Method for predicting concrete damage based on critical moderation theory of acoustic emission parameters

Abstract

The invention discloses a method for predicting concrete damage based on a critical moderation theory of acoustic emission parameters. According to the method, an acoustic emission monitoring system is constructed, data such as stress, strain and temperature of a concrete structure are collected in real time, and the variance and the autocorrelation coefficient of the concrete structure are calculated. And in combination with the critical moderation model, the damage state of the concrete structure is evaluated, the damage evolution trend is predicted, and a damage early warning signal is sent out when the damage is close to a critical point. The method has high sensitivity and accuracy, can be widely applied to health monitoring and life prediction of structures such as concrete bridges, buildings and tunnels, and provides a scientific basis for structural reinforcement and maintenance.

Description

Technical Field

[0001] The invention relates to a concrete damage prediction technology, in particular to a method for predicting concrete damage based on a critical slowing-down theory of acoustic emission parameters. Background Art

[0002] As the main structural material in buildings and infrastructure, concrete may experience damage, crack expansion and other phenomena under long-term loads, affecting its durability and safety. Existing concrete damage prediction methods are mainly based on stress-strain models, finite element analysis, etc., but these methods cannot effectively cope with the nonlinear damage evolution process under complex environments. However, the damage and fracture evolution of concrete are sensitive to complex cracking behaviors. The destruction of concrete is the dynamic nonlinear evolution of crack expansion and cohesion from initial microcracks to large cracks, which eventually leads to softening. In other words, the process of concrete damage corresponds to the multi-scale propagation of cracks. Therefore, the morphology of cracks must be monitored to prevent the failure of materials and structures. Acoustic emission (AE) is a non-destructive technique to determine the complex cracking behavior of concrete based on the rapid release of elastic waves from cracks. Critical slowing down (CSD) is a statistical physics parameter that refers to the transition process of a dynamic system from one stage to another under the action of disturbance. When approaching the critical point, the wave phenomenon is conducive to the formation of a new phase. Zhang reported that critical slowing down (CSD) provides an effective precursor to sandstone failure. Maghadam applied critical slowing down (CSD) to study a complex biological system. Stephanov published a paper in which they described the relationship between fluid dynamics and critical slowing down (CSD). Based on the above literature, recent attention to critical slowing down (CSD) has focused on the provision of complex dynamic systems. However, there are few reports on critical slowing down (CSD) as a method to characterize AE parameters to reveal the precursor failure of concrete. The critical slowing down theory (CSD) believes that when a system approaches a critical point, its recovery rate will become slower and slower, and statistical characteristics such as variance and autocorrelation coefficient will also change significantly. By monitoring these changes in real time, potential damage or instability can be detected in advance before a major change occurs in the system. Based on this theory, the present invention proposes a method for predicting concrete damage based on the critical slowing down theory of acoustic emission parameters. Summary of the invention

[0003] The purpose of the present invention is to provide a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters. The method analyzes the dynamic recovery process of concrete after disturbance by statistical means and predicts the potential damage risk by using the changes in autocorrelation coefficient and variance.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] The present invention provides a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters, and analyzes the recovery process of concrete after being disturbed by a statistical method, comprising the following steps:

[0006] S1. Construct an acoustic emission system to obtain the acoustic emission signal generated by the concrete sample during the stress process;

[0007] S2. Install sensors in the concrete structure to collect concrete stress, strain and temperature data in real time through the sensors;

[0008] S3. Calculate the variance and autocorrelation coefficient of the data, and analyze its changing trend over time or stress;

[0009] S4. Based on the critical slowing-down model, using the variance and the autocorrelation coefficient to analyze and evaluate the damage state of the concrete structure;

[0010] S5. Perform numerical simulation on the concrete structure to obtain the damage distribution at different positions in the concrete structure;

[0011] S6. Predicting the evolution trend of concrete damage according to the damage distribution, and issuing a damage warning signal when approaching a critical point.

[0012] Preferably, in step S1, the acoustic emission system includes an acoustic emission sensor, a preamplifier, a data acquisition system and signal processing software.

[0013] Preferably, in step S2, the sensor includes an acoustic emission sensor, a stress sensor, a temperature and humidity sensor and / or a vibration sensor.

[0014] Preferably, in step S3, the calculation formula of the variance is:

[0015]

[0016] where x i is the i-th data, n is the number of data in the sample;

[0017] The formula for calculating the autocorrelation coefficient is:

[0018]

[0019] Where j is the lag time, is the average value of the data.

[0020] Preferably, in step S4, the critical slowing-down model is:

[0021] x n+1 =e λΔt x n +sε n ;

[0022] where x n is the deviation of the system state variables from the equilibrium state, ε n is a random quantity that conforms to the normal distribution, s is the mean square error, and λ is the recovery rate.

[0023] Preferably, if λ and Δt do not depend on x n ,but:

[0024] x n+1 =αx n +sε n ;

[0026] Among them, α is the autocorrelation coefficient.

[0027] Then the following analysis of variance was performed:

[0028]

[0029] When a dynamic system approaches a critical point, the recovery rate from small perturbations will slow down, the recovery rate will tend to zero, and the variance will tend to infinity.

[0030] Preferably, in step S5, the numerical simulation uses a finite element analysis method to simulate and analyze the concrete structure and obtain damage distribution of the structure.

[0031] Preferably, in step S6, the predicted evolution trend of concrete damage includes the critical time point of damage occurrence and the degree of damage, and is used to provide reinforcement or repair suggestions.

[0032] Preferably, the method is applied to health monitoring and life prediction of concrete bridges, buildings, and tunnel concrete structures.

[0033] Preferably, steps S1-S6 are all automatically implemented by a computer program.

[0034] Compared with the prior art, the present invention has achieved the following beneficial technical effects:

[0035] 1. High sensitivity: By analyzing the stress-strain response of concrete structures in real time, potential damage can be discovered in a timely manner, especially at the stage of minor damage that traditional prediction methods cannot reach.

[0036] 2. Accurate prediction: Based on the critical slowing-down theory, it can effectively predict the behavior of concrete structures when they approach the critical point, providing a scientific basis for reinforcement and maintenance.

[0037] 3. Wide applicability: This method is not only applicable to concrete bridges, buildings and other structures, but can also be extended to health monitoring of other large concrete structures such as tunnels and dams. 4 / 6

[0038] 4. Automated implementation: This method can be automated through computer programs, with high operational efficiency and repeatability.

[0039] In summary, the present invention provides a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters, which can monitor and predict the damage evolution of concrete structures in real time and accurately, and has important theoretical significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 It is a flow chart of the method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters of the present invention, describing the whole process from data collection to damage prediction.

[0042] Figure 2 It is a graph of the variance change trend under the same lag step and different window lengths, showing the significant change of variance when approaching the failure point.

[0043] Figure 3 It is a trend diagram of the autocorrelation coefficient under the same lag step and different window lengths, indicating its limitation in describing the damage status.

[0044] Figure 4 It is a graph of the variance change trend under the same window length and different lag steps, showing the significant change of variance when approaching the failure point.

[0045] Figure 5 It is a trend diagram of the autocorrelation coefficient under the same window length and different lag steps, indicating its limitation in describing the damage status. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The purpose of the present invention is to provide a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters, so as to solve the problems existing in the prior art.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Embodiment 1:

[0050] This embodiment provides a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters, and concrete samples with water-binder ratios of 0.45 and 0.55 are selected for experimental research.

[0051] The real-time data obtained by the acoustic emission system is used to calculate its variance and autocorrelation coefficient, and a trend chart of changes over time is drawn (such as Figure 2 , Figure 3 , Figure 4 and Figure 5 shown).

[0052] Here are the specific steps:

[0053] S1. Construct an acoustic emission system to obtain the acoustic emission signal generated by the concrete specimen during the stress process; use the DS5AE system for acoustic emission monitoring, which is mainly composed of sensors, amplifiers, data acquisition systems and personal computers. The model of the sensor is RS-2A, and the frequency range is 50-400khz. Then fix two AE sensors on both sides of the specimen, and use vaseline as a coupling agent. Set the signal threshold of 18mV, eliminate the background noise, and record the number of AE events. At the same time, the gain of the preamplifier and the main amplifier are both 40dB, and the sampling frequency is 3MHz. The peak recognition time, impact recognition time and impact locking time are set to 50μs, 300μs and 1000μs respectively. The sampling frequency of the data acquisition system is also 3MHz. The interpolation method is used to obtain the time consistency between the AE acquisition system and the electro-hydraulic servo universal testing machine. A standard broken pencil test is performed before each compression test to verify the accuracy and sensitivity of the acoustic emission. The work of the high-frequency acoustic wave transmitter and receiver is non-invasive and will not cause physical damage to the concrete structure, so it is suitable for non-destructive evaluation.

[0054] S2. Install multiple sensors in the concrete structure to collect the stress, strain and temperature of the concrete in real time; the sensors include: acoustic emission sensors, stress sensors, temperature and humidity sensors, vibration sensors, etc.

[0055] S3. Calculate the variance and autocorrelation coefficient of the collected data and analyze its trend over time or stress;

[0056] Variance(s 2 ) is calculated as:

[0057]

[0058] where x i is the i-th data, n is the number of data in the sample;

[0059] The formula for calculating the autocorrelation coefficient (α) is:

[0060]

[0061] Where j is the lag time, is the average value of the data.

[0062] S4. Based on the critical slowing-down model, the damage state of the concrete structure is evaluated using the autocorrelation coefficient and variance analysis. The critical slowing-down model can be expressed as:

[0063] x n+1 =e λΔt x n +sε n ;

[0064] where x n is the deviation of the system state variables from the equilibrium state, ε n is a random quantity that conforms to the normal distribution, s is the mean square error, and λ is the recovery rate.

[0065] If λ and △t do not depend on x n ,but:

[0068] x n+1 =αx n +sε n ;

[0069] Among them, α is the autocorrelation coefficient.

[0070] Then the following analysis of variance was performed:

[0071]

[0072] When a dynamic system approaches a critical point, the recovery rate of small perturbations will slow down. Therefore, the recovery rate will tend to zero and the variance will tend to infinity. Therefore, the increase in autocorrelation coefficient and variance can serve as a precursor signal that the system is approaching a critical point.

[0073] Hysteresis step consistency: In order to ensure the consistency of the comparison between the variance and the autocorrelation coefficient, the present invention keeps the same hysteresis step for both calculations (set to 200), which helps to reveal the similarities and differences between the variance and the autocorrelation coefficient in describing the concrete damage and failure process. The effect of the window length on the autocorrelation coefficient and variance in the critical deceleration characteristics of the acoustic emission counting sequence is observed, and the window length is set to 500, 1000 and 1500 respectively;

[0074] Figure 2 The changes in the variance of the AE series of two groups of samples with water-to-binder ratios of 0.45 and 0.55 when the lag step size is 200 and the window lengths are 500, 1000 and 1500 are depicted.

[0075] Figure 3 The changes in the autocorrelation coefficients of the AE series of two groups of samples with water-to-binder ratios of 0.45 and 0.55, respectively, when the lag step size is 200 and the window lengths are 500, 1000 and 1500, are depicted.

[0076] Window length consistency: In order to ensure the consistency of the comparison between the variance and the autocorrelation coefficient, the present invention keeps the same window length (set to 1000) for both calculations, which helps to reveal the similarities and differences between the variance and the autocorrelation coefficient in describing the concrete damage and failure process. To observe the effect of the lag step on the autocorrelation coefficient and variance in the critical deceleration characteristics of the acoustic emission count sequence, the window length is set to 1000, and the lag step is set to 100, 200 and 300.

[0077] Figure 4 The changes in the variance of the acoustic emission sequences of the AE sequences of two groups of samples with water-binder ratios of 0.45 and 0.55, respectively, are depicted when the window length is 1000 and the lag step is 100, 200 and 300.

[0078] Figure 5 The changes in the autocorrelation coefficients of the AE sequences of two groups of samples with water-binder ratios of 0.45 and 0.55, respectively, are depicted when the window length is 1000 and the lag step lengths are 100, 200 and 300.

[0079] The variance trends are consistent under different window lengths and lag steps; the variance fluctuates significantly near the failure point and increases sharply when close to failure. The changes in the autocorrelation coefficient are different. Comparing the two groups of samples, it is found that the autocorrelation coefficient has no obvious evolution law under different lag steps.

[0080] The advantage of variance is to identify precursor points and main fault surfaces. This study found that variance is more suitable as a precursor signal than autocorrelation coefficient because variance is less affected by window length and lag step length, has high sensitivity and low false alarm rate.

[0081] In concrete compression failure, as stress increases, cracks inside the specimen initiate and expand steadily. Then, the stress reaches the yield strength and further increases the compressive strength of concrete, the cracks develop instability, and finally lead to concrete instability. In crack expansion, crack initiation and stable expansion can be regarded as one stage, and unstable crack expansion can be regarded as another stage. The morphological transformation crack of the concrete specimen can be regarded as a transition from one stage to another. This transition is accompanied by a sharp increase in the acoustic emission count rate, which is manifested as an increase in signal amplitude, and is accompanied by a series of critical slowing-down features, such as a slower recovery rate and a weakened ability to return to the old phase. The destruction process of concrete can be regarded as the evolution of cracks from stable expansion to unstable expansion. Due to the slowing-down features in the crack formation process, such as a slow recovery rate and a weak ability to return to the old stage, there is a significant variance fluctuation at the precursor point. In addition, the variance shows a sharp upward trend on the main crack surface. Therefore, the critical slowing-down theory can be used to analyze the destruction process of concrete.

[0082] S5. Perform numerical simulation on the concrete structure to obtain the damage distribution at different positions in the structure. The numerical simulation uses the finite element analysis method to simulate and analyze the concrete structure and obtain the damage distribution of the structure.

[0083] S6. Predict the damage evolution trend of concrete based on the damage distribution, and determine the early warning signal of damage when approaching the critical point; the damage evolution trend prediction includes the critical time point of damage occurrence and the degree of damage, and is used to provide reinforcement or maintenance suggestions.

[0084] The experiment shows that when approaching the damage point, the variance increases significantly, and the variation range is higher than the autocorrelation coefficient. The above results show that the changes in variance and autocorrelation coefficient can be used as effective characterization indicators for concrete damage early warning, among which the variance has higher sensitivity and reliability.

[0085] The present invention discloses a method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters. By analyzing the stress-strain response of the concrete structure in real time, potential damage can be discovered in a timely manner, especially at the stage of micro-damage that traditional prediction methods cannot reach. Based on the critical slowing-down theory, the behavior of the concrete structure when it approaches the critical point can be effectively predicted, providing a scientific basis for reinforcement and maintenance. This method is not only applicable to structures such as concrete bridges and buildings, but can also be extended to health monitoring of other large concrete structures such as tunnels and dams.

[0086] The method can be realized automatically by computer program and has high operation. The invention not only has important social value, but also can bring significant economic benefits, and at the same time technically realizes in-depth understanding and accurate monitoring of concrete damage.

[0087] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters, characterized by: The recovery process of concrete after disturbance is analyzed by statistical methods, including the following steps: S1. Construct an acoustic emission system to obtain the acoustic emission signal generated by the concrete sample during the stress process; S2. Install sensors in the concrete structure to collect concrete stress, strain and temperature data in real time through the sensors; S3. Calculate the variance and autocorrelation coefficient of the data, and analyze its changing trend over time or stress; S4. Based on the critical slowing-down model, using the variance and the autocorrelation coefficient to analyze and evaluate the damage state of the concrete structure; S5. Perform numerical simulation on the concrete structure to obtain the damage distribution at different positions in the concrete structure; S6. Predicting the evolution trend of concrete damage according to the damage distribution, and issuing a damage warning signal when approaching a critical point.

2. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S1, the acoustic emission system includes an acoustic emission sensor, a preamplifier, a data acquisition system and signal processing software.

3. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S2, the sensor includes an acoustic emission sensor, a stress sensor, a temperature and humidity sensor and / or a vibration sensor.

4. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S3, the calculation formula of the variance is: where x i is the i-th data, n is the number of data in the sample; The formula for calculating the autocorrelation coefficient is: Where j is the lag time and x is the mean of the data.

5. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S4, the critical slowing-down model is: x n+1 =yes λΔt x n +Sε n ; where x n is the deviation of the system state variables from the equilibrium state, ε n is a random quantity that conforms to the normal distribution, s is the mean square error, and λ is the recovery rate.

6. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 4 is characterized in that: If λ and △t do not depend on x n ,but: x n+1 =αx n +Sε n ; Among them, α is the autocorrelation coefficient. Then the following analysis of variance was performed: When a dynamic system approaches a critical point, the recovery rate from small perturbations will slow down, the recovery rate will tend to zero, and the variance will tend to infinity.

7. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S5, the numerical simulation uses the finite element analysis method to simulate and analyze the concrete structure and obtain the damage distribution of the structure.

8. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to claim 1 is characterized in that: In step S6, the evolution trend of concrete damage is predicted, including the critical time point of damage occurrence and the degree of damage, and is used to provide reinforcement or repair suggestions.

9. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to any one of claims 1 to 8, characterized in that: The method is applied to health monitoring and life prediction of concrete bridges, buildings and tunnel concrete structures.

10. The method for predicting concrete damage based on the critical slowing-down theory of acoustic emission parameters according to any one of claims 1 to 8, characterized in that: Steps S1-S6 are all automatically implemented by a computer program.

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