Freeze-thaw damage detection and early warning method and system based on sensor network

Abstract

The invention discloses a freeze-thaw damage detection and early warning method and system based on a sensor network, belongs to the technical field of concrete detection, aims to solve the problems that tiny damage cannot be timely and accurately found and a large amount of labor cost is needed, and comprises the steps that various sensors are installed on the inner surface, the outer surface, important nodes and key positions of a concrete structure; a sensor network is formed, historical data and real-time data, detected by the sensor network, of the temperature, humidity, stress and cracks of the concrete structure are collected, and the current freeze-thaw damage degree of the concrete structure is evaluated based on the historical data and the real-time data; according to the invention, through combined monitoring of various types of sensors, various health parameters of the concrete structure under the freezing and thawing effect are comprehensively reflected, and the accuracy and comprehensiveness of data are improved; the system not only can monitor the structure state in real time, but also predicts the damage degree in combination with historical data and real-time data, and gives the development trend of freeze-thaw damage in a period of time in the future.

Description

Technical Field

[0001] The present invention belongs to the technical field of concrete detection, and particularly relates to a freeze-thaw damage detection and early warning method and system based on a sensor network. Background Art

[0002] Concrete refers to the general term for engineering composite materials in which aggregate is cemented into a whole by a cementitious material. Generally, the term "concrete" refers to cement concrete, which uses cement as the cementitious material, sand and stone as aggregate; it is mixed with water (which may contain additives and admixtures) in a certain proportion and obtained by stirring. It is also called ordinary concrete and is widely used in civil engineering. Freeze-thaw damage is a common problem in concrete structures in cold regions. During the freeze-thaw cycle of concrete, due to the positive and negative alternation of temperature (such as the rise and fall of air temperature or the change of water level), the water in its internal pores forms freezing expansion pressure, osmotic pressure, etc., resulting in fatigue stress in the concrete, and then causing the phenomenon of gradual erosion and damage from the surface to the inside. In severe cases, it will affect the stability and safety of the structure.

[0003] Currently, most traditional freeze-thaw damage detection methods rely on manual inspection. The results of the detection methods largely depend on the experience and judgment ability of the inspectors, which is subjective, unable to detect minor damages in a timely and accurate manner, and requires a large amount of labor costs.

[0004] Therefore, there is a need for a freeze-thaw damage detection and early warning method and system based on a sensor network to solve the problems in the prior art that minor damages cannot be detected in a timely and accurate manner and a large amount of labor costs are required. Summary of the Invention

[0005] The purpose of the present invention is to provide a freeze-thaw damage detection and early warning method and system based on a sensor network to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A freeze-thaw damage detection and early warning method and system based on a sensor network, including the steps of:

[0007] Install a variety of sensors on the inner and outer surfaces, important nodes and key positions of the concrete structure to form a sensor network;

[0008] Collect historical data and real-time data of the temperature, humidity, stress, and cracks of the concrete structure detected by the sensor network;

[0009] Among them, the sensors transmit the collected data to the data processing center in real time through wireless communication technology for subsequent analysis; the frequency and accuracy of data collection are set according to the actual situation to ensure the timeliness and accuracy of the data, so as to reflect the real-time state change of the structure under freeze-thaw action.

[0010] Based on historical data and real-time data, evaluate the current freeze-thaw damage degree of the concrete structure;

[0011] According to the safety standards and historical data of the concrete structure, preset the threshold of the damage rate, divide the warning levels according to the threshold, and automatically classify the current freeze-thaw damage rate into the corresponding warning levels;

[0012] Among them, the warning levels are divided into: Green warning: The structure is in good condition and the freeze-thaw damage degree is within the acceptable range;

[0013] Yellow warning: The structure has slight freeze-thaw damage, and there is a risk of progressive expansion of the damage. It is recommended to conduct regular inspections;

[0014] Orange warning: The freeze-thaw damage of the structure has reached a medium level, the damage expands rapidly, and it may affect the safety of the structure's use. It is recommended to take emergency repair measures;

[0015] Red warning: The freeze-thaw damage of the structure is serious, which has posed an obvious threat to the bearing capacity and safety of the structure. It is necessary to immediately take repair or reinforcement measures.

[0016] Predict the development trend of the freeze-thaw damage of the concrete structure in the next period of time, and automatically classify it into different warning levels according to the damage degree and prediction trend;

[0017] Through the classification of warning levels, send warning notifications to relevant management personnel, engineers or maintenance personnel, and provide a detailed freeze-thaw damage assessment report. The warning notifications can be sent to the monitoring personnel and maintenance personnel via text messages, emails, APP push, etc.

[0018] It should be noted in the solution that the multiple sensors include but are not limited to temperature sensors, humidity sensors, stress sensors, and crack sensors, which form a complete sensor network through wireless communication technology. These sensors can monitor key indicators such as temperature, humidity, stress, and crack width in real time. The change in temperature is the core driving factor of the freeze-thaw cycle, affecting the ice-water transformation, expansion, and contraction inside the concrete; humidity affects the moisture absorption and freezing in the concrete, and thus affects the occurrence of freeze-thaw damage; the mechanical stress suffered by the concrete during the freeze-thaw cycle, especially the internal stress caused by the expansion of ice; the crack sensor can monitor the cracking condition of the concrete surface, which is crucial for evaluating the actual degree of freeze-thaw damage.

[0019] It is further worth noting that the historical data includes the number of freeze-thaw cycles monitored by the sensor network in the past period of time, as well as environmental data and structural data;

[0020] Among them, the relationship between the freeze-thaw damage rate (D) and temperature, humidity, and stress can be calculated by the following formula:

[0021] D = α1·T max ·T min + α2·H·N + α3·σ·N + α4·C

[0022] Where D is the freeze - thaw damage rate (%), T max is the maximum temperature (°C) in the freeze - thaw cycle, T min is the minimum temperature (°C) in the freeze - thaw cycle, H is the humidity (%), N is the number of freeze - thaw cycles, σ is the stress (Pa), C is the crack width (mm) monitored by the crack sensor, and α1, α2, α3, α4 are empirical parameters obtained through experimental data and statistical analysis, used to adjust the influence weights of various factors.

[0023] The relationship between the freeze - thaw damage rate (D) and the stress can be calculated by the following formula:

[0024] D σ = β1·σ·N

[0025] Where D σ is the freeze - thaw damage rate caused by stress; σ is the stress (Pa), N is the number of freeze - thaw cycles, and β1 is the stress influence coefficient.

[0026] The relationship between the freeze - thaw damage rate (D) and the crack can be calculated by the following formula:

[0027] D C = β2·ΔC·N

[0028] Where D C is the freeze - thaw damage rate caused by the crack, ΔC is the crack width (mm), N is the number of freeze - thaw cycles, and β2 is the crack influence coefficient.

[0029] The final freeze - thaw damage assessment can be obtained by comprehensively considering the influence of the above - mentioned various factors to get a comprehensive freeze - thaw damage rate (D total ):

[0030] D total = α1·T max ·T min + α2·H·N + α3·σ·N + α4·C + D σ + D C .

[0031] Furthermore, it should be noted that the freeze - thaw damage prediction formula is established through the following multiple linear regression model:

[0032] D(t) = β0 + β1T(t) + β2H(t) + β3σ(t) + β4C(t) + β5N(t) + ε

[0033] Among them, D(t) is the freeze-thaw damage rate (predicted value) at a future time (t), T(t) is the temperature at the future time, H(t) is the humidity at the future time, σ(t) is the stress at the future time, C(t) is the crack width at the future time, N(t) is the number of freeze-thaw cycles at the future time, β0, β1, β2, β3, β4, β5 are regression coefficients obtained by fitting historical data, and ε is an error term representing the prediction error of the model.

[0034] Furthermore, based on the same inventive concept as the freeze-thaw damage detection and warning method based on a sensor network, this solution proposes a freeze-thaw damage detection and warning system based on a sensor network, including:

[0035] The sensor module includes various types of sensors, and transmits the collected data to the data processing module through a wireless communication module.

[0036] The data processing center is responsible for receiving, storing, processing, and analyzing data from each sensor node, including a data processing module and a historical data storage module.

[0037] The data processing module is used to receive, clean, and preliminarily process sensor data, and convert it into a format suitable for analysis;

[0038] The historical data storage module is used to store past environmental data, monitoring data, and freeze-thaw damage data;

[0039] The evaluation and analysis module evaluates the current freeze-thaw damage degree of the concrete structure based on historical data and real-time data through a statistical model;

[0040] The prediction module is used to perform prediction analysis of freeze-thaw damage and predict the possible freeze-thaw damage that the structure may suffer in a future period of time;

[0041] The warning module is used to automatically classify the current freeze-thaw damage degree or the predicted freeze-thaw damage degree into warning levels and send out warning notifications in a timely manner for relevant personnel to handle.

[0042] Compared with the prior art, the freeze-thaw damage detection and warning method and system based on a sensor network provided by the present invention have at least the following beneficial effects:

[0043] Through the combined monitoring of multiple types of sensors, it comprehensively reflects various health parameters of the concrete structure under freeze-thaw action, improving the accuracy and comprehensiveness of the data. The system can not only monitor the structural state in real time, but also combine historical data and real-time data to predict the degree of damage and give the development trend of freeze-thaw damage in a future period, providing a scientific basis for early warning. Based on the prediction of the degree of freeze-thaw damage, the system automatically divides different early warning levels to ensure that an early warning can be issued before the structure has problems, so as to take countermeasures in advance to prevent catastrophic consequences. Through automated monitoring, it reduces the frequency of manual inspections, lowers the maintenance cost, and improves the accuracy of freeze-thaw damage detection. Description of the Drawings

[0044] Figure 1 It is a flowchart of the freeze-thaw damage detection and early warning method based on a sensor network according to the present invention;

[0045] Figure 2 It is a block diagram of the structure of the freeze-thaw damage detection and early warning system based on a sensor network according to the present invention. Detailed Embodiments

[0046] The following further describes the present invention in conjunction with embodiments.

[0047] Refer to Figure 1 As shown, the present invention provides a freeze-thaw damage detection and early warning method based on a sensor network, including the steps:

[0048] S1. Install multiple sensors on the inner and outer surfaces, important nodes and key positions of the concrete structure to form a sensor network;

[0049] Among them, the multiple sensors include but are not limited to temperature sensors, humidity sensors, stress sensors, and crack sensors. A complete sensor network is formed through wireless communication technology. These sensors can monitor key indicators such as temperature, humidity, stress, and crack width in real time. The change in temperature is the core driving factor of the freeze-thaw cycle, affecting the ice-water transformation, expansion and contraction inside the concrete; humidity affects the moisture absorption and freezing of water in the concrete, and thus affects the occurrence of freeze-thaw damage; the mechanical stress suffered by the concrete during the freeze-thaw cycle, especially the internal stress caused by the expansion of ice; the crack sensor can monitor the cracking situation of the concrete surface, which is crucial for evaluating the actual degree of freeze-thaw damage.

[0050] S2. Collect historical data and real-time data of the temperature, humidity, stress, and cracks of the concrete structure detected by the sensor network;

[0051] Among them, the sensor transmits the collected data to the data processing center in real time through wireless communication technology for subsequent analysis; the frequency and accuracy of data collection are set according to the actual situation to ensure the timeliness and accuracy of the data, so as to reflect the real-time state change of the structure under freeze-thaw action.

[0052] S3. Based on the historical data and real-time data, evaluate the current freeze-thaw damage degree of the concrete structure; the historical data includes the number of freeze-thaw cycles in the past period monitored by the sensor network, as well as environmental data and structural data.

[0053] Among them, the relationship between the freeze-thaw damage rate (D) and temperature, humidity, and stress can be calculated by the following formula:

[0054] D = α1·T max ·T min +α2·H·N + α3·σ·N + α4·C

[0055] Among them, D is the freeze-thaw damage rate (%), T max is the maximum temperature (°C) in the freeze-thaw cycle, T min is the minimum temperature (°C) in the freeze-thaw cycle, H is the humidity (%), N is the number of freeze-thaw cycles, σ is the stress (Pa), C is the crack width (mm) monitored by the crack sensor, and α1, α2, α3, α4 are empirical parameters obtained through experimental data and statistical analysis, which are used to adjust the influence weights of various factors.

[0056] The relationship between the freeze-thaw damage rate (D) and stress can be calculated by the following formula:

[0057] D σ = β1·σ·N

[0058] Among them, D σ is the freeze-thaw damage rate caused by stress; σ is the stress (Pa), N is the number of freeze-thaw cycles, and β1 is the stress influence coefficient.

[0059] The relationship between the freeze-thaw damage rate (D) and cracks can be calculated by the following formula:

[0060] D C = β2·ΔC·N

[0061] Among them, D C is the freeze-thaw damage rate caused by cracks, ΔC is the crack width (mm), N is the number of freeze-thaw cycles, and β2 is the crack influence coefficient.

[0062] The final freeze-thaw damage assessment can be obtained by comprehensively considering the influence of the above various factors to get a comprehensive freeze-thaw damage rate (D total ):

[0063] D total = α1·T max ·T min + α2·H·N + α3·σ·N + α4·C + D σ + D C

[0064] S4. According to the safety standards and historical data of the concrete structure, preset the threshold of the damage rate, divide the early warning levels according to the threshold, and automatically classify the currently obtained freeze-thaw damage rate into the corresponding early warning levels;

[0065] Among them, the early warning levels are divided into:

[0066] Green early warning: The structure is in good condition, and the degree of freeze-thaw damage is within an acceptable range;

[0067] Yellow early warning: Slight freeze-thaw damage appears in the structure, and there is a risk of gradual expansion of the damage. It is recommended to conduct regular inspections;

[0068] Orange early warning: The freeze-thaw damage of the structure has reached a medium level, the damage expands rapidly, and it may affect the safety of the structure's use. It is recommended to take emergency repair measures;

[0069] Red early warning: The freeze-thaw damage of the structure is serious, which has posed an obvious threat to the bearing capacity and safety of the structure. It is necessary to immediately take repair or reinforcement measures.

[0070] S5. Predict the development trend of the freeze-thaw damage of the concrete structure in the future for a period of time, and automatically classify it into different early warning levels according to the predicted trend of the damage degree;

[0071] Establish a freeze-thaw damage prediction formula through the following multiple linear regression model:

[0072] D(t)= β0 + β1T(t)+ β2H(t)+ β3σ(t)+ β4C(t)+ β5N(t)+ ε

[0073] Among them, D(t) is the freeze-thaw damage rate (predicted value) at the future time (t), T(t) is the temperature at the future time, H(t) is the humidity at the future time, σ(t) is the stress at the future time, C(t) is the crack width at the future time, N(t) is the number of freeze-thaw cycles at the future time, β0, β1, β2, β3, β4, β5 are regression coefficients obtained by fitting historical data, and ε is the error term, representing the prediction error of the model.

[0074] S6. Through the division of the early warning levels, send early warning notifications to relevant management personnel, engineers or maintenance personnel, and provide a detailed freeze-thaw damage assessment report. The early warning notifications can be sent to the monitoring personnel and maintenance personnel via text messages, emails, APP push, etc.

[0075] Further, please refer to Figure 2 As shown, an invention concept identical to the freeze-thaw damage detection and warning method based on a sensor network is proposed. This solution proposes a freeze-thaw damage detection and warning system based on a sensor network, including:

[0076] The sensor module includes various types of sensors and transmits the collected data to the data processing module through a wireless communication module.

[0077] The data processing center is responsible for receiving, storing, processing, and analyzing data from each sensor node, including a data processing module and a historical data storage module.

[0078] The data processing module is used to receive, clean, and preliminarily process sensor data and convert it into a format suitable for analysis;

[0079] The historical data storage module is used to store past environmental data, monitoring data, and freeze-thaw damage data;

[0080] The evaluation and analysis module evaluates the current freeze-thaw damage degree of the concrete structure based on historical data and real-time data through a statistical model;

[0081] The prediction module is used to perform prediction analysis of freeze-thaw damage and predict the possible freeze-thaw damage that the structure may suffer in a future period;

[0082] The warning module is used to automatically classify the warning level of the current freeze-thaw damage degree or predicted freeze-thaw damage degree and issue a warning notice in a timely manner for relevant personnel to handle.

[0083] The usage process of the above freeze-thaw damage detection and warning system based on a sensor network is as follows:

[0084] Step 1: Install sensors at key parts of the concrete structure and areas that may be affected by freeze-thaw, and each sensor is equipped with a wireless communication module to ensure that data can be transmitted to the data processing center;

[0085] Step 2: The sensors start to collect data in real time and transmit the data to the data processing center through the wireless communication module;

[0086] Step 3: The data processing module receives the sensor data, cleans and preliminarily processes the data, and converts it into a standard format suitable for further analysis; at the same time, stores the data of each real-time monitoring into the historical data storage module for subsequent comparative analysis and long-term trend tracking;

[0087] Step 4: The system sets different warning thresholds according to historical data, the design standards of the structure, and expert experience;

[0088] Step Five: Based on real-time data and historical data, the evaluation and analysis module evaluates and analyzes the degree of freeze-thaw damage to the current concrete structure;

[0089] Step Six: The prediction module conducts prediction and analysis of freeze-thaw damage to predict the degree of freeze-thaw damage that the structure may suffer in a future period;

[0090] Step Seven: The early warning module is used to automatically classify the current degree of freeze-thaw damage or the predicted degree of freeze-thaw damage into early warning levels and issue early warning notifications in a timely manner for relevant personnel to handle.

[0091] In summary, the advantages of the present invention are as follows: Through the combined monitoring of various types of sensors provided, it comprehensively reflects various health parameters of the concrete structure under freeze-thaw action, improving the accuracy and comprehensiveness of the data; The system can not only monitor the structural state in real time, but also combine historical data and real-time data to predict the degree of damage, and give the development trend of freeze-thaw damage in a future period, providing a scientific basis for early warning; Based on the prediction of the degree of freeze-thaw damage, the system automatically classifies different early warning levels to ensure that early warnings can be issued before problems occur in the structure, so as to take countermeasures in advance to prevent catastrophic consequences; Through automated monitoring, the frequency of manual inspections is reduced, the maintenance cost is lowered, and the accuracy of freeze-thaw damage detection is improved.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A freeze-thaw damage detection and early warning method based on a sensor network, characterized in that, Including the steps: Install a variety of sensors on the inner and outer surfaces, important joints and key positions of the concrete structure to form a sensor network; Collect historical data and real-time data of temperature, humidity, stress and cracks of the concrete structure detected by the sensor network; According to the safety standards and historical data of the concrete structure, preset the threshold of the damage rate, and divide the early warning levels according to the threshold; Based on the historical data and real-time data, evaluate the current freeze-thaw damage degree of the concrete structure, and automatically classify it into the corresponding early warning level according to the obtained freeze-thaw damage degree; Predict the development trend of the freeze-thaw damage of the concrete structure in the future for a period of time, and automatically classify it into different early warning levels according to the damage degree prediction trend; Through the division of the early warning level, send early warning notifications to relevant management personnel, engineers or maintenance personnel, and provide a detailed freeze-thaw damage assessment report.

2. The method for freeze-thaw damage detection and warning based on a sensor network according to claim 1, characterized in that: The various sensors include, but are not limited to, temperature sensors, humidity sensors, stress sensors, and crack sensors, and form a complete sensor network through wireless communication technology. These sensors can real-time monitor key indicators such as temperature, humidity, stress and crack width.

3. The freeze-thaw damage detection and warning method based on a sensor network according to claim 1, characterized in that: The historical data includes the number of freeze-thaw cycles in the past period of time monitored by the sensor network, as well as environmental data and structural data; Among them, the relationship between the freeze-thaw damage rate (D) and temperature, humidity and stress can be calculated by the following formula: D = α1·T max ·T min + α2·H·N + α3·σ·N + α4·C Among them, D is the freeze-thaw damage rate (%), T max is the maximum temperature (°C) in the freeze-thaw cycle, T min is the minimum temperature (°C) in the freeze-thaw cycle, H is the humidity (%), N is the number of freeze-thaw cycles, σ is the stress (Pa), C is the crack width (mm) monitored by the crack sensor, and α1, α2, α3, α4 are empirical parameters obtained through experimental data and statistical analysis, and are used to adjust the influence weights of various factors. The relationship between the freeze-thaw damage rate (D) and stress can be calculated by the following formula: D σ = β1·σ·N Among them, D σ The freeze-thaw damage rate caused by stress; σ is the stress (Pa), N is the number of freeze-thaw cycles, and β1 is the stress influence coefficient. The relationship between the freeze-thaw damage rate (D) and cracks can be calculated by the following formula: D C = β2·ΔC·N Among them, D C is the freeze-thaw damage rate caused by cracks, ΔC is the crack width (mm), N is the number of freeze-thaw cycles, and β2 is the crack influence coefficient. The final freeze-thaw damage assessment can obtain a comprehensive freeze-thaw damage rate (D total ) by integrating the influences of the above factors: D total = α1·T max ·T min + α2·H·N + α3·σ·N + α4·C + D σ + D C 。 4. The method for freeze-thaw damage detection and early warning based on a sensor network according to claim 1, wherein: The early warning levels are divided into green early warning, which means the structure is in good condition and the freeze-thaw damage degree is within an acceptable range; Yellow early warning means that the structure has slight freeze-thaw damage and there is a risk of gradual expansion of the damage. It is recommended to conduct regular inspections; Orange early warning means that the freeze-thaw damage of the structure has reached a medium level, the damage expands quickly, and it may affect the use safety of the structure. It is recommended to take emergency repair measures; Red early warning means that the freeze-thaw damage of the structure is serious, and it has posed an obvious threat to the bearing capacity and safety of the structure. Immediate repair or reinforcement measures are required.

5. The freeze-thaw damage detection and warning method based on a sensor network according to claim 1, characterized in that Establish a freeze-thaw damage prediction formula through the following multiple linear regression model: D(t) = β0 + β1T(t) + β2H(t) + β3σ(t) + β4C(t) + β5N(t) + ε Among them, D(t) is the freeze-thaw damage rate (predicted value) at the future time (t), T(t) is the temperature at the future time, H(t) is the humidity at the future time, σ(t) is the stress at the future time, C(t) is the crack width at the future time, N(t) is the number of freeze-thaw cycles at the future time, β0, β1, β2, β3, β4, β5 are regression coefficients obtained by fitting historical data, and ε is the error term, representing the prediction error of the model.

6. The freeze-thaw damage detection and warning system based on a sensor network is used to implement the freeze-thaw damage detection and warning method based on a sensor network according to any one of claims 1-5, characterized in that, Including: The sensor module includes various types of sensors, and transmits the collected data to the data processing module through the wireless communication module. The data processing center is responsible for receiving, storing, processing and analyzing data from each sensor node, including the data processing module and the historical data storage module. The data processing module is used to receive, clean, and preliminarily process sensor data and convert it into a format suitable for analysis; The historical data storage module is used to store past environmental data, monitoring data, and data on freeze-thaw damage; The evaluation and analysis module evaluates the current degree of freeze-thaw damage of the concrete structure based on historical data and real-time data through a statistical model; The prediction module is used to conduct predictive analysis of freeze-thaw damage and predict the possible freeze-thaw damage that the structure may suffer in a future period of time; The warning module is used to automatically classify the current degree of freeze-thaw damage or the predicted degree of freeze-thaw damage into warning levels and issue warning notifications in a timely manner for relevant personnel to handle.

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