Experimental study on damage characteristics of early-age concrete under blasting load in high-temperature tunnel and its characterization method

Abstract

The present application provides a high-temperature tunnel blasting load early-age concrete damage characteristic test and characterization method, relates to the concrete damage detection technical field, and realizes the accurate prediction and active prevention and control of the high-temperature tunnel concrete multi-factor coupling damage through the fusion of microbial metagenome analysis, multi-physical field coupling modeling and three-dimensional dynamic visualization technology. By screening high-corrosion characteristic flora and quantifying the metabolic activity, a nonlinear prediction model is constructed, breaking through the limitations of traditional single factor evaluation; combined with the metabolic substance diffusion path and the stress field dynamic correction crack propagation direction, the biological-chemical-mechanical synergistic damage mechanism is revealed; through the diffusion coefficient correction of the pore structure adaptation, the universality of the model for different density concretes is improved. Finally, the three-dimensional atlas of the superposition of flora and damage path is generated, supporting the targeted bacteriostasis and dynamic maintenance decision, and improving the safety evaluation efficiency of the concrete structure in the complex environment.

Description

Technical Field

[0001] This invention relates to the field of concrete damage detection technology, specifically to experimental and characterization methods for early-age concrete damage characteristics under high-temperature tunnel blasting loads. Background Technology

[0002] In complex geological environments (such as high-temperature tunnels), early-age concrete is prone to hidden damage due to the coupled effects of multiple factors, including blasting loads, high temperature and humidity, and microbial erosion. Traditional assessment methods often focus on the analysis of single mechanical or chemical factors, making it difficult to reveal the damage mechanism under the synergistic effect of microbial metabolism and temperature and humidity, resulting in large prediction biases and delayed maintenance.

[0003] The existing technology, disclosed in CN116975969A, presents a method and system for real-time location and damage quantification of crack propagation in concrete dams under explosive loading. This method includes: Step 1, preprocessing; Step 2, constructing a numerical simulation framework under explosive loading; Step 2-1, setting the constitutive model of dam particles and the fracture criteria for dam particles; Step 2-2, converting the contact force of interactions between particles of different materials into stress; using water pressure and uplift pressure as a mechanical boundary layer containing pressure information; Step 2-3, determining the fracture failure of dam particles and solving for characteristic information; Step 2-4, simulating concrete fracture propagation; Step 3, obtaining the damage type of damaged particles and the number of damaged particles corresponding to each damage type through the numerical simulation framework, thereby determining the damage type in real time and classifying and quantifying the damage evolution process of concrete dams under explosive loading in real time, either individually or overall. However, it cannot quantify the nonlinear influence of microbial colonization on crack propagation and lacks a multi-field coupled (bio-chemical-mechanical) dynamic damage model, resulting in the inability to accurately identify high-risk damage areas. Moreover, relying heavily on empirical parameters and neglecting the regulatory role of pore structure on the diffusion of microbial metabolites makes it difficult to adapt to the evaluation needs of concrete with different densities, thus restricting the ability to actively control the spread of microorganisms under complex working conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting loads, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Test and characterization methods for early-age concrete damage characteristics under high-temperature tunnel blasting loads, including the following steps:

[0008] S1: Construct early-age concrete specimens based on tunnel building materials. After applying blasting loads to the concrete specimens, coat the surface with soil from the tunnel environment to inoculate microbial communities and place them in a high-temperature environment.

[0009] S2: Periodically collect microbial samples from the surface of concrete specimens, record crack propagation parameters on the surface of concrete specimens, screen out characteristic microbial communities that are strongly correlated with crack propagation, and generate microbial community data of characteristic microbial communities.

[0010] S3: Based on the microbial community data of the characteristic microbial community and the crack propagation parameters, construct a nonlinear prediction model about the relationship between the characteristic microbial community and the crack.

[0011] S4: Perform 3D modeling of concrete specimens, generate crack prediction paths based on the nonlinear prediction model between characteristic microbial communities and cracks, and overlay the prediction paths onto the 3D model to complete the visualization of crack propagation.

[0012] Preferably, the crack propagation parameters include crack propagation rate, crack length, porosity, and directional angle, and the microbial community data includes microbial community abundance and metabolite concentration;

[0013] The logic for screening characteristic bacterial communities strongly correlated with crack propagation is as follows:

[0014] Microbial community samples were collected at multiple points on the surface and cracks of concrete specimens. Metagenomic sequencing technology was used to analyze the types of microbial communities in the samples and to calculate the abundance of each type of microbial community.

[0015] Spearman correlation coefficients between the abundance of various bacterial communities and the crack propagation rate were calculated, and their confidence levels were calculated using a significance test. Bacteria with correlation coefficients greater than or equal to 0.6 and confidence levels greater than or equal to 0.95, and whose metabolic function annotations include pathways for organic acid synthesis or mineral dissolution, were identified as characteristic bacterial communities.

[0016] Preferably, the nonlinear prediction model between the characteristic microbial community and the crack is expressed as follows:

[0017] ;

[0018] In the formula Indicates the crack length. Indicates the placement time. , , These represent the sensitivity coefficients of the pre-defined concrete specimens to bacterial flora, temperature, and humidity, respectively. Indicates the first Abundance of characteristic bacterial communities, Indicates the first Abundance reference values ​​for characteristic bacterial groups, Indicates the first Metabolic activity index of bacterial community, subscript An index representing characteristic bacterial groups, Indicates the total number of characteristic bacterial groups. , These represent ambient temperature and ambient humidity, respectively. , These represent the reference temperature and reference humidity, respectively. This represents the overall diffusion coefficient.

[0019] Preferably, the first Metabolic activity index of bacterial population The calculation method is as follows:

[0020] ;

[0021] In the formula , , They represent the first The formation rate, metabolite concentration, and power-law exponent of characteristic bacterial colonies;

[0022] The generation logic for the generation rate and the power-law exponent is as follows:

[0023] Take another identical concrete specimen, and... The characteristic bacterial groups were individually inoculated onto the surface of concrete specimens and placed in the same high geothermal environment for observation;

[0024] Determination of the first The change in the concentration of metabolites of a characteristic bacterial community over time affects the production rate. The fitting equation is as follows:

[0025] ;

[0026] In the formula Indicates the reference concentration of metabolites;

[0027] Then, a log-linear regression was performed to fit the concentration and crack propagation rate, and the fitting equation is:

[0028] ;

[0029] The power law exponent is obtained from the fitted equation. .

[0030] Preferably, the comprehensive diffusion coefficient is calculated as follows:

[0031] ;

[0032] In the formula Indicates the first Reference diffusion coefficient of characteristic bacterial communities in the middle, , These represent porosity and reference porosity, respectively. Indicates the decay exponent, and .

[0033] Preferably, the construction logic of the 3D model is as follows:

[0034] The overall shape, internal pores, and initial crack network of concrete specimens were constructed using microfocus CT scanning.

[0035] Based on the nonlinear prediction model between characteristic microbial communities and cracks, the required prediction time is set, and the crack prediction path after the corresponding prediction time is calculated.

[0036] Different characteristic bacterial communities are distinguished by color, and a 3D model is generated by superimposing the characteristic bacterial communities with the crack prediction path to complete the visualization output.

[0037] Preferably, the required prediction time for calculating the crack prediction path is expressed as:

[0038] ;

[0039] In the formula Indicates the required forecast time. Indicates the time step. This indicates the total number of model updates;

[0040] The change in crack length after each time step is expressed as:

[0041] ;

[0042] After each time step, the directional angle of the crack prediction path is expressed as:

[0043] ;

[0044] In the formula and These represent the initial direction angle and the distance traveled, respectively. The direction angle after the next update Index indicating the number of updates, , Indicates to Find the partial derivatives in three-dimensional coordinates. Indicates the gradient magnitude. This represents the maximum principal stress.

[0045] The preferred method for calculating the maximum principal stress is as follows:

[0046] ;

[0047] In the formula This represents the instantaneous pressure of the blasting load. This indicates the area where the blasting load is applied. , These represent the coefficient of thermal expansion and the modulus of elasticity of the concrete specimen, respectively.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] This invention achieves accurate prediction and proactive prevention of multi-factor coupled damage in high-temperature tunnel concrete by integrating microbial metagenomic analysis, multiphysics coupled modeling, and 3D dynamic visualization technologies. By screening highly corrosive microbial communities and quantifying their metabolic activity, a nonlinear prediction model is constructed, overcoming the limitations of traditional single-factor assessments. Combining metabolite diffusion pathways with stress field dynamic correction of crack propagation direction reveals a bio-chemical-mechanical synergistic damage mechanism. Through diffusion coefficient correction adapted to pore structure, the model's universality for concrete with different densities is improved. Finally, a 3D map superimposed with microbial colonization heat zones and damage pathways is generated, supporting targeted antimicrobial and dynamic maintenance decisions, significantly improving the efficiency of safety assessment and life prediction accuracy of concrete structures in complex environments. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0051] Figure 2 This is a schematic diagram illustrating the fitting relationship between crack propagation rate and abundance product in this invention;

[0052] Figure 3 This is a schematic diagram illustrating the fitting relationship between crack propagation rate and metabolite concentration in this invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0054] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] Example:

[0056] Please see Figures 1-3 The present invention provides a technical solution:

[0057] Test and characterization methods for early-age concrete damage characteristics under high-temperature tunnel blasting loads, including the following steps:

[0058] S1: Construct early-age concrete specimens based on tunnel building materials. After applying blasting loads to the concrete specimens, coat the surface with soil from the tunnel environment to inoculate microbial communities and place them in a high-temperature environment.

[0059] Specifically, when constructing concrete specimens, various components of concrete in tunnel building materials can be collected first, and concrete specimens of the same material can be constructed according to the components of concrete in tunnel building materials. When placed in a high geothermal environment, the concrete specimens can be placed in the same natural environment as the tunnel location for observation, or a test chamber with temperature and humidity control functions can be used to simulate a high geothermal environment.

[0060] In this step, standardized specimen preparation, microbial inoculation, and high-temperature environment simulation are used to ensure that the test conditions are consistent with the actual tunnel working conditions, thereby improving the comparability and repeatability of data. Microbial data and crack parameters are collected simultaneously, providing multimodal input for microbial-damage correlation research. This breaks through the limitations of traditional single mechanical or chemical analysis and is conducive to building a complete technology chain from damage simulation to mechanism analysis, providing basic data support for subsequent model construction and visualization.

[0061] S2: Periodically collect microbial samples from the surface of concrete specimens, record crack propagation parameters on the surface of concrete specimens, screen out characteristic microbial communities that are strongly correlated with crack propagation, and generate microbial community data of characteristic microbial communities.

[0062] Crack propagation parameters include crack propagation rate, crack length, porosity, and directional angle. Microbial community data includes bacterial abundance and metabolite concentration. The directional angle can be the angle between the crack and the horizontal direction or the vertical direction, which can be determined based on the specific circumstances. Porosity represents the ratio between pore volume and the total volume of concrete. Other parameters can be directly measured or have corresponding definitions and calculation formulas in existing technologies, so they will not be elaborated upon here.

[0063] The logic for screening characteristic bacterial communities strongly correlated with crack propagation is as follows:

[0064] Microbial community samples were collected at multiple points on the surface and cracks of concrete specimens. Metagenomic sequencing technology was used to analyze the types of microbial communities in the samples and to calculate the abundance of each type of microbial community.

[0065] Spearman correlation coefficients between the abundance of various bacterial communities and the crack propagation rate were calculated, and their confidence levels were calculated using a significance test. Bacteria with correlation coefficients greater than or equal to 0.6 and confidence levels greater than or equal to 0.95, and whose metabolic function annotations include pathways for organic acid synthesis or mineral dissolution, were identified as characteristic bacterial communities.

[0066] Specifically, metagenomic sequencing is performed on microbial community samples from the surface and cracks of concrete specimens to obtain DNA sequence data of the microbial community. FastQC is then used to assess the quality of the raw data, and Trimmomatic or Cutadapt is used to remove low-quality sequences and adapter contaminants, retaining high-quality reads. Next, MetaGeneMark or Prodigal is used to predict open reading frames (ORFs) in the sequencing data to identify functional genes in the microorganisms. Finally, the predicted gene sequences are aligned to functional databases (such as KEGG, COG, eggNOG, or MetaCyc) to obtain functional annotation information for each gene. This allows for the screening of pathways related to organic acid synthesis and mineral dissolution, such as KEGG pathway map00020: citric acid cycle, map00620: pyruvate metabolism, or map00910: sulfur metabolism, map02010: ABC transporter. This screens for microorganisms with corrosive metabolic capabilities (such as acid-producing bacteria and sulfur-oxidizing bacteria), avoiding interference from irrelevant microorganisms.

[0067] In this step, using correlation coefficients and confidence levels to screen microbial communities can reduce the impact of extreme data, reduce noise interference, and ensure the accuracy of model input. By identifying high-risk microbial communities through metabolic function annotation (organic acid synthesis / mineral dissolution), we can focus on the biochemical mechanisms that truly drive damage, clarify the quantitative standards of damage-associated microbial communities, provide high-quality feature variables for model construction, and improve the interpretability and engineering guidance value of the model.

[0068] S3: Based on the microbial community data of the characteristic microbial community and the crack propagation parameters, construct a nonlinear prediction model about the relationship between the characteristic microbial community and the crack.

[0069] The nonlinear prediction model between characteristic microbial communities and cracks is expressed as follows:

[0070] ;

[0071] In the formula Indicates the crack length. Indicates the placement time. , , These represent the sensitivity coefficients of the pre-defined concrete specimens to bacterial flora, temperature, and humidity, respectively. Indicates the first Abundance of characteristic bacterial communities, Indicates the first Abundance reference values ​​for characteristic bacterial groups, Indicates the first Metabolic activity index of bacterial community, subscript An index representing characteristic bacterial groups, Indicates the total number of characteristic bacterial groups. , These represent ambient temperature and ambient humidity, respectively. , These represent the reference temperature and reference humidity, respectively. This represents the overall diffusion coefficient. The abundance reference value for the characteristic bacterial community can be set to the abundance value of this characteristic bacterial community under the natural environment of the tunnel. The reference temperature and humidity can be set to standard conditions of normal temperature and humidity, i.e., 25℃ and 50% humidity. The bacterial community sensitivity coefficient can be obtained by changing the abundance of the characteristic bacterial community under constant temperature and humidity conditions, and by fitting the exponential relationship between the crack propagation rate and the bacterial community abundance using the least squares method. The calculation method can be expressed as:

[0072] ;

[0073] The temperature sensitivity coefficient can be fitted by setting a temperature gradient under constant humidity conditions, and the calculation method can be expressed as:

[0074] ;

[0075] Similarly, the humidity sensitivity coefficient can be fitted by setting a humidity gradient under constant temperature conditions. The calculation method can be expressed as follows:

[0076] ;

[0077] As can be seen from the above prediction model, it mainly involves four major influencing factors. The first is the influence of characteristic microbial communities, which is reflected in the following part of the model:

[0078] ;

[0079] In this section, the product form quantifies the combined amplification effect of multi-microbial abundance on damage, reflecting the symbiotic or competitive relationship of the microbial community;

[0080] The second influencing factor is temperature, which is reflected in the following part of the model:

[0081] ;

[0082] This shows that increased temperature accelerates the metabolic and chemical reaction rates of microorganisms, which can be used to characterize the temperature sensitivity of concrete specimens inoculated with various bacteria.

[0083] The third influencing factor is humidity, which is reflected in the following part of the model:

[0084] ;

[0085] Similar to temperature, this section describes how high humidity promotes microbial colonization and metabolite diffusion, thus characterizing the amplification effect of humidity on damage.

[0086] The final part is the overall diffusion coefficient. This parameter is used to integrate the diffusion capacity of multi-microbial metabolites and quantify the impact of pore structure on damage transmission.

[0087] No. Metabolic activity index of bacterial population The calculation method is as follows:

[0088] ;

[0089] In the formula , , They represent the first The formation rate, metabolite concentration, and power-law exponent of characteristic bacterial colonies;

[0090] The generation logic for the generation rate and the power-law exponent is as follows:

[0091] Take another identical concrete specimen, and... The characteristic bacterial groups were individually inoculated onto the surface of concrete specimens and placed in the same high geothermal environment for observation;

[0092] Determination of the first The change in the concentration of metabolites of a characteristic bacterial community over time affects the production rate. The fitting equation is as follows:

[0093] ;

[0094] In the formula Indicates the reference concentration of metabolites;

[0095] Then, a log-linear regression was performed to fit the concentration and crack propagation rate, and the fitting equation is:

[0096] ;

[0097] The power law exponent is obtained from the fitted equation. .

[0098] The calculation method of the metabolic activity index shows that the formation rate of characteristic colonies is used to characterize the generation efficiency of metabolites from characteristic bacterial communities. First-order reaction kinetics are used to reflect the change in bacterial activity over time. Log-linear regression is used to improve the statistical significance of parameter calibration, and a power-law exponent is used to quantify the nonlinear amplification effect of metabolite concentration on damage rate. The metabolic activity index generated in this way comprehensively reflects the metabolite generation efficiency of the bacterial community and its corrosive ability on concrete. In the prediction model, it influences the crack propagation rate through an exponential amplification effect, reflecting the correlation between "bacterial community quantity - metabolic activity - damage intensity". Furthermore, by ranking the metabolic activity indices of different characteristic bacterial communities, high-risk bacterial communities that need to be preferentially inhibited can be quickly identified, which helps guide targeted antibacterial strategies.

[0099] The overall diffusion coefficient is calculated as follows:

[0100] ;

[0101] In the formula Indicates the first Reference diffusion coefficient of characteristic bacterial communities in the middle, , These represent porosity and reference porosity, respectively. Indicates the decay exponent, and The reference diffusion coefficient here can be determined through expert experience, or it can be calculated using Fick's second law after preparing the same concrete specimen, inoculating it with a single bacterial community. Specifically, it is generally calculated using... For highly corrosive bacteria such as sulfur oxidizing bacteria, a higher value is required. The reference porosity can be set according to the type of concrete; ordinary concrete is generally between 8% and 15%, while high-strength concrete is generally between 3% and 8%.

[0102] In the formula for calculating the comprehensive diffusion coefficient, the reference diffusion coefficient is used to characterize the diffusion ability of a single bacterial community under standard porosity. The latter part, which combines porosity, reference porosity, and attenuation index, is used to quantify the influence of pore structure on diffusion. Therefore, the final comprehensive diffusion coefficient can reflect the linear superposition of the diffusion ability of multiple bacterial communities, can adapt to the combined effect of complex microbial communities, and can also improve the universality of the prediction model for concrete with different densities.

[0103] S4: Perform 3D modeling of concrete specimens, generate crack prediction paths based on the nonlinear prediction model between characteristic microbial communities and cracks, and overlay the prediction paths onto the 3D model to complete the visualization of crack propagation.

[0104] The construction logic of 3D modeling is as follows:

[0105] The overall shape, internal pores, and initial crack network of concrete specimens were constructed using microfocus CT scanning.

[0106] Based on the nonlinear prediction model between characteristic microbial communities and cracks, the required prediction time is set, and the crack prediction path after the corresponding prediction time is calculated.

[0107] Different characteristic bacterial communities are distinguished by color, and a 3D model is generated by superimposing the characteristic bacterial communities with the crack prediction path to complete the visualization output.

[0108] This method uses microfocus CT scanning to capture the micron-scale structure of pores and cracks, providing a real geometric basis for damage evolution. It transforms abstract model output into intuitive 3D maps, enabling engineers to quickly locate damage hotspots. Color coding is then used to distinguish between bacterial community types and damage levels, thereby improving decision-making efficiency.

[0109] The required prediction time for calculating the crack prediction path is expressed as:

[0110] ;

[0111] In the formula Indicates the required forecast time. Indicates the time step. This indicates the total number of model updates;

[0112] The change in crack length after each time step is expressed as:

[0113] ;

[0114] After each time step, the directional angle of the crack prediction path is expressed as:

[0115] ;

[0116] In the formula and These represent the initial direction angle and the distance traveled, respectively. The direction angle after the next update Index indicating the number of updates, , Indicates to Find the partial derivatives in three-dimensional coordinates. Indicates the gradient magnitude. This represents the maximum principal stress. The initial direction angle and specific location of the crack can be determined during the CT scan. Therefore, in each subsequent update, knowing the corresponding change in crack length and the changed direction angle allows for the determination of the corresponding crack prediction path.

[0117] The formula for calculating the crack propagation angle reveals two main influencing factors: the concentration of metabolites from the characteristic bacterial colony and the blast load—one chemical and one mechanical. Using the maximum principal stress as the denominator balances the influence of the metabolite concentration gradient on the crack propagation direction. A larger value indicates that the mechanical factor (stress concentration) dominates the propagation direction, while the influence of the chemical factor (microbial metabolism) is relatively weakened, and vice versa. The spatial gradient calculation in the upper part characterizes the combined diffusion effect of multiple microbial metabolites. In practice, this drives the crack to deflect towards areas with higher concentrations of characteristic bacterial metabolites, reflecting a chemical-mechanical synergistic damage mechanism. Dividing by the gradient modulus is for normalization, unifying the dimensions.

[0118] The maximum principal stress is calculated as follows:

[0119] ;

[0120] In the formula This represents the instantaneous pressure of the blasting load. This indicates the area where the blasting load is applied. , These represent the coefficient of thermal expansion and the modulus of elasticity of the concrete specimen, respectively.

[0121] In the calculation method of maximum principal stress, This represents the blast load term, used to quantify the mechanical drive of instantaneous impact on the crack tip. The thermal stress term is used to characterize the thermal expansion effect caused by high ground temperature and to explain the temperature-stress coupled damage mechanism. Therefore, the maximum principal stress integrates dynamic load and temperature effect, which can accurately characterize the stress state under complex working conditions, provide mechanical input for the direction correction formula, and ensure the integrity of the multi-field coupled model.

[0122] In this embodiment, a group of concrete specimens were sampled daily. The surface microbial communities mainly included the genera *Bacillus*, *Acidithiobacillus*, *Pseudomonas*, and *Sulfurimonas*. *Bacillus* is characterized by its organic acid synthesis function, while *Acidithiobacillus* and *Sulfurimonas* are characterized by their mineral dissolution function. *Pseudomonas*, however, did not exhibit any related function. Therefore, *Bacillus*, *Acidithiobacillus*, and *Sulfurimonas* were designated as the characteristic microbial communities and numbered SP1 to SP3 for analysis. The specific data are shown in the table below.

[0123]

[0124] Reference Figures 2-3 As shown in the table above, the crack propagation rate exhibits a non-linear power function relationship with the abundance product. This is because in the model: ;

[0125] This section represents the product of the abundance ratios of all characteristic bacterial communities, with each community's abundance ratio multiplied by a weighted power of its metabolic activity index. This reflects the synergistic or competitive effects of multiple characteristic bacterial communities on concrete damage. The product form implies that the combined effect among the communities is not a simple additive effect, but rather has an amplifying or inhibiting effect. Since the metabolic activity index is generally positive and not equal to 1, the effect of the product term on the crack propagation rate is non-linear and has an amplifying effect compared to simple linear growth. The abundance product in the table is the direct multiplication of the abundances of the three characteristic bacterial communities, reflecting the overall activity of the characteristic bacterial communities. It is positively correlated with this part of the model; therefore, its fitting graph with the crack propagation rate can be used as a reference and conforms to the model's expectations.

[0126] The fitted curves also show a logarithmic linear relationship (i.e., a power-law relationship) between crack propagation rate and metabolite concentration. The concentration of metabolites (such as organic acids) affects the chemical corrosion strength of concrete, and the power-law exponent represents the nonlinear amplification effect of concentration on corrosion rate. The logarithmic linear relationship simplifies complex reaction kinetics and facilitates parameter estimation, which is consistent with the model's expectations. The fitting method for crack propagation rate is similar to that for other parameters, such as temperature, humidity, and porosity, and will not be elaborated upon here.

[0127] In summary, this invention achieves accurate prediction and proactive control of multi-factor coupled damage in high-temperature tunnel concrete by integrating microbial metagenomic analysis, multiphysics coupled modeling, and 3D dynamic visualization technology. By screening highly corrosive microbial communities and quantifying their metabolic activity, a nonlinear prediction model is constructed, overcoming the limitations of traditional single-factor assessments. Combining metabolite diffusion paths with dynamic stress field correction of crack propagation direction reveals a bio-chemical-mechanical synergistic damage mechanism. Furthermore, by adjusting the diffusion coefficient to suit the pore structure, the model's universality for concrete with different densities is improved. Finally, a 3D map superimposed with microbial colonization hotspots and damage paths is generated, supporting targeted antimicrobial and dynamic maintenance decisions, significantly improving the efficiency of safety assessment and life prediction accuracy of concrete structures in complex environments.

[0128] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0129] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0130] 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; 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, depending on actual needs.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load, characterized in that, The specific steps include: S1: Construct early-age concrete specimens based on tunnel building materials. After applying blasting loads to the concrete specimens, coat the surface with soil from the tunnel environment to inoculate microbial communities and place them in a high-temperature environment. S2: Periodically collect microbial samples from the surface of concrete specimens, record crack propagation parameters on the surface of concrete specimens, screen out characteristic microbial communities that are strongly correlated with crack propagation, and generate microbial community data of characteristic microbial communities. The crack propagation parameters include crack propagation rate, crack length, porosity, and directional angle; the microbial community data includes microbial community abundance and metabolite concentration. The logic for screening characteristic bacterial communities strongly correlated with crack propagation is as follows: Microbial community samples were collected at multiple points on the surface and cracks of concrete specimens. Metagenomic sequencing technology was used to analyze the types of microbial communities in the samples and to calculate the abundance of each type of microbial community. The Spearman correlation coefficient between the abundance of various bacterial communities and the crack propagation rate was calculated, and the confidence level was calculated using the significance test. Bacteria with a correlation coefficient greater than or equal to 0.6 and a confidence level greater than or equal to 0.95, and whose metabolic function annotations include organic acid synthesis or mineral dissolution pathways, were identified as characteristic bacterial communities. S3: Based on the microbial community data of the characteristic microbial community and the crack propagation parameters, construct a nonlinear prediction model about the relationship between the characteristic microbial community and the crack. The nonlinear prediction model between the characteristic bacterial community and the crack is expressed as follows: In the formula Indicates the crack length. Indicates the placement time. , , These represent the sensitivity coefficients of the pre-defined concrete specimens to bacterial flora, temperature, and humidity, respectively. Indicates the first Abundance of characteristic bacterial communities, Indicates the first Abundance reference values ​​for characteristic bacterial groups, Indicates the first Metabolic activity index of bacterial community, subscript An index representing characteristic bacterial communities. Indicates the total number of characteristic bacterial groups. , These represent ambient temperature and ambient humidity, respectively. , These represent the reference temperature and reference humidity, respectively. Indicates the overall diffusion coefficient; S4: Perform 3D modeling of concrete specimens, generate crack prediction paths based on the nonlinear prediction model between characteristic microbial communities and cracks, and overlay the prediction paths onto the 3D model to complete the visualization of crack propagation.

2. The test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load as described in claim 1, characterized in that: The first Metabolic activity index of bacterial population The calculation method is as follows: In the formula , , They represent the first The formation rate, metabolite concentration, and power-law exponent of characteristic bacterial colonies; The generation logic for the generation rate and the power-law exponent is as follows: Take another identical concrete specimen, and... The characteristic bacterial groups were individually inoculated onto the surface of concrete specimens and placed in the same high geothermal environment for observation; Determination of the first The change in the concentration of metabolites of a characteristic bacterial community over time affects the production rate. The fitting equation is as follows: In the formula Indicates the reference concentration of metabolites; Then, a log-linear regression was performed to fit the concentration and crack propagation rate, and the fitting equation is: The power law exponent is obtained from the fitted equation. .

3. The test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load as described in claim 1, characterized in that: The comprehensive diffusion coefficient is calculated as follows: In the formula Indicates the first Reference diffusion coefficient of a characteristic bacterial community. , These represent porosity and reference porosity, respectively. Indicates the decay exponent, and .

4. The test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load as described in claim 1, characterized in that: The construction logic of the 3D model is as follows: The overall shape, internal pores, and initial crack network of concrete specimens were constructed using microfocus CT scanning. Based on the nonlinear prediction model between characteristic microbial communities and cracks, the required prediction time is set, and the crack prediction path after the corresponding prediction time is calculated. Different characteristic bacterial communities are distinguished by color, and a three-dimensional model is generated by superimposing the characteristic bacterial communities with the crack prediction path to complete the visualization output.

5. The test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load as described in claim 4, characterized in that: The required prediction time for calculating the crack prediction path is expressed as: In the formula Indicates the required forecast time. Indicates the time step. This indicates the total number of model updates; The change in crack length after each time step is expressed as: After each time step, the directional angle of the crack prediction path is expressed as: In the formula and These represent the initial direction angle and the distance traveled, respectively. The direction angle after the next update Index indicating the number of updates. , Indicates to Find the partial derivatives in three-dimensional coordinates. Indicates the gradient magnitude. This represents the maximum principal stress.

6. The test and characterization method for early-age concrete damage characteristics under high-temperature tunnel blasting load as described in claim 5, characterized in that: The maximum principal stress is calculated as follows: In the formula This represents the instantaneous pressure of the blasting load. This indicates the area where the blasting load is applied. , These represent the coefficient of thermal expansion and the modulus of elasticity of the concrete specimen, respectively.

Images