Railway fiber reinforced concrete structure shrinkage stress crack whole life intelligent operation and maintenance method and system

Abstract

The application discloses a railway fiber concrete structure shrinkage stress crack whole life intelligent operation and maintenance method and system, and the method comprises the following steps: S1, railway fiber concrete structure multi-source parameter acquisition and material characteristic modeling; S2, fiber bridge connection constraint capacity quantitative analysis; S3, environmental field, operation load field and material field coupling analysis.The application realizes the collaborative analysis of fiber concrete material characteristics, environmental factors, operation load factors and structural damage evolution process by constructing the progressive correlation mechanism among the fiber bridge connection constraint factor FBCF, the environmental-load-material three-field coupling index ELMCI, the shrinkage stress evolution index SSEI, the damage accumulation index DAI, the crack life index CLPI and the operation and maintenance decision index OMDI, can accurately reveal the formation and evolution law of the railway fiber concrete structure shrinkage stress, and improve the accuracy of crack initiation risk identification, damage accumulation evaluation and residual life prediction.

Description

Technical Field

[0001] This invention relates to the field of railway engineering construction technology, and in particular to a method and system for intelligent operation and maintenance of shrinkage stress cracks in railway fiber-reinforced concrete structures throughout their entire lifespan. Background Technology

[0002] Railway bridges, track slabs, tunnel linings, and subgrade ancillary structures are all located in complex service environments. To improve the crack resistance, toughness, and durability of these structures, fiber-reinforced concrete has been widely used in railway engineering construction. Fiber-reinforced concrete can effectively inhibit crack propagation and improve the tensile strength and energy dissipation capacity of the structure through fiber bridging. However, during long-term service, the structure is still affected by factors such as hydration heat, autogenous shrinkage, drying shrinkage, temperature and humidity changes, and train cyclic loads, which gradually generate shrinkage stress inside. When the shrinkage stress continues to accumulate and exceeds the local tensile strength, it can easily induce crack initiation and propagation, thereby affecting the safety, durability, and service life of the railway structure.

[0003] In existing technologies, the operation and maintenance management of cracks in railway concrete structures mainly adopts methods such as manual inspection, image recognition monitoring, online sensor monitoring, and early warning analysis. These methods assess the structural condition using apparent parameters such as crack width, crack length, and crack quantity, which improves the monitoring capability of railway structural defects to some extent. However, existing technologies still have the following shortcomings:

[0004] 1. Existing crack monitoring and assessment methods mainly focus on identifying the state after crack formation, lacking analysis of crack formation mechanisms and shrinkage stress evolution processes. In particular, they do not fully consider the impact of fiber bridging within fiber-reinforced concrete on stress transmission, stress release, and crack suppression processes. At the same time, there is a lack of a unified analysis mechanism for the coupling effects between temperature field, humidity field, operational load field, and material field, making it difficult to accurately characterize the formation and evolution of shrinkage stress within railway fiber-reinforced concrete structures. This results in insufficient crack initiation prediction capabilities and discrepancies between risk assessment results and actual service conditions.

[0005] 2. Existing operation and maintenance systems typically treat monitoring, assessment, and maintenance as independent management links, lacking a collaborative mechanism between crack damage evolution, life prediction, and operation and maintenance decisions. It is difficult to dynamically generate the optimal operation and maintenance plan based on crack development trends and the remaining life of the structure, and it is impossible to achieve closed-loop management of the entire life cycle from crack initiation prediction, damage accumulation analysis, life assessment to operation and maintenance decision optimization. This can easily lead to problems such as delayed maintenance, over-maintenance, and unreasonable allocation of maintenance resources.

[0006] Therefore, there is an urgent need to provide an intelligent operation and maintenance method and system for the entire life cycle of shrinkage stress cracks in railway fiber reinforced concrete structures. Summary of the Invention

[0007] Based on the technical problems existing in the background technology, this invention proposes an intelligent operation and maintenance method and system for shrinkage stress cracks in railway fiber reinforced concrete structures throughout their entire life cycle.

[0008] The intelligent operation and maintenance method for shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in this invention includes the following steps:

[0009] S1: Multi-source parameter acquisition and material feature modeling of railway fiber-reinforced concrete structures: Acquire material parameters, environmental parameters, operational load parameters and structural state parameters of railway fiber-reinforced concrete structures, and establish corresponding data mapping relationships;

[0010] S2: Quantitative analysis of fiber bridging constraint capacity: Based on the material parameters obtained in S1, a fiber bridging constraint characterization model is constructed, and the fiber bridging constraint factor FBCF is calculated to quantitatively characterize the constraint effect of fibers on concrete shrinkage deformation, the inhibition effect on crack propagation, and the enhancement effect on stress transfer process.

[0011] S3: Coupling analysis of environmental field, operational load field and material field: Using the fiber bridging constraint factor FBCF calculated in S2 as the basic parameter of the material field, the environmental action field is constructed in combination with the environmental parameters obtained in S1, and the operational load field is constructed in combination with the operational load parameters. The coupling relationship between the environmental action field, the operational load field and the material field is established, and the coupling index ELMCI of the three fields of environment, load and material is calculated to characterize the comprehensive action strength of railway fiber reinforced concrete structure under actual service conditions.

[0012] S4: Shrinkage stress evolution analysis and risk characterization: Based on the three-field coupling index ELMCI calculated in S3, the fiber bridging constraint factor FBCF calculated in S2, and the structural shrinkage deformation parameters, a shrinkage stress evolution model is constructed, and the shrinkage stress evolution index SSEI is calculated to characterize the degree of internal shrinkage stress accumulation and the risk level of crack initiation.

[0013] S5: Damage accumulation analysis and crack stage identification: Based on the shrinkage stress evolution index SSEI calculated in S4, a damage accumulation model is constructed, the damage accumulation index DAI is calculated, and the initiation stage, propagation stage, penetration stage and stable stage of structural cracks are identified based on the damage accumulation index DAI. At the same time, crack propagation rate parameters and damage evolution trend parameters are obtained.

[0014] S6: Crack life prediction and remaining service life assessment: Based on the damage accumulation index (DAI), crack propagation rate parameter, durability degradation parameter calculated in S5 and the shrinkage stress evolution index (SSEI) calculated in S4, a crack life prediction model is constructed to calculate the crack life index (CLPI) and the remaining service life of the structure, which are used to characterize the future development trend of cracks and the degree of degradation of the structure's service capacity.

[0015] S7: Operation and Maintenance Decision Evaluation and Strategy Generation: Based on the Crack Life Index (CLPI), Shrinkage Stress Evolution Index (SSEI), Damage Accumulation Index (DAI), and remaining service life of the structure, an operation and maintenance decision model is constructed. The operation and maintenance decision index (OMDI) is calculated, and corresponding operation and maintenance schemes are matched from the preset operation and maintenance strategy library according to the operation and maintenance decision index (OMDI). The operation and maintenance schemes include one or more of the following: inspection optimization scheme, crack repair scheme, structural reinforcement scheme, maintenance resource allocation scheme, and operation control scheme.

[0016] S8: Operation and Maintenance Feedback Closed-Loop Optimization: Obtain structural status feedback data after operation and maintenance implementation, and perform parameter correction and adaptive updates on the fiber bridging constraint characterization model, three-field coupling model, shrinkage stress evolution model, damage accumulation model, crack life prediction model and operation and maintenance decision model to realize dynamic intelligent operation and maintenance of shrinkage stress cracks in railway fiber-reinforced concrete structures throughout their entire life cycle.

[0017] Preferably, in S1, the structural state parameters include strain parameters, displacement parameters, crack parameters, durability degradation parameters, and shrinkage deformation parameters, and the shrinkage deformation parameters include drying shrinkage strain, autogenous shrinkage strain, and temperature shrinkage strain.

[0018] The material parameters include fiber type, fiber volume fraction, fiber length, fiber aspect ratio, fiber elastic modulus, fiber orientation distribution, and fiber dispersion uniformity.

[0019] The environmental parameters include temperature, humidity, rainfall, and freeze-thaw cycle parameters;

[0020] The operating load parameters include train axle load, train operating frequency, operating speed, and vibration response parameters.

[0021] Compared with existing technologies, the beneficial effects of this invention are:

[0022] 1. By constructing a fiber bridging constraint characterization model and establishing a fiber bridging constraint factor (FBCF), material properties such as fiber volume fraction, fiber orientation characteristics, fiber dispersion uniformity, and fiber bridging capacity are incorporated into the crack evolution analysis process. This achieves a quantitative characterization of the fiber bridging effect on stress transmission, stress release, and crack suppression capabilities. It overcomes the problem that existing fiber-reinforced concrete evaluation methods use ordinary concrete analysis models and are difficult to reflect the fiber reinforcement effect, thus improving the accuracy of the shrinkage stress crack formation mechanism analysis of railway fiber-reinforced concrete structures.

[0023] 2. By constructing the coupling relationship between the environmental action field, the operational load field, and the material constraint field, an environment-load-material three-field coupling index (ELMCI) is established. This enables a unified analysis of the synergistic effects of multiple factors, such as temperature changes, humidity changes, freeze-thaw cycles, train cyclic loads, and fiber material properties. This allows for a more realistic reflection of the formation and evolution of shrinkage stress in railway fiber-reinforced concrete structures under actual service conditions, improving the accuracy of crack initiation risk identification and prediction, and reducing structural defect assessment errors.

[0024] 3. By establishing a progressive analysis relationship between the shrinkage stress evolution index (SSEI), damage accumulation index (DAI), and crack life index (CLPI), a continuous prediction process from shrinkage stress formation, damage accumulation and development, crack propagation and evolution to remaining life assessment is realized. This breaks through the limitation of existing technologies that only monitor and assess the apparent state of cracks. It can identify crack development trends and structural life decay processes in advance, and improve the whole life cycle prediction capability of railway fiber reinforced concrete structures.

[0025] 4. Based on the Crack Life Index (CLPI), Damage Accumulation Index (DAI), Shrinkage Stress Evolution Index (SSEI), and remaining service life of the structure, an operation and maintenance decision model is constructed. The operation and maintenance decision index (OMDI) is calculated, and the corresponding operation and maintenance plan is automatically matched with the preset operation and maintenance strategy library. This realizes closed-loop management of the entire process from crack initiation prediction, damage analysis, life assessment to operation and maintenance decision generation. It can dynamically optimize the timing of maintenance, maintenance level, and allocation of maintenance resources, avoid over-maintenance and maintenance lag, and improve the intelligent operation and maintenance level and maintenance resource utilization efficiency of railway infrastructure.

[0026] This invention constructs a progressive correlation mechanism among the fiber bridging constraint factor (FBCF), the environment-load-material three-field coupling index (ELMCI), the shrinkage stress evolution index (SSEI), the damage accumulation index (DAI), the crack life index (CLPI), and the operation and maintenance decision index (OMDI). This mechanism enables the synergistic analysis of fiber-reinforced concrete material properties, environmental factors, operational load factors, and structural damage evolution processes. It can accurately reveal the formation and evolution laws of shrinkage stress in railway fiber-reinforced concrete structures, improving the accuracy of crack initiation risk identification, damage accumulation assessment, and remaining life prediction. Simultaneously, by establishing a linkage mechanism between crack life prediction and operation and maintenance decision-making, it achieves closed-loop management throughout the entire life cycle, from crack initiation prediction, damage evolution analysis, life assessment to operation and maintenance strategy generation. This improves the intelligent operation and maintenance level of railway infrastructure, the efficiency of maintenance resource allocation, and the structural service safety and durability. Attached Figure Description

[0027] Figure 1 This is a flowchart of the intelligent operation and maintenance method for the entire life cycle of shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in Embodiment 1 of the present invention;

[0028] Figure 2 This is a block diagram of the intelligent operation and maintenance system for the entire life cycle of shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in Embodiment 2 of the present invention.

[0029] Figure 3 This is a curve showing the variation of the shrinkage stress evolution index (SSEI) in the intelligent operation and maintenance method for the entire life cycle of shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in this invention.

[0030] Figure 4 This is a graph showing the relationship between the Crack Life Index (CLPI) and the Remaining Service (RUL) in the intelligent operation and maintenance method for shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in this invention.

[0031] Figure 5 This is a graph showing the time variation of the crack propagation rate and damage evolution trend parameter DET in the intelligent operation and maintenance method for shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in this invention. Detailed Implementation

[0032] The present invention will be further explained below with reference to specific embodiments.

[0033] Example 1

[0034] Reference Figure 1 , 3 4.5. This embodiment proposes a full-lifecycle intelligent operation and maintenance method for shrinkage stress cracks in railway fiber-reinforced concrete structures, including the following steps:

[0035] S1: Multi-source parameter acquisition and material feature modeling of railway fiber-reinforced concrete structures: Acquire material parameters, environmental parameters, operational load parameters and structural state parameters of railway fiber-reinforced concrete structures, and establish corresponding data mapping relationships;

[0036] The structural state parameters include strain parameters, displacement parameters, crack parameters, durability degradation parameters, and shrinkage deformation parameters. The shrinkage deformation parameters include drying shrinkage strain, autogenous shrinkage strain, and temperature shrinkage strain.

[0037] Material parameters include fiber type, fiber volume fraction, fiber length, fiber aspect ratio, fiber elastic modulus, fiber orientation distribution, and fiber dispersion uniformity.

[0038] Environmental parameters include temperature, humidity, rainfall, and freeze-thaw cycle parameters;

[0039] Operating load parameters include train axle load, train operating frequency, operating speed, and vibration response parameters;

[0040] S2: Quantitative analysis of fiber bridging constraint capacity: Based on the material parameters obtained in S1, a fiber bridging constraint characterization model is constructed, and the fiber bridging constraint factor FBCF is calculated to quantitatively characterize the constraint effect of fibers on concrete shrinkage deformation, the inhibition effect on crack propagation, and the enhancement effect on stress transfer process.

[0041] S3: Coupling analysis of environmental field, operational load field and material field: Using the fiber bridging constraint factor FBCF calculated in S2 as the basic parameter of the material field, the environmental action field is constructed in combination with the environmental parameters obtained in S1, and the operational load field is constructed in combination with the operational load parameters. The coupling relationship between the environmental action field, the operational load field and the material field is established, and the coupling index ELMCI of the three fields of environment, load and material is calculated to characterize the comprehensive action strength of railway fiber reinforced concrete structure under actual service conditions.

[0042] S4: Shrinkage stress evolution analysis and risk characterization: Based on the three-field coupling index ELMCI calculated in S3, the fiber bridging constraint factor FBCF calculated in S2, and the structural shrinkage deformation parameters, a shrinkage stress evolution model is constructed, and the shrinkage stress evolution index SSEI is calculated to characterize the degree of internal shrinkage stress accumulation and the risk level of crack initiation.

[0043] S5: Damage accumulation analysis and crack stage identification: Based on the shrinkage stress evolution index SSEI calculated in S4, a damage accumulation model is constructed, the damage accumulation index DAI is calculated, and the initiation stage, propagation stage, penetration stage and stable stage of structural cracks are identified based on the damage accumulation index DAI. At the same time, crack propagation rate parameters and damage evolution trend parameters are obtained.

[0044] S6: Crack life prediction and remaining service life assessment: Based on the damage accumulation index (DAI), crack propagation rate parameter, durability degradation parameter calculated in S5 and the shrinkage stress evolution index (SSEI) calculated in S4, a crack life prediction model is constructed to calculate the crack life index (CLPI) and the remaining service life of the structure, which are used to characterize the future development trend of cracks and the degree of degradation of the structure's service capacity.

[0045] S7: Operation and Maintenance Decision Evaluation and Strategy Generation: Based on the Crack Life Index (CLPI), Shrinkage Stress Evolution Index (SSEI), Damage Accumulation Index (DAI), and remaining service life of the structure, an operation and maintenance decision model is constructed. The operation and maintenance decision index (OMDI) is calculated, and corresponding operation and maintenance solutions are matched from the preset operation and maintenance strategy library according to the operation and maintenance decision index (OMDI). The operation and maintenance solutions include one or more of the following: inspection optimization solution, crack repair solution, structural reinforcement solution, maintenance resource allocation solution, and operation control solution.

[0046] S8: Operation and Maintenance Feedback Closed-Loop Optimization: Obtain structural status feedback data after operation and maintenance implementation, and perform parameter correction and adaptive updates on the fiber bridging constraint characterization model, three-field coupling model, shrinkage stress evolution model, damage accumulation model, crack life prediction model and operation and maintenance decision model to realize dynamic intelligent operation and maintenance of shrinkage stress cracks in railway fiber-reinforced concrete structures throughout their entire life cycle.

[0047] In this embodiment, the specific logical steps of S2 are as follows:

[0048] S201: Fiber Basic Parameter Extraction: Based on the material parameters obtained in S1, extract parameters such as fiber type, fiber volume fraction, fiber length, fiber diameter, fiber elastic modulus, fiber orientation distribution, and fiber dispersion uniformity to construct a fiber basic feature vector. ;

[0049] in: For fiber basic feature vectors, This refers to the fiber volume fraction. For fiber length, The diameter of the fiber. For fiber elastic modulus, This is the fiber orientation coefficient. This is the fiber dispersion uniformity coefficient;

[0050] S202: Fiber Aspect Ratio Evaluation: The fiber aspect ratio is calculated based on fiber length and fiber diameter, and is used to characterize the fiber's ability to bridge cracks. The calculation formula is as follows:

[0051] ;

[0052] AR represents the fiber aspect ratio. For fiber length, The larger the fiber aspect ratio, the stronger the fiber's ability to inhibit crack propagation.

[0053] S203: Calculation of Fiber Orientation Contribution: The effective constraint capability of fibers on the principal tensile stress direction is evaluated based on the spatial distribution direction of the fibers. The orientation contribution coefficient is calculated using the following formula:

[0054] ;

[0055] in The orientation contribution coefficient, Let be the angle between the i-th fiber and the direction of the principal tensile stress, and n be the number of fibers involved in the statistics. The closer the fiber direction is to the direction of the principal tensile stress, the stronger the constraint ability.

[0056] S204: Calculation of Fiber Dispersion Uniformity: The uniformity coefficient is calculated based on the spatial distribution of fibers in concrete to evaluate the degree of fiber aggregation. The calculation formula is as follows:

[0057] ;

[0058] in The dispersion uniformity coefficient, The standard deviation of fiber quantity in each sampling area. This represents the average number of fibers.

[0059] S205: Calculation of Fiber Bridging Capacity: The fiber bridging capacity index is calculated by considering the fiber volume fraction, fiber aspect ratio, elastic modulus, and orientation contribution. This index reflects the bridging load-bearing capacity of the fiber after it crosses a crack. The calculation formula is as follows:

[0060] ;

[0061] in The fiber bridging capacity index. This refers to the fiber volume fraction. The aspect ratio of the fiber. For fiber elastic modulus, Contribution coefficient for orientation;

[0062] S206: Construction of Fiber Bridging Constraint Factor: Combining the fiber bridging capacity index and uniformity coefficient, a fiber bridging constraint characterization model is established, and the fiber bridging constraint factor FBCF is calculated to characterize the constraint ability of fiber materials on crack propagation and stress release. Its calculation formula is as follows:

[0063] ;

[0064] in For fiber bridging constraint factor, The fiber bridging capacity index. This is the fiber dispersion uniformity coefficient. , These are the weighting coefficients;

[0065] S207: Classification of Fiber Confinement Capacity: The crack resistance of fiber-reinforced concrete is evaluated based on the fiber bridging constraint factor (FBCF). The evaluation criteria are as follows:

[0066] ;

[0067] in The fiber constraint capability level, , The preset level threshold;

[0068] This step primarily addresses the issue of insufficient utilization of the properties of fiber-reinforced concrete materials in existing technologies. By extracting key parameters such as fiber type, volume fraction, aspect ratio, elastic modulus, spatial orientation, and dispersion uniformity, a fiber bridging constraint characterization model is established. A fiber bridging constraint factor (FBCF) that reflects the fiber bridging load-bearing capacity and crack suppression capability is constructed, thereby realizing the digital characterization of the fiber material field. This provides a foundation for subsequent coupled analysis of environmental field, operational load field, and material field, as well as the analysis of the formation mechanism of shrinkage stress cracks.

[0069] In this embodiment, the specific logical steps of S3 are as follows:

[0070] S301: Construction of Environmental Action Field: Based on the temperature, humidity, rainfall, and freeze-thaw cycle parameters obtained in S1, an environmental action field is constructed, and the environmental impact index is calculated to characterize the comprehensive influence of environmental factors on the formation of structural shrinkage stress. The calculation formula is as follows:

[0071] ;

[0072] in For environmental impact index, To normalize the temperature parameter, For normalized humidity parameters, To normalize the rainfall parameters, For normalized freeze-thaw cycle parameters, , , , Environmental impact weighting coefficient;

[0073] S302: Construction of the Operational Load Field: Based on the train axle load, train operating frequency, train operating speed, and vibration response parameters obtained in S1, an operational load field is constructed, and the operational load index is calculated to characterize the influence of train load on structural fatigue damage and crack propagation. The calculation formula is as follows:

[0074] ;

[0075] in For the operating load index, For normalized axle load parameters, To normalize the operating frequency parameters, To normalize the running speed parameters, To normalize the vibration response parameters, , , , For operational load weighting coefficients;

[0076] S303: Material Constraint Field Construction: Obtain the fiber bridging constraint factor FBCF output from S2, establish the material constraint field, and calculate the material constraint capacity index to characterize the ability of fiber-reinforced concrete to inhibit shrinkage deformation and crack propagation. The calculation formula is as follows:

[0077] ;

[0078] in This refers to the material constraint capability index. For fiber bridging constraint factor, This is a material correction factor;

[0079] S304: Coupling Analysis of Environmental Field and Operational Load Field: An environment-load coupling model is established based on the Environmental Impact Index (EI) and the Operational Load Index (LI). The environmental load coupling strength is calculated to characterize the damage-promoting effect under the combined action of environmental factors and train load. The calculation formula is as follows:

[0080] ;

[0081] in Environmental load coupling strength;

[0082] S305: Environment-Load-Material Three-Field Coupling Analysis: The material constraint capacity index MI is introduced into the environmental load coupling model to establish a three-field coupling model. The three-field coupling coefficients are calculated to characterize the comprehensive load state of the structure under fiber material constraint. The calculation formula is as follows:

[0083] ;

[0084] in The coupling coefficient between the environment, load, and material fields;

[0085] S306: Generation of the Three-Field Coupling Index: Based on the Environmental Impact Index EI, the Operational Load Index LI, and the Three-Field Coupling Coefficient The Environment-Load-Material Three-Field Coupling Index (ELMCI) is used to characterize the comprehensive strength of railway fiber-reinforced concrete structures under actual service conditions. Its calculation formula is as follows:

[0086] ;

[0087] in The coupling index of the environment-load-material three fields. , , These are the environmental field influence weighting coefficient, the operational load field influence weighting coefficient, and the three-field coupling effect weighting coefficient, respectively.

[0088] This step primarily addresses the issues of separate analysis of environmental factors, operational load factors, and fiber-reinforced concrete material properties in existing technologies, as well as the lack of a unified coupled evaluation mechanism. Using the fiber bridging constraint factor FBCF obtained from S2 as the basic parameter of the material field, a three-field coupled model of environment, load, and material is constructed by combining the environmental action field and the operational load field. This model quantitatively analyzes the synergistic effects of temperature and humidity changes, freeze-thaw cycles, train cyclic loads, and fiber constraint effects on the structural stress state, forming the three-field coupled index ELMCI that can reflect the comprehensive intensity of the structure under actual service conditions. This provides key input parameters for subsequent analysis of shrinkage stress formation mechanisms and prediction of crack initiation risks.

[0089] Reference Figure 3 In this embodiment, the specific logical steps of S4 are as follows:

[0090] S401: Shrinkage Deformation Parameter Extraction: Obtain the shrinkage deformation parameters from S1, including drying shrinkage strain, autogenous shrinkage strain, and temperature shrinkage strain, and construct a comprehensive shrinkage strain parameter to characterize the overall shrinkage deformation degree of the railway fiber-reinforced concrete structure during service. The formula used is:

[0091] ;

[0092] in To comprehensively account for contraction strain, For drying shrinkage strain, For autogenous contraction strain, This is due to temperature contraction strain;

[0093] S402: Construction of shrinkage stress influence factors: Obtain the fiber bridging constraint factor FBCF output from S2 and the environment-load-material three-field coupling index ELMCI output from S3, and establish a set of shrinkage stress influence factors to provide input parameters for subsequent shrinkage stress evolution analysis;

[0094] S403: Construction of Shrinkage Stress Evolution Model: A shrinkage stress evolution model is established based on comprehensive shrinkage strain parameters, fiber bridging constraint factors, and three-field coupling indices to calculate the internal shrinkage stress level of the structure. The formula used is as follows:

[0095] ;

[0096] in For the level of contraction stress, The elastic modulus of concrete;

[0097] S404: Calculation of Shrinkage Stress Evolution Index: The Shrinkage Stress Evolution Index (SSEI) is established based on the shrinkage stress level and its changing trend. It reflects the degree of accumulation and growth trend of shrinkage stress within the structure. The formula used is as follows:

[0098] ;

[0099] in The contractile stress evolution index, The rate of change of contraction stress, , These are the weighting coefficients;

[0100] S405: Crack Initiation Risk Characterization: Based on the relationship between the Shrinkage Stress Evolution Index (SSEI) and the tensile strength threshold of the structure, the crack initiation risk is assessed and classified into low-risk, medium-risk, and high-risk levels. At the same time, the Shrinkage Stress Evolution Index (SSEI) is output to S5 as the basic parameter for calculating the Damage Accumulation Index (DAI).

[0101] This step primarily addresses the lack of analysis on the formation mechanism of shrinkage stress and the difficulty in predicting crack initiation in existing technologies. Based on the fiber bridging constraint factor FBCF constructed in S2 and the environment-load-material three-field coupling index ELMCI constructed in S3, a shrinkage stress evolution model is established in conjunction with structural shrinkage deformation parameters. The formation, accumulation, and growth process of shrinkage stress are dynamically analyzed, resulting in the shrinkage stress evolution index SSEI, which reflects the internal stress state of the structure and the risk of crack initiation. This achieves a mechanistic characterization of the shrinkage stress evolution process from material constraint effects and multi-field coupling effects, providing a basis for subsequent damage accumulation analysis, life prediction, and intelligent operation and maintenance decisions.

[0102] Reference Figure 5 In this embodiment, the specific logical steps of S5 are as follows:

[0103] S501: Acquisition of Shrinkage Stress Evolution Data: Acquire the Shrinkage Stress Evolution Index (SSEI) output by S404, as well as the crack width, crack length, strain parameters, and displacement parameters for the corresponding time period. Combine this with historical monitoring data to construct a structural damage evolution analysis dataset, providing basic data support for damage accumulation analysis.

[0104] S502: Damage Accumulation Model Construction: A damage accumulation model is established based on the correlation between the shrinkage stress evolution index (SSEI) and structural response parameters. The damage accumulation index (DAI) is calculated to characterize the degree of damage development and crack propagation within the structure. The calculation formula is as follows:

[0105] ;

[0106] in The damage accumulation index, The contractile stress evolution index, For crack width parameters, For crack length parameters, , , These are the weighting coefficients;

[0107] S503: Crack Stage Identification: Based on the change characteristics and growth trend of the damage accumulation index (DAI) calculated in S502, the stage of the structural crack is identified. When DAI is at a low level and grows slowly, it is determined to be the crack initiation stage. When DAI continues to grow, it is determined to be the crack propagation stage. When DAI reaches the preset penetration threshold, it is determined to be the crack penetration stage. When DAI tends to stabilize, it is determined to be the crack stabilization stage.

[0108] S504: Crack propagation rate analysis: Based on the changes in crack width and crack length during the continuous monitoring period, the crack propagation rate parameter is calculated to reflect the crack development speed. The formula used is:

[0109] ;

[0110] in For crack propagation rate parameters, This represents the change in crack width. This represents the change in crack length. For monitoring time intervals;

[0111] S505: Combining the damage accumulation index (DAI) and crack propagation rate parameter, analyze the structural damage development trend, calculate the damage evolution trend parameter to characterize the future development direction and speed of structural damage, and output the damage accumulation index (DAI), crack propagation rate parameter and damage evolution trend parameter to S6 as input parameters for the crack life prediction model.

[0112] The formula used is:

[0113] ;

[0114] in For damage evolution trend parameters, The rate of change of the cumulative damage index. For crack propagation rate parameters, , These are the weighting coefficients;

[0115] This step primarily addresses the shortcomings of existing technologies, such as the difficulty in continuously characterizing crack damage development and the lack of ability to analyze the entire life cycle evolution of cracks. Based on the shrinkage stress evolution index (SSEI) obtained from S4, and combined with structural response information including crack width, crack length, strain, and displacement, a damage accumulation analysis model is established. This model enables continuous analysis from shrinkage stress accumulation to structural damage development and crack propagation, resulting in a damage accumulation index (DAI) reflecting the degree of structural damage and a crack propagation rate parameter characterizing the crack development speed. In addition, the damage evolution trend parameter DET, which characterizes the future damage development trend, enables dynamic identification of the crack initiation, propagation, penetration and stabilization stages, providing a basis for subsequent crack life prediction and operation and maintenance decisions.

[0116] Reference Figure 4 In this embodiment, the specific logical steps of S6 are as follows:

[0117] S601: Construction of Lifetime Prediction Parameter Set: Obtain the Damage Accumulation Index (DAI) and Crack Propagation Rate Parameters from S5 Output. In addition, the damage evolution trend parameter DET was obtained, along with the shrinkage stress evolution index SSEI output from S4 and the durability degradation parameter from S1. Construct a set of parameters for predicting crack lifetime:

[0118] ;

[0119] in It is a set of lifetime prediction parameters used to characterize the current damage state and future degradation trend of the structure;

[0120] S602: Crack Life Prediction Model Construction: A crack life prediction model is established based on the life prediction parameter set, and the crack life index CLPI is calculated to comprehensively characterize the future development risk of cracks and the degree of structural life degradation. The formula used is as follows:

[0121] ;

[0122] in This refers to the crack life index. The damage accumulation index, For crack propagation rate parameters, For damage evolution trend parameters, The contractile stress evolution index, , , , , These are the weighting coefficients;

[0123] S603: Crack Development Trend Assessment: Based on the changes in the Crack Life Index (CLPI), the future development trend of cracks is assessed. When the CLPI continues to increase, it indicates that the crack is in an accelerated propagation state. When the CLPI tends to stabilize, it indicates that the crack development is becoming more gradual, thus forming a crack development trend evaluation result.

[0124] S604: Calculation of Remaining Service of Structure: The remaining service life of the structure is calculated based on the Crack Life Index (CLPI) and the structural design service life parameters. The formula used is as follows:

[0125] ;

[0126] in For the remaining service life of the structure, For structural design life, The crack lifetime index threshold is the threshold value for the failure state. As the CLPI gradually approaches the failure threshold, the remaining service life gradually decreases.

[0127] S605: Life assessment result output: Generate life assessment results based on crack life index CLPI and structural remaining service life RUL. The life assessment results include crack future development trend, current life health level, remaining service life level and crack failure risk level. Output crack life index CLPI and structural remaining service life RUL to S7 as the basic parameters for calculating operation and maintenance decision index OMDI.

[0128] This step primarily addresses the problem of disconnect between crack monitoring results and life prediction in existing technologies, making it difficult to accurately assess the remaining service capacity of structures. Based on the damage accumulation state and crack propagation trend information obtained from S5, and combined with the characteristics of shrinkage stress evolution and durability degradation, a crack life prediction model is established. This enables continuous deduction from damage accumulation analysis to life status assessment, resulting in the Crack Life Index (CLPI) which reflects the future development risk of cracks, and the Remaining Service (RUL) which characterizes the remaining service capacity of structures. This provides support for the transformation of operation and maintenance management from reactive maintenance to predictive maintenance.

[0129] In this embodiment, the specific logical steps of S7 are as follows:

[0130] S701: Construction of Operation and Maintenance Decision Parameter Set: Obtain the Crack Life Index (CLPI) and Remaining Service Life (RUL) output from S6, the Damage Accumulation Index (DAI) output from S5, and the Shrinkage Stress Evolution Index (SSEI) output from S4 to construct the operation and maintenance decision parameter set: ,in This is a set of operation and maintenance decision parameters used to characterize the current damage state, lifespan, and future risk level of the structure.

[0131] S702: Construction of Operation and Maintenance Decision Model: An operation and maintenance decision model is established based on the set of operation and maintenance decision parameters. The Operation and Maintenance Decision Index (OMDI) is calculated to comprehensively reflect the current risk level and the degree of operation and maintenance needs of the structure. The formula used is as follows:

[0132] ;

[0133] in As an index for operation and maintenance decision-making, This refers to the crack life index. The damage accumulation index, The contractile stress evolution index, For the remaining service life of the structure, For structural design life, , , , These are the crack lifetime risk weight, damage accumulation risk weight, shrinkage stress risk weight, and remaining lifetime risk weight, respectively.

[0134] S703: Structural Risk Level Assessment: The structural operating status is classified into five risk levels based on the Operation and Maintenance Decision Index (OMDI): Level 1, Level 2, Level 3, Level 4, and Level 5. The higher the OMDI, the higher the structural risk level.

[0135] S704: Operation and maintenance strategy matching analysis: Establish a preset operation and maintenance strategy library, and match the corresponding operation and maintenance plan in the operation and maintenance strategy library according to the structural risk level and the operation and maintenance decision index OMDI. The operation and maintenance strategy library includes inspection optimization strategy, crack sealing and repair strategy, fiber reinforcement strategy, structural reinforcement strategy, load-limiting operation strategy and replacement and reconstruction strategy.

[0136] S705: Operation and Maintenance Plan Generation and Output: Outputs the target operation and maintenance plan and corresponding execution suggestions, and feeds back the operation and maintenance implementation results to the S8 closed-loop optimization module for subsequent model adaptive correction. The execution suggestions include: suggested maintenance time, suggested maintenance location, suggested maintenance level, suggested maintenance resource allocation, and next evaluation cycle.

[0137] This step addresses the problem in existing technologies where life prediction results and operation and maintenance management are independent and it is difficult to form a dynamic decision-making mechanism. Based on crack life prediction results and structural health status evaluation results, an operation and maintenance decision evaluation model is established to realize structural risk level identification, operation and maintenance demand analysis, and automatic generation of operation and maintenance strategies. This results in the Operation and Maintenance Decision Index (OMDI), which reflects the degree of comprehensive operation and maintenance needs of the structure, thereby realizing predictive maintenance and refined management of railway fiber-reinforced concrete structures.

[0138] In this embodiment, the specific logical steps of S8 are as follows:

[0139] S801: Operation and maintenance feedback data collection: Obtain structural status feedback data after the implementation of the operation and maintenance plan generated by S7, and establish an operation and maintenance feedback database. The structural status feedback data includes crack width change data, crack length change data, strain change data, displacement change data, temperature and humidity change data, vibration response data, and maintenance effect evaluation data.

[0140] S802: Operation and Maintenance Effectiveness Evaluation and Analysis: The feedback data after operation and maintenance implementation is compared and analyzed with the predicted results before implementation to obtain the crack control effect, damage suppression effect, lifespan recovery effect, and operation and maintenance resource utilization effect. An operation and maintenance effectiveness deviation parameter is calculated to characterize the degree of difference between the predicted results and the actual operating status. The calculation formula is as follows:

[0141] ;

[0142] in For parameters indicating deviation in operation and maintenance performance, This is the actual monitoring result after operation and maintenance. The model's prediction results;

[0143] S803: Model Parameter Error Identification: Based on the deviation parameters of operation and maintenance effect, identify the sources of prediction errors of each model and generate a set of model correction parameters. The error sources include fiber bridging constraint model error, three-field coupling model error, shrinkage stress evolution model error, damage accumulation model error, crack life prediction model error, and operation and maintenance decision model error.

[0144] S804: Model Adaptive Correction: Based on the model correction parameter set, the weight coefficients, influence factors and threshold parameters in each level of the model are dynamically adjusted. Specifically, this includes: correcting the calculation parameters of the fiber bridging constraint factor FBCF, the calculation parameters of the three-field coupling index ELMCI, the calculation parameters of the shrinkage stress evolution index SSEI, the calculation parameters of the damage accumulation index DAI, the calculation parameters of the crack life index CLPI, and the calculation parameters of the operation and maintenance decision index OMDI, so that the model output results gradually approach the actual service state.

[0145] S805: Closed-loop optimization throughout the entire life cycle: The modified model is reapplied to the subsequent monitoring, evaluation, life prediction and operation and maintenance decision-making process of railway fiber-reinforced concrete structures, and new structural status feedback data is continuously received. Steps S801 to S804 are repeated to finally form a dynamic intelligent operation and maintenance closed loop for the entire life cycle of shrinkage stress cracks in railway fiber-reinforced concrete structures.

[0146] This step addresses the problem in existing technologies where model parameters are fixed and prediction results are difficult to continuously adapt to changes in structural service conditions. By establishing an operation and maintenance feedback closed-loop optimization mechanism, the crack status, damage status, and life status after operation and maintenance are fed back to the analysis models at all levels. The mechanism adaptively learns and dynamically optimizes the fiber bridging constraint mechanism, three-field coupling mechanism, shrinkage stress evolution mechanism, damage accumulation mechanism, life prediction mechanism, and operation and maintenance decision-making mechanism. This achieves closed-loop management throughout the entire life cycle, from monitoring and analysis, life prediction, operation and maintenance decision-making to feedback optimization, continuously improving the system's prediction accuracy and intelligent operation and maintenance capabilities.

[0147] This embodiment constructs a progressive correlation mechanism among the fiber bridging constraint factor FBCF, the environment-load-material three-field coupling index ELMCI, the shrinkage stress evolution index SSEI, the damage accumulation index DAI, the crack life index CLPI, and the operation and maintenance decision index OMDI. This enables the synergistic analysis of fiber-reinforced concrete material properties, environmental factors, operational load factors, and structural damage evolution processes. It can accurately reveal the formation and evolution law of shrinkage stress in railway fiber-reinforced concrete structures, and improve the accuracy of crack initiation risk identification, damage accumulation assessment, and remaining life prediction. At the same time, by establishing a linkage mechanism between crack life prediction and operation and maintenance decision-making, it realizes closed-loop management of the entire life cycle from crack initiation prediction, damage evolution analysis, life assessment to operation and maintenance strategy generation, thereby improving the intelligent operation and maintenance level of railway infrastructure, the efficiency of maintenance resource allocation, and the structural service safety and durability.

[0148] Example 2

[0149] Reference Figure 2 This embodiment proposes an intelligent operation and maintenance system for the entire life cycle of shrinkage stress cracks in railway fiber-reinforced concrete structures, including a multi-source data acquisition module, a fiber bridging constraint analysis module, a three-field coupling analysis module, a shrinkage stress evolution analysis module, a damage accumulation analysis module, a crack life prediction module, an intelligent operation and maintenance decision-making module, and a closed-loop optimization module.

[0150] The multi-source data acquisition module includes temperature and humidity sensors, strain sensors, vibration sensors, displacement sensors, crack monitoring devices, and data communication units. It is used to acquire material parameters, environmental parameters, operational load parameters, and structural state parameters of railway fiber-reinforced concrete structures, and to establish corresponding data mapping relationships.

[0151] The fiber bridging constraint analysis module is connected to the multi-source data acquisition module. It is used to construct a fiber bridging constraint characterization model based on material parameters and calculate the fiber bridging constraint factor FBCF to characterize the constraint ability of fiber concrete against shrinkage deformation and crack propagation.

[0152] The three-field coupling analysis module is connected to the fiber bridging constraint analysis module. It is used to construct the environmental action field based on environmental parameters, the operational load field based on operational load parameters, and the material constraint field based on fiber bridging constraint factors. It also establishes the coupling relationship between the environmental action field, the operational load field, and the material constraint field, and calculates the three-field coupling index ELMCI.

[0153] The shrinkage stress evolution analysis module is connected to the three-field coupling analysis module. It is used to construct a shrinkage stress evolution model based on the three-field coupling index ELMCI, fiber bridging constraint factor FBCF and structural shrinkage deformation parameters, and to calculate the shrinkage stress evolution index SSEI.

[0154] The damage accumulation analysis module is connected to the shrinkage stress evolution analysis module. It is used to establish a damage accumulation model based on the shrinkage stress evolution index SSEI, calculate the damage accumulation index DAI, and identify the crack initiation stage, crack propagation stage, crack penetration stage, and crack stabilization stage.

[0155] The crack life prediction module is connected to the damage accumulation analysis module. It is used to build a crack life prediction model based on the damage accumulation index (DAI), shrinkage stress evolution index (SSEI), crack propagation rate parameters, and durability degradation parameters, and to calculate the crack life index (CLPI) and the remaining service life of the structure.

[0156] The intelligent operation and maintenance decision module is connected to the crack life prediction module. It is used to build an operation and maintenance decision model based on the crack life index CLPI, damage accumulation index DAI, shrinkage stress evolution index SSEI and the remaining service life of the structure, calculate the operation and maintenance decision index OMDI, and generate the corresponding operation and maintenance plan.

[0157] The closed-loop optimization module is connected to the intelligent operation and maintenance decision module to obtain structural status feedback data after operation and maintenance implementation, and to dynamically correct and adaptively update the fiber bridging constraint characterization model, the three-field coupling model, the shrinkage stress evolution model, the damage accumulation model, the crack life prediction model, and the operation and maintenance decision model.

[0158] This embodiment achieves full lifecycle management of shrinkage stress cracks in railway fiber-reinforced concrete structures, from formation mechanism analysis and damage evolution assessment to life prediction and operation and maintenance optimization, by constructing modules such as fiber bridging constraint analysis, environment-load-material three-field coupling analysis, shrinkage stress evolution analysis, damage accumulation analysis, crack life prediction, and intelligent operation and maintenance decision-making. It can improve the accuracy of crack risk identification, life prediction, and operation and maintenance decision-making, and enhance the intelligent operation and maintenance level and service safety of railway infrastructure.

[0159] Example 3

[0160] Taking the ballastless track slab fiber-reinforced concrete structure of a high-speed railway section as an example, the following application description of the intelligent operation and maintenance method for shrinkage stress cracks in railway fiber-reinforced concrete structures proposed in this invention is provided:

[0161] The track slab is made of polypropylene fiber reinforced concrete and has a service life of 5 years. Temperature and humidity sensors, strain sensors, vibration sensors, displacement sensors and crack monitoring devices are installed inside and around the track slab. The monitoring data is uploaded to the operation and maintenance platform in real time through the data communication unit.

[0162] First, material parameters, environmental parameters, operational load parameters, and structural state parameters were collected through a multi-source data acquisition module. Among them, the fiber volume fraction was 0.9%, the fiber length was 18 mm, the fiber diameter was 0.03 mm, and the fiber elastic modulus was 4.2 GPa. The average temperature during the monitoring period was 32℃, the relative humidity was 65%, the daily train passing frequency was 280 times, and local microcracks were detected in the structure with an average crack width of 0.08 mm.

[0163] Subsequently, the fiber bridging constraint analysis module established a fiber bridging constraint characterization model based on fiber volume fraction, aspect ratio, elastic modulus, orientation distribution, and dispersion uniformity, and calculated the fiber bridging constraint factor: FBCF=0.82, indicating that fiber-reinforced concrete has strong crack inhibition and stress release capabilities.

[0164] Next, the three-field coupling analysis module established an environment-load-material three-field coupling model based on environmental parameters, operational load parameters, and fiber bridging constraint factors. The calculated ELMCI=0.74 indicates that there is a significant coupling effect between environmental factors, train load, and material properties.

[0165] Then, the shrinkage stress evolution analysis module establishes a shrinkage stress evolution model based on the comprehensive shrinkage strain parameters, fiber bridging constraint factor FBCF and three-field coupling index ELMCI, and calculates that: SSEI=0.68. According to the risk level assessment, the current structure is determined to be in the medium-high risk range, and there is a trend of further crack propagation.

[0166] Furthermore, the damage accumulation analysis module establishes a damage accumulation model based on the shrinkage stress evolution index (SSEI) and parameters such as crack width and crack length, calculates DAI=0.61, and identifies the current crack as being in the propagation stage; simultaneously, it calculates the crack propagation rate parameter. and damage evolution trend parameters This indicates that the crack is still in a state of continuous growth. Subsequently, the crack lifetime prediction module uses the damage accumulation index (DAI) and crack propagation rate parameters to predict the crack life. A crack life prediction model was established using the damage evolution trend parameter DET and the shrinkage stress evolution index SSEI. The calculated CLPI = 0.73, predicting the remaining service life of the track slab to be... This indicates that the structural durability has deteriorated to a certain extent.

[0167] Finally, the intelligent operation and maintenance decision module establishes an operation and maintenance decision model based on CLPI, DAI, SSEI and RUL, and calculates OMDI=0.71. The system automatically matches the "preventive repair + key monitoring" operation and maintenance strategy, and recommends that the cracked area be grouted and sealed within 3 months, and that the monitoring frequency be increased from once a month to once a week.

[0168] After the operation and maintenance is completed, the closed-loop optimization module continuously collects feedback data on the structural status after repair, and performs parameter correction and adaptive updates on the fiber bridging constraint model, three-field coupling model, shrinkage stress evolution model, damage accumulation model, crack life prediction model and operation and maintenance decision model, so that the subsequent prediction results are closer to the actual service state of the structure, thereby realizing intelligent operation and maintenance of shrinkage stress cracks in railway fiber-reinforced concrete structures throughout their entire life cycle.

[0169] Implementation Results: By adopting this invention, it is possible to achieve synergistic analysis of fiber-reinforced concrete material characteristics, environmental factors, and operational load factors, and to predict in advance the risk of crack initiation, damage accumulation process, and remaining lifespan. Based on the prediction results, an operation and maintenance plan is automatically generated. Compared with the traditional operation and maintenance method based on crack appearance parameters, this invention improves the accuracy of crack risk identification and the scientific nature of operation and maintenance decisions, and realizes predictive maintenance and full life cycle management of railway fiber-reinforced concrete structures.

[0170] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent operation and maintenance of shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures, characterized in that: Includes the following steps: S1: Multi-source parameter acquisition and material feature modeling of railway fiber-reinforced concrete structures: Acquire material parameters, environmental parameters, operational load parameters and structural state parameters of railway fiber-reinforced concrete structures, and establish corresponding data mapping relationships; S2: Quantitative analysis of fiber bridging constraint capacity: Based on the material parameters obtained in S1, a fiber bridging constraint characterization model is constructed, and the fiber bridging constraint factor FBCF is calculated to quantitatively characterize the constraint effect of fibers on concrete shrinkage deformation, the inhibition effect on crack propagation, and the enhancement effect on stress transfer process. S3: Coupling analysis of environmental field, operational load field and material field: Using the fiber bridging constraint factor FBCF calculated in S2 as the basic parameter of the material field, the environmental action field is constructed in combination with the environmental parameters obtained in S1, and the operational load field is constructed in combination with the operational load parameters. The coupling relationship between the environmental action field, the operational load field and the material field is established, and the coupling index ELMCI of the three fields of environment, load and material is calculated to characterize the comprehensive action strength of railway fiber reinforced concrete structure under actual service conditions. S4: Shrinkage stress evolution analysis and risk characterization: Based on the three-field coupling index ELMCI calculated in S3, the fiber bridging constraint factor FBCF calculated in S2, and the structural shrinkage deformation parameters, a shrinkage stress evolution model is constructed, and the shrinkage stress evolution index SSEI is calculated to characterize the degree of internal shrinkage stress accumulation and the risk level of crack initiation. S5: Damage accumulation analysis and crack stage identification: Based on the shrinkage stress evolution index SSEI calculated in S4, a damage accumulation model is constructed, the damage accumulation index DAI is calculated, and the initiation stage, propagation stage, penetration stage and stable stage of structural cracks are identified based on the damage accumulation index DAI. At the same time, crack propagation rate parameters and damage evolution trend parameters are obtained. S6: Crack life prediction and remaining service life assessment: Based on the damage accumulation index (DAI), crack propagation rate parameter, durability degradation parameter calculated in S5 and the shrinkage stress evolution index (SSEI) calculated in S4, a crack life prediction model is constructed to calculate the crack life index (CLPI) and the remaining service life of the structure, which are used to characterize the future development trend of cracks and the degree of degradation of the structure's service capacity. S7: Operation and Maintenance Decision Evaluation and Strategy Generation: Based on the Crack Life Index (CLPI), Shrinkage Stress Evolution Index (SSEI), Damage Accumulation Index (DAI), and remaining service life of the structure, an operation and maintenance decision model is constructed. The operation and maintenance decision index (OMDI) is calculated, and corresponding operation and maintenance schemes are matched from the preset operation and maintenance strategy library according to the operation and maintenance decision index (OMDI). The operation and maintenance schemes include one or more of the following: inspection optimization scheme, crack repair scheme, structural reinforcement scheme, maintenance resource allocation scheme, and operation control scheme. S8: Operation and Maintenance Feedback Closed-Loop Optimization: Obtain structural status feedback data after operation and maintenance implementation, and perform parameter correction and adaptive updates on the fiber bridging constraint characterization model, three-field coupling model, shrinkage stress evolution model, damage accumulation model, crack life prediction model and operation and maintenance decision model to realize dynamic intelligent operation and maintenance of shrinkage stress cracks in railway fiber-reinforced concrete structures throughout their entire life cycle.

2. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 1, characterized in that, In S1, the structural state parameters include strain parameters, displacement parameters, crack parameters, durability degradation parameters, and shrinkage deformation parameters. The shrinkage deformation parameters include drying shrinkage strain, autogenous shrinkage strain, and temperature shrinkage strain. The material parameters include fiber type, fiber volume fraction, fiber length, fiber aspect ratio, fiber elastic modulus, fiber orientation distribution, and fiber dispersion uniformity. The environmental parameters include temperature, humidity, rainfall, and freeze-thaw cycle parameters; The operating load parameters include train axle load, train operating frequency, operating speed, and vibration response parameters.

3. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 2, characterized in that, The specific logical steps of S2 are as follows: S201: Fiber Basic Parameter Extraction: Based on the material parameters obtained in S1, extract parameters such as fiber type, fiber volume fraction, fiber length, fiber diameter, fiber elastic modulus, fiber orientation distribution, and fiber dispersion uniformity to construct a fiber basic feature vector. ; in: For fiber basic feature vectors, This refers to the fiber volume fraction. For fiber length, The diameter of the fiber. For fiber elastic modulus, This is the fiber orientation coefficient. This is the fiber dispersion uniformity coefficient; S202: Fiber Aspect Ratio Evaluation: The fiber aspect ratio is calculated based on fiber length and fiber diameter, and is used to characterize the fiber's ability to bridge cracks. The calculation formula is as follows: ； Where AR is the fiber aspect ratio. For fiber length, The larger the fiber aspect ratio, the stronger the fiber's ability to inhibit crack propagation. S203: Calculation of Fiber Orientation Contribution: The effective constraint capability of fibers on the principal tensile stress direction is evaluated based on the spatial distribution direction of the fibers. The orientation contribution coefficient is calculated using the following formula: ； in The orientation contribution coefficient, Let be the angle between the i-th fiber and the direction of the principal tensile stress, and n be the number of fibers involved in the statistics. The closer the fiber direction is to the direction of the principal tensile stress, the stronger the constraint ability. S204: Calculation of Fiber Dispersion Uniformity: The uniformity coefficient is calculated based on the spatial distribution of fibers in concrete to evaluate the degree of fiber aggregation. The calculation formula is as follows: ； in The dispersion uniformity coefficient, The standard deviation of fiber quantity in each sampling area. This represents the average number of fibers. S205: Calculation of Fiber Bridging Capacity: The fiber bridging capacity index is calculated by considering the fiber volume fraction, fiber aspect ratio, elastic modulus, and orientation contribution. This index reflects the bridging load-bearing capacity of the fiber after it crosses a crack. The calculation formula is as follows: ； in The fiber bridging capacity index. This refers to the fiber volume fraction. The aspect ratio of the fiber. For fiber elastic modulus, Contribution coefficient for orientation; S206: Construction of Fiber Bridging Constraint Factor: Combining the fiber bridging capacity index and uniformity coefficient, a fiber bridging constraint characterization model is established, and the fiber bridging constraint factor FBCF is calculated to characterize the constraint ability of fiber materials on crack propagation and stress release. Its calculation formula is as follows: ； in For fiber bridging constraint factor, The fiber bridging capacity index. This is the fiber dispersion uniformity coefficient. , These are the weighting coefficients; S207: Classification of Fiber Confinement Capacity: The crack resistance of fiber-reinforced concrete is evaluated based on the fiber bridging constraint factor (FBCF). The evaluation criteria are as follows: ； in The fiber constraint capability level, , This is the preset level threshold.

4. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 3, characterized in that, The specific logical steps of S3 are as follows: S301: Construction of Environmental Action Field: Based on the temperature, humidity, rainfall, and freeze-thaw cycle parameters obtained in S1, an environmental action field is constructed, and the environmental impact index is calculated to characterize the comprehensive influence of environmental factors on the formation of structural shrinkage stress. The calculation formula is as follows: ； in For environmental impact index, To normalize the temperature parameter, For normalized humidity parameters, To normalize the rainfall parameters, For normalized freeze-thaw cycle parameters, , , , Environmental impact weighting coefficient; S302: Construction of the Operational Load Field: Based on the train axle load, train operating frequency, train operating speed, and vibration response parameters obtained in S1, an operational load field is constructed, and the operational load index is calculated to characterize the influence of train load on structural fatigue damage and crack propagation. The calculation formula is as follows: ； in For the operating load index, For normalized axle load parameters, To normalize the operating frequency parameters, To normalize the running speed parameters, To normalize the vibration response parameters, , , , For operational load weighting coefficients; S303: Material Constraint Field Construction: Obtain the fiber bridging constraint factor FBCF output from S2, establish the material constraint field, and calculate the material constraint capacity index to characterize the ability of fiber-reinforced concrete to inhibit shrinkage deformation and crack propagation. The calculation formula is as follows: ； in This refers to the material constraint capability index. For fiber bridging constraint factor, This is a material correction factor; S304: Coupling Analysis of Environmental Field and Operational Load Field: An environment-load coupling model is established based on the Environmental Impact Index (EI) and the Operational Load Index (LI). The environmental load coupling strength is calculated to characterize the damage-promoting effect under the combined action of environmental factors and train load. The calculation formula is as follows: ； in Environmental load coupling strength; S305: Environment-Load-Material Three-Field Coupling Analysis: The material constraint capacity index MI is introduced into the environmental load coupling model to establish a three-field coupling model. The three-field coupling coefficients are calculated to characterize the comprehensive load state of the structure under fiber material constraint. The calculation formula is as follows: ； in The coupling coefficient between the environment, load, and material fields; S306: Generation of the Three-Field Coupling Index: Based on the Environmental Impact Index EI, the Operational Load Index LI, and the Three-Field Coupling Coefficient The Environment-Load-Material Three-Field Coupling Index (ELMCI) is used to characterize the comprehensive strength of railway fiber-reinforced concrete structures under actual service conditions. Its calculation formula is as follows: ； in The coupling index of the environment-load-material three fields. , , These are the environmental field influence weighting coefficient, the operational load field influence weighting coefficient, and the three-field coupling effect weighting coefficient, respectively.

5. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 4, characterized in that, The specific logical steps of S4 are as follows: S401: Shrinkage Deformation Parameter Extraction: Obtain the shrinkage deformation parameters from S1, including drying shrinkage strain, autogenous shrinkage strain, and temperature shrinkage strain, and construct a comprehensive shrinkage strain parameter to characterize the overall shrinkage deformation degree of the railway fiber-reinforced concrete structure during service. The formula used is: ； in To comprehensively account for contraction strain, For drying shrinkage strain, For autogenous contraction strain, This is due to temperature contraction strain; S402: Construction of shrinkage stress influence factors: Obtain the fiber bridging constraint factor FBCF output from S2 and the environment-load-material three-field coupling index ELMCI output from S3, and establish a set of shrinkage stress influence factors to provide input parameters for subsequent shrinkage stress evolution analysis; S403: Construction of Shrinkage Stress Evolution Model: A shrinkage stress evolution model is established based on comprehensive shrinkage strain parameters, fiber bridging constraint factors, and three-field coupling indices to calculate the internal shrinkage stress level of the structure. The formula used is as follows: ； in For the level of contraction stress, The elastic modulus of concrete; S404: Calculation of Shrinkage Stress Evolution Index: The Shrinkage Stress Evolution Index (SSEI) is established based on the shrinkage stress level and its changing trend. It reflects the degree of accumulation and growth trend of shrinkage stress within the structure. The formula used is as follows: ； in The contractile stress evolution index, The rate of change of contraction stress, , These are the weighting coefficients; S405: Crack Initiation Risk Characterization: Based on the relationship between the Shrinkage Stress Evolution Index (SSEI) and the structural tensile strength threshold, the crack initiation risk is assessed and classified into low, medium, and high risk levels. At the same time, the Shrinkage Stress Evolution Index (SSEI) is output to S5 as the basic parameter for calculating the Damage Accumulation Index (DAI).

6. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 5, characterized in that, The specific logical steps of S5 are as follows: S501: Acquisition of Shrinkage Stress Evolution Data: Acquire the Shrinkage Stress Evolution Index (SSEI) output by S404, as well as the crack width, crack length, strain parameters, and displacement parameters for the corresponding time period. Combine this with historical monitoring data to construct a structural damage evolution analysis dataset, providing basic data support for damage accumulation analysis. S502: Damage Accumulation Model Construction: A damage accumulation model is established based on the correlation between the shrinkage stress evolution index (SSEI) and structural response parameters. The damage accumulation index (DAI) is calculated to characterize the degree of damage development and crack propagation within the structure. The calculation formula is as follows: ； in The damage accumulation index, The contractile stress evolution index, For crack width parameters, For crack length parameters, , , These are the weighting coefficients; S503: Crack Stage Identification: Based on the change characteristics and growth trend of the damage accumulation index (DAI) calculated in S502, the stage of the structural crack is identified. When DAI is at a low level and grows slowly, it is determined to be the crack initiation stage. When DAI continues to grow, it is determined to be the crack propagation stage. When DAI reaches the preset penetration threshold, it is determined to be the crack penetration stage. When DAI tends to stabilize, it is determined to be the crack stabilization stage. S504: Crack propagation rate analysis: Based on the changes in crack width and crack length during the continuous monitoring period, the crack propagation rate parameter is calculated to reflect the crack development speed. The formula used is: ； in For crack propagation rate parameters, This represents the change in crack width. This represents the change in crack length. For monitoring time intervals; S505: Combining the damage accumulation index (DAI) and crack propagation rate parameter, analyze the structural damage development trend, calculate the damage evolution trend parameter to characterize the future development direction and speed of structural damage, and output the damage accumulation index (DAI), crack propagation rate parameter and damage evolution trend parameter to S6 as input parameters for the crack life prediction model. The formula used is: ； in For damage evolution trend parameters, The rate of change of the cumulative damage index. For crack propagation rate parameters, , These are the weighting coefficients.

7. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 6, characterized in that, The specific logical steps of S6 are as follows: S601: Construction of Lifetime Prediction Parameter Set: Obtain the Damage Accumulation Index (DAI) and Crack Propagation Rate Parameters from S5 Output. In addition, the damage evolution trend parameter DET was obtained, along with the shrinkage stress evolution index SSEI output from S4 and the durability degradation parameter from S1. Construct a set of parameters for predicting crack lifetime: ； in It is a set of lifetime prediction parameters used to characterize the current damage state and future degradation trend of the structure; S602: Crack Life Prediction Model Construction: A crack life prediction model is established based on the life prediction parameter set, and the crack life index CLPI is calculated to comprehensively characterize the future development risk of cracks and the degree of structural life degradation. The formula used is as follows: ； in This refers to the crack life index. The damage accumulation index, For crack propagation rate parameters, For damage evolution trend parameters, The contractile stress evolution index, , , , , These are the weighting coefficients; S603: Crack Development Trend Assessment: Based on the changes in the Crack Life Index (CLPI), the future development trend of cracks is assessed. When the CLPI continues to increase, it indicates that the crack is in an accelerated propagation state. When the CLPI tends to stabilize, it indicates that the crack development is becoming more gradual, thus forming a crack development trend evaluation result. S604: Calculation of Remaining Service of Structure: The remaining service life of the structure is calculated based on the Crack Life Index (CLPI) and the structural design service life parameters. The formula used is as follows: ； in For the remaining service life of the structure, For structural design life, The crack lifetime index threshold is the threshold value for the failure state. As the CLPI gradually approaches the failure threshold, the remaining service life gradually decreases. S605: Life Assessment Result Output: Generate life assessment results based on the Crack Life Index (CLPI) and the Remaining Service Life (RUL) of the structure. The life assessment results include the future development trend of cracks, the current life health level, the remaining service life level, and the crack failure risk level. The Crack Life Index (CLPI) and the Remaining Service Life (RUL) of the structure are then output to S7 as the basic parameters for calculating the Operation and Maintenance Decision Index (OMDI).

8. The intelligent operation and maintenance method for shrinkage stress cracks throughout the entire life cycle of railway fiber-reinforced concrete structures according to claim 7, characterized in that, In S7, the Operation and Maintenance Decision Index (OMDI) is calculated to comprehensively reflect the current risk level of the structure and the degree of operation and maintenance needs. The formula used is as follows: ； in As an index for operation and maintenance decision-making, This refers to the crack life index. The damage accumulation index, The contractile stress evolution index, For the remaining service life of the structure, For structural design life, , , , These are the crack lifetime risk weight, damage accumulation risk weight, shrinkage stress risk weight, and remaining lifetime risk weight, respectively.

9. A full-lifecycle intelligent operation and maintenance system for shrinkage stress cracks in railway fiber-reinforced concrete structures, used to implement the method described in any one of claims 1-8, characterized in that, It includes a multi-source data acquisition module, a fiber bridging constraint analysis module, a three-field coupling analysis module, a shrinkage stress evolution analysis module, a damage accumulation analysis module, a crack life prediction module, an intelligent operation and maintenance decision-making module, and a closed-loop optimization module; The multi-source data acquisition module includes a temperature and humidity sensor, a strain sensor, a vibration sensor, a displacement sensor, a crack monitoring device, and a data communication unit. It is used to acquire material parameters, environmental parameters, operational load parameters, and structural state parameters of the railway fiber-reinforced concrete structure, and to establish corresponding data mapping relationships. The fiber bridging constraint analysis module is connected to the multi-source data acquisition module and is used to construct a fiber bridging constraint characterization model based on material parameters and calculate the fiber bridging constraint factor FBCF to characterize the constraint ability of fiber concrete against shrinkage deformation and crack propagation. The three-field coupling analysis module is connected to the fiber bridging constraint analysis module. It is used to construct an environmental action field based on environmental parameters, an operational load field based on operational load parameters, and a material constraint field based on fiber bridging constraint factors. It also establishes the coupling relationship between the environmental action field, the operational load field, and the material constraint field, and calculates the environment-load-material three-field coupling index ELMCI. The shrinkage stress evolution analysis module is connected to the three-field coupling analysis module and is used to construct a shrinkage stress evolution model based on the three-field coupling index ELMCI, fiber bridging constraint factor FBCF and structural shrinkage deformation parameters, and to calculate the shrinkage stress evolution index SSEI. The damage accumulation analysis module is connected to the shrinkage stress evolution analysis module and is used to establish a damage accumulation model based on the shrinkage stress evolution index SSEI, calculate the damage accumulation index DAI, and identify the crack initiation stage, crack propagation stage, crack penetration stage and crack stabilization stage. The crack life prediction module is connected to the damage accumulation analysis module and is used to construct a crack life prediction model based on the damage accumulation index DAI, the shrinkage stress evolution index SSEI, the crack propagation rate parameter and the durability degradation parameter, and to calculate the crack life index CLPI and the remaining service life of the structure. The intelligent operation and maintenance decision module is connected to the crack life prediction module. It is used to build an operation and maintenance decision model based on the crack life index CLPI, damage accumulation index DAI, shrinkage stress evolution index SSEI and the remaining service life of the structure, calculate the operation and maintenance decision index OMDI, and generate the corresponding operation and maintenance plan. The closed-loop optimization module is connected to the intelligent operation and maintenance decision module and is used to obtain structural state feedback data after operation and maintenance implementation. It dynamically corrects and adaptively updates the fiber bridging constraint characterization model, the three-field coupling model, the shrinkage stress evolution model, the damage accumulation model, the crack life prediction model, and the operation and maintenance decision model.

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