Real-time monitoring device for concrete hardening process

Abstract

The invention relates to the technical field of concrete monitoring, in particular to a concrete hardening process real-time monitoring device which comprises a stage recognition unit for recognizing the state stage of concrete; the state judgment unit determines that the strain sensor is debonded based on the non-thermal strain residual error, determines that the humidity sensor fails in function based on the humidity change relation of the temperature rise time period, and determines that the temperature sensor has zero drift based on the proportional relation of the net temperature rise and the net strain; the risk root cause unit obtains a thermally induced risk factor and a shrinkage risk factor based on the net temperature rise and the net strain, determines a concrete shrinkage main cause based on the thermally induced risk factor and the shrinkage risk factor in combination with the state stage of the concrete, and adjusts the spraying power of the spraying maintenance device; and the checking and correcting unit is used for adjusting the spatial consistency weight and the physical association weight of the sensor. The monitoring strategy is adaptively adjusted according to the interference of the extreme environment on the concrete state monitored by the sensor, and the detection precision of the sensor is improved.

Description

Technical Field

[0001] This invention relates to the field of concrete monitoring technology, and in particular to a real-time monitoring device for the concrete hardening process. Background Technology

[0002] As the most widely used and consumed building material in the world today, the quality of concrete's hardening process (i.e., hydration process) directly determines the final mechanical properties, durability, and long-term reliability of the structure. The hardening process is not a simple "solidification," but a complex physicochemical process involving the dynamic interaction of multiple key parameters such as temperature, humidity, internal stress, and microstructure evolution.

[0003] Traditional methods for monitoring and controlling the hardening process of concrete mainly rely on point-based, intermittent on-site monitoring, using insertion temperature sensors or bonded surface strain gauges to measure individual points on the structure. The data obtained by this method is localized and isolated, making it difficult to comprehensively reflect the overall hardening status of large-volume concrete or complex structures. It also has monitoring blind spots, and the wiring is cumbersome and easily damaged during construction.

[0004] In recent years, with the development of sensing technology, some advanced monitoring technologies have begun to be explored and applied. For example, fiber optic grating (FBG) sensors are used to monitor temperature and strain, or ultrasonic and resistivity methods are used to indirectly assess intensity development. However, these technologies still face many limitations. They can usually only measure a single parameter (such as temperature or strain), making it difficult to acquire multi-parameter coupled information simultaneously and failing to fully characterize the hydration process. They also lack real-time and continuity capabilities, with some technologies having low data acquisition frequencies or requiring complex external excitation and data processing, thus failing to achieve true real-time, online monitoring and feedback.

[0005] Therefore, current technologies lack an integrated device capable of long-term, real-time, synchronous, and in-situ monitoring of core multi-dimensional parameters of the concrete hardening process (such as internal temperature field, humidity field, and strain / stress development), and possessing high robustness and intelligent analysis functions. This deficiency leads to the inability to promptly identify potential problems during construction, such as the risk of temperature cracks, substandard strength development, and unsuitable curing conditions, which may affect structural quality and even cause safety accidents.

[0006] Chinese Patent Publication No. CN120009402A discloses a method and system for detecting the hardening of shotcrete, comprising: determining detection requirements based on the physical properties of shotcrete and establishing a progressive multi-frequency excitation sequence; after each multi-frequency excitation, real-time monitoring of the echo signal inside the concrete and dynamically adjusting the excitation parameters according to the quality of the echo signal; preprocessing the acquired echo signal; extracting multi-dimensional feature parameters from the preprocessed echo signal, including time-domain features, frequency-domain features, and energy-domain features, to form a representative feature vector; removing redundant features based on the feature vector and calculating a hardening score using the remaining features; and outputting detection results and early warning information based on the hardening score. It is evident that the aforementioned method and system for detecting the hardening of shotcrete has the following problems: The monitoring strategy cannot be adapted to the interference of extreme environments on the sensor monitoring of concrete condition. Sensor errors or debonding lead to inaccurate or incomplete sensor detection. Summary of the Invention

[0007] To address this issue, the present invention provides a real-time monitoring device for the concrete hardening process, thereby overcoming the problem in the prior art that it is impossible to adaptively adjust the monitoring strategy to address interference from extreme environments affecting the sensor's monitoring of the concrete state.

[0008] To achieve the above objectives, the present invention provides a real-time monitoring device for the concrete hardening process, comprising: The stage identification unit, which is connected to the monitoring unit, is used to obtain the net temperature rise and net strain of the concrete structure and extract the key features of the net temperature rise curve and net strain curve to identify the stage of the concrete. The state determination unit, which is connected to the stage identification unit, is used to obtain the non-thermal strain residual of the strain sensor by combining net temperature rise and net strain, determine strain sensor debonding based on the non-thermal strain residual, determine humidity sensor malfunction based on humidity change relationship during temperature rise period, and determine zero-point drift of temperature sensor based on ratio of net temperature rise and net strain. The risk root cause unit is used to obtain heat-induced risk factors and shrinkage risk factors based on net temperature rise and net strain, determine the main cause of concrete shrinkage based on heat-induced risk factors and shrinkage risk factors combined with the state stage of concrete, and adjust the spraying power of the spray curing device. The verification and correction unit has spatial consistency weights and physical correlation weights for sensors in each spatial homogeneous region. It is used to identify spatial outliers, determine the rolling correlation coefficient between the data and the data of neighboring sensors, and determine the cause of data outliers by combining the changing trends of spatial outlier data and data in spatial homogeneous regions, and adjust the spatial consistency weights of the sensors. It triggers a humidity response sluggish warning based on the net temperature rise rate and net humidity change, and reduces the physical correlation weight of the corresponding humidity sensor. It triggers a strain temperature response misalignment warning based on the ratio of net strain to net temperature rise, and reduces the physical correlation weight of the strain sensor. The negative feedback unit, connected to the inspection and correction unit, is used to obtain the comprehensive reliability weight of the sensor based on the spatial consistency weight and physical correlation weight of the sensor, and to determine the reliability of the sensor. It adjusts the risk thresholds of shrinkage risk factor and thermal risk factor by combining the state stage of concrete and the overall data quality index of each spatial homogeneous area. The data quality index is the average value of the comprehensive reliability weight of several sensors in the spatial homogeneous area.

[0009] Furthermore, the key features include the first sustained positive temperature rise rate, the maximum temperature rise rate, the inflection point where the temperature rise rate turns from positive to negative, and the moment when the temperature rise rate approaches zero; Within a set short time window, the stage identification unit obtains the linear regression slope of the net temperature rise of the physical structure; when the linear regression slope continuously exceeds the set positive threshold to reach the minimum duration period, the stage identification unit records the linear regression slope as the first sustained positive temperature rise rate.

[0010] Furthermore, if the first sustained positive temperature rise rate is triggered, the stage identification unit continuously calculates the instantaneous temperature rise rate, takes the maximum value of several instantaneous temperature rise rates as the maximum temperature rise rate, and obtains the maximum temperature rise duration corresponding to the maximum temperature rise rate. If the first sustained positive temperature rise rate is not triggered, the stage identification unit determines that the concrete is in the plastic stage; If the current duration after triggering the first sustained positive temperature rise rate is less than the maximum temperature rise duration, the stage identification unit determines that the concrete is in the hydration acceleration period. After the temperature rise rate turns from positive to negative at the inflection point, if the instantaneous temperature rise rate remains within the limit range and the cumulative duration exceeds the set duration, the stage identification unit determines that the temperature rise process is nearing its end and the concrete is in a temperature stabilization period.

[0011] Furthermore, if the non-thermal strain residual of any strain sensor continues to deviate and the degree of deviation increases with the net temperature rise, the state determination unit determines that the corresponding sensor interface is debonded. If the net temperature rise during the temperature rise period is greater than the critical temperature rise value, and the absolute value of the corresponding humidity slope is less than the critical slope, and this continues for more than a fixed duration, then the state determination unit determines that there is a physical contradiction between the detected temperature and humidity, and the humidity sensor function fails. If the actual difference between the actual ratio of net temperature rise and net strain and the theoretical ratio is greater than the difference evaluation value, the state determination unit determines that the sensor has zero-point drift.

[0012] Furthermore, the mean and standard deviation of several non-thermal strain residuals of any strain sensor are calculated. If the non-thermal strain residuals exceed the residual threshold range, the state determination unit determines that the non-thermal strain residuals are continuously deviating. Calculate the rolling correlation coefficient between the non-thermal strain residual and the net temperature rise. If the absolute value of the rolling correlation coefficient is greater than the coefficient threshold, the state determination unit will determine that the deviation of the non-thermal strain residual increases with the net temperature rise.

[0013] Furthermore, after the stage identification unit determines that the concrete is in the accelerated hydration period, when it identifies the inflection point where the temperature rise rate changes from positive to negative, the stage identification unit determines that the concrete is in the peak hydration period. After the stage identification unit detects the inflection point where the temperature rise rate changes from positive to negative, if the maximum temperature rise rate continues to decrease and the net temperature rise is positive, it is determined that the concrete is in the hydration deceleration period. If the heat-induced risk factor is less than the first risk threshold and the shrinkage risk factor exceeds the second risk threshold, the risk root cause unit determines that the drying effect of the environment is the main cause of concrete shrinkage. Excessive wind speed accelerates the evaporation of surface moisture, leading to an increase in the depth of drying shrinkage, and thus increases the spraying power of the spray curing device.

[0014] Furthermore, if any sensor data exceeds the valid range, the inspection and correction unit marks the corresponding sensor as a spatial outlier. If the trend of spatial outlier data is consistent with the trend of data from several sensors within a homogeneous spatial region, and the rolling correlation coefficient is greater than the first coefficient threshold, then the verification and correction unit determines that the outlier data of the sensor is caused by a real local anomaly, and the spatial consistency weight of the sensor remains unchanged. If the rolling correlation coefficient is less than the second coefficient threshold, or the trend is opposite, the inspection and correction unit determines that the corresponding sensor itself is faulty, such as sensor interface loss of adhesion, functional failure, or zero-point drift, and reduces the spatial consistency weight of the sensor.

[0015] Furthermore, the net temperature rise rate is obtained based on the ratio of net temperature rise to unit time. If the net temperature rise rate is greater than the rate threshold and the net humidity change absolute value is continuously less than the humidity change threshold, the inspection and correction unit triggers a humidity response sluggish warning and reduces the physical correlation weight of the corresponding humidity sensor. If the ratio of net strain to net temperature rise continues to exceed the reasonable range, the inspection correction unit will trigger a strain-temperature response misalignment warning and reduce the physical correlation weight of the strain sensor.

[0016] Furthermore, the negative feedback unit obtains the sensor's overall reliability weight based on the sensor's spatial consistency weight and physical association weight; If the overall confidence weight of the sensor is lower than the first weight threshold, the negative feedback unit determines that the sensor has low confidence and its data is not included in the calculation. The data representative value of the corresponding point of the sensor is replaced by the data interpolation of the neighboring high-weight sensors. If the overall confidence weight of the sensor is greater than the second weight threshold, the negative feedback unit sensor is determined to be a high-weight sensor.

[0017] Furthermore, the stage identification unit identifies whether the concrete is in the hydration deceleration period or the temperature stabilization period, and the negative feedback unit reduces the risk threshold of the shrinkage risk factor; The negative feedback unit obtains the overall data quality index of each spatially homogeneous region, and the data quality index is the average value of the comprehensive reliability weights of several sensors in the spatially homogeneous region; If the data quality index corresponding to any spatially homogeneous region is less than the index threshold, the negative feedback unit judges that there is uncertainty in the monitoring data of the corresponding spatially homogeneous region and increases the risk threshold of the thermal risk factor.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: by constructing a multi-layered intelligent logic system that includes state recognition, state determination, risk root cause analysis, verification and correction, and negative feedback adjustment, the system achieves accurate perception and reliable diagnosis of the concrete hardening process under extreme environments; it effectively removes strong environmental noise, extracts pure hydration signals, and automatically verifies the health status of sensors, significantly improving data reliability; based on physical laws and multi-source data fusion, the system dynamically quantifies the risks of hydration heat-induced shrinkage and adaptively adjusts the risk threshold according to real-time data quality and environmental intensity, greatly reducing false alarms and missed alarms; ultimately forming an intelligent closed loop of "monitoring-diagnosis-early warning-adjustment," providing data-driven decision support for high-quality construction and maintenance of high-performance concrete under harsh conditions, ensuring structural safety and durability.

[0019] Furthermore, under extreme conditions such as daily temperature fluctuations of up to 30-40℃, strong solar radiation, and strong winds, extracting the temperature rise and strain changes caused solely by cement hydration from the raw temperature and strain signals measured by sensors inside the concrete is a crucial prerequisite for all subsequent advanced analyses, such as stage identification and risk warning. This invention achieves precise extraction of concrete hydration reaction signals by setting up a physically symmetrical anhydrous reference body and monitoring it synchronously with the solid structure. By directly subtracting temperature and moisture changes caused by environmental and non-hydration factors from the raw data, the system obtains net temperature rise, net strain, and net moisture change that only reflect the hydration process. Based on these pure signals, the system can accurately identify the hardening stages of concrete (such as the plastic stage, acceleration stage, and stabilization stage), significantly improving the accuracy and anti-interference ability of state judgment under extreme conditions, and providing a reliable data foundation for subsequent risk warning and maintenance control.

[0020] Furthermore, under sealed or semi-sealed conditions, the rise in internal temperature of concrete inevitably leads to two results: an increase in air saturated vapor pressure, which, if pore water is sufficient, should cause relative humidity to rise; and an acceleration of hydration reaction, consuming capillary water, leading to a decrease in relative humidity. Actual humidity is the superposition of these two effects, but it is impossible for it to remain absolutely constant for a long time under continuous temperature rise. Within a very short time window, the change in strain should also have a reasonable proportional relationship with the change in temperature. This invention establishes a theoretical thermal strain baseline and calculates non-thermal strain residuals, achieving effective separation and diagnosis of concrete shrinkage strain and sensor interface debonding. The system uses statistical methods and rolling correlation coefficients to accurately identify sensor interface failure caused by environmental stress. At the same time, by combining the detection of physical contradictions in temperature and humidity changes and the rationality verification of the strain-temperature rise ratio, it can intelligently judge the malfunction of the humidity sensor and the zero-point drift of the sensor. This significantly improves the reliability of monitoring data and the online self-diagnostic capability of sensor health status, ensuring that the input signals for subsequent risk analysis are true and reliable, and providing a solid guarantee for concrete curing decisions under extreme environments.

[0021] Furthermore, this invention achieves a quantitative assessment of cracking risk during the concrete hardening process by real-time monitoring and calculation of thermal and shrinkage risk factors. Based on precise stage identification (acceleration phase, peak phase, and deceleration phase), the system dynamically decouples the stress contribution caused by hydration heat release and shrinkage strain. It calculates thermal stress through the core-surface temperature difference and directly measures constrained shrinkage strain using free shrinkage test blocks, significantly improving the targeting and accuracy of risk diagnosis. When a shrinkage-dominant risk is identified during the deceleration phase, the system automatically traces it back to the primary cause of environmental dryness and triggers an increase in spray power, forming an intelligent closed loop from precise diagnosis to proactive intervention, effectively preventing the generation of concrete shrinkage cracks under extreme conditions.

[0022] Furthermore, this invention constructs a dual verification and dynamic reliability assessment system for sensor data through a verification correction and negative feedback adjustment unit. The system uses spatial consistency analysis to identify local anomalies or sensor failures, verifies the rationality of the data through physical association rules, and intelligently adjusts the weights of each sensor. Based on the comprehensive reliability weight, the system can automatically eliminate low-reliability data and perform interpolation repair to ensure the overall reliability of the input data. At the same time, by combining concrete condition and data quality index, the system adaptively adjusts the risk judgment threshold, effectively overcoming misjudgments caused by noise interference and occasional sensor failures in extreme environments, significantly improving the accuracy and robustness of risk warning, and realizing the self-maintenance and stable operation of the monitoring system under complex working conditions.

[0023] Furthermore, this invention achieves intelligent adaptive adjustment of risk monitoring strategies and thresholds through a negative feedback unit. The system can dynamically switch monitoring priorities based on the concrete hardening stage (such as the deceleration period and the stabilization period) and tighten the judgment criteria for shrinkage risk. Simultaneously, by combining the data quality index of each monitoring area, the system can automatically raise the risk threshold to avoid false alarms when data reliability decreases, and maintain sensitivity to prevent missed alarms when data quality is good. This dual negative feedback mechanism based on process status and data reliability ensures the accuracy and reliability of risk warnings under extremely complex working conditions, enabling maintenance decisions to effectively respond to real risks while resisting interference from the uncertainty of the monitoring system itself. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of a unit of the real-time monitoring device for the concrete hardening process in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for determining the de-adhesion of the corresponding sensor interface in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for determining sensor reliability in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for adjusting the risk threshold of the thermal risk factor in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0026] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0028] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Please see Figures 1-4 As shown, Figure 1 This is a schematic diagram of a unit of the real-time monitoring device for the concrete hardening process in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for determining the de-adhesion of the corresponding sensor interface in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for determining sensor reliability in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for adjusting the risk threshold of the thermal risk factor in an embodiment of the present invention.

[0030] This invention provides a real-time monitoring device for the concrete hardening process, comprising: The stage identification unit, which is connected to the monitoring unit, is used to obtain the net temperature rise and net strain of the concrete structure and extract the key features of the net temperature rise curve and net strain curve to identify the stage of the concrete. The state determination unit, which is connected to the stage identification unit, is used to obtain the non-thermal strain residual of the strain sensor by combining net temperature rise and net strain, determine strain sensor debonding based on the non-thermal strain residual, determine humidity sensor malfunction based on humidity change relationship during temperature rise period, and determine zero-point drift of temperature sensor based on ratio of net temperature rise and net strain. The risk root cause unit is used to obtain heat-induced risk factors and shrinkage risk factors based on net temperature rise and net strain, determine the main cause of concrete shrinkage based on heat-induced risk factors and shrinkage risk factors combined with the state stage of concrete, and adjust the spraying power of the spray curing device. The verification and correction unit has spatial consistency weights and physical correlation weights for sensors in each spatial homogeneous region. It is used to identify spatial outliers, determine the rolling correlation coefficient between the data and the data of neighboring sensors, and determine the cause of data outliers by combining the changing trends of spatial outlier data and data in spatial homogeneous regions, and adjust the spatial consistency weights of the sensors. It triggers a humidity response sluggish warning based on the net temperature rise rate and net humidity change, and reduces the physical correlation weight of the corresponding humidity sensor. It triggers a strain temperature response misalignment warning based on the ratio of net strain to net temperature rise, and reduces the physical correlation weight of the strain sensor. The negative feedback unit, connected to the inspection and correction unit, is used to obtain the comprehensive reliability weight of the sensor based on the spatial consistency weight and physical correlation weight of the sensor, and to determine the reliability of the sensor. It adjusts the risk thresholds of shrinkage risk factor and thermal risk factor by combining the state stage of concrete and the overall data quality index of each spatial homogeneous area. The data quality index is the average value of the comprehensive reliability weight of several sensors in the spatial homogeneous area.

[0031] Specifically, by constructing a multi-layered intelligent logic encompassing state recognition, state determination, risk root cause analysis, verification and correction, and negative feedback adjustment, the system achieves accurate perception and reliable diagnosis of the concrete hardening process under extreme environments. It effectively isolates strong environmental noise, extracts pure hydration signals, and automatically verifies sensor health status, significantly improving data reliability. Based on physical laws and multi-source data fusion, the system dynamically quantifies the risks of hydration heat-induced shrinkage and adaptively adjusts risk thresholds according to real-time data quality and environmental intensity, greatly reducing false alarms and missed alarms. Ultimately, it forms an intelligent closed loop of "monitoring-diagnosis-early warning-adjustment," providing data-driven decision support for high-quality construction and maintenance of high-performance concrete under harsh conditions, ensuring structural safety and durability.

[0032] The application scenario in this embodiment is to pour large-volume high-performance concrete (such as wind power foundations and special military facilities) in extremely dry, high-ultraviolet, and high-temperature-dark regions (such as the Northwest Desert and the Qinghai-Tibet Plateau).

[0033] In this embodiment, the monitoring unit consists of intelligent monitoring units and environmental sensor groups distributed inside and on the surface of the concrete structure, as well as an execution terminal; The intelligent monitoring unit is a waterproof probe integrating a microelectromechanical system (MEMS) that can collect core parameters of the concrete hardening process in situ, synchronously, and continuously. The environmental sensor array is used to collect external boundary conditions such as ambient temperature, humidity, wind speed, and rainfall in real time. The execution terminal is used to receive instructions from the upper level and control the maintenance equipment, such as adjusting the spray power of the spray valve of the maintenance device.

[0034] In strong ambient noise, the "effective signal" generated purely by the concrete hydration reaction is extracted, and the reliability of the sensor itself is evaluated in real time.

[0035] Set up a concrete reference body with the same shape, size, and surface treatment as the concrete-cast solid structure; The concrete inside the reference body does not contain cement, or uses inert materials with very weak hydration reaction, such as base materials mixed with appropriate amounts of retarders and inert fillers, to ensure that no significant heat of hydration is generated inside. Temperature and strain sensors of the same type and batch were installed in the same spatial location as the physical structure within the reference body.

[0036] The reference body is placed near the solid structure and exposed to the exact same environmental conditions, including sunlight, wind speed, and temperature.

[0037] At the moment of completion of pouring, the initial readings of all corresponding sensors inside the solid concrete and the reference body, the initial temperature of the solid and the initial temperature of the reference, the initial strain of the solid and the initial strain of the reference, and the initial humidity of the solid and the initial humidity of the reference are recorded simultaneously.

[0038] Real-time acquisition of the actual temperatures of the solid structure and the reference body. Net temperature rise of the solid structure = (actual temperature of the solid structure - initial temperature of the solid structure) - (actual temperature of the reference body - initial temperature of the reference body). Real-time acquisition of the actual strain of the solid structure and the reference body. Net strain of the solid structure = (actual strain of the solid structure - initial strain of the solid structure) - (actual strain of the reference body - initial strain of the reference body). The actual humidity of the entity structure and the reference body is obtained in real time. The net humidity change of the entity structure = (actual humidity of the entity structure - initial humidity of the entity) - (actual humidity of the reference body - initial humidity of the reference body).

[0039] State stage identification is performed based on the net temperature rise and net strain of the solid structure. Key features of the net temperature rise curve and net strain curve of the solid structure are extracted. The key features include the first sustained positive temperature rise rate, the maximum temperature rise rate, the inflection point of the temperature rise rate turning from positive to negative, and the moment when the temperature rise rate approaches zero. Within a set short time window, calculate the linear regression slope of the net temperature rise of the solid structure.

[0040] When the linear regression slope continuously exceeds the set positive threshold and reaches the minimum duration period, the stage identification unit records the linear regression slope as the first sustained positive temperature rise rate. In practice, the short time window is 30 minutes, the positive threshold is 0.05°C / h, and the minimum duration is 3 consecutive short time windows; If the first sustained positive temperature rise rate is not triggered, the stage identification unit determines that the concrete is in the plastic stage; If the first sustained positive temperature rise rate is triggered, the stage identification unit continuously calculates the instantaneous temperature rise rate, takes the maximum value of several instantaneous temperature rise rates as the maximum temperature rise rate, and obtains the maximum temperature rise duration corresponding to the maximum temperature rise rate. If the current duration after triggering the first sustained positive temperature rise rate is less than the maximum temperature rise duration, the stage identification unit determines that the concrete is in the hydration acceleration period. After the temperature rise rate turns from positive to negative, if the instantaneous temperature rise rate remains within the limit range and the cumulative duration exceeds the set duration, the stage identification unit determines that the temperature rise process is about to end and the concrete is in a temperature stabilization period. The limit range is -0.02°C / h to +0.02°C / h, and the set duration is 12 hours.

[0041] Specifically, under extreme conditions such as daily temperature fluctuations of up to 30-40℃, strong solar radiation, and strong winds, extracting the temperature rise and strain changes caused solely by cement hydration from the raw temperature and strain signals measured by sensors inside the concrete is a crucial prerequisite for all subsequent advanced analyses, such as stage identification and risk warning. This invention achieves precise extraction of concrete hydration reaction signals by setting up a physically symmetrical anhydrous reference body and monitoring it synchronously with the solid structure. By directly subtracting temperature and moisture changes caused by environmental and non-hydration factors from the raw data, the system obtains net temperature rise, net strain, and net moisture change that reflect only the hydration process. Based on these pure signals, the system can accurately identify the hardening stages of concrete (such as the plastic stage, acceleration stage, and stable stage), significantly improving the accuracy and anti-interference ability of state judgment under extreme conditions, and providing a reliable data foundation for subsequent risk warning and maintenance control.

[0042] The net temperature rise of the solid structure is the product of the thermal strain caused by the temperature change and the thermal expansion coefficient of the concrete and the net temperature rise of the solid structure. The theoretical thermal strain baseline of any strain sensor = thermal expansion coefficient of concrete × net temperature rise provided by the nearest adjacent temperature sensor; The non-thermal strain residual of any strain sensor = net strain - theoretical thermal strain baseline. The non-thermal strain residual mainly consists of contraction strain and possible constraint strain. If the non-thermal strain residual of any strain sensor continues to deviate and the degree of deviation increases with the net temperature rise, the state determination unit determines that the corresponding sensor interface is debonded. Specifically, the mean and standard deviation of several non-thermal strain residuals of any strain sensor are calculated. If the non-thermal strain residuals exceed the residual threshold range, the state determination unit determines that the non-thermal strain residuals are continuously deviating. Calculate the rolling correlation coefficient between the non-thermal strain residual and the net temperature rise. If the absolute value of the rolling correlation coefficient is greater than the coefficient threshold, the state determination unit will determine that the deviation of the non-thermal strain residual increases with the net temperature rise. The residual threshold range is the range consisting of the mean of the non-thermal strain residuals and the sum of positive and negative three times the standard deviation, and the coefficient threshold is 0.6.

[0043] For several sensors that have not experienced interface debonding, the state determination unit identifies the temperature rise period during which the net temperature rise is continuously positive and calculates the humidity slope during the temperature rise period. If the net temperature rise during the temperature rise period is greater than the critical temperature rise value, and the absolute value of the corresponding humidity slope is less than the critical slope, and this continues for more than 3 hours, then the status determination unit determines that there is a serious physical contradiction between the detected temperature and humidity, and the humidity sensor function fails. Furthermore, the actual ratio of net temperature rise to net strain is calculated according to the initial detection cycle. If the actual difference between the actual ratio and the theoretical ratio is greater than the difference evaluation value, the state determination unit determines that the sensor has zero-point drift. Wherein, the critical temperature rise value is 2℃, the critical slope is 0.1%RH / h, the initial detection cycle is 30min, the theoretical ratio is 10με / °C, and the difference evaluation value is 15με / °C.

[0044] Specifically, under sealed or semi-sealed conditions, the rise in internal temperature of concrete inevitably leads to two results: an increase in air saturated vapor pressure, which, if pore water is sufficient, should cause relative humidity to rise; and an acceleration of hydration reaction, consuming capillary water, resulting in a decrease in relative humidity. Actual humidity is the superposition of these two effects, but it is impossible for it to remain absolutely constant for a long time under continuous temperature rise. Within a very short time window, the change in strain should also have a reasonable proportional relationship with the change in temperature. This invention establishes a theoretical thermal strain baseline and calculates non-thermal strain residuals, achieving effective separation and diagnosis of concrete shrinkage strain and sensor interface debonding. The system uses statistical methods and rolling correlation coefficients to accurately identify sensor interface failure caused by environmental stress. At the same time, by combining the detection of physical contradictions in temperature and humidity changes and the rationality verification of the strain-temperature rise ratio, it can intelligently determine the malfunction of the humidity sensor and the zero-point drift of the sensor. This significantly improves the reliability of monitoring data and the online self-diagnostic capability of sensor health status, ensuring that the input signals for subsequent risk analysis are true and reliable, and providing a solid guarantee for concrete curing decisions under extreme environments.

[0045] After the stage identification unit determines that the concrete is in the accelerated hydration period, when it identifies the inflection point when the temperature rise rate changes from positive to negative, the stage identification unit determines that the concrete is in the peak hydration period. After the stage identification unit detects the inflection point where the temperature rise rate changes from positive to negative, if the maximum temperature rise rate continues to decrease and the net temperature rise is positive, it is determined that the concrete is in the hydration deceleration period.

[0046] The risk root cause unit obtains the net temperature rise of the concrete core and surface, and calculates the net temperature difference of hydration caused by hydration. The net temperature difference of hydration = net temperature rise of the core - net temperature rise of the surface. The net temperature difference of hydration is caused by uneven heat release during hydration. Calculate the free strain caused by the hydration temperature difference based on the net hydration temperature difference: Thermally induced free strain = Coefficient of thermal expansion × Net hydration temperature difference; If the coefficient of thermal expansion is 10×10^-6 / °C and the net hydration temperature difference is 15°C, then the thermally induced free strain = 150×10^-6, that is, 150 micro-strains; Hydration thermal stress = current elastic modulus × thermally induced free strain × constraint coefficient, normalizing hydration thermal stress into a thermally induced risk factor; Thermo-induced risk factor = hydration thermal stress / estimated tensile strength at current age. The higher the thermo-induced risk factor, the higher the risk of cracking due to uneven hydration heat release.

[0047] Specifically, the current elastic modulus is determined by measuring the stress and strain of concrete. The current elastic modulus represents the ability of concrete to resist elastic deformation at the current age, and is the ratio of stress to strain, with units of Pa and N / m². The coefficient of thermal expansion is an inherent material property of concrete, obtained through querying or experimentation. It represents the amount of expansion (or contraction) per unit length of concrete when the temperature rises (or falls) by 1 degree Celsius, and the unit is / ℃. The constraint coefficient is a dimensionless coefficient between 0 and 1, used to describe the degree to which the deformation of concrete is constrained by external or internal forces. When the deformation is completely free, the constraint coefficient = 0, and no stress is generated; when the deformation is fully constrained, the constraint coefficient = 1, and the maximum theoretical stress will be generated.

[0048] Optionally, the point constraint coefficient inside the concrete is 0.5 to 0.8, the point constraint coefficient near the surface of the structure is 0.3 to 0.6, and the constraint coefficient of the completely free edge is 0.1 to 0.3.

[0049] In practice, tensile strength values ​​are obtained by conducting tensile tests on sample blocks according to a standard cycle (days), and the estimated tensile strength value at the current age is obtained by combining the current age of the concrete that has been poured.

[0050] While pouring the main structure (constrained by the foundation, reinforcement or adjacent components), under the same environmental and curing conditions, simultaneously pour a miniature, unreinforced free shrinkage test block with a slip layer at the bottom; The risk root cause unit obtains the net strain of the concrete solid structure and the free shrinkage test block. The strain generated by the confinement stress = net strain of the concrete solid structure - net strain of the free shrinkage test block. Shrinkage stress = current elastic modulus × strain caused by constraint stress; Shrinkage risk factor = shrinkage stress / estimated tensile strength at current age. The stage identification unit determines that the concrete is in the hydration deceleration period. If the heat-induced risk factor is less than the first risk threshold and the shrinkage risk factor exceeds the second risk threshold, the risk root cause unit determines that the environmental drying effect is the main cause of concrete shrinkage. The high environmental wind speed accelerates the evaporation of surface moisture, resulting in an increase in the drying shrinkage depth, and increases the spraying power of the spray curing device.

[0051] The first risk threshold is 0.4, and the second risk threshold is 0.7.

[0052] Specifically, this invention achieves a quantitative assessment of cracking risk during the concrete hardening process by real-time monitoring and calculation of thermal and shrinkage risk factors. The system dynamically decouples the stress contribution caused by hydration heat release and shrinkage strain based on precise stage identification (acceleration phase, peak phase, and deceleration phase). It calculates thermal stress through the core-surface temperature difference and directly measures constrained shrinkage strain using free shrinkage test blocks, significantly improving the targeting and accuracy of risk diagnosis. When a shrinkage-dominant risk is identified during the deceleration phase, the system automatically traces it back to the primary cause of environmental dryness and triggers an increase in spray power, forming an intelligent closed loop from precise diagnosis to proactive intervention, effectively preventing the generation of concrete shrinkage cracks under extreme environments.

[0053] The inspection and correction unit sets spatial consistency weights and physical correlation weights for several sensors of the physical structure, and the values ​​of the spatial consistency weights and physical correlation weights range from 0.0 to 1.0.

[0054] The inspection and correction unit obtains the net temperature rise and net strain of the physical structure from several temperature sensors and strain sensors. Combined with the characteristics of the large-volume concrete structure, the structure is divided into several spatially homogeneous regions. In this embodiment, the concrete structure features include a core area, a transition area, and a surface area.

[0055] The verification correction unit calculates the median and interquartile range of the current net temperature rise of all sensors within any homogeneous spatial region. The effective data range is defined as [median - k × interquartile range, median + k × interquartile range], where k is an empirical coefficient, which is taken as 1.5 in practice.

[0056] If any sensor data exceeds the valid range, the inspection and correction unit marks the corresponding sensor as a spatial outlier. The verification correction unit calculates the rolling correlation coefficient between the sensor data of the spatial outlier and the three nearest neighbor sensor data of the spatial homogeneous region within the past two hours; The change trend of outlier data is calculated. If the change trend of spatial outlier data is consistent with the change trend of data from several sensors in a spatially homogeneous region, and the rolling correlation coefficient is greater than the first coefficient threshold, then the inspection and correction unit determines that the outlier data of the sensor is caused by a real local anomaly (such as proximity to a cooling water pipe or local insulation failure), and the spatial consistency weight of the sensor remains unchanged. If the rolling correlation coefficient is less than the second coefficient threshold, or the trend is opposite, the inspection and correction unit determines that the corresponding sensor itself is faulty, such as sensor interface loss of adhesion, functional failure, or zero-point drift. The spatial consistency weight of the sensor is reduced, and in subsequent calculations, the representative value of the sensor data is replaced by interpolation of data from neighboring sensors.

[0057] The first coefficient threshold is 0.8, and the second coefficient threshold is 0.3.

[0058] Obtain the net temperature rise, net strain, and net humidity after spatial consistency verification and weight adjustment; The net temperature rise rate is obtained based on the ratio of net temperature rise to unit time. When the net temperature rise rate is greater than the rate threshold, the absolute value of the net humidity change should be greater than the humidity change threshold. If the absolute value of net humidity change is continuously less than the humidity change threshold, the inspection and correction unit triggers a humidity response sluggish warning and reduces the physical association weight of the corresponding humidity sensor. During implementation, if the net moisture change is less than the moisture change threshold for three consecutive initial detection cycles, the inspection and correction unit determines that the net moisture change is continuously less than the moisture change threshold.

[0059] The change in net strain should be in the same direction as the change in net temperature rise. Calculate the ratio of net strain to net temperature rise. If the ratio of net strain to net temperature rise continues to exceed the reasonable range, the inspection and correction unit will trigger a strain-temperature response misalignment warning and reduce the physical correlation weight of the strain sensor. If the ratio of net strain to net temperature rise exceeds the reasonable range for four consecutive initial testing cycles, the inspection correction unit will determine that the ratio of net strain to net temperature rise continues to exceed the reasonable range.

[0060] Wherein, the rate threshold is 0.5°C / h, the wet deformation threshold is 0.05%, and the reasonable ratio range is ±50% of the thermal expansion coefficient of concrete.

[0061] The negative feedback unit calculates the overall reliability weight of the sensor, which is calculated as: Overall Reliability Weight = α × Spatial Consistency Weight + β × Physical Association Weight, where α and β are adjustable coefficients. In practice, α = 0.6 and β = 0.4. The adjustable coefficients reflect the emphasis on different verification dimensions. If the overall confidence weight of the sensor is lower than the first weight threshold, the negative feedback unit determines that the sensor has low confidence and its data is not included in the calculation. The data representative value of the corresponding point of the sensor is replaced by the data interpolation of the neighboring high-weight sensors. In practice, if the overall confidence weight of a sensor is greater than the second weight threshold, the negative feedback unit sensor is determined to be a high-weight sensor.

[0062] The first weight threshold is 0.3, and the second weight threshold is 0.7.

[0063] Specifically, this invention constructs a dual verification and dynamic reliability assessment system for sensor data through a verification correction and negative feedback adjustment unit. The system uses spatial consistency analysis to identify local anomalies or sensor failures, verifies the rationality of the data through physical association rules, and intelligently adjusts the weights of each sensor. Based on the comprehensive reliability weight, the system can automatically eliminate low-reliability data and perform interpolation repair to ensure the overall reliability of the input data. At the same time, by combining concrete condition and data quality index, the system adaptively adjusts the risk judgment threshold, effectively overcoming misjudgments caused by noise interference and occasional sensor failures in extreme environments, significantly improving the accuracy and robustness of risk warning, and realizing the self-maintenance and stable operation of the monitoring system under complex working conditions.

[0064] When the stage identification unit identifies that concrete is in the hydration deceleration period and temperature stabilization period, the material strength growth slows down, but it is more sensitive to shrinkage. The negative feedback unit will shift the monitoring focus from heat-induced risk factors to shrinkage risk factors and reduce the risk threshold of shrinkage risk factors. During implementation, when the concrete is in the hydration deceleration period and temperature stabilization period, the negative feedback unit reduces the second risk threshold to 0.5.

[0065] The negative feedback unit obtains the overall data quality index of each spatially homogeneous region, and the data quality index is the average value of the comprehensive reliability weights of several sensors in the spatially homogeneous region; When the data quality index corresponding to any spatially homogeneous region is less than the index threshold, the negative feedback unit judges that there is uncertainty in the monitoring data of the corresponding spatially homogeneous region and increases the risk threshold of the thermal risk factor. In practice, the negative feedback unit raises the first risk threshold based on the ratio of the data quality index to the index threshold.

[0066] Specifically, this invention achieves intelligent adaptive adjustment of risk monitoring strategies and thresholds through a negative feedback unit. The system can dynamically switch monitoring priorities based on the concrete hardening stage (such as the deceleration period and the stabilization period) and tighten the judgment criteria for shrinkage risk. Simultaneously, by combining the data quality index of each monitoring area, the system can automatically raise the risk threshold to avoid false alarms when data reliability decreases, and maintain sensitivity to prevent missed alarms when data quality is good. This dual negative feedback mechanism based on process status and data reliability ensures the accuracy and reliability of risk warnings under extremely complex working conditions, enabling maintenance decisions to effectively respond to real risks while resisting interference from the uncertainty of the monitoring system itself.

[0067] The technical solution of the present invention has 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 the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, 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 the present invention.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A real-time monitoring device for the concrete hardening process, characterized in that, include: The stage identification unit, which is connected to the monitoring unit, is used to obtain the net temperature rise and net strain of the concrete structure and extract the key features of the net temperature rise curve and net strain curve to identify the stage of the concrete. The state determination unit, which is connected to the stage identification unit, is used to combine net temperature rise and net strain to obtain the non-thermal strain residual of the strain sensor, determine strain sensor debonding based on the non-thermal strain residual, determine humidity sensor malfunction based on humidity change relationship during temperature rise period, and determine zero-point drift of temperature sensor based on ratio of net temperature rise and net strain. The risk root cause unit is used to obtain heat-induced risk factors and shrinkage risk factors based on net temperature rise and net strain, determine the main cause of concrete shrinkage based on heat-induced risk factors and shrinkage risk factors combined with the state stage of concrete, and adjust the spraying power of the spray curing device. The verification and correction unit has spatial consistency weights and physical correlation weights for sensors in each spatial homogeneous region. It is used to identify spatial outliers, determine the rolling correlation coefficient between the data and the data of neighboring sensors, and determine the cause of data outliers by combining the changing trends of spatial outlier data and data in spatial homogeneous regions, and adjust the spatial consistency weights of the sensors. It triggers a humidity response sluggish warning based on the net temperature rise rate and net humidity change, and reduces the physical correlation weight of the corresponding humidity sensor. It triggers a strain temperature response misalignment warning based on the ratio of net strain to net temperature rise, and reduces the physical correlation weight of the strain sensor. The negative feedback unit, which is connected to the verification and correction unit, is used to obtain the comprehensive reliability weight of the sensor based on the spatial consistency weight and physical correlation weight of the sensor, and to determine the reliability of the sensor. The risk thresholds for shrinkage risk factors and thermal risk factors are adjusted by combining the state stage of concrete and the overall data quality index of each spatial homogeneous area. The data quality index is the average value of the comprehensive credibility weights of several sensors within the spatial homogeneous area.

2. The real-time monitoring device for concrete hardening process according to claim 1, characterized in that, The key features include the first sustained positive temperature rise rate, the maximum temperature rise rate, the inflection point where the temperature rise rate turns from positive to negative, and the moment when the temperature rise rate approaches zero. Within a set short time window, the stage identification unit obtains the linear regression slope of the net temperature rise of the physical structure; when the linear regression slope continuously exceeds the set positive threshold to reach the minimum duration period, the stage identification unit records the linear regression slope as the first sustained positive temperature rise rate.

3. The real-time monitoring device for concrete hardening process according to claim 2, characterized in that, If the first sustained positive temperature rise rate is triggered, the stage identification unit continuously calculates the instantaneous temperature rise rate, takes the maximum value of several instantaneous temperature rise rates as the maximum temperature rise rate, and obtains the maximum temperature rise duration corresponding to the maximum temperature rise rate. If the first sustained positive temperature rise rate is not triggered, the stage identification unit determines that the concrete is in the plastic stage; If the current duration after triggering the first sustained positive temperature rise rate is less than the maximum temperature rise duration, the stage identification unit determines that the concrete is in the hydration acceleration period. After the temperature rise rate turns from positive to negative at the inflection point, if the instantaneous temperature rise rate remains within the limit range and the cumulative duration exceeds the set duration, the stage identification unit determines that the temperature rise process is nearing its end and the concrete is in a temperature stabilization period.

4. The real-time monitoring device for concrete hardening process according to claim 3, characterized in that, If the non-thermal strain residual of any strain sensor continues to deviate and the degree of deviation increases with the net temperature rise, the state determination unit determines that the corresponding sensor interface is debonded. If the net temperature rise during the temperature rise period is greater than the critical temperature rise value, and the absolute value of the corresponding humidity slope is less than the critical slope, and this continues for more than a fixed duration, then the state determination unit determines that there is a physical contradiction between the detected temperature and humidity, and the humidity sensor function fails. If the actual difference between the actual ratio of net temperature rise and net strain and the theoretical ratio is greater than the difference evaluation value, the state determination unit determines that the sensor has zero-point drift.

5. The real-time monitoring device for concrete hardening process according to claim 4, characterized in that, Calculate the mean and standard deviation of several non-thermal strain residuals for any strain sensor. If the non-thermal strain residuals exceed the residual threshold range, the state determination unit determines that the non-thermal strain residuals are continuously deviating. Calculate the rolling correlation coefficient between the non-thermal strain residual and the net temperature rise. If the absolute value of the rolling correlation coefficient is greater than the coefficient threshold, the state determination unit will determine that the deviation of the non-thermal strain residual increases with the net temperature rise.

6. The real-time monitoring device for concrete hardening process according to claim 5, characterized in that, After the stage identification unit determines that the concrete is in the accelerated hydration period, when it identifies the inflection point when the temperature rise rate changes from positive to negative, the stage identification unit determines that the concrete is in the peak hydration period. After the stage identification unit detects the inflection point where the temperature rise rate changes from positive to negative, if the maximum temperature rise rate continues to decrease and the net temperature rise is positive, it is determined that the concrete is in the hydration deceleration period. If the heat-induced risk factor is less than the first risk threshold and the shrinkage risk factor exceeds the second risk threshold, the risk root cause unit determines that the drying effect of the environment is the main cause of concrete shrinkage. Excessive wind speed accelerates the evaporation of surface moisture, leading to an increase in the depth of drying shrinkage, and thus increases the spraying power of the spray curing device.

7. The real-time monitoring device for concrete hardening process according to claim 6, characterized in that, If any sensor data exceeds the valid range, the inspection and correction unit marks the corresponding sensor as a spatial outlier. If the trend of spatial outlier data is consistent with the trend of data from several sensors within a homogeneous spatial region, and the rolling correlation coefficient is greater than the first coefficient threshold, then the verification and correction unit determines that the outlier data of the sensor is caused by a real local anomaly, and the spatial consistency weight of the sensor remains unchanged. If the rolling correlation coefficient is less than the second coefficient threshold, or the trend is opposite, the inspection and correction unit determines that the corresponding sensor itself is faulty, such as sensor interface loss of adhesion, functional failure, or zero-point drift, and reduces the spatial consistency weight of the sensor.

8. The real-time monitoring device for concrete hardening process according to claim 7, characterized in that, The net temperature rise rate is obtained based on the ratio of net temperature rise to unit time. If the net temperature rise rate is greater than the rate threshold and the net humidity change absolute value is continuously less than the humidity change threshold, the inspection and correction unit triggers a humidity response sluggish warning and reduces the physical correlation weight of the corresponding humidity sensor. If the ratio of net strain to net temperature rise continues to exceed the reasonable range, the inspection correction unit will trigger a strain-temperature response misalignment warning and reduce the physical correlation weight of the strain sensor.

9. The real-time monitoring device for concrete hardening process according to claim 8, characterized in that, The negative feedback unit obtains the sensor's overall reliability weight based on the sensor's spatial consistency weight and physical association weight; If the overall confidence weight of the sensor is lower than the first weight threshold, the negative feedback unit determines that the sensor has low confidence and its data is not included in the calculation. The data representative value of the corresponding point of the sensor is replaced by the data interpolation of the neighboring high-weight sensors. If the overall confidence weight of the sensor is greater than the second weight threshold, the negative feedback unit sensor is determined to be a high-weight sensor.

10. The real-time monitoring device for concrete hardening process according to claim 9, characterized in that, The stage identification unit identifies whether the concrete is in the hydration deceleration period or the temperature stabilization period, and the negative feedback unit reduces the risk threshold of the shrinkage risk factor. The negative feedback unit obtains the overall data quality index of each spatially homogeneous region, and the data quality index is the average value of the comprehensive reliability weights of several sensors in the spatially homogeneous region; If the data quality index corresponding to any spatially homogeneous region is less than the index threshold, the negative feedback unit judges that there is uncertainty in the monitoring data of the corresponding spatially homogeneous region and increases the risk threshold of the thermal risk factor.

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