Steel strand-BFRC reinforcing component crack control method based on strain monitoring

Abstract

The invention relates to the technical field of crack control, in particular to a steel strand-BFRC reinforcing member crack control method based on strain monitoring, which comprises the following steps: arranging a distributed strain sensor network at an interface between a steel strand net of a reinforcing member and a BFRC layer; establishing a crack propagation prediction model based on historical test data, wherein model input comprises a steel strand reinforcement ratio, a BFRC tensile strength and an interface strain threshold value; collecting monitoring data of the strain sensor network in real time, identifying an interface strain mutation area and marking the interface strain mutation area as a potential crack initiation point; dynamically calculating a crack expansion path and a critical width; steel strand pretightening force adjusting parameters are generated; the pre-tightening force of the steel strand is dynamically adjusted through the executing mechanism, and closed-loop feedback control is formed. According to the method, continuous optimization and self-adaptive adjustment of a crack inhibition strategy are achieved, the crack propagation speed is effectively delayed, and the durability and safety of a reinforcing component in a complex service environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crack control, and in particular to a crack control method for a steel strand-BFRC reinforced component based on strain monitoring. Background Art

[0002] The existing reinforcement technology combining steel strands and basalt fiber reinforced composites (BFRC) mainly relies on the initially set steel strand preload and BFRC material ratio for crack control, and lacks the ability to dynamically adjust based on the actual stress state during service. When components are subjected to local strain concentration due to load changes, environmental erosion or material aging during long-term use, cracks often initiate and expand rapidly, making it difficult for traditional manual inspection methods to capture early signs of cracks in a timely manner. In addition, current conventional reinforcement designs only consider static load conditions, ignoring the dynamic changes in stress transfer characteristics at the interface over time, resulting in widespread hysteresis and uncertainty in crack control.

[0003] Regarding the dynamic prediction and adaptive control of crack propagation, existing technologies still have many deficiencies. On the one hand, crack monitoring methods mostly use single-point or locally distributed strain measurement, which makes it difficult to fully cover key stress areas and accurately identify interface strain mutations and crack initiation locations. On the other hand, existing crack prediction methods are generally based on static empirical formulas or simplified models, and lack an accurate prediction mechanism that dynamically correlates multiple factors such as steel strand reinforcement ratio, BFRC tensile strength, and interface strain threshold. At the same time, after crack propagation is identified, there is a lack of technical paths that can dynamically generate steel strand preload adjustment amounts based on propagation trends and continuously optimize adjustment strategies through real-time closed-loop feedback. As a result, crack control measures are delayed and have limited effects, and cannot meet the needs of high-reliability structural reinforcement projects for real-time, accurate, and intelligent crack control. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a crack control method for steel strand-BFRC reinforced components based on strain monitoring.

[0005] The crack control method of the steel strand-BFRC reinforced component based on strain monitoring includes the following steps: S1: A distributed strain sensor network is deployed at the interface between the steel strand mesh and the BFRC layer of the reinforcement component, with the sensors covering the key stress transfer area; S2: Develop a crack propagation prediction model based on historical test data. The model inputs include the strand reinforcement ratio, BFRC tensile strength, and interface strain threshold. S3: collecting monitoring data of the strain sensor network in real time, identifying the interface strain mutation area and marking it as a potential crack initiation point; S4: inputting the strain data of the potential crack initiation point into a crack propagation prediction model to dynamically calculate the crack propagation path and critical width; S5: Generate steel strand preload adjustment parameters based on the crack propagation path and critical width; S6: Dynamically adjust the preload force of the steel strand through the actuator to form a closed-loop feedback control.

[0006] Furthermore, the S1 includes: S11: Determine the interface between the steel strand mesh and the BFRC layer of the reinforced component and identify the key areas of stress transfer; S12: Determine the density and distribution of sensors based on the geometry and stress distribution model of the key stress transfer area, and optimize by minimizing the objective function; S13: Based on the stress distribution in the stress transfer area, the finite element analysis model is used to simulate the stress changes at the interface and determine the layout area of ​​the strain sensor.

[0007] Furthermore, the S2 includes: S21: Collect historical test data, including strand reinforcement ratio, BFRC tensile strength, and interface strain threshold, and construct a regression model; S22: Use historical test data to perform regression analysis and use the least squares method to optimize the regression coefficient and constant term; S23: By minimizing the objective function, the optimal regression coefficient and constant term are obtained, and the crack propagation prediction model is obtained.

[0008] Furthermore, the S23 includes: S231, calculate the gradient of the objective function: calculate the partial derivative of the objective function with respect to the regression coefficient and the constant term, that is, the gradient; S232, update regression coefficient and constant term: update the regression coefficient and constant term using the obtained gradient; S233, forming a crack propagation prediction model: constructing a complete crack propagation prediction model based on the obtained optimal regression coefficient and constant term.

[0009] Furthermore, the S3 includes: S31, real-time collection of strain sensor data: real-time collection of strain data at the interface between the steel strand mesh and the BFRC layer of the reinforcement component through a distributed strain sensor network. The sensor data includes strain signals and sensor positions. S32, strain mutation identification: Sudden changes are detected on the real-time collected strain data. Based on the time series analysis method, the change point detection algorithm is used to identify the sudden change area of ​​the interface strain; S33, marking the mutation region as a potential crack initiation point: marking the identified strain mutation region as a potential crack initiation point, and outputting a coordinate set of the potential crack initiation point according to the spatiotemporal position of the strain mutation region.

[0010] Furthermore, the S4 includes: S41, standardization and input processing of potential crack initiation point data: normalizing the strain data and spatial positions of the detected potential crack initiation points, and constructing a standardized input vector, which is input into the crack propagation prediction model; S42, dynamic calculation of crack propagation path and critical crack width: Based on the constructed standardized input vector, the crack propagation path length and critical crack width corresponding to the potential crack initiation point are dynamically calculated.

[0011] Furthermore, the S41 includes: S411, normalization processing: normalize the strain value of the initiation point; S412, position normalization: normalizing the three-dimensional position coordinates; S413, standard input vector construction: combine the normalized strain value and the three-dimensional position coordinates into a standardized input vector.

[0012] Furthermore, the S42 includes: S421, crack propagation path prediction: Calculate the crack propagation path length based on the relationship between the normalized strain value and the material elastic modulus; S422, Critical crack width prediction: Based on the principles of fracture mechanics, the critical crack width is estimated using the maximum stress in the local area.

[0013] Furthermore, the S5 includes: S51, Crack Extension Severity Assessment: Calculate the crack extension severity index based on the crack extension path and critical crack width; S52, generating a steel strand preload adjustment parameter according to the severity of the crack expansion.

[0014] Furthermore, the S6 includes: S61, actuator control signal generation: constructing an execution control vector based on the strand preload adjustment parameter; S62, closed-loop feedback and parameter correction mechanism: After executing control, the system continuously monitors the strain sensor feedback, compares it with the predicted strain value under the previous input, and calculates the dynamic error value; based on the dynamic error value, it dynamically corrects the next round of adjustment coefficients.

[0015] Beneficial effects of the present invention: The present invention deploys a distributed strain sensor network at the interface between the steel strand mesh and the BFRC layer to monitor strain changes in key areas of the component in real time. This can promptly identify sudden changes in interface strain and accurately mark potential crack initiation points, effectively avoiding the problems of high hysteresis and limited coverage of traditional detection methods. Combined with the crack propagation prediction model established based on the steel strand reinforcement ratio, BFRC tensile strength and interface strain threshold, it can dynamically predict the crack propagation path and critical width, achieve early prediction of crack development trends, greatly improve prediction accuracy and response speed, and provide a reliable basis for subsequent reinforcement control.

[0016] The present invention further dynamically generates steel strand preload adjustment parameters based on the crack propagation prediction results, and adjusts the preload in real time through the actuator, forming a closed-loop feedback control during the BFRC construction and reinforcement use stages; by real-time acquisition of strain feedback errors and dynamic correction of control parameters, continuous optimization and adaptive adjustment of crack suppression strategies are achieved, effectively delaying the crack propagation speed and improving the durability and safety of reinforced components in complex service environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 This is a prediction model diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0020] like Figure 1-Figure 2 As shown, the crack control method of the steel strand-BFRC reinforced member based on strain monitoring includes the following steps: S1: A distributed strain sensor network is deployed at the interface between the steel strand mesh and the BFRC layer of the reinforcement component, with the sensors covering the key stress transfer area; S2: Develop a crack propagation prediction model based on historical test data. The model inputs include the strand reinforcement ratio, BFRC tensile strength, and interface strain threshold. S3: Real-time collection of monitoring data from the strain sensor network to identify areas of sudden interface strain changes and mark them as potential crack initiation points; S4: Input the strain data of the potential crack initiation point into the crack propagation prediction model to dynamically calculate the crack propagation path and critical width; S5: Generate steel strand preload adjustment parameters based on the crack propagation path and critical width; S6: Dynamically adjust the preload force of the steel strand through the actuator to form a closed-loop feedback control.

[0021] S1 includes: S11: Determine the interface between the steel strand mesh and the BFRC layer of the reinforced component and identify the key areas of stress transfer; S12: Determine the density and distribution of sensors based on the geometry and stress distribution model of the key stress transfer area, and optimize by minimizing the objective function, which is expressed as: ; Where N is the number of sensors, is the stress value of the area covered by the I-th sensor, is the maximum stress value in the region; S13: Based on the stress distribution in the stress transfer area, the finite element analysis (FEA) model is used to simulate the stress changes at the interface and determine the layout area of ​​the strain sensor, which is expressed as: ; in, is the stress change, The length change of the sensor monitoring area, is the original length.

[0022] S2 includes: S21: Collect historical test data, including strand reinforcement ratio, BFRC tensile strength, and interface strain threshold, and construct a regression model to predict the crack propagation path and critical width, expressed as: ; in, is the predicted crack propagation path or critical width, is the reinforcement ratio of steel strand, ranging from 0.5% to 4%. is the tensile strength of BFRC (MPa), , is the interface strain threshold, ranging from 0.001 to 0.005, , , is the regression coefficient of the model (obtained by fitting historical data), is the constant term of the model, ; S22: Use historical test data for regression analysis and use the least squares method to optimize the regression coefficients , , and constant term , expressed as: ; in, is the objective function, n is the number of historical data points, are the steel strand reinforcement ratio, BFRC tensile strength and interface strain threshold of the i-th data point, is the actual crack extension path or critical width of the i-th data point; S23: By minimizing the objective function, the optimal regression coefficient and constant term are obtained, and the crack propagation prediction model is obtained.

[0023] S23 includes: S231, calculate the gradient of the objective function: calculate the gradient of the objective function relative to the regression coefficient , , and constant term The partial derivatives, or gradients, include: (1) Yes Partial derivatives of : ; (2) Yes Partial derivatives of : ; (3) Yes Partial derivatives of : ; (4) Yes Partial derivatives of : ; S232, Update regression coefficients and constant terms: Update the regression coefficients using the obtained gradients , , and constant term , the update formula is expressed as follows through the gradient descent method: ; ; ; ; in, , , , It is from the previous iteration , , , value, is the learning rate, which controls the step size of each update and takes a value of 0.01-0.1. It is the partial derivative of the objective function with respect to the regression coefficient and the constant term. The above parameters are updated iteratively until the objective function Until convergence, the optimal regression coefficient is obtained 、 、 and constant term ; S233, forming a crack propagation prediction model: Based on the obtained optimal regression coefficient and constant term, a complete crack propagation prediction model is constructed, which is expressed as: .

[0024] S3 includes: S31, real-time collection of strain sensor data: Through the distributed strain sensor network, real-time collection of strain data at the interface between the steel strand network and the BFRC layer of the reinforcement component. The sensor data includes strain signals , sensor location ; S32, strain mutation identification: Sudden changes are detected on the real-time collected strain data. Based on the time series analysis method, the change point detection algorithm is used to identify the sudden change area of ​​the interface strain, which is expressed as: ; in, is the change in strain between time t and the previous time t-1, is the strain value at time t, when When , the region is marked as the strain mutation region; S33, mark the mutation area as a potential crack initiation point: mark the identified strain mutation area as a potential crack initiation point, and output the potential crack initiation point coordinate set according to the spatiotemporal position of the strain mutation area. and strain data of potential crack initiation points If multiple sensors detect an obvious strain mutation in the area and meet the aforementioned strain mutation threshold , then the risk of crack expansion in this area is greater.

[0025] S4 includes: S41, standardization and input processing of potential crack initiation point data: normalizing the strain data and spatial positions of the detected potential crack initiation points, and constructing a standardized input vector, which is input into the crack propagation prediction model; S42, dynamic calculation of crack propagation path and critical crack width: Based on the constructed standardized input vector, the crack propagation path length and critical crack width corresponding to the potential crack initiation point are dynamically calculated.

[0026] S41 includes: S411, standardization: the strain value of the initiation point Normalized, expressed as: ; in, is the normalized strain value, is the minimum strain value recorded in the monitoring history, is the maximum strain value recorded in the monitoring history; S412, Position Standardization (Spatial Coordinate Normalization): For three-dimensional position coordinates After normalization, it is expressed as: ; ; ; in, are the minimum and maximum values ​​of the three-dimensional boundary coordinates of the monitoring area, is the normalized value of the three-dimensional spatial position coordinate of the J-th point; S413, standard input vector construction: combine the normalized strain value and the three-dimensional position coordinates into a standardized input vector , expressed as: ; in, is the normalized input vector of the Jth potential crack initiation point, is the normalized strain value of the J-th point.

[0027] S42 includes: S421, Crack propagation path prediction: The crack propagation path length is calculated based on the relationship between the normalized strain value and the material elastic modulus, expressed as: ; in, is the crack extension path length, , are the upper and lower bounds of the strain normalization interval, E is the elastic modulus of the material (MPa), k is the crack growth rate coefficient, ; S422, Critical crack width prediction: Based on the principles of fracture mechanics, the critical crack width is estimated using the maximum stress in the local area, expressed as: ; ; in, is the critical crack width, , is the fracture toughness of the material , is the local maximum stress value at the potential crack initiation point (megapascals, MPa).

[0028] S5 includes: S51, Crack propagation severity assessment: Based on the crack propagation path and the critical crack width , calculate the crack growth severity index , which is used to comprehensively evaluate the impact of cracks on structural performance and is expressed as: ; in, is the crack extension severity index, are the weight coefficients of crack path length and crack path width, respectively. , is the reference value of the crack extension path, which is the maximum crack extension length allowed by the structure. The minimum elastic modulus method is used to ensure the consistency of units. , is the minimum value of the elastic modulus of each material in the system (MPa), Is the crack width reference value, which is the maximum crack width allowed by the structure, based on the limit crack width conversion method to ensure consistent units. , is the maximum crack width allowed by the structure (unit: m); S52, based on the severity of crack expansion, generates the steel strand preload adjustment parameter, expressed as: ; in, is the adjustment value of the preload force of the steel strand, is the preload adjustment sensitivity coefficient (value range 0.1~0.5), is the initial design preload of the steel strand, is the severity of crack growth.

[0029] S6 includes: S61, actuator control signal generation: adjustment parameters based on strand preload , build execution control vector , expressed as: ; in, is the control instruction vector, It is the initial parameter of the system for adjusting the preload force of the steel strand, and the control vector Issued by the upper control system to execute the target strand preload ; S62, closed-loop feedback and parameter correction mechanism: After executing the control, the system continuously monitors the strain sensor feedback , and the predicted strain value under the previous input By comparison, the dynamic error value is calculated and expressed as: ; in, is the measured strain value from the sensor network, collected in region D, is the predicted strain value calculated from the crack growth model and the last control command input, is the strain error between the measured and predicted strains; Based on dynamic error value , dynamically correct the next round of adjustment coefficients , expressed as: ; in, It is the closed loop adjustment gain coefficient, with a value range of 0.01-0.05. is the strain deviation.

[0030] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

Claims

1. A crack control method for steel strand-BFRC reinforced components based on strain monitoring, characterized in that: The following steps are involved: S1: A distributed strain sensor network is deployed at the interface between the steel strand mesh and the BFRC layer of the reinforcement component, with the sensors covering the key stress transfer area; S2: Develop a crack propagation prediction model based on historical test data. The model inputs include the strand reinforcement ratio, BFRC tensile strength, and interface strain threshold. S3: collecting monitoring data of the strain sensor network in real time, identifying the interface strain mutation area and marking it as a potential crack initiation point; S4: inputting the strain data of the potential crack initiation point into a crack propagation prediction model to dynamically calculate the crack propagation path and critical width; S5: Generate steel strand preload adjustment parameters based on the crack propagation path and critical width; S6: Dynamically adjust the preload force of the steel strand through the actuator to form a closed-loop feedback control.

2. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 1 is characterized in that: Said S1 comprises: S11: Determine the interface between the steel strand mesh and the BFRC layer of the reinforced component and identify the key areas of stress transfer; S12: Determine the density and distribution of sensors based on the geometry and stress distribution model of the key stress transfer area, and optimize by minimizing the objective function; S13: Based on the stress distribution in the stress transfer area, the finite element analysis model is used to simulate the stress changes at the interface and determine the layout area of ​​the strain sensor.

3. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 2 is characterized in that: The S2 includes: S21: Collect historical test data, including strand reinforcement ratio, BFRC tensile strength, and interface strain threshold, and construct a regression model; S22: Use historical test data to perform regression analysis and use the least squares method to optimize the regression coefficient and constant term; S23: By minimizing the objective function, the optimal regression coefficient and constant term are obtained, and the crack propagation prediction model is obtained.

4. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 3 is characterized in that: The S23 includes: S231, calculate the gradient of the objective function: calculate the partial derivative of the objective function with respect to the regression coefficient and the constant term, that is, the gradient; S232, update regression coefficient and constant term: update the regression coefficient and constant term using the obtained gradient; S233, forming a crack propagation prediction model: constructing a complete crack propagation prediction model based on the obtained optimal regression coefficient and constant term.

5. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 4 is characterized in that: The S3 includes: S31, real-time collection of strain sensor data: real-time collection of strain data at the interface between the steel strand network and the BFRC layer of the reinforcement component through a distributed strain sensor network. The sensor data includes strain signals and sensor positions. S32, strain mutation identification: Sudden changes are detected on the real-time collected strain data. Based on the time series analysis method, the change point detection algorithm is used to identify the sudden change area of ​​the interface strain; S33, marking the mutation region as a potential crack initiation point: marking the identified strain mutation region as a potential crack initiation point, and outputting a coordinate set of the potential crack initiation point according to the spatiotemporal position of the strain mutation region.

6. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 5 is characterized in that: The S4 includes: S41, standardization and input processing of potential crack initiation point data: normalizing the strain data and spatial positions of the detected potential crack initiation points, and constructing a standardized input vector, which is input into the crack propagation prediction model; S42, dynamic calculation of crack propagation path and critical crack width: Based on the constructed standardized input vector, the crack propagation path length and critical crack width corresponding to the potential crack initiation point are dynamically calculated.

7. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 6, characterized in that: The S41 includes: S411, normalization processing: normalize the strain value of the initiation point; S412, position normalization: normalizing the three-dimensional position coordinates; S413, standard input vector construction: combine the normalized strain value and the three-dimensional position coordinates into a standardized input vector.

8. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 6, characterized in that: The S42 includes: S421, crack propagation path prediction: Calculate the crack propagation path length based on the relationship between the normalized strain value and the material elastic modulus; S422, Critical crack width prediction: Based on the principles of fracture mechanics, the critical crack width is estimated using the maximum stress in the local area.

9. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 8, characterized in that: The S5 includes: S51, Crack Extension Severity Assessment: Calculate the crack extension severity index based on the crack extension path and critical crack width; S52: generating a preload adjustment parameter for the steel strand according to the severity of the crack expansion.

10. The crack control method for steel strand-BFRC reinforced components based on strain monitoring according to claim 9, characterized in that: The S6 includes: S61, actuator control signal generation: constructing an execution control vector based on the strand preload adjustment parameter; S62, closed-loop feedback and parameter correction mechanism: After executing control, the system continuously monitors the strain sensor feedback, compares it with the predicted strain value under the previous input, and calculates the dynamic error value; based on the dynamic error value, it dynamically corrects the next round of adjustment coefficients.

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