enlarged schematic view and (b) is a diagram of a model of a single equivalent rusted steel bar;
FIG. 3 is a graph of Monte Carlo simulation results of the present invention;
FIG. 4 is a graph showing the results of the changes of the mechanical properties of the steel bars according to the present invention with respect to the corrosion rate, wherein (a) is a normalized yield stress-corrosion rate change curve, (b) is a normalized ultimate stress-corrosion rate change curve, and (c) is a normalized ultimate strain-corrosion rate change curve.
Detailed Description
The technical scheme of the invention is further explained by combining the examples.
A finite element numerical simulation and parameter analysis method for evaluating the residual mechanical properties of rusted steel bars, as shown in fig. 1, comprising:
firstly, removing the rusted steel bars from the building, or preparing a rusted steel bar sample under the laboratory condition, carrying out rust removal treatment on the rusted steel bars according to the requirements in the specification of removing corrosion products on corrosion test pieces of metals and alloys (GB/T16545-2015), and recording the corrosion rate of the steel bars. Because the shape of the corroded steel bar has great influence on the tensile strength and ductility of the corroded steel bar, in order to ensure the accuracy of finite element simulation, a 3D scanner is adopted to carry out 3D scanning on the corroded steel bar to obtain a point cloud file of a three-dimensional model of a sample, a repairing function in Geomagic Wrap of reverse modeling software is utilized to repair some holes and redundant nodes possibly existing in an initial scanning model, the holes and the redundant nodes are led out to be a 3D entity model, the model is segmented every 1mm along the length direction of the steel bar through three-dimensional analysis Pro/E software, and the residual sectional area of the corroded steel bar is extracted.
The method comprises the following steps of processing and analyzing the cut sectional area data by using a mathematical analysis tool, establishing a probability distribution model of the residual sectional area of the rusted steel bar, and developing and introducing research results of the probability distribution model of the residual sectional area of the rusted steel bar by a foreign student Kashani (Use of a 3D optical medical measurement technique for a stored concrete correlation pattern analysis of detailed concrete [ J ] reliability Science,2013,73: 208-:
defining the pitting corrosion section coefficient R of the rusted steel barAThe relationship between the area of each section of the steel bar and the overall average corrosion rate is shown as follows:
Aavethe average area of the whole rusted steel bar is A '(x), and the A' (x) is the residual sectional area of any position of the steel bar along the axial direction. Coefficient of pitting corrosion R of KashaniA(x) After statistical analysis, it is found that the probability density function follows a lognormal distribution with a confidence of 95%:
wherein, the parameters mu and sigma and the average corrosion rate n of the steel bar are in an exponential relationship, and the following regression equation is fitted according to the data provided by the test:
μ＝-0.000052*n1.825 (3)
σ＝0.0006491*n1.526 (4)
the data of the cross section of the corroded steel bar is regenerated according to the probability distribution model, and a steel bar with the average corrosion rate n of 40% is taken as an example for explanation. The original cross-sectional area of the reinforcing bar is A0Calculated from the formulae (3) and (4), μ ═ 0.04363 and σ ═ 0.180991, i.e., RALognomial (-0.04363, 0.180991), average cross-sectional area A of the baraveCan be formed by the average rust rate n and the original cross-sectional area A0Calculating to obtain the area A' of each section of the steel bar from the average area AaveAnd RAAnd (4) multiplying the two. The length of the model is determined by the length of the scale distance of the extensometer in the tensile test, and the common 50mm of the scale distance of the extensometer is taken as the length of the model, so that 51 cross-section data need to be generated at random again by using the probability distribution model obtained in the last step according to the segmentation precision of 1 mm. Assuming that the cross-sectional area of the steel bar changes linearly within 1mm in length, a 50mm long rusted steel bar can be regarded as being formed by equivalent butt joint of 50 round tables with the length of 1mm, as shown in fig. 2 (a).
According to the corrosion appearance of the corrosion steel bar, the influence of the pitting corrosion pit on the residual sectional area of the steel bar is considered, certain correlation necessarily exists in the numerical value of the sectional area near the corrosion pit, namely the sectional area of the steel bar is relatively small in the length spanned by the corrosion pit, a core corrosion pit with the width of 10mm is assumed to exist in the middle of each corrosion steel bar, the minimum 11 data in the generated random section data are rearranged to simulate the corrosion pit in the corrosion steel bar, the minimum section data are placed in the middle of a sequence, and the rest 10 data are sequentially arranged on two sides of the minimum section from small to large. And regions outside the rust pit are still arranged at two sides of the rust pit in a random distribution mode, and regions outside the rust sections are simulated by adopting an even rust model obtained by calculating the average rust rate. The recombined equivalent steel bar corrosion finite element model is shown in fig. 2 (b).
Taking a finite element software MSC.Marc as an example, establishing the numerical model, taking a force-displacement curve of the non-rusted steel bar as an input structure of the model, dividing grids, applying axial load to a clamping end of the model, performing static analysis, extracting a load and displacement result corresponding to each incremental step, completing a numerical tensile test of a single steel bar, and deriving a corresponding command stream file (md).
According to the Monte Carlo method, through python batch modification command stream files (md.), the modeling operation is firstly copied for 100 times, then each modeling operation is modified, the randomly generated corrosion steel bar section data is subjected to pairwise assignment and replaced to the circular truncated cone unit in each steel bar sample according to the combination rule of the step S2, so that 100 corrosion steel bar samples are generated, the F-D curve of each steel bar is finally obtained, and the average value of the F-D curves is the final residual mechanical property of the steel bar under the corrosion rate, as shown in figure 3.
By the method for establishing the random probability distribution model, a large number of steel bar models with different corrosion appearances can be obtained, the new corrosion steel bar model is established on the original real corrosion steel bar sample, and the corrosion appearance of the surface of the new corrosion steel bar model has greater relevance to the corrosion condition of the real corrosion steel bar. Therefore, after the problem of the number of corroded steel bar samples is solved, according to the scheme of the invention, the residual mechanical property of the steel bar under corresponding parameters can be obtained by modifying the parameters such as the probability distribution model, the complete steel bar structure, the corrosion rate, the diameter of the steel bar, the length of the steel bar and the like, and the influence of different parameters on the mechanical property of the steel bar can be researched.
Taking the modification of the corrosion rate as an example, the parameter analysis is carried out according to the scheme of the invention. Still using the calculated parameters in the above embodiment, the simulated tensile test is performed on the samples of the pitting corrosion steel bars with the corrosion rates of 10%, 15%, 20%, 25%, 30%, 35% and 40%, respectively, and the obtained results are shown in fig. 4. In order to fully consider the variability of the mechanical property of the steel bar, the average value (shown by a dot in figure 4) of the mechanical property under each corrosion rate and a numerical value (shown by a shadow in figure 4) within a one-time standard deviation range of the average value are extracted, the obtained steel bar has the degradation trend of the mechanical property along with the increase of the corrosion rate, and the degradation trend is close to an empirical formula obtained by summarizing Zhangiping (Zhang iping, the like, the random constitutive relation of the corrosion steel bar, the building material science report, 2014) based on a real corrosion steel bar tensile test, and the reliability of the scheme is verified.
By adopting the technical scheme of the embodiment, limited resources can be utilized to the maximum extent, more corrosion reinforcing steel bar equivalent finite element models can be obtained, influence factors of mechanical properties of the corroded reinforcing steel bars can be quantitatively researched through Monte Carlo simulation, the residual mechanical properties of the reinforcing steel bars in the corrosion environment can be predicted, and guidance and suggestion are provided for evaluating the service life and the mechanical properties of the RC structure in the coastal environment.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions and substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (6)
1. A finite element simulation and parameter analysis method for the residual mechanical property of a corrosion steel bar is characterized by comprising the following steps:
step S1, obtaining the area data of the residual cross section of the steel bar under different corrosion rates through the existing corrosion steel bar test piece, and performing probability distribution fitting on the obtained data to obtain probability distribution models of the residual cross section of the steel bar with different corrosion rates;
step S2, steel bar section data which accord with the probability distribution model in the step S1 are generated randomly through mathematical software again, the data are assigned to a finite element round table model, and a new equivalent corrosion steel bar model is combined;
and step S3, editing the command stream file of the finite element software through python according to the Monte Carlo method to generate a large number of corrosion steel bar models, performing batch tensile numerical tests in the finite element software, and extracting the calculation results to obtain the final residual mechanical properties of the steel bars under the corresponding corrosion rates.
2. A finite element simulation and parameter analysis method for residual mechanical properties of rusted steel bars according to claim 1, wherein step S1 includes: the method comprises the steps of carrying out 3D scanning on a corrosion steel bar test piece by adopting a 3D scanner to obtain a 3D point cloud file of the test piece, further converting to obtain a three-dimensional entity model, segmenting the steel bar model along the length direction of a steel bar at intervals of less than or equal to 2mm through three-dimensional analysis software to obtain cross section residual area data of a corrosion steel bar, and carrying out probability distribution fitting on the obtained data through mathematical software to obtain probability distribution models of residual cross sections of the steel bars with different corrosion rates.
3. A finite element simulation and parameter analysis method of the residual mechanical property of the rusted steel bar according to claim 2, wherein the fitting of the probability distribution model of the residual sectional area of the steel bar under different corrosion rates comprises the following steps:
normalizing the sectional area of each section obtained in the step S1 to define the pitting section coefficient R of the rusted steel barA，
Wherein A isaveThe average area of the whole rusted steel bar is shown, and A' (x) is the residual sectional area of any position of the steel bar along the axial direction;
analyzing and fitting the normalized sectional area data by using a mathematical analysis tool,obtaining the pitting cross-section coefficient R of the rusted steel barAAnd (4) repeating the operation of step S1 to fit the distribution of all the steel bar models to obtain different parameter values of the distribution model under the corresponding corrosion rate, drawing a parameter scatter diagram corresponding to the distribution model under the different corrosion rates, and performing regression analysis to obtain a regression equation of the distribution model parameters related to the corrosion rate, wherein each steel bar has different corrosion rates.
4. A finite element simulation and parameter analysis method of the residual mechanical property of the rusted steel bar according to claim 1, wherein a new equivalent rusted steel bar model is combined, comprising the steps of:
the new equivalent corrosion steel bar model is a geometric body formed by splicing a series of circular truncated cone units and can be established in general finite element software, the length of the model is determined according to the length of the scale distance of an extensometer in a tensile test, a group of data meeting the distribution model is regenerated through mathematical software according to a probability distribution model obtained by fitting in the step S1, the number of the data is determined by the segmentation precision of the solid steel bar model and the length of the model together, and if the length of the model is L mm and the segmentation precision is S mm, the number of the newly generated data is (L/S + 1).
According to the corrosion appearance of the corrosion reinforcing steel bars, assuming that a core rust pit with the width of 10mm exists in the middle of each corrosion reinforcing steel bar, rearranging the minimum (10/S +1) data in the generated random section data to simulate the rust pit in the corrosion reinforcing steel bars, wherein the minimum section data is arranged in the middle of the sequence, and the rest 10 data are sequentially arranged on two sides of the minimum section from small to large; the areas outside the rust pit are still arranged at two sides of the rust pit in a random distribution mode, and after the sequence of the areas is combined, the generated section data is the pitting section coefficient R of the steel barATherefore, all data still need to be multiplied by the average cross section area A of the corrosion reinforcing steel baraveAs the cross-sectional area of the new model; and in the area outside the corrosion section, simulating the clamping section of the steel bar by adopting an even corrosion model obtained by calculating the average corrosion rate.
5. A finite element simulation and parameter analysis method of the residual mechanical properties of the rusted steel bars according to claim 1, wherein a batch tensile numerical test is performed on a newly generated rusted steel bar model in finite element software according to a monte carlo method, comprising the steps of:
establishing the model by using general finite element software, taking a force-displacement curve of the non-rusted steel bar as an input structure of the model, dividing grids, applying axial load to a clamping end of the model, performing static analysis, extracting a load and a displacement result corresponding to each increment step, completing a numerical value tensile test of a single steel bar, and deriving a corresponding command stream file;
according to the Monte Carlo method, command stream files are modified in batches through python, the modeling operation is firstly copied for not less than 50 times, then each modeling operation is modified, the randomly generated corrosion steel bar section data are assigned pairwise and replaced to the circular truncated cone unit in each steel bar sample according to the combination rule of the step S2, so that a large number of corrosion steel bar samples are generated and are reintroduced into finite element software to perform a batch numerical tensile test, and finally the F-D curve of each steel bar is obtained, and the average value of the F-D curves is taken as the final residual mechanical property of the steel bar under the corrosion rate.
6. A finite element simulation and parameter analysis method of corroded steel bar residual mechanical property according to claim 1, characterized in that the steel bar residual mechanical property under corresponding parameters can be obtained by modifying the parameters of probability distribution model, non-corroded steel bar constitutive relation, corrosion rate, steel bar diameter and steel bar length according to the above steps, and the influence of different parameters on the steel bar mechanical property is researched.
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