Method, device and equipment for testing performance of concrete in extremely low-temperature environment

Abstract

The invention belongs to the field of material performance testing in an extreme low-temperature environment, particularly relates to a concrete performance testing method, device and equipment in an extreme low-temperature environment, and aims to solve the problem that a concrete local fracture precursor cannot be effectively predicted in an extreme low-temperature testing environment. The method comprises the following steps: acquiring a concrete test piece provided with a strain sensing unit, an acoustic emission sensing unit and a temperature field sensing unit; placing the concrete test piece in an extreme low-temperature test environment, and applying a load; in the load applying process, strain parameters output by the strain sensing unit, acoustic emission parameters output by the acoustic emission sensing unit and temperature field parameters output by the temperature field sensing unit are synchronously collected; and identifying the local fracture precursor of the concrete test piece based on the space-time correlation among the strain parameter, the acoustic emission parameter and the temperature field parameter. According to the method, comprehensive characterization of the concrete damage process from multiple physical dimensions is realized, and the local fracture precursor can be identified in an extremely low-temperature test environment.

Description

Technical Field

[0001] This application belongs to the field of material performance testing under extreme low temperature environments, and specifically relates to a method, apparatus and equipment for testing concrete performance under extreme low temperature environments. Background Technology

[0002] Concrete, as a critical structural material, is widely used in major projects requiring operation in extremely low-temperature environments, such as liquefied natural gas (LNG) storage tanks and the construction of polar and outer space bases. In these environments, concrete structures are subjected to the coupled effects of ultra-low temperatures and loads for extended periods. The hydration products within the concrete structure are prone to physicochemical changes due to temperatures below -165°C, leading to the initiation of microcracks, degradation of the material's mechanical properties, and potentially causing structural safety accidents. Therefore, accurately identifying and understanding the damage evolution of concrete under extreme low temperatures, especially the effective prediction of localized fractures, is crucial for ensuring project safety.

[0003] Existing technologies for performance testing of low-temperature concrete have significant shortcomings. On the one hand, single-parameter testing methods, such as collecting stress-strain data solely through a cryogenic mechanical testing machine, cannot comprehensively reflect the material's damage state. Although infrared thermal imagers can be used to monitor temperature field anomalies and characterize local damage at room temperature, the need for cryogenic insulation chambers in cryogenic environments prevents the practical application of thermal imagers. On the other hand, while finite parameter combination testing methods have been attempted, they also face numerous technical bottlenecks. For example, existing strain and acoustic emission combined tests typically use room-temperature sensors for simple low-temperature adaptation, without optimization for extreme low-temperature environments around -165°C. This results in strain gauge data drift due to binder failure at extreme low temperatures, and acoustic emission sensors failing to effectively capture critical microcrack signals due to reduced performance.

[0004] In addition, conventional test environment setup and data acquisition systems also have problems such as low temperature field control accuracy and lack of unified clock synchronization for various signals. This results in large time errors in the acquired multi-parameter data, making it impossible to perform effective spatiotemporal correlation analysis and thus impossible to establish an intrinsic relationship model between mechanical behavior, crack propagation and thermal response.

[0005] In summary, existing technologies can only obtain isolated or poorly correlated physical parameters, lack quantitative judgment criteria for local rupture precursors, and are unable to meet the urgent need for early safety warning of concrete structures under extreme low temperature environments. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, namely the inability to effectively predict the precursors of localized concrete cracking in extreme low-temperature testing environments, one embodiment of this application provides a method for testing concrete performance in extreme low-temperature environments, comprising:

[0007] Obtain concrete specimens equipped with strain sensing units, acoustic emission sensing units, and temperature field sensing units; Concrete specimens were placed in an extreme low-temperature testing environment and subjected to loads; During the application of load, the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit are collected simultaneously. Based on the spatiotemporal correlation between strain parameters, acoustic emission parameters, and temperature field parameters, a multi-parameter collaborative analysis model was used to identify the precursors of local fracture in concrete specimens.

[0008] As a preferred embodiment, identifying precursors to localized cracking in concrete specimens includes: At different times, the standardized anomaly strength of strain parameters, acoustic emission parameters and temperature field parameters of concrete specimens at each coordinate is calculated. Based on the weighting coefficients corresponding to each parameter, the standardized anomaly intensity of the strain parameter, the standardized anomaly intensity of the acoustic emission parameter, and the standardized anomaly intensity of the temperature field parameter are weighted and summed to obtain the consistency index of multi-parameter coupling. If the consistency index is greater than the preset consistency threshold, it is determined that there is a precursor to local rupture at the corresponding coordinate at the corresponding time.

[0009] As a preferred implementation, the calculation steps for standardized anomaly intensity include: Subtract the corresponding baseline value from each parameter collected in real time to obtain the abnormal signal value of each parameter at different times and coordinates; Normalize each abnormal signal value to obtain the standardized abnormal intensity of the corresponding parameter.

[0010] As a preferred implementation, the steps for obtaining the weighting coefficients corresponding to each parameter include: Extreme low temperature loading tests were conducted on multiple groups of standard concrete specimens. Strain parameters, acoustic emission parameters, and temperature field parameters were collected simultaneously during the test process, and the actual cracking conditions of each group of standard concrete specimens were recorded. A historical dataset is composed of strain parameters, acoustic emission parameters, temperature field parameters, and actual fracture conditions corresponding to multiple sets of standard concrete specimens. Through statistical analysis, the contribution of each parameter in the historical dataset to the actual rupture situation is quantified and used as the weighting coefficient for each parameter.

[0011] As a preferred implementation, the step of obtaining the weight coefficients corresponding to each parameter further includes: A cross-validation method was adopted, with the goal of optimizing the accuracy of multi-parameter collaborative determination of rupture precursors. The weight coefficients of each parameter were adjusted until the consistency index matched the actual rupture situation.

[0012] As a preferred embodiment, identifying precursors to localized cracking in concrete specimens includes: When the strain rate of change of the strain parameter, the acoustic emission energy of the acoustic emission parameter, and the spatial gradient of the temperature field parameter all exceed their respective preset critical thresholds at the same time or within a preset time window, it is determined that there is a precursor to local rupture.

[0013] In a preferred embodiment, the strain sensing unit is an ultra-low temperature strain gauge, the acoustic emission sensing unit is an ultra-low temperature acoustic emission sensor, and the temperature field sensing unit is an ultra-low temperature fiber optic temperature sensor; furthermore, the temperature field sensing unit is embedded inside the concrete specimen, and the strain sensing unit and the acoustic emission sensing unit are arranged on the surface of the concrete specimen.

[0014] As a preferred implementation, the extreme low temperature test environment is provided by an ultra-low temperature test chamber, which has a built-in constant temperature control system that stabilizes the temperature inside the chamber within a preset extreme low temperature range through a dual-loop adaptive proportional-integral-derivative control structure.

[0015] On the other hand, one embodiment of this application proposes a concrete performance testing device under extreme low temperature conditions, used to perform the above-described concrete performance testing method under extreme low temperature conditions, including: The concrete specimen acquisition module is used to acquire concrete specimens equipped with strain sensing units, acoustic emission sensing units and temperature field sensing units. The testing module is used to place concrete specimens in an extreme low-temperature testing environment and apply loads; The data acquisition module is used to simultaneously acquire the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit during the application of load. The performance testing module is used to identify the precursors of local fracture in concrete specimens through a multi-parameter collaborative analysis model based on the spatiotemporal correlation between strain parameters, acoustic emission parameters, and temperature field parameters.

[0016] Thirdly, one embodiment of this application provides an apparatus comprising: At least one processor; and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by the processor to implement the above-described method for testing concrete performance under extreme low-temperature conditions.

[0017] Compared with the prior art, the technical solution provided in this application has at least one of the following beneficial effects: (1) In the extreme low temperature test environment, the three types of parameters of concrete specimens during the loading process are collected simultaneously: strain, acoustic emission and temperature field. A collaborative analysis model is established to realize the comprehensive characterization of concrete damage process from multiple physical dimensions. It can identify local rupture precursors that cannot be revealed by a single parameter, and realize the technical breakthrough from post-observation to pre-prediction. It provides a quantitative basis for the mechanical behavior and damage evolution of concrete under extreme low temperature environment.

[0018] (2) By precisely correlating the strain mutation reflecting mechanical response, the acoustic emission energy release reflecting crack activity, and the temperature field anomaly reflecting energy dissipation in time and space, it is possible not only to predict the precursors of concrete specimen failure in extreme low-temperature testing environments, but also to further analyze the evolution of failure. For example, the propagation chain of cracks from initiation to propagation can be traced, and the coupling sequence of different physical phenomena can be analyzed. This provides an unprecedented research method for revealing the complex damage mechanism of concrete under extreme low temperatures, and its test results can provide strong quantitative criteria and theoretical support for structural health monitoring and safety assessment of major projects such as LNG storage tanks, polar and deep space facilities. Attached Figure Description

[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a concrete performance testing method under extreme low temperature conditions provided in one embodiment of this application; Figure 2 This is a block diagram of a concrete performance testing device under extreme low temperature conditions, provided in one embodiment of this application. Figure 3 This is a schematic diagram of the structure of a computer system used to implement the methods, apparatus, and electronic devices of this application. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] This application provides a method for testing the performance of concrete under extreme low-temperature conditions. The method involves acquiring concrete specimens equipped with strain sensing units, acoustic emission sensing units, and temperature field sensing units. The concrete specimens are placed in an extreme low-temperature testing environment and subjected to load. During the loading process, strain parameters output by the strain sensing unit, acoustic emission parameters output by the acoustic emission sensing unit, and temperature field parameters output by the temperature field sensing unit are simultaneously collected. Based on the spatiotemporal correlation between strain parameters, acoustic emission parameters, and temperature field parameters, a multi-parameter collaborative analysis model is used to identify localized fracture precursors in the concrete specimens. In the extreme low-temperature testing environment, the simultaneous collection of strain, acoustic emission, and temperature field parameters of the concrete specimens during the loading process, and the establishment of a collaborative analysis model, achieves a comprehensive characterization of the concrete damage process from multiple physical dimensions. This method can identify localized fracture precursors that cannot be revealed by a single parameter, achieving a technological breakthrough from post-event observation to pre-event prediction, and providing quantitative evidence for the mechanical behavior and damage evolution of concrete under extreme low-temperature conditions.

[0023] To more clearly explain the concrete performance testing method under extreme low temperature conditions proposed in this application, the following is a combination of... Figure 1 The steps in the embodiments of this application are described in detail.

[0024] The first embodiment of this application provides a method for testing the performance of concrete under extreme low-temperature conditions, including steps S10-S40, each of which is described in detail below: Step S10: Obtain a concrete specimen equipped with a strain sensing unit, an acoustic emission sensing unit, and a temperature field sensing unit.

[0025] Optionally, the concrete specimens are cylindrical concrete specimens of standard dimensions that meet the requirements of GB / T50081-2019 "Standard for Test Methods of Physical and Mechanical Properties of Concrete". After the concrete specimens are prepared, they should be cured in a standard curing room for 28 days in accordance with the standard.

[0026] In one embodiment of this application, the strain sensing unit is an ultra-low temperature strain gauge, the acoustic emission sensing unit is an ultra-low temperature acoustic emission sensor, and the temperature field sensing unit is an ultra-low temperature fiber optic temperature sensor; furthermore, the temperature field sensing unit is embedded inside the concrete specimen, and the strain sensing unit and the acoustic emission sensing unit are arranged on the surface of the concrete specimen.

[0027] It should be noted that, in the embodiments of this application, ultra-low temperature or extreme low temperature refers to a temperature below -165°C.

[0028] The specific setup of each sensing unit is as follows: Cryogenic strain gauges are uniformly arranged on the unloaded surface of the specimen, and the surface strain gauges are fixed to the key stress area (unloaded surface) of the specimen using cryogenic adhesive to ensure that the bonding strength of the adhesive and the sensitivity of the strain gauges are not affected by the ambient temperature under cryogenic conditions; at the same time, four cryogenic acoustic emission sensors are arranged at intervals along the main stress direction on the unloaded surface of the specimen. The cryogenic acoustic emission sensors are used to capture the acoustic signals of the propagation of tiny cracks generated in the concrete during the compression process; Cryogenic fiber optic temperature sensors are arranged by pre-embedding distributed optical fibers along the axial direction of the specimen or by spirally winding them on the unloaded surface. The cryogenic fiber optic temperature sensors are used to monitor the dynamic changes of the surface and internal temperature field of the specimen in real time.

[0029] As an example, the cryogenic acoustic emission sensor is model AE154DL.

[0030] Furthermore, after the sensor arrangement is completed, the stability of the sensor is verified, and then the specimen is placed in an ultra-low temperature environment of -165℃ for pre-curing to bring the specimen and sensor to a stable state.

[0031] By employing a dedicated cryogenic sensor and combining pre-embedded and surface-mounted sensors, the authenticity, stability, and high signal-to-noise ratio of the signal are guaranteed from the data source, which is a necessary prerequisite for achieving high-precision collaborative analysis.

[0032] Step S20: Place the concrete specimen in an extreme low temperature test environment and apply a load.

[0033] Optionally, the extreme low temperature test environment is provided by the ultra-low temperature test chamber, which has a built-in constant temperature control system. Through a dual-loop adaptive proportional-integral-derivative control structure, the temperature inside the chamber is stabilized within the preset extreme low temperature range.

[0034] In one embodiment of this application, the preset extreme low temperature range is -165℃±2℃. A concrete specimen with the sensor unit installed is placed inside an ultra-low temperature test chamber. The test chamber adopts a vacuum insulation and cold plate heat exchange coupling structure and has a built-in constant temperature control system. Stable long-term operation within the temperature range of 165℃±2℃. The cabin thermal balance meets the following requirements: ; in, U represents the overall heat transfer coefficient, and A represents the effective heat transfer area. Indicates ambient temperature. This indicates the set temperature.

[0035] It should be noted that the set temperature is the temperature that the cryogenic test chamber needs to maintain, which is the temperature specified in this embodiment. 165℃.

[0036] Temperature monitoring points are installed inside the cabin to control the maximum temperature difference. Its thermal response time constant is defined as: This ensures that the temperature field response is faster than the loading rate. Among other things, Let c represent the density of the medium, c represent the specific heat capacity, V represent the relevant volume, and h represent the surface heat transfer coefficient. This indicates the convective heat transfer area.

[0037] In one embodiment of this application, in order to improve the uniformity of the temperature field, a local temperature homogenization module is integrated on the periphery of the test chamber. It consists of a microchannel cold plate and an adjustable heating belt, which can cancel the local thermal gradient in real time. The data acquisition system is arranged in the low-temperature shielded area outside the chamber and includes a stress-strain signal processing module, an acoustic emission signal processing module, and an optical fiber temperature field signal processing module. The three types of signal channels each adopt an independent low-temperature differential amplifier circuit to eliminate the interference of environmental noise on the test data.

[0038] Furthermore, the analog-to-digital converter (ADC) for the strain and temperature channels has a resolution of no less than 16 bits and a sampling frequency of no less than 1 kHz; the acoustic emission channel uses a preamplifier with a bandwidth of 20 kHz–1 MHz and a high-frequency ADC (≥2 MHz) to capture microcrack acoustic events.

[0039] In this embodiment, each module in the data acquisition system acquires data through a unified clock synchronization mechanism to ensure that the timestamp error between strain parameters, acoustic emission parameters and temperature field parameters is less than one millisecond.

[0040] Through a unified clock synchronization mechanism and adaptive noise cancellation, the precise alignment of different physical signals in the time dimension is ensured.

[0041] Furthermore, to address the issue of zero drift in electronic components at extremely low temperatures, the front-end amplifier employs a temperature-compensated feedback structure: when the temperature... Changes cause input offset voltage shift At that time, the bias and gain are automatically adjusted to improve the output stability. The signal chain incorporates multiple levels of shielding and single-point grounding to eliminate ground loop noise; all sensing channels employ differential input and double-shielded cables to ensure data integrity under conditions of strong electromagnetic interference and low-temperature vibration.

[0042] In one embodiment of this application, the constant temperature control system employs a dual-loop adaptive PID control structure, with the outer loop maintaining the overall temperature of the cabin and the inner loop performing local temperature compensation. The discrete form of the controller is as follows: ; in, This represents the controller output signal at time k. Represents the proportionality coefficient. Represents the integral coefficient. Denotes the differential coefficient. This represents the temperature deviation at time k. , This represents the actual temperature of the cabin at time k. Indicates the sampling period. This represents the temperature deviation at time k-1, and m represents the time step index of the historical error.

[0043] The constant temperature control system estimates the cabin's inertial parameters in real time through an online identification algorithm and adaptively adjusts them. Improve control precision.

[0044] The high-precision dual-loop adaptive control system creates a stable and uniform extreme low-temperature environment, effectively eliminating the interference of ambient temperature fluctuations on the test results. In particular, it provides reliable background conditions for capturing weak local temperature anomalies caused by material damage.

[0045] The fiber optic temperature sensing module employs distributed temperature measurement technology, extracting the spatial gradient of the temperature field through differential calculation. ; in, This represents the gradient vector of temperature in two-dimensional space. Represents coordinate points Temperature at location Represents coordinate points Temperature at location Represents coordinate points Temperature at location Represents coordinate points Temperature at location This indicates the spacing of the sensors in the x-direction. This indicates the spacing of the sensors in the y-direction.

[0046] In one embodiment of this application, in order to achieve high-precision identification of weak thermal signals generated by the initiation of localized material damage, the signal quality of the temperature monitoring channel can be strictly screened, for example, by quantifying the effectiveness of the signal through the signal-to-noise ratio (SNR). ,in, This indicates the amount of temperature change in the signal. This represents the standard deviation of temperature measurement noise. To ensure that the signal can be distinguished from noise with high confidence, this embodiment sets the identification threshold to [value missing]. To meet this signal-to-noise ratio requirement and achieve the identification of abnormal temperature rises on the order of 0.1℃, i.e. According to the signal-to-noise ratio formula, the standard deviation of temperature measurement noise must meet the following requirements. .

[0047] By strictly controlling the quality of the signal acquisition front end, it is possible to reliably distinguish minute temperature anomalies at the 0.1℃ level from a complex low-temperature background.

[0048] Furthermore, potential abnormal heating zones can be identified by combining local extreme value detection.

[0049] In one embodiment of this application, after synchronous sampling, each module of the data acquisition system enters the digital filtering and adaptive noise cancellation stage, using the least mean square error (LMS) algorithm to dynamically filter out random interference from the cooling system and hydraulic loading. ; in, This represents the weight vector at time n+1. This represents the weight vector at time n. Indicates the step size factor. This represents the temperature deviation at time n. This represents the reference noise signal at time n.

[0050] Cross-channel time alignment is achieved through a cross-correlation function, which can be expressed as follows: , ; in, Indicates signal and signal The cross-correlation function, Signals to be aligned Adding time delay at time t The signal value after that, Indicates time delay. Represents the cross-correlation function The time delay to reach the maximum value is the actual time difference between two signals.

[0051] In this embodiment, the timing alignment of acoustic emission and strain or temperature signals is achieved through a cross-correlation function, ensuring that the timing synchronization error is less than 1 millisecond.

[0052] In one embodiment of this application, the data acquisition system automatically performs multi-point temperature drift calibration and thermal pulse response calibration after power-on, wherein the pulse response fitting function is: ; in, This indicates the initial temperature, which is -165℃ in this embodiment. This represents the temperature change between the surface temperature of the concrete specimen and its initial temperature. The temperature at which the impulse response is fitted is represented by t.

[0053] The local thermal time constant is estimated by fitting an impulse response function, and the optical fiber reading deviation is corrected. After processing by this self-calibration and filtering algorithm, a stable, accurate, and interference-resistant extreme low-temperature test environment and synchronous data acquisition system are constructed to ensure that the influence of environmental factors (such as temperature fluctuations, electromagnetic interference, signal noise, etc.) on the test data can be effectively avoided when applying loads to concrete specimens. At the same time, high-precision synchronous acquisition of three key parameters—stress-strain, acoustic emission, and temperature field—is achieved, providing a foundation for subsequent analysis of the mechanical behavior and fracture precursors of concrete at -165℃ ultra-low temperature.

[0054] Furthermore, after providing an extreme low-temperature testing environment through an ultra-low temperature test chamber, the concrete specimen is placed in this environment and a load is applied. In this embodiment, a uniaxial compressive load is applied to the specimen using an ultra-low temperature hydraulic loading device. The hydraulic loading device, located inside the test chamber, includes a low-temperature resistant hydraulic cylinder, a loading piston, and a force sensing element. It can apply a uniaxial compressive load to the concrete specimen according to a control signal. The loading rate is automatically adjusted by the central control unit according to a preset curve. The loading plate of the ultra-low temperature hydraulic loading device adopts a structure with a sensor clearance groove to ensure close contact between the loading plate and the non-sensor area of ​​the specimen. The load rate is preset according to the compressive strength range of the concrete.

[0055] Step S30: During the application of load, the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit are collected simultaneously.

[0056] Optionally, during the load application process, the data acquisition system is started synchronously, the cryogenic strain gauge provides real-time feedback on the strain parameters of the specimen, the cryogenic acoustic emission sensor records the acoustic emission parameters during crack propagation, and the cryogenic fiber optic sensor generates temperature field parameters through distributed sensing technology.

[0057] Among them, strain parameters are strain distribution data, acoustic emission parameters are acoustic emission time characteristics, such as energy, frequency, amplitude, etc., and temperature field parameters are real-time images of the temperature field on the surface of the concrete specimen.

[0058] Due to stress and strain The physical meanings and dimensions of the three types of signals—acoustic emission (AE), temperature field (T), and acoustic emission (AE)—are completely different. Directly using the original signals to construct an analysis model will cause the large numerical scale difference to mask the small numerical signal, making it impossible to fairly reflect the abnormal characteristics of each type of signal.

[0059] Therefore, in this embodiment of the application, the synchronously acquired channel signals are time-normalized throughout the entire process of load application.

[0060] As an example, standardization can be achieved using the following formula: ; in, This represents the standardized result of the parameters of the signal labeled i at time t. This represents the original acquired signal of the parameter identified as i at time t. This represents the mean of the parameter identified as signal i. This represents the standard deviation of the parameter with signal identifier i.

[0061] It should be noted that the signal identifier i is When the signal identifier i is AE, the corresponding parameter is the strain parameter; when the signal identifier i is T, the corresponding parameter is the acoustic emission parameter; when the signal identifier i is T, the corresponding parameter is the temperature field parameter.

[0062] After standardization by formula, all signals are transformed into dimensionless data with a mean of 0 and a standard deviation of 1, achieving same-scale comparison and ensuring that the three types of signals have balanced weights in subsequent fusion analysis.

[0063] Step S40: Based on the spatiotemporal correlation between strain parameters, acoustic emission parameters and temperature field parameters, a multi-parameter collaborative analysis model is used to identify the precursors of local rupture in the concrete specimen.

[0064] Optionally, after obtaining the parameters, a joint feature vector of stress-strain, acoustic emission, and temperature field is established using a multimodal feature fusion algorithm: ; in, This represents the feature fusion vector at time t. This represents the strain value at time t. This represents the strain rate at time t. This represents the acoustic emission energy at time t. This represents the peak frequency of acoustic emission at time t. This represents the peak amplitude of acoustic emission at time t. Represents the surface coordinates of the concrete specimen at time t. The temperature gradient vector at that point, Indicates the surface coordinates of the concrete specimen at time t. The amount of temperature change at that location.

[0065] It should be noted that the strain value is obtained through cryogenic strain gauges, reflecting the degree of deformation of the concrete specimen under external force, and is used to determine the mechanical response; the strain rate is obtained by performing time-domain differential calculation on the strain value, characterizing the change of the strain value over time, and is used to capture abrupt strain characteristics, such as a sudden acceleration of strain rate caused by microcrack initiation; the acoustic emission energy is obtained through cryogenic acoustic emission sensors, reflecting the intensity of microcrack propagation inside the concrete; the higher the energy, the more active the crack propagation; the acoustic emission peak frequency is obtained by spectral analysis of the original acoustic emission signal, and is used to distinguish crack propagation modes; different frequencies correspond to different crack types, for example, low-frequency signals may correspond to macroscopic cracks, and high-frequency signals may correspond to microscopic cracks; the acoustic emission peak amplitude is the maximum amplitude value of the acoustic emission signal at time t, which is directly acquired by the cryogenic acoustic emission sensor and pre-amplified, reflecting the intensity of the acoustic emission event, and is used to determine whether the crack has entered the rapid propagation stage; the temperature gradient vector reflects the rate of temperature change in space; crack friction can cause local heating, so the gradient anomaly region may be a microcrack concentration area; the temperature change is the surface coordinate of the concrete specimen at time t. The difference between the temperature at the current location and the initial temperature is used to directly mark local abnormal temperature changes.

[0066] By constructing a joint feature vector, high-fidelity acquisition of three core parameters is achieved, breaking the limitations of traditional testing that can only obtain single mechanical or temperature parameters. This provides a multi-dimensional and highly consistent data foundation for the behavioral analysis of concrete under extreme low temperatures. It enables the analysis of precursors to localized fracture based on various data, improving the accuracy of the analysis results and minimizing interference from background noise.

[0067] In one embodiment of this application, when the strain rate of change of the strain parameter, the acoustic emission energy of the acoustic emission parameter, and the temperature field spatial gradient of the temperature field parameter all exceed their respective preset critical thresholds at the same time or within a preset time window, it is determined that there is a precursor to local rupture.

[0068] Specifically, principal component analysis (PCA) and temporal clustering analysis are used to extract potential anomalous patterns from the joint eigenvectors. If a synchronous abrupt change occurs at time t that satisfies the following conditions, a precursor to local rupture is determined to exist at that time: ; ; ; in, This represents the critical threshold for the rate of change of strain. This represents the critical threshold of sound reflection energy. This represents the critical threshold of the temperature gradient.

[0069] As an example, the critical threshold for the rate of change of strain. It can be 0.002 / s, the critical threshold for acoustic reflection energy. It can be 100 Temperature gradient critical threshold It can be 0.5℃ / mm.

[0070] By using the coordinated determination of three critical thresholds, the judgment logic for local fracture precursors has a reliable data source, avoiding misalignment of multi-parameter correlations caused by noise, and improving the accuracy of identifying local fracture precursors in concrete.

[0071] It should be noted that the critical thresholds corresponding to each parameter can be calibrated through a large number of standard concrete specimens under extreme low temperature loading tests, and are not limited in the embodiments of this application.

[0072] Furthermore, the propagation chain between parameters can be determined based on the time when each parameter exceeds its corresponding critical threshold.

[0073] Specifically, construct the three-signal time delay matrix: ; in, This represents the time delay between the strain signal and the acoustic emission signal. This represents the time delay between the strain signal and the temperature field signal. This represents the time delay between the acoustic emission signal and the strain signal. This represents the time delay between the acoustic emission signal and the temperature field signal. This represents the time delay between the temperature field signal and the strain signal. This represents the time delay between the temperature field signal and the acoustic emission signal. The diagonal element 0 indicates that the same signal has no time delay.

[0074] By calculating the coupling sequence of different parameters, the propagation chain between mechanical response, acoustic emission, and temperature anomaly is analyzed.

[0075] Furthermore, adaptive filtering can be performed using the LMS algorithm to enhance robustness and ensure the purity of the three types of test signals.

[0076] In another embodiment of this application, the standardized anomaly intensities of strain parameters, acoustic emission parameters, and temperature field parameters at different coordinates of the concrete specimen are calculated at different times. Based on the weighting coefficients corresponding to each parameter, the standardized anomaly intensities of strain parameters, acoustic emission parameters, and temperature field parameters are weighted and summed to obtain a consistency index of multi-parameter coupling. If the consistency index is greater than a preset consistency critical threshold, it is determined that there is a precursor to local rupture at the corresponding coordinate at the corresponding time.

[0077] Since the acquired parameters are continuous data sequences, data dimensionality reduction can be performed through anomaly filtering to avoid processing massive amounts of data.

[0078] First, a full-field temperature distribution matrix is ​​generated based on fiber optic distributed sensing data. The spatial gradient operator is used to extract local extrema and abrupt boundary points of temperature changes, and the temperature gradient energy density function is calculated. Identify abnormally high-energy regions. When or local temperature change rate When the threshold is exceeded, the area is marked as a potential thermal anomaly zone. .

[0079] Subsequently, the selection was made Exceeding the critical threshold of strain change rate strain abrupt change region ,as well as Acoustic emission energy concentration area The spatial coordinates of the three abnormal regions are spatially superimposed to obtain the initial candidate abnormal regions.

[0080] Furthermore, the standardized anomaly intensity corresponding to each parameter in the candidate anomaly region is calculated: the corresponding baseline value is subtracted from each parameter collected in real time to obtain the anomaly signal value of each parameter at different times and coordinates; the anomaly signal value is normalized to obtain the standardized anomaly intensity of the corresponding parameter.

[0081] As an example, a normalization method could be to subtract the mean of the abnormal signal for the corresponding parameter from the abnormal signal value at each time step, and then divide the result by the standard deviation of all abnormal signal values ​​for that parameter.

[0082] Furthermore, the standardized anomaly intensities of strain parameters, acoustic emission parameters, and temperature field parameters are weighted and summed to obtain the consistency index of multi-parameter coupling.

[0083] As an example, the consistency index can be calculated using the following formula: ; in, Indicates the surface coordinates of the concrete specimen at time t. Consistency index at the location. Indicates the surface coordinates of the concrete specimen at time t. The normalized anomaly intensity of the strain parameters at the location, Indicates the surface coordinates of the concrete specimen at time t. The normalized anomaly intensity of the acoustic emission parameters at the location, Indicates the surface coordinates of the concrete specimen at time t. The normalized anomaly intensity of the temperature field parameters at that location, , and These are the corresponding weighting coefficients.

[0084] It should be noted that the weighting coefficients were obtained through extreme low-temperature loading tests on a large number of standard-sized concrete specimens. Multiple sets of standard concrete specimens were prepared according to the same standards as the concrete specimens, and the arrangement of ultra-low temperature strain gauges, acoustic emission sensors and fiber optic temperature sensors was completed. A uniaxial compressive load at a preset rate was applied in a test environment of -165℃±2℃ to achieve the extreme low-temperature loading test.

[0085] In one embodiment of this application, the method for obtaining the weighting coefficients is as follows: Extreme low-temperature loading tests were conducted on multiple groups of standard concrete specimens. Strain parameters, acoustic emission parameters, and temperature field parameters were collected simultaneously during the test process, and the actual fracture conditions of each group of standard concrete specimens were recorded. A historical dataset was formed by the strain parameters, acoustic emission parameters, temperature field parameters, and actual fracture conditions of multiple groups of standard concrete specimens. Through statistical analysis, the contribution of each parameter in the historical dataset to the actual fracture conditions was quantified and used as the weighting coefficient for each parameter.

[0086] Through multiple rounds of data fitting and verification, weighting coefficients are obtained to ensure that they can objectively reflect the importance of each physical field in the identification of rupture precursors.

[0087] when At that time, the area was determined to be a precursor zone for localized concrete cracking. It is the critical threshold for localized rupture, which can be calibrated through numerous extreme low-temperature concrete loading tests. In the embodiments of this application, the critical threshold for localized rupture is... The value can be 0.7.

[0088] In some embodiments, to improve reliability, a cross-validation method can also be used, with the accuracy of multi-parameter collaborative determination of rupture precursors as the optimization objective, adjusting the weight coefficients of each parameter until the consistency index matches the actual rupture situation.

[0089] Furthermore, it can output comprehensive analysis results including abnormal temperature distribution maps, strain abrupt change time series and acoustic emission energy superposition maps, clearly marking the critical point and failure mode of local cracking of concrete specimens, which can be used to quantify crack evolution and local failure mode of concrete under extreme low temperature, and provide a basis for judgment for safety assessment of concrete structures under extreme environment.

[0090] Please see Figure 2The second embodiment of this application provides a concrete performance testing device under extreme low temperature conditions, which is used to perform the above-described concrete performance testing method under extreme low temperature conditions, and includes: a concrete specimen acquisition module 100, a testing module 200, a data acquisition module 300, and a performance testing module 400.

[0091] The concrete specimen acquisition module 100 is used to acquire concrete specimens equipped with strain sensing units, acoustic emission sensing units and temperature field sensing units. Test module 200 is used to place concrete specimens in an extreme low-temperature test environment and apply loads; The data acquisition module 300 is used to simultaneously acquire the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit during the application of load. The performance testing module 400 is used to identify the precursors of local fracture in concrete specimens based on the spatiotemporal correlation between strain parameters, acoustic emission parameters and temperature field parameters through a multi-parameter collaborative analysis model.

[0092] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the device described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] It should be noted that the concrete performance testing method and apparatus under extreme low temperature conditions provided in the above embodiments are only illustrative examples of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of this application can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of this application are only for distinguishing the various modules or steps and are not considered as an improper limitation of this application.

[0094] A device according to a third embodiment of this application includes: At least one processor; and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by the processor to implement the above-described method for testing concrete performance under extreme low-temperature conditions.

[0095] A computer-readable storage medium according to a fourth embodiment of this application stores computer instructions that are executed by the computer to implement the above-described method for testing concrete performance under extreme low-temperature conditions.

[0096] A computer program product according to the fifth embodiment of this application, when run on an electronic device, causes the electronic device to execute the above-described method for testing concrete performance under extreme low temperature conditions.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and related descriptions of the electronic devices, computer-readable storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0098] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system for implementing embodiments of the systems, methods, and electronic devices of this application. Figure 3 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0099] like Figure 3 As shown, the computer system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0100] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0101] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0102] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0104] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0105] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0106] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for testing the performance of concrete under extreme low-temperature conditions, characterized in that, include: Obtain concrete specimens equipped with strain sensing units, acoustic emission sensing units, and temperature field sensing units; The concrete specimens were placed in an extreme low-temperature test environment and subjected to loads; During the application of load, the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit are collected simultaneously. Based on the spatiotemporal correlation between the strain parameters, the acoustic emission parameters, and the temperature field parameters, a multi-parameter collaborative analysis model is used to identify the precursors of local rupture in the concrete specimen.

2. The method for testing concrete performance under extreme low temperature conditions according to claim 1, characterized in that, The identification of precursory localized cracking in the concrete specimen includes: At different times, the normalized anomaly strength of the strain parameters, acoustic emission parameters and temperature field parameters at each coordinate of the concrete specimen is calculated respectively; Based on the weighting coefficients corresponding to each parameter, the standardized anomaly intensity of the strain parameter, the standardized anomaly intensity of the acoustic emission parameter, and the standardized anomaly intensity of the temperature field parameter are weighted and summed to obtain the consistency index of multi-parameter coupling. If the consistency index is greater than the preset consistency threshold, it is determined that there is a precursor to the local rupture at the corresponding coordinate at the corresponding time.

3. The method for testing concrete performance under extreme low temperature conditions according to claim 2, characterized in that, The calculation steps for the standardized anomaly intensity include: Subtract the corresponding baseline value from each parameter collected in real time to obtain the abnormal signal value of each parameter at different times and coordinates; The abnormal signal values ​​are normalized to obtain the standardized abnormal intensity of the corresponding parameters.

4. The method for testing concrete performance under extreme low temperature conditions according to claim 2, characterized in that, The steps for obtaining the weight coefficients corresponding to each parameter include: Extreme low temperature loading tests were conducted on multiple groups of standard concrete specimens. Strain parameters, acoustic emission parameters, and temperature field parameters were collected simultaneously during the test process, and the actual cracking conditions of each group of standard concrete specimens were recorded. A historical dataset is composed of strain parameters, acoustic emission parameters, temperature field parameters, and actual fracture conditions corresponding to multiple sets of standard concrete specimens. Through statistical analysis, the contribution of each parameter in the historical dataset to the actual rupture situation is quantified and used as the weighting coefficient for each parameter.

5. The method for testing concrete performance under extreme low temperature conditions according to claim 4, characterized in that, The steps for obtaining the weight coefficients corresponding to each parameter also include: A cross-validation method is adopted, with the accuracy of multi-parameter collaborative determination of rupture precursors as the optimization objective. The weight coefficients of each parameter are adjusted until the consistency index matches the actual rupture situation.

6. The method for testing concrete performance under extreme low temperature conditions according to claim 1, characterized in that, The identification of precursory localized cracking in the concrete specimen includes: When the strain rate of change of the strain parameter, the acoustic emission energy of the acoustic emission parameter, and the spatial gradient of the temperature field parameter all exceed their respective preset critical thresholds at the same time or within a preset time window, it is determined that there is a precursor to the local rupture.

7. The method for testing concrete performance under extreme low temperature conditions according to claim 1, characterized in that, The strain sensing unit is an ultra-low temperature strain gauge, the acoustic emission sensing unit is an ultra-low temperature acoustic emission sensor, and the temperature field sensing unit is an ultra-low temperature fiber optic temperature sensor; furthermore, the temperature field sensing unit is embedded inside the concrete specimen, and the strain sensing unit and the acoustic emission sensing unit are arranged on the surface of the concrete specimen.

8. The method for testing concrete performance under extreme low temperature conditions according to claim 1, characterized in that, The extreme low temperature test environment is provided by an ultra-low temperature test chamber, which has a built-in constant temperature control system. Through a dual-loop adaptive proportional-integral-derivative control structure, the temperature inside the chamber is stabilized within a preset extreme low temperature range.

9. A concrete performance testing device under extreme low temperature conditions, used to perform the concrete performance testing method under extreme low temperature conditions according to any one of claims 1-8, characterized in that, include: The concrete specimen acquisition module is used to acquire concrete specimens equipped with strain sensing units, acoustic emission sensing units and temperature field sensing units. The testing module is used to place the concrete specimen in an extreme low-temperature testing environment and apply loads; The data acquisition module is used to simultaneously acquire the strain parameters output by the strain sensing unit, the acoustic emission parameters output by the acoustic emission sensing unit, and the temperature field parameters output by the temperature field sensing unit during the application of load. The performance testing module is used to identify the precursors of local rupture in the concrete specimen based on the spatiotemporal correlation between the strain parameters, the acoustic emission parameters, and the temperature field parameters, through a multi-parameter collaborative analysis model.

10. A device, characterized in that, include: At least one processor; and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by the processor to implement the concrete performance testing method under extreme low temperature conditions as described in any one of claims 1-8.

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