A real-time dynamic monitoring and early warning system and method for house crack deformation

Abstract

The application discloses a kind of housing crack deformation real-time dynamic monitoring early warning system and method, related to building safety monitoring technical field, by underground water level relevant data acquisition, can be mastered the influence of underground water level fluctuation to foundation in real time, ensure the accuracy of crack propagation analysis, and obtain crack risk index Lf, carry out housing safety evaluation, identify housing safety anomaly, and calculate soil expansion factor Tr with water level change influence factor Ψ, provide scientific support for crack prediction, simultaneously predict crack development trend, carry out risk assessment, improve the initiative of housing maintenance, and collect future crack change data, feedback to crack change prediction model, improve long-term monitoring accuracy, reduce error, reduce housing maintenance cost, improve the scientificity and reliability of safety warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building safety monitoring, in particular to a house crack deformation real-time dynamic monitoring and early warning system and method. BACKGROUND

[0002] In the field of civil engineering and building safety, the stability and durability of buildings have always been the focus of engineers and researchers. Among them, foundation engineering and structural health monitoring are important research directions to ensure the long-term safety of buildings. In this subfield, house crack monitoring is a key technical means for assessing whether a building has abnormal deformation, material degradation, foundation settlement, or external environmental impact. Currently, intelligent monitoring and early warning systems for cracks have been widely used in high-rise buildings, old houses, bridges, and other structures. However, the expansion of house cracks is not only caused by structural loads, but also by groundwater level fluctuations, which are a key but often overlooked factor. In areas with periodic changes in groundwater level, the swelling and shrinking of foundation soil can cause uneven settlement of the house foundation, leading to the expansion of cracks in walls, beams, and columns. Therefore, studying the impact of groundwater level changes on house crack deformation and establishing an effective real-time monitoring and early warning system is of great significance for improving building safety and service life.

[0003] In the Chinese invention patent with application publication number CN119268562A, a house wall crack monitoring method and system are disclosed. The method includes: S1, continuously collecting wall surface image data through an image collection module; S2, analyzing the collected wall surface image data through an analysis control module to obtain a wall danger characteristic value curve over time and determine whether to generate a wall danger warning signal. The invention analyzes the collected wall surface image data through the analysis control module to obtain a wall danger characteristic value curve over time and determine whether to generate a wall danger warning signal, which alerts the house wall monitoring personnel. This achieves the purpose of accurately and objectively monitoring house wall cracks, avoids subjective judgment during manual observation, and allows real-time monitoring of house wall cracks, improving the accuracy of wall crack monitoring.

[0004] The above method can monitor house wall cracks in real time, improving the accuracy of wall crack monitoring. However, in addition to this, existing house wall crack monitoring methods mainly focus on the geometric features of cracks (such as width, depth, and length). By monitoring the geometric features of cracks in real time, it is determined whether the house is in a dangerous state.

[0005] However, this method can judge whether the house is in a dangerous state, but it does not fully consider the external causes of the crack, such as the influence of groundwater level change on the foundation. In soft soil area, groundwater level fluctuation is more frequent, and foundation soil body is easy to swell and shrink, causing uneven settlement, but due to the lack of groundwater monitoring data support of crack monitoring system, it is often difficult to identify the real cause of crack expansion.

[0006] Therefore, the application provides a housing crack deformation real-time dynamic monitoring and early warning system and method. SUMMARY

[0007] In view of the deficiencies of the prior art, the application provides a housing crack deformation real-time dynamic monitoring and early warning system and method, which solves the problems in the above background art.

[0008] To achieve the above purpose, the application is implemented by the following technical scheme: a housing crack deformation real-time dynamic monitoring and early warning system, comprising a groundwater level data acquisition module, a crack deformation monitoring module, a data analysis module, a crack prediction and intelligent early warning module, and an intelligent feedback module;

[0009] The groundwater level data acquisition module is used for real-time monitoring of the groundwater level fluctuation around the house, and obtaining a set of groundwater level related data H;

[0010] The crack deformation monitoring module is used for obtaining housing crack related data and performing feature extraction, obtaining crack expansion rate , and preliminarily evaluating the safety state of the house;

[0011] The data analysis module is used for summarizing and calculating the obtained set of groundwater level related data H, obtaining a soil expansion factor Tr, and obtaining a water level change influence factor Ψ according to the soil expansion factor Tr;

[0012] The crack prediction and intelligent early warning module is used for constructing a crack change prediction model, predicting the crack width at a future time point , evaluating the safety state of the future house, and triggering the early warning mechanism;

[0013] The intelligent feedback module is used for collecting crack change data in a future period of time in real time, and feeding back to the crack change prediction model, and optimizing the model prediction result.

[0014] Preferably, the groundwater level data acquisition module is used for collecting geological reports of the target house area, determining the soil type of the target house area, and setting geological monitoring points, and deploying an intelligent sensor group in the set geological monitoring points, wherein the soil type includes soft soil, sand and clay, and the intelligent sensor group includes a static pressure type groundwater level meter, a capacitive soil moisture content sensor and a micro ground displacement sensor.

[0015] According to the intelligent sensor group deployed at the geological monitoring point, the underground water level related data of the target house is collected in real time, and an underground water level related data set H is constructed, wherein the underground water level related data includes underground water level height W, soil water content and foundation settlement amount .

[0016] Preferably, the crack deformation monitoring module includes a crack data acquisition unit and a preliminary evaluation unit.

[0017] The crack data acquisition unit is used to set data acquisition nodes in the target house, and install intelligent sensors at the data acquisition nodes to obtain house crack related data, wherein the data acquisition nodes include house bearing walls, beam column nodes, door and window edges, and house soft soil foundation areas, the intelligent sensors include crack depth ultrasonic measuring instruments and laser range finders, and the house crack related data includes crack width and crack depth .

[0018] According to the obtained house crack related data, feature extraction is performed to obtain crack expansion rate and crack depth change rate , wherein the crack expansion rate and the crack depth change rate are obtained in the following manner:

[0019]

[0020]

[0021] In the formula, and respectively represent the crack width and crack depth of the house at the current data monitoring time point t, and respectively represent the crack width and crack depth of the house at the last data monitoring time point , represents the time interval between the two data monitoring time points.

[0022] Preferably, the preliminary evaluation unit is used to perform summary calculation according to the crack expansion rate and the crack depth change rate to obtain a crack risk index Lf, wherein the crack risk index Lf is obtained in the following manner:

[0023]

[0024] wherein, and respectively represent the weight coefficients of crack propagation rate and crack depth change rate , C represents a first correction constant;

[0025] a preset crack risk threshold Lfyz, the crack risk threshold Lfyz and the crack risk index Lf are compared and analyzed, the safety degree of the house at the current data monitoring time point t is evaluated, and the specific evaluation content is as follows:

[0026] If the crack risk index Lf is less than the crack risk threshold Lfyz, i.e. Lf < Lfyz, it is determined that the safety of the house at this time is within the normal range, and no processing is required;

[0027] If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz, i.e. Lf ≥ Lfyz, it is determined that the safety of the house at this time is in a dangerous state, the alarm system is triggered, the alarm information is automatically generated and sent to the house owner, and the emergency reinforcement operation of the house crack is carried out.

[0028] Preferably, the data analysis module comprises a preprocessing unit and a groundwater level related data analysis unit;

[0029] The preprocessing unit is used for denoising and data cleaning of the data in the groundwater level related data set H, and dimensionless processing of the cleaned data;

[0030] The groundwater level related data analysis unit is used for feature extraction according to the preprocessed groundwater level related data set H when the safety of the house is within the normal range, to obtain the foundation material sensitivity coefficient and the residual effect factor , wherein the foundation material sensitivity coefficient is obtained in the following manner:

[0031]

[0032] wherein, represents the foundation settlement amount, represents the groundwater level change amount, represents the soil water content;

[0033] The residual effect factor is obtained in the following manner:

[0034]

[0035] wherein, represents an empirical attenuation coefficient, represents the groundwater level change amount at the i-th data monitoring time point, the soil moisture content at the i-th data monitoring time point, and n represents the total number of data monitoring time points, .

[0036] Preferably, according to the foundation material sensitivity coefficient and the residual effect factor , combined with the groundwater level height W and the soil moisture content in the groundwater level related data set H, a soil expansion factor Tr is obtained by summary calculation, and a water level change influence factor Ψ is further obtained according to the soil expansion factor Tr, wherein the soil expansion factor Tr is obtained in the following manner:

[0037]

[0038] In the formula, represents the groundwater level height of the area where the house is located at the current data monitoring time point t, represents the groundwater level height of the area where the house is located at the last data monitoring time point .

[0039] The water level change influence factor Ψ is obtained in the following manner:

[0040]

[0041] In the formula, represents the cumulative influence of the change of the groundwater level on the crack expansion within the crack response lag time period , that is, the water level change influence factor, represents the crack lag response coefficient, and represents the sensitivity of the crack to the expansion and contraction of the foundation, represents the integral independent variable, .

[0042] Preferably, the crack prediction and intelligent early warning module comprises a crack prediction unit and an early warning unit.

[0043] The crack prediction unit is used to collect historical groundwater level related data and house crack related data, and construct a time series model using a linear regression method, to obtain the crack expansion rate in the following manner and the water level change influence factor in the following manner, to obtain the crack expansion rate and the water level change influence factor of the house in the past period of time, and use the time series model to obtain the crack expansion rate at a future time point and the water level change influence factor at the future time point , wherein the future time point Crack propagation rate at time and future time points Factors affecting water level changes The method of obtaining it is:

[0044]

[0045]

[0046] In the formula, and These represent the crack propagation rate and water level change influencing factor at the current data monitoring time point, respectively; J represents the size of the time window used for linear regression calculation; and j represents the index of the historical data. and Represents the regression coefficient. and Representing past time points The changes in crack propagation rate and the changes in factors affecting water level changes;

[0047] Construct a crack change prediction model to predict future time points. Crack width Among them, future time points Crack width The calculation formula is:

[0048]

[0049] In the formula, This represents the crack width at the current data monitoring time point t. Indicates a future point in time. Crack propagation rate at time, Indicates the structural load influence coefficient. This represents the groundwater level influence coefficient. Indicates a future point in time. Factors affecting water level changes during the period This represents the anomaly correction term, where K represents the number of time steps within the prediction time range, and k represents the index of the time step. , Indicates a predicted future point in time. This indicates the time from the current data monitoring point to the future prediction point. At any point in time between, This model represents the influence of groundwater level on crack propagation, avoiding negative values ​​for the groundwater influence factor. ∈(t, ).

[0050] Preferably, the early warning unit is used to preset the first crack safety threshold. and the second crack safety threshold , and the crack width at the future time point is compared with the first crack safety threshold and the second crack safety threshold to evaluate the safety state of the future house, and the specific evaluation content is as follows:

[0051] If the crack width at the future time point is less than or equal to the first crack safety threshold , it is determined that the safety state of the future house is in a normal state, and no processing is needed;

[0052] If the crack width at the future time point is greater than the first crack safety threshold and less than the second crack safety threshold , it is determined that the safety state of the future house is in a risk state, at this time, the frequency of house crack detection is increased from once a month to once a week, the house foundation settlement is detected, the underground water level fluctuation is observed, and the foundation drainage measures are taken;

[0053] If the crack width at the future time point is greater than or equal to the second crack safety threshold , it is determined that the safety state of the future house is in a dangerous state, at this time, the house personnel are immediately notified to evacuate the room, and an alarm is immediately given to start the emergency response mechanism and take house structure reinforcement, wherein the house structure reinforcement includes using high-strength epoxy resin and polymer mortar to block cracks, adding steel plates and carbon fiber reinforced layers in the crack concentration area, and carrying out high-pressure grouting to stabilize the foundation.

[0054] Preferably, the intelligent feedback module is used to collect crack change data in the future period of time in real time, and feed the collected crack change data and the predicted crack width at the future time point back to the crack change prediction model to intelligently adjust the crack change prediction model and optimize the error of the crack change prediction model.

[0055] Preferably, a real-time dynamic monitoring and early warning method for house crack deformation comprises the following steps,

[0056] Step 1, real-time monitoring of underground water level fluctuation around the house to obtain a set of underground water level related data H;

[0057] Step 2, obtaining house crack related data and performing feature extraction to obtain crack expansion rate And conduct a preliminary assessment of the building's safety status;

[0058] Step 3: Based on the obtained groundwater level related data set H, perform summary calculations to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, obtain the water level change influencing factor Ψ.

[0059] Step 4: Construct a crack change prediction model to predict future time points. Crack width Assess the future safety status of the house and trigger an early warning mechanism;

[0060] Step 5: Collect crack change data in real time for a future period of time and feed it back to the crack change prediction model to optimize the model prediction results.

[0061] This invention provides a real-time dynamic monitoring and early warning system and method for building crack deformation, which has the following beneficial effects:

[0062] (1) By combining real-time groundwater level monitoring, crack deformation monitoring, data analysis, crack prediction and intelligent early warning, as well as intelligent feedback function modules, multiple factors affecting crack expansion can be accurately obtained. Through the linear regression time series prediction model, the system can predict the crack width at future time points and assess the future safety status of the building. Compared with the traditional static crack monitoring method, this system can predict the crack expansion trend in advance, provide early warning for building safety management, avoid building structural damage caused by uncontrolled crack expansion, and improve the long-term stability and service life of the building.

[0063] (2) By adopting a groundwater level data acquisition module and deploying intelligent sensor groups, such as static pressure groundwater level gauges, capacitive soil moisture sensors and miniature ground displacement sensors, the groundwater level height W and soil moisture content in the building foundation area can be monitored in real time. and foundation settlement The system constructs a groundwater level-related data set H and calculates the water level change impact factor Ψ through a data analysis module. This accurately assesses the lag effect of groundwater level changes on crack propagation, thereby distinguishing the true causes of crack propagation. For example, in soft soil foundation areas, groundwater level fluctuations may cause soil expansion and contraction, which in turn affects crack propagation. This system can accurately identify this phenomenon and effectively distinguish between foundation settlement cracks and structural load cracks, thus providing more targeted maintenance measures for different types of cracks and improving the scientific nature of building structural health management.

[0064] (3) By using intelligent sensors such as static pressure groundwater level gauge, capacitive soil moisture sensor, crack depth ultrasonic measuring instrument and laser rangefinder, the system can realize real-time monitoring of multiple parameters such as building crack deformation, foundation settlement and groundwater level fluctuation. The system also supports remote data transmission, which can upload monitoring data to cloud data center to realize remote real-time monitoring. Building managers can check the status of building cracks, receive early warning information and conduct safety assessments at any time through PC or mobile terminal. Compared with the traditional manual periodic inspection method, this system greatly improves monitoring efficiency, reduces labor costs and makes building safety monitoring more intelligent and convenient. Attached Figure Description

[0065] Figure 1 This is a block diagram of a real-time dynamic monitoring and early warning system for building crack deformation according to the present invention.

[0066] Figure 2 This is a schematic diagram of the process for a real-time dynamic monitoring and early warning method for building crack deformation according to the present invention.

[0067] Figure 3 This is a block diagram of the crack deformation monitoring module of a real-time dynamic monitoring and early warning system for building crack deformation according to the present invention.

[0068] Figure 4 This is a line graph showing the predicted crack width for the next three months, as provided by the real-time dynamic monitoring and early warning system for building crack deformation according to the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1

[0071] Please see Figure 1 , Figure 3 and Figure 4 The present invention provides a real-time dynamic monitoring and early warning system for building crack deformation, including a groundwater level data acquisition module, a crack deformation monitoring module, a data analysis module, a crack prediction and intelligent early warning module, and an intelligent feedback module.

[0072] The groundwater level data acquisition module is used to monitor the fluctuation of groundwater level around the building in real time and obtain groundwater level related data set H;

[0073] The crack deformation monitoring module is used to acquire data related to building cracks, extract features, and obtain the crack expansion rate. And conduct a preliminary assessment of the building's safety status;

[0074] The data analysis module is used to summarize and calculate based on the acquired groundwater level related data set H, obtain the soil expansion factor Tr, and obtain the water level change influencing factor Ψ based on the soil expansion factor Tr.

[0075] The crack prediction and intelligent early warning module is used to build a crack change prediction model to predict future time points. Crack width Assess the future safety status of the house and trigger an early warning mechanism;

[0076] The intelligent feedback module is used to collect crack change data in real time over a future period and feed it back to the crack change prediction model to optimize the model's prediction results.

[0077] In this embodiment, through full-cycle dynamic monitoring and intelligent early warning of building crack deformation, external environmental factors such as groundwater level fluctuations, soil expansion effects, and foundation settlement are fully considered. By acquiring groundwater level-related data set H, the soil expansion factor Tr and the water level change influence factor Ψ are analyzed to assess the lag effect of hydrogeological factors on crack propagation, thereby improving the scientific rigor and accuracy of monitoring. Simultaneously, the system combines a linear regression time series model to predict crack deformation trends at future time points, based on a first crack safety threshold. Second crack safety threshold The system conducts building risk assessments, provides early warnings for safety, and offers a scientific basis for building managers to make decisions. In addition, the system collects real-time data on actual crack changes through an intelligent feedback module, compares and analyzes the data with predicted values, optimizes the crack change prediction model, improves the system's adaptability and long-term monitoring accuracy, thereby reducing false alarms and missed alarms, improving the level of intelligent building safety management, reducing building maintenance costs, and extending the service life of buildings.

[0078] Example 2

[0079] Please refer to Figure 1 Specifically: The groundwater level data acquisition module is used to collect geological reports of the area where the target house is located, determine the soil type of the area where the target house is located, and set up geological monitoring points. The intelligent sensor group is deployed in the set geological monitoring points. The soil types include soft soil, sandy soil and clay. The intelligent sensor group includes a static pressure groundwater level gauge, a capacitive soil moisture sensor and a miniature ground displacement sensor.

[0080] Based on the intelligent sensor array deployed at the geological monitoring points, real-time data on groundwater levels in the area where the target house is located is collected, and a groundwater level-related data set H is constructed. This data includes groundwater level height W and soil moisture content. and the amount of foundation settlement .

[0081] wherein the groundwater level height W is obtained by using a static groundwater level gauge;

[0082] soil moisture content obtained by using a capacitive soil moisture sensor;

[0083] the amount of foundation settlement obtained by using a micro ground displacement sensor.

[0084] In the embodiment, by collecting the geological report of the area where the target house is located, the soil type where the house is located is analyzed in advance, including soft soil, sand and clay, to ensure that the monitoring system can adopt an optimized monitoring strategy for different geological environments. In addition, by deploying an intelligent sensor group in the set geological monitoring point, the system can monitor in real time and build a set of underground water level related data H. Compared with the traditional house crack monitoring which only focuses on the change of the crack itself, the system fully considers the influence of underground water level fluctuation on foundation and crack expansion, so that the monitoring is more comprehensive and accurate. Through real-time collection and transmission of data, reliable data support can be provided for foundation settlement trend analysis, soil swelling and shrinking effect calculation and underground water influence evaluation, to provide accurate input for subsequent crack deformation prediction and early warning, which is suitable for houses with soft soil foundation easily affected by underground water fluctuation, such as coastal and riverbank areas, can effectively improve the scientificity of house structure safety evaluation, and discover the crack expansion risk caused by foundation settlement and water level fluctuation in advance, reduce the possibility of sudden structure damage, so as to ensure the long-term stability and living safety of the house.

[0085] Embodiment 3

[0086] Please refer to Figure 1 and Figure 3 , specifically: the crack deformation monitoring module includes a crack data acquisition unit and a preliminary evaluation unit;

[0087] The crack data acquisition unit is used to set data acquisition nodes in the target house and install intelligent sensors at the data acquisition nodes to obtain house crack related data, wherein the data acquisition nodes include house bearing walls, beam column nodes, door and window edges and house soft soil foundation areas, the intelligent sensors include crack depth ultrasonic measuring instruments and laser range finders, and the house crack related data includes crack width and crack depth ;

[0088] wherein the crack width is obtained by using a laser range finder;

[0089] the crack depth Obtained by using a crack depth ultrasonic measuring instrument;

[0090] According to the obtained housing crack related data, feature extraction is performed to obtain a crack propagation rate and a crack depth change rate , wherein the crack propagation rate and the crack depth change rate are obtained in the following manner:

[0091]

[0092]

[0093] wherein, and respectively represent the crack width and the crack depth of the house at the current data monitoring time point t, and respectively represent the crack width and the crack depth of the house at the last data monitoring time point, represents the time interval between the two data monitoring time points.

[0094] The preliminary evaluation unit is configured to perform summary calculation according to the crack propagation rate and the crack depth change rate to obtain a crack risk index Lf, wherein the crack risk index Lf is obtained in the following manner:

[0095]

[0096] wherein, and respectively represent the weight coefficients of the crack propagation rate and the crack depth change rate , and C represents a first correction constant, wherein the specific numerical value of the weight coefficients is set by the customer according to the actual situation, 0 < 1, 0 < 1, and + = 1.

[0097] A preset crack risk threshold Lfyz is compared and analyzed with the crack risk index Lf to evaluate the safety degree of the house at the current data monitoring time point t, and the specific evaluation content is as follows:

[0098] ​If the crack risk index Lf is less than the crack risk threshold Lfyz, i.e., Lf < Lfyz, it is determined that the house safety at this time is in the normal range, and no treatment is needed.

[0099] If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz, i.e., Lf ≥ Lfyz, it is determined that the house safety at this time is in a dangerous state, triggering the alarm system, automatically generating an alarm information, and sending the alarm information to the house owner, and performing the house crack emergency reinforcement operation.

[0100] Specific examples are as follows:

[0101] Suppose there is a residential building on a soft soil foundation area, and an intelligent sensor group is deployed in the middle of the residential building to regularly monitor the house safety, evaluate the house safety state, and obtain the monitoring data of adjacent two house safety monitoring, as shown in Table 1:

[0102]

[0103] Table 1

[0104] According to Table 1, the crack propagation rate and the crack depth change rate are calculated:

[0105]

[0106]

[0107] According to the obtained crack propagation rate and crack depth change rate , the crack risk index Lf is calculated:

[0108]

[0109] The crack risk index Lf is compared with the preset crack risk threshold Lfyz, Lfyz = 0.8, at this time < 0.8, i.e., the crack risk index Lf is less than the crack risk threshold Lfyz, it is determined that the house safety at this time is in the normal range, and no treatment is needed, and the house crack data is continuously monitored.

[0110] In the embodiment, by deploying the crack depth ultrasonic measuring instrument and the laser range finder at the key structure positions of the house, such as the load-bearing wall, the beam column joint, the door and window edge, and the soft soil foundation, the crack width and depth data are collected with high precision, and the crack propagation rate and the crack depth change rate , provide efficient data support, based on which, the system further calculates the crack risk index Lf, combines the preset crack risk threshold Lfyz, realizes real-time housing safety evaluation, compared with the traditional crack monitoring method, this scheme not only improves the automation and precision of monitoring, but also can trigger emergency maintenance response in time when the crack expands too fast or exceeds the safety range through the real-time alarm mechanism, notify the housing manager to reinforce and repair, prevent the crack from further expanding, improve the safety of the housing structure, in addition, through comprehensive analysis of the crack expansion rate and the crack depth change rate , the false alarm and missed alarm problems caused by single crack width judgment can be avoided, the scientificity and reliability of the evaluation are improved, and the housing management is more intelligent and efficient.

[0111] Embodiment 4

[0112] Please refer to Figure 1 , specifically: the data analysis module includes a preprocessing unit and a groundwater level related data analysis unit;

[0113] The preprocessing unit is used to denoise and clean the data in the groundwater level related data set H, and to perform dimensionless processing on the cleaned data;

[0114] The groundwater level related data analysis unit is used to perform feature extraction according to the preprocessed groundwater level related data set H when the housing safety is within the normal range, to obtain the foundation material sensitivity coefficient and the residual effect factor , wherein the foundation material sensitivity coefficient is obtained in the following manner:

[0115]

[0116] In the formula, represents the foundation settlement amount, represents the groundwater level change amount, represents the soil water content;

[0117] The foundation material sensitivity coefficient is calculated and obtained by comparing historical settlement data of different geological types;

[0118] The residual effect factor is obtained in the following manner:

[0119]

[0120] In the formula, represents the empirical attenuation coefficient, represents the groundwater level change amount at the i-th data monitoring time point, the soil moisture content at the i th data monitoring time point, and n represents the total number of data monitoring time points, .

[0121] residual effect factor depending on the cumulative effect of the previous n water level changes;

[0122] according to the foundation material sensitivity coefficient and the residual effect factor , combined with the groundwater level height W and the soil moisture content in the groundwater level related data set H, the soil expansion factor Tr is obtained by summary calculation, and the water level change influence factor Ψ is further obtained according to the soil expansion factor Tr, wherein the soil expansion factor Tr is obtained in the following manner:

[0123]

[0124] In the formula, represents the groundwater level height of the building area at the current data monitoring time point t, represents the groundwater level height of the building area at the previous data monitoring time point .

[0125] The water level change influence factor Ψ is obtained in the following manner:

[0126]

[0127] In the formula, represents the cumulative influence of the groundwater level change on the crack propagation within the crack response lag time period , that is, the water level change influence factor, represents the crack lag response coefficient, which represents the sensitivity of the crack to the foundation expansion and contraction, represents the integral independent variable, wherein the crack lag response coefficient is obtained by applying simulated soil shrinkage and expansion stress on different building materials and recording the crack propagation amount using a crack length meter.

[0128] In the embodiment, the groundwater level related data set H is deeply processed by the preprocessing unit and the groundwater level related data analysis unit to improve the prediction accuracy and monitoring reliability. First, the preprocessing unit denoises, cleans and dimensionless processes the original groundwater level data to ensure the stability and comparability of the data, thereby reducing the monitoring error and improving the data quality. Subsequently, the groundwater level related data analysis unit extracts the foundation material sensitivity coefficient and the residual effect factor based on the cleaned data under the condition that the safety state of the house is normal, accurately evaluates the response capability of the foundation to the groundwater fluctuation and the long-term cumulative influence of the soil expansion and contraction, ensures that the crack prediction has more physical realistic meaning, and further calculates the soil expansion factor Tr combining the groundwater level height and the soil moisture content, quantifies the expansion and contraction effect caused by the change of the groundwater level, and calculates the water level change influence factor based on the soil expansion factor Tr, which is used to predict the cumulative influence of the groundwater fluctuation on the crack expansion. Compared with the traditional method based only on crack monitoring data, the system accurately identifies the external driving factors of crack expansion through comprehensive analysis of geology, hydrology and structure, thereby reducing false positives and false negatives, ensuring the accuracy of crack prediction, improving the intelligent level of house structure safety management, predicting crack expansion risk in advance, and providing scientific basis for house maintenance decision.

[0129] Embodiment 5

[0130] Please refer to Figure 1 and Figure 4 , specifically: the crack prediction and intelligent early warning module includes a crack prediction unit and a warning unit;

[0131] The crack prediction unit is used to collect historical groundwater level related data and house crack related data, and uses a linear regression method to construct a time series model, and according to the crack expansion rate Obtaining method and water level change influence factor Obtaining method, obtaining the crack expansion rate of the house in the past period of time and water level change influence factor , and using a time series model, obtaining the crack expansion rate at the future time point and the water level change influence factor at the future time point , wherein the crack expansion rate at the future time point and the water level change influence factor at the future time point are obtained as follows:

[0132]

[0133]

[0134] wherein, and respectively represent the crack propagation rate and water level change influence factor at the current data monitoring time point, J represents the time window size for linear regression calculation, j represents the index of historical data, and represent the regression coefficients, and respectively represent the crack propagation rate change amount and water level change influence factor change amount at the past time point ;

[0135] A crack change prediction model is constructed to predict the crack width at the future time point , wherein, the crack width at the future time point is calculated by the formula:

[0136]

[0137] wherein, represents the crack width at the current data monitoring time point t, represents the crack propagation rate at the future time point , represents the structure load influence coefficient, represents the groundwater level influence coefficient, represents the water level change influence factor at the future time point , represents the anomaly correction term, K represents the number of time steps in the prediction time range, and k represents the index of the time step, , represents the predicted future time point, represents any one time point between the current data monitoring time point and the future predicted time point , represents the groundwater level influence on crack propagation model, avoiding negative values of the groundwater influence factor, ∈(t, ).

[0138] The structure load influence coefficient is obtained by stress monitoring coefficient;

[0139] The groundwater level influence coefficient is obtained by using linear regression method to analyze the historical data;

[0140] The anomaly correction term is set by experts according to historical experience, and the value range is usually 0.01~0.05mm;​

[0141] The early warning unit is used to preset a first crack safety threshold and a second crack safety threshold , and compares and analyzes the crack width at a future time point with the first crack safety threshold and the second crack safety threshold to evaluate the safety state of the future house, and the specific evaluation content is as follows:

[0142] If the crack width at the future time point is less than or equal to the first crack safety threshold , it is determined that the safety state of the future house is in a normal state, and no processing is needed;

[0143] If the crack width at the future time point is greater than the first crack safety threshold and less than the second crack safety threshold , it is determined that the safety state of the future house is in a risk state, at which time the crack detection frequency of the house is increased from once a month to once a week, the foundation settlement of the house is detected, the underground water level fluctuation is observed, and the foundation drainage measures are taken;

[0144] If the crack width at the future time point is greater than or equal to the second crack safety threshold , it is determined that the safety state of the future house is in a dangerous state, at which time the house personnel is immediately notified to evacuate the room, an alarm is immediately given, an emergency response mechanism is started, and the house structure is reinforced, wherein the house structure reinforcement includes using high-strength epoxy resin and polymer mortar to block the cracks, adding steel plates and carbon fiber reinforced layers in the crack concentration area, and performing high-pressure grouting to stabilize the foundation.

[0145] Specific examples are as follows:

[0146] Suppose a residential building in a soft soil area region has introduced a house crack deformation real-time dynamic monitoring and early warning system, at which time the house crack related data and underground water level related data in the past 6 months are collected, as shown in Table 2 below:

[0147]

[0148] Table 2

[0149] The linear regression is used to predict the crack expansion rate and the water level change influence factor Ψ in the next three months, and the results are as shown in Table 3:

[0150]

[0151] Table 3

[0152] Given that the width of the crack in the house was 4.1 mm in February 2025, =0.2, =0.8, =0.02, calculate the crack width in May 2025 based on Tables 2 and 3 above. :

[0153]

[0154] Set the first crack safety threshold Second crack safety threshold The crack widths were 5.0 mm and 6.0 mm respectively, representing May 2025. Greater than the first crack safety threshold And less than the second crack safety threshold It was determined that the building's safety was at risk in May 2025. At this point, it was necessary to immediately increase the frequency of crack monitoring to once a week, check the foundation settlement, and carry out foundation drainage treatment.

[0155] In this embodiment, a time series model is constructed using linear regression, and combined with historical groundwater level data and building crack propagation data to accurately predict future time points. Crack propagation rate at time and future time points Factors affecting water level changes The system dynamically analyzes the long-term evolution of crack propagation to assess the future safety status of buildings, improving the foresight and reliability of crack early warning systems. Through the early warning unit, the system presets a first crack safety threshold. Second crack safety threshold And based on future time points Crack width The prediction result is divided into three housing safety states of normal, risk and danger, and scientific and reasonable measures are provided for different risk levels. For the housing with cracks in the risk state, the system will automatically increase the crack detection frequency, and combined with the underground water level monitoring and foundation settlement detection, timely measures such as foundation drainage are taken to delay the crack expansion speed; when the crack reaches the dangerous state, the system can immediately alarm and notify the housing personnel to evacuate, and start emergency reinforcement measures, such as filling cracks with epoxy resin, carbon fiber reinforced structure reinforcement, and stabilizing the foundation through high-pressure grouting to ensure the long-term stability of the housing structure. Through the crack change prediction model + dynamic early warning mechanism, precise prediction, intelligent decision and active protection are realized, not only reducing the sudden housing safety accidents caused by crack expansion, but also reducing the housing maintenance cost and improving the service life and safety of the building.

[0156] Embodiment 6

[0157] Please refer to Figure 1 , specifically: the intelligent feedback module is used to collect crack change data in the future period of time in real time, and feed back the collected crack change data and the predicted crack width of the future time point to the crack change prediction model, and intelligently adjust the crack change prediction model to optimize the error of the crack change prediction model. In the embodiment, by collecting crack change data in the future period of time and comparing with the predicted value, the prediction error is calculated, and then the crack change prediction model is adjusted. Compared with the traditional fixed parameter model, the system has self-learning ability, can dynamically correct the prediction parameters according to the latest crack change trend, optimize the model precision, ensure the rationality of the reinforcement scheme, avoid invalid maintenance, reduce resource waste, and finally improve the intelligent level of crack monitoring and enhance the scientific nature of housing safety management, providing accurate decision support for structure maintenance.

[0158] Embodiment 7

[0159] Please refer to

[0160] , specifically: a real-time dynamic monitoring and early warning method for housing crack deformation, comprising the following steps, Figure 2 Step one, real-time monitoring of underground water level fluctuation around the housing, obtaining an underground water level related data set H;

[0161] Step two, obtaining housing crack related data and performing feature extraction, obtaining crack expansion rate

[0162] , and preliminarily evaluating the housing safety state;

[0163] ​Step three, according to the obtained groundwater level related data set H, perform summary calculation to obtain soil expansion factor Tr, and according to the soil expansion factor Tr, obtain water level change influence factor ;

[0164] Step four, construct a crack change prediction model to predict the crack width at a future time point , evaluate the safety state of the future house, and trigger the early warning mechanism;

[0165] Step five, collect crack change data in the future for a period of time in real time, and feed back to the crack change prediction model to optimize the model prediction result.

[0166] In the embodiment, through the underground water level data acquisition, the influence of the underground water level fluctuation on the foundation can be mastered in real time, the accuracy of the crack expansion analysis is ensured, the crack expansion rate is obtained, the safety anomaly is identified in advance in combination with the house safety evaluation, the soil expansion factor Tr and the water level change influence factor are calculated, the scientific support is provided for the crack prediction, the crack development trend is predicted, the risk evaluation is performed, the initiative of the house maintenance is improved, the future crack change data is collected, is fed back to the crack change prediction model, the long-term monitoring accuracy is improved, the error is reduced, the house maintenance cost is reduced, and the scientificity and reliability of the safety early warning are improved.

[0167] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, the scope of the present application is defined by the appended claims and their equivalents.​

Claims

1. A real-time dynamic monitoring and early warning system for building crack deformation, characterized in that: It includes a groundwater level data acquisition module, a crack deformation monitoring module, a data analysis module, a crack prediction and intelligent early warning module, and an intelligent feedback module; The groundwater level data acquisition module is used to monitor the fluctuation of groundwater level around the building in real time and obtain groundwater level related data set H; The crack deformation monitoring module is used for acquiring house crack related data, performing feature extraction, acquiring crack expansion rate and preliminarily evaluating the safety state of the house The data analysis module is used to summarize and calculate based on the acquired groundwater level related data set H, obtain the soil expansion factor Tr, and obtain the water level change influencing factor Ψ based on the soil expansion factor Tr. The crack prediction and intelligent early warning module is used to build a crack change prediction model to predict future time points. Crack width Assess the future safety status of the house and trigger an early warning mechanism; The intelligent feedback module is used to collect crack change data in real time over a future period and feed it back to the crack change prediction model to optimize the model's prediction results.

2. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 1, characterized in that: The groundwater level data acquisition module is used to collect geological reports of the area where the target house is located, determine the soil type of the area where the target house is located, and set up geological monitoring points. The intelligent sensor group is deployed in the set geological monitoring points. The soil types include soft soil, sandy soil and clay. The intelligent sensor group includes a static pressure groundwater level gauge, a capacitive soil moisture sensor and a miniature ground displacement sensor. According to the intelligent sensor group deployed at the geological monitoring point, the underground water level related data of the target house is collected in real time, and an underground water level related data set H is constructed, wherein the underground water level related data includes underground water level height W, soil water content and foundation settlement amount .

3. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 1, characterized in that: The crack deformation monitoring module includes a crack data acquisition unit and a preliminary assessment unit; The crack data acquisition unit is used for setting data acquisition nodes in the target house and installing intelligent sensors at the data acquisition nodes to obtain house crack related data, wherein the data acquisition nodes include house bearing walls, beam column nodes, door and window edges and house soft soil foundation areas, the intelligent sensors include crack depth ultrasonic measuring instruments and laser range finders, and the house crack related data includes crack width and crack depth ; According to the obtained house crack related data, feature extraction is performed to obtain a crack propagation rate and a crack depth change rate , wherein the crack propagation rate and the crack depth change rate The acquisition mode is: ； wherein and respectively represent the crack width and the crack depth of the house at the current data monitoring time point t and respectively represent the crack width and the crack depth of the house at the previous data monitoring time point , represents the time interval between the two data monitoring time points.

4. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 3, characterized in that: The preliminary evaluation unit is configured to obtain a crack risk index Lf based on the crack propagation rate and the crack depth change rate , and perform a summary calculation to obtain the crack risk index Lf, where the crack risk index Lf is obtained in the following manner: ； In the formula, and respectively represent the weight coefficients of the crack propagation rate and the crack depth change rate C represents the first correction constant; A preset crack risk threshold Lfyz is established. The crack risk threshold Lfyz and the crack risk index Lf are compared and analyzed to assess the safety level of the house at the current data monitoring time point t. The specific assessment content is as follows: If the crack risk index Lf is less than the crack risk threshold Lfyz, i.e., Lf < Lfyz, then the house is considered to be within the normal range and no action is required. If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz (i.e., Lf≥Lfyz), the house is deemed to be in a dangerous state, triggering the alarm system to automatically generate alarm information and send it to the homeowner, prompting emergency reinforcement work to be carried out on the cracks.

5. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 4, characterized in that: The data analysis module includes a preprocessing unit and a groundwater level-related data analysis unit; The preprocessing unit is used to denoise and clean the data in the groundwater level related data set H, and to perform dimensionless processing on the cleaned data. The underground water level related data analysis unit is used to extract features according to the preprocessed underground water level related data set H when the house safety is in the normal range, and obtain the foundation material sensitive coefficient and residual effect factor wherein the foundation material sensitive coefficient The obtaining mode is: ； In the formula, represents the amount of ground settlement, represents the amount of change in the underground water level, represents the soil water content; Residual effect factor The acquisition mode is: ； wherein represents an empirical attenuation coefficient, represents the groundwater level change amount at the i-th data monitoring time point, represents the soil water content at the i-th data monitoring time point, and n represents the total number of data monitoring time points, .

6. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 5, characterized in that: According to the ground material sensitivity coefficient and the residual effect factor , combined with the groundwater level height W and the soil water content in the groundwater level related data set H , the soil expansion factor Tr is obtained by summary calculation, and the water level change influence factor Ψ is further obtained according to the soil expansion factor Tr, wherein the soil expansion factor Tr is obtained in the following manner: ； In the formula, represents the underground water level height of the region where the house is located at the current data monitoring time point t, represents the underground water level height of the region where the house is located at the last data monitoring time point t-1. The method for obtaining the water level change influencing factor Ψ is as follows: ； wherein represents the cumulative effect of groundwater level change on crack propagation within the crack response lag time period, represents the crack lag response coefficient, which represents the sensitivity of the crack to the swelling and shrinking of the ground, represents the integral independent variable, .​ 7. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 6, characterized in that: The crack prediction and intelligent early warning module includes a crack prediction unit and an early warning unit; The crack prediction unit is used to collect historical groundwater level related data and house crack related data, and construct a time series model using linear regression method, according to the crack expansion rate acquisition method and water level change influencing factor acquisition method, acquire the crack expansion rate of the house in the past period of time and the water level change influencing factor , and use the time series model to acquire the crack expansion rate at the future time point and the water level change influencing factor at the future time point , wherein the acquisition method of the crack expansion rate at the future time point and the water level change influencing factor at the future time point is: ； In the formula, and These represent the crack propagation rate and water level change influencing factor at the current data monitoring time point, respectively; J represents the size of the time window used for linear regression calculation; and j represents the index of the historical data. and Represents the regression coefficient. and These represent past time points. The changes in crack propagation rate and the changes in factors affecting water level changes; A crack change prediction model is constructed to predict the crack width at a future time point wherein the crack width at the future time point is calculated by the following formula:​​ ； wherein, denotes the crack width at the current data monitoring time point t, denotes the crack propagation rate at the future time point denotes the structure load influence coefficient, denotes the groundwater level influence coefficient, denotes the water level change influence factor at the future time point denotes the anomaly correction term, K denotes the number of time steps in the prediction time range, and k denotes the index of the time step, , denotes the predicted future time point, denotes any one time point between the current data monitoring time point and the future predicted time point denotes the groundwater level influence on crack propagation model, avoiding negative values of the groundwater influence factor, ∈ (t, ).​​​ 8. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 7, characterized in that: The early warning unit is used to preset a first crack safety threshold and a second crack safety threshold , and compare and analyze the crack width of a future time point with the first crack safety threshold and the second crack safety threshold to evaluate the safety state of the future house, and the specific evaluation contents are as follows: If future time point Crack width Less than or equal to the first crack safety threshold If the condition is met, the future safety status of the house is determined to be normal, and no action is required. If future time point Crack width Greater than the first crack safety threshold And less than the second crack safety threshold If the building is found to be in a state of risk, the frequency of crack detection should be increased from once a month to once a week. The foundation settlement should also be checked, groundwater level fluctuations should be observed, and foundation drainage measures should be implemented. If future time point Crack width Greater than or equal to the second crack safety threshold If the building is deemed to be in a dangerous state, the occupants should be immediately notified to evacuate the building, and an emergency response mechanism should be activated to reinforce the building structure. This reinforcement includes sealing cracks with high-strength epoxy resin and polymer mortar, adding steel plates and carbon fiber reinforcement layers in areas with concentrated cracks, and performing high-pressure grouting to stabilize the foundation.

9. The real-time dynamic monitoring and early warning system for house crack deformation according to claim 7, characterized in that: The intelligent feedback module is configured to collect crack change data in real time for a future period of time, and feed the collected crack change data and predicted crack width at a future time point to the crack change prediction model, intelligently adjust the crack change prediction model, and optimize the crack change prediction model error. ​ 10. A real-time dynamic monitoring and early warning method for building crack deformation, used for realizing a real-time dynamic monitoring and early warning system for building crack deformation according to any one of claims 1-9, characterized in that: Includes the following steps, Step 1: Monitor the fluctuations of groundwater levels around the house in real time and obtain a set of groundwater level-related data H; Step two, get the house crack related data, and carry out feature extraction, get crack expansion rate And make a preliminary assessment of the safety of the house; Step 3: Based on the obtained groundwater level related data set H, perform summary calculations to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, obtain the water level change influencing factor Ψ. Step four, build a crack change prediction model to predict the crack width at a future time point , assess the safety state of the future house, and trigger the early warning mechanism;​ Step 5: Collect crack change data in real time for a future period of time and feed it back to the crack change prediction model to optimize the model prediction results.

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