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

Abstract

The invention discloses a real-time dynamic monitoring and early warning system and method for house crack deformation, relates to the technical field of building safety monitoring, and aims at mastering the influence of underground water level fluctuation on a foundation in real time, ensuring the accuracy of crack propagation analysis, obtaining a crack risk index Lf and performing house safety assessment through underground water level related data acquisition. House safety abnormity is identified, a soil expansion factor Tr and a water level change influence factor psi are calculated, scientific support is provided for crack prediction, crack development trend is predicted, risk assessment is carried out, house maintenance initiative is improved, future crack change data are collected and fed back to a crack change prediction model, long-term monitoring accuracy is improved, and the method is suitable for popularization and application. Errors are reduced, the house maintenance cost is reduced, and the scientificity and reliability of safety early warning are improved.

Description

Technical Field

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

[0002] In the fields of civil engineering and building safety, the stability and durability of buildings have always been key concerns for engineers and researchers. Foundation engineering and structural health monitoring are important research areas for ensuring the long-term safety of buildings. Within this niche area, house crack monitoring is a key technical approach used to assess whether a building has experienced abnormal deformation, material degradation, foundation settlement, or external environmental impacts. Currently, intelligent crack monitoring and early warning systems are widely used in high-rise buildings, older homes, bridges, and other structures. However, the expansion of house cracks is not solely caused by structural loads; groundwater level fluctuations are also a key but often overlooked influencing factor. In areas where the groundwater level fluctuates periodically, the expansion and contraction of the foundation soil can lead to uneven settlement of the building foundation, which in turn triggers the expansion of cracks in walls, beams, and columns. Therefore, studying the impact of groundwater level fluctuations on house crack deformation and establishing an effective real-time monitoring and early warning system are of great significance for improving building safety and service life.

[0003] In the Chinese invention patent application publication number CN119268562A, a method and system for monitoring cracks in house walls are disclosed, which include: S1, continuously collecting wall surface image data through an image acquisition module; S2, analyzing the collected wall surface image data through an analysis control module, obtaining a curve of wall hazard characteristic value changes over time, and judging whether to generate a wall hazard warning signal; the invention analyzes the collected wall surface image data through an analysis control module, obtains a curve of wall hazard characteristic value changes over time, and then judges whether to generate a wall hazard warning signal, and warns house wall monitoring personnel. In this way, the purpose of accurately and objectively monitoring house wall cracks can be achieved, avoiding the subjective judgment in the manual observation process, and can monitor house wall cracks in real time, thereby improving the accuracy of wall crack monitoring.

[0004] The above method can monitor cracks in house walls in real time, improving the accuracy of wall crack monitoring. However, in addition to this, the existing house wall crack monitoring method mainly focuses on the geometric characteristics of the cracks themselves (such as width, depth and length), and judges whether the house is in a dangerous state by real-time monitoring of the geometric characteristics of the cracks.

[0005] However, although this method can determine whether a house is in a dangerous state, it fails to fully consider the external causes of cracks, such as the impact of groundwater level changes on the foundation. In soft soil areas, groundwater levels fluctuate more frequently, and foundation soil is prone to expansion and contraction, causing uneven settlement. However, due to the lack of groundwater monitoring data support for the crack monitoring system, it is often difficult to identify the true cause of crack expansion.

[0006] To this end, the present invention provides a real-time dynamic monitoring and early warning system and method for house crack deformation. Summary of the Invention

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

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time dynamic monitoring and early warning system for house 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;

[0009] The groundwater level data acquisition module is used to monitor the groundwater level fluctuation around the house in real time and obtain the groundwater level related data set H;

[0010] The crack deformation monitoring module is used to obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate. , and conduct a preliminary assessment of the safety status of the house;

[0011] The data analysis module is used to perform summary calculations based on the acquired groundwater level related data set H to obtain the soil expansion factor Tr, and to obtain the water level change impact factor Ψ based on the soil expansion factor Tr;

[0012] The crack prediction and intelligent early warning module is used to build a crack change prediction model and predict future time points Crack width , assess the future safety status of the house and trigger the early warning mechanism;

[0013] The intelligent feedback module is used to collect crack change data in real time over a period of time in the future and feed it back to the crack change prediction model to optimize the model prediction results.

[0014] Preferably, the groundwater level data acquisition module is used to collect a geological report of the area where the target house is located, determine the soil type of the area where the target house is located, and set geological monitoring points. The intelligent sensor group is deployed in the set geological monitoring points, wherein the soil types include soft soil, sandy soil and clay. The intelligent sensor group includes a static pressure groundwater level meter, a capacitive soil moisture sensor and a micro ground displacement sensor;

[0015] Based on the intelligent sensor group deployed at the geological monitoring point, the groundwater level data in the area where the target house is located is collected in real time, and a groundwater level data set H is constructed, where the groundwater level data includes the groundwater level height W, soil moisture content and foundation settlement .

[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 up data acquisition nodes in the target house and install smart sensors at the data acquisition nodes to obtain data related to house cracks. The data acquisition nodes include the load-bearing walls, beam-column nodes, door and window edges and the soft soil foundation area of ​​the house. The smart sensors include crack depth ultrasonic measuring instruments and laser rangefinders. The house crack related data include crack width and crack depth ;

[0018] Based on the acquired house crack related data, feature extraction is performed to obtain the crack expansion rate and crack depth change rate , where the crack growth rate and crack depth change rate The way to obtain it is:

[0019]

[0020]

[0021] Where, and Respectively represent the crack width of the house at the current data monitoring time point t and crack depth , and Respectively represent the last data monitoring time point The width of the crack in the house and crack depth , Indicates the time interval between two data monitoring time points.

[0022] Preferably, the preliminary evaluation unit is used to evaluate the crack growth rate and crack depth change rate , perform summary calculation to obtain the crack risk index Lf, where the crack risk index Lf is obtained as follows:

[0023]

[0024] Where, and Represent the crack growth rate and crack depth change rate The weight coefficient, C represents the first correction constant;

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

[0026] If the crack risk index Lf is less than the crack risk threshold Lfyz, that is, Lf<Lfyz, then it is determined that the house safety is within the normal range and no treatment is required;

[0027] If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz, that is, Lf ≥ Lfyz, the house safety is determined to be in a dangerous state at this time, the alarm system is triggered, an alarm message is automatically generated, and the alarm message is sent to the owner of the house for emergency reinforcement work on the house cracks.

[0028] Preferably, the data analysis module includes a pre-processing unit and a groundwater level related data analysis unit;

[0029] The pre-processing unit is used to perform denoising and data cleaning on the data in the groundwater level related data set H, and perform dimensionless processing on the cleaned data;

[0030] The groundwater level related data analysis unit is used to extract features based on the pre-processed groundwater level related data set H when the house safety is within the normal range, and obtain the foundation material sensitivity coefficient and residual effect factors , where the foundation material sensitivity coefficient is The way to obtain it is:

[0031]

[0032] Where, represents the amount of foundation settlement, represents the change in groundwater level, Indicates soil moisture content;

[0033] Residual effect factor The way to obtain it is:

[0034]

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

[0036] Preferably, based on the sensitivity coefficient of foundation material and residual effect factors , combined with the groundwater level height W and soil moisture content in the groundwater level related data set H , perform summary calculations to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, further obtain the water level change impact factor Ψ. The soil expansion factor Tr is obtained as follows:

[0037]

[0038] Where, Indicates the groundwater level height of the area where the house is located at the current data monitoring time point t, Indicates the last data monitoring time point The height of the groundwater level in the area where the house is located;

[0039] The water level change influencing factor Ψ is obtained as follows:

[0040]

[0041] Where, Indicates the crack response lag time period The cumulative effect of groundwater level change on crack expansion, i.e., the water level change factor, It represents the crack hysteresis response coefficient, which indicates the sensitivity of the crack to the expansion and contraction of the foundation. represents the independent variable of integration, .

[0042] Preferably, the crack prediction and intelligent early warning module includes 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 use linear regression method to build a time series model based on the crack expansion rate. Acquisition methods and factors affecting water level changes Acquisition method: Get the crack expansion rate of the house in the past period of time and water level change factors , and use the time series model to obtain future time points The crack growth rate and future time points The water level change factors , where the future time point The crack growth rate and future time points The water level change factors The way to obtain is:

[0044]

[0045]

[0046] Where, and They represent the crack expansion rate and water level change influencing factor at the current data monitoring time point, J represents the time window size used for linear regression calculation, and j represents the index of historical data. and represents the regression coefficient, and Represents past time points The change in crack growth rate and the change in the water level change influencing factor;

[0047] Construct a crack change prediction model to predict future time points Crack width , where the future time point Crack width The calculation formula is:

[0048]

[0049] Where, represents the crack width at the current data monitoring time point t, Indicates a future time point The crack growth rate, represents the structural load influence coefficient, represents the groundwater level influence coefficient, Indicates a future time point The water level change factor at time represents the anomaly correction term, K represents the number of time steps in the prediction time range, k represents the index of the time step, , represents the predicted future time point, Indicates the time from the current data monitoring point to the future prediction point At any point in time between Represents the influence model of groundwater level on crack expansion, avoiding the groundwater influence factor taking a negative value, ∈(t, ).

[0050] Preferably, the early warning unit is used to preset the first crack safety threshold and the second crack safety threshold , and the future time point Crack width and the first crack safety threshold and the second crack safety threshold Conduct comparative analysis and assess the future safety status of the house. The specific assessment contents are as follows:

[0051] If the future time Crack width Less than or equal to the first crack safety threshold , then it is determined that the future safety status of the house is in normal state and no treatment is required;

[0052] If the future time Crack width Greater than the first crack safety threshold , and is less than the second crack safety threshold , then the future safety status of the house is judged to be at risk. At this time, the frequency of house crack inspection is increased from once a month to once a week. The house foundation settlement is also inspected, groundwater level fluctuations are observed, and foundation drainage measures are implemented.

[0053] If the future time Crack width Greater than or equal to the second crack safety threshold , it is determined that the future safety status of the house is in a dangerous state. At this time, the people in the house are immediately notified to evacuate the room, and the alarm is immediately called to activate the emergency response mechanism to reinforce the structure of the house. Among them, the structural reinforcement of the house includes using high-strength epoxy resin and polymer mortar to seal cracks, adding steel plates and carbon fiber reinforcement layers in areas where cracks are concentrated, and performing high-pressure grouting to stabilize the foundation.

[0054] Preferably, the intelligent feedback module is used to collect crack change data in real time for a period of time in the future, and to compare the collected crack change data with the predicted future time point. Crack width Feedback is given to the crack change prediction model, the crack change prediction model is intelligently adjusted, and the crack change prediction model error is optimized.

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

[0056] Step 1: Monitor the groundwater level fluctuations around the house in real time and obtain the groundwater level related data set H;

[0057] Step 2: Obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate , and conduct a preliminary assessment of the safety status of the house;

[0058] Step 3: Based on the acquired groundwater level related data set H, perform summary calculation to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, obtain the water level change impact factor Ψ;

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

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

[0061] The present invention provides a real-time dynamic monitoring and early warning system and method for house 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, and intelligent feedback function modules, it is possible to accurately obtain multiple factors that affect crack expansion. Through the linear regression time series prediction model, the system can predict the crack width at a future time point and evaluate the future safety status of the house. Compared with the traditional static crack monitoring method, this system can predict the crack expansion trend in advance, provide advanced early warning for house safety management, avoid damage to the house structure caused by uncontrolled crack expansion, and improve the long-term stability and service life of the house.

[0063] (2) By adopting the groundwater level data acquisition module, deploying intelligent sensor groups such as static pressure groundwater level meter, capacitive soil moisture sensor and micro ground displacement sensor, the groundwater level height W and soil moisture content in the house foundation area are monitored in real time. and foundation settlement , and construct a groundwater level-related data set H. Through the data analysis module, the water level change influence factor Ψ is calculated to accurately evaluate the hysteresis effect of groundwater level changes on crack expansion, thereby distinguishing the true cause of crack expansion. For example, in soft soil foundation areas, groundwater level fluctuations may cause soil expansion and contraction, thereby affecting crack expansion. The system can accurately identify this phenomenon and effectively distinguish between foundation settlement cracks and structural load cracks, thereby providing more targeted maintenance measures for different types of cracks and improving the scientific nature of building structural health management.

[0064] (3) By adopting intelligent sensors such as static pressure groundwater level meter, capacitive soil moisture sensor, crack depth ultrasonic measuring instrument and laser rangefinder, real-time monitoring of multiple parameters such as house crack deformation, foundation settlement and groundwater level fluctuation can be achieved. The system also supports remote data transmission and can upload monitoring data to the cloud data center to achieve remote real-time monitoring. House management personnel can check the status of house cracks at any time through PC or mobile terminal, receive early warning information and conduct safety assessment. Compared with the traditional manual regular inspection method, this system greatly improves monitoring efficiency, reduces labor costs, and makes house safety monitoring more intelligent and convenient. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0066] Figure 2 The present invention is a flow chart of a real-time dynamic monitoring and early warning method for house crack deformation.

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

[0068] Figure 4 This is a broken line graph of crack width prediction for the next three months predicted by a real-time dynamic monitoring and early warning system for house crack deformation according to the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] Example 1

[0071] See also Figure 1 、 Figure 3 and Figure 4 , the present invention provides a real-time dynamic monitoring and early warning system for house 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 groundwater level fluctuation around the house in real time and obtain the groundwater level related data set H;

[0073] The crack deformation monitoring module is used to obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate. , and conduct a preliminary assessment of the safety status of the house;

[0074] The data analysis module is used to perform summary calculations based on the acquired groundwater level related data set H to obtain the soil expansion factor Tr, and to obtain the water level change impact 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 and predict future time points Crack width , assess the future safety status of the house and trigger the early warning mechanism;

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

[0077] In the embodiment, through full-cycle dynamic monitoring and intelligent early warning of house crack deformation, full consideration is given to external environmental factors such as groundwater level fluctuations, soil expansion effect, foundation settlement, etc., by obtaining the groundwater level related data set H, analyzing the soil expansion factor Tr and the water level change influencing factor Ψ, and evaluating the hysteresis effect of hydrogeological factors on crack expansion, thereby improving the scientificity and accuracy of monitoring. At the same time, the system combines the linear regression time series model to predict the crack deformation trend at future time points, and based on the first crack safety threshold and the second crack safety threshold Conduct housing risk assessments, implement safety warnings, and provide housing managers with a scientific basis for decision-making. In addition, the system collects actual crack change data in real time through an intelligent feedback module, compares and analyzes it with the predicted values, optimizes the crack change prediction model, and improves the system's adaptability and long-term monitoring accuracy, thereby reducing false alarms and missed alarms, improving the intelligence level of housing safety management, reducing building maintenance costs, and extending the service life of the house.

[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 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 meter, a capacitive soil moisture sensor and a micro ground displacement sensor;

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

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

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

[0083] Foundation settlement Obtained by using a micro ground displacement sensor.

[0084] In one embodiment, by collecting geological reports on the area where the target house is located and analyzing the soil type of the house, including soft soil, sand, and clay, the monitoring system can adopt optimized monitoring strategies for different geological environments. Furthermore, by deploying intelligent sensor groups at designated geological monitoring points, the system can monitor in real time and construct a groundwater level-related data set H. Compared to traditional house crack monitoring, which focuses solely on changes in the cracks themselves, this system fully considers the impact of groundwater level fluctuations on the foundation and crack propagation, making monitoring more comprehensive and accurate. Through real-time data collection and transmission, it can provide reliable data support for foundation settlement trend analysis, soil expansion and contraction effect calculations, and groundwater impact assessments, providing accurate input for subsequent crack deformation prediction and early warning. This system is suitable for houses with soft soil foundations susceptible to groundwater fluctuations, such as those along coastal and riverbanks. It can effectively improve the scientific nature of house structural safety assessments, detect crack propagation risks caused by foundation settlement and water level fluctuations in advance, reduce the possibility of sudden structural damage, and thus ensure the long-term stability and resident safety of the house.

[0085] Example 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 up data acquisition nodes in the target house and install smart sensors at the data acquisition nodes to obtain data related to house cracks. The data acquisition nodes include the load-bearing walls, beam-column nodes, door and window edges and the soft soil foundation area of ​​the house. The smart sensors include crack depth ultrasonic measuring instruments and laser rangefinders. The house crack related data include crack width and crack depth ;

[0088] The crack width Acquired by using a laser rangefinder;

[0089] Crack depth Obtained by using a crack depth ultrasonic measurement instrument;

[0090] Based on the acquired house crack related data, feature extraction is performed to obtain the crack expansion rate and crack depth change rate , where the crack growth rate and crack depth change rate The way to obtain it is:

[0091]

[0092]

[0093] Where, and Respectively represent the crack width of the house at the current data monitoring time point t and crack depth , and Respectively represent the last data monitoring time point The width of the crack in the house and crack depth , Indicates the time interval between two data monitoring time points.

[0094] The initial evaluation unit is used to determine the crack growth rate and crack depth change rate , perform summary calculation to obtain the crack risk index Lf, where the crack risk index Lf is obtained as follows:

[0095]

[0096] Where, and Represent the crack growth rate and crack depth change rate The weight coefficient of C represents the first correction constant, wherein the specific value of the weight coefficient is set by the customer according to the actual situation, 0< <1,0< <1, and + =1;

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

[0098] If the crack risk index Lf is less than the crack risk threshold Lfyz, that is, Lf<Lfyz, then it is determined that the house safety is within the normal range and no treatment is required;

[0099] If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz, that is, Lf ≥ Lfyz, the house safety is determined to be in a dangerous state at this time, the alarm system is triggered, an alarm message is automatically generated, and the alarm message is sent to the owner of the house for emergency reinforcement work on the house cracks.

[0100] The following are some examples:

[0101] Assume that there is a residential building on a soft soil foundation. A smart sensor group is deployed in the residential building to regularly monitor the building's safety and assess its safety status. The monitoring data from two consecutive building safety monitoring sessions is obtained, as shown in Table 1 below:

[0102] Table 1

[0103] According to Table 1 above, the crack growth rate is calculated as and crack depth change rate :

[0104]

[0105]

[0106] According to the obtained crack growth rate and crack depth change rate , the crack risk index Lf is calculated:

[0107]

[0108] Compare and analyze the crack risk index Lf with the preset crack risk threshold Lfyz, Lfyz=0.8, then <0.8, that is, the crack risk index Lf is less than the crack risk threshold Lfyz, and it is determined that the house safety is within the normal range at this time. No processing is required, and the house crack data continues to be monitored.

[0109] In the embodiment, by deploying crack depth ultrasonic measuring instruments and laser rangefinders at key structural locations of the house, such as load-bearing walls, beam-column joints, door and window edges, and soft soil foundations, high-precision data collection of crack width and depth is performed, and the crack expansion rate is calculated. and crack depth change rate , providing efficient data support. Based on this, the system further calculates the crack risk index Lf, combined with the preset crack risk threshold Lfyz, to achieve real-time housing safety assessment. Compared with traditional crack monitoring methods, this solution not only improves the automation and accuracy of monitoring, but also can trigger emergency maintenance response in the first time through the real-time alarm mechanism when the cracks expand too fast or exceed the safety range, and notify the housing manager to carry out reinforcement and repair to prevent the cracks from further expanding and improve the safety of the housing structure. In addition, by comprehensively analyzing the crack expansion rate and crack depth change rate It can avoid the false alarm and missed alarm problems caused by single crack width judgment, improve the scientificity and reliability of the assessment, and make housing management more intelligent and efficient.

[0110] Example 4

[0111] Please refer to Figure 1 ,Specifically: the data analysis module includes a pre-processing unit and a groundwater level ,related data analysis unit;

[0112] The pre-processing unit is used to perform denoising and data cleaning on the data in the groundwater level related data set H, and perform dimensionless processing on the cleaned data;

[0113] The groundwater level related data analysis unit is used to extract features based on the pre-processed groundwater level related data set H when the house safety is within the normal range, and obtain the foundation material sensitivity coefficient and residual effect factors , where the foundation material sensitivity coefficient is The way to obtain it is:

[0114]

[0115] Where, represents the amount of foundation settlement, represents the change in groundwater level, Indicates soil moisture content;

[0116] Sensitivity coefficient of foundation material It is calculated by comparing historical settlement data of different geological types;

[0117] Residual effect factor The way to obtain it is:

[0118]

[0119] Where, represents the empirical attenuation coefficient, represents the groundwater level change at the i-th data monitoring time point, represents the soil moisture content at the i-th data monitoring time point, n represents the total number of data monitoring time points, .

[0120] Residual effect factor Depends on the cumulative effect of the previous n water level changes;

[0121] Based on the sensitivity coefficient of foundation material and residual effect factors , combined with the groundwater level height W and soil moisture content in the groundwater level related data set H , perform summary calculations to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, further obtain the water level change impact factor Ψ. The soil expansion factor Tr is obtained as follows:

[0122]

[0123] Where, Indicates the groundwater level height of the area where the house is located at the current data monitoring time point t, Indicates the last data monitoring time point The height of the groundwater level in the area where the house is located;

[0124] The water level change influencing factor Ψ is obtained as follows:

[0125]

[0126] Where, Indicates the crack response lag time period The cumulative effect of groundwater level change on crack expansion, i.e., the water level change factor, It represents the crack hysteresis response coefficient, which indicates the sensitivity of the crack to the expansion and contraction of the foundation. represents the independent variable of integration, , where the crack hysteresis response coefficient is The result is obtained by applying simulated soil shrinkage and expansion stress on different building materials and using a crack expansion meter to record the crack expansion.

[0127] In the embodiment, the groundwater level related data set H is deeply processed by the pre-processing unit and the groundwater level related data analysis unit to improve the prediction accuracy and monitoring reliability. First, the pre-processing unit performs denoising, data cleaning and dimensionless processing on the original groundwater level data to ensure the stability and comparability of the data, thereby reducing monitoring errors and improving data quality. Subsequently, the groundwater level related data analysis unit extracts the foundation material sensitivity coefficient and residual effect factor based on the cleaned data under the condition that the house is in normal safety status, accurately evaluates the foundation's response ability to groundwater fluctuations and the long-term cumulative impact of soil expansion and contraction, and ensures crack prediction. It has more physical and realistic significance. Combining the groundwater level height and soil moisture content, this system further calculates the soil expansion factor Tr, quantifies the expansion and contraction effect caused by groundwater level changes, and calculates the water level change impact factor Ψ based on the soil expansion factor Tr, which is used to predict the cumulative impact of groundwater fluctuations on crack expansion. Compared with the traditional method based only on crack monitoring data, this system accurately identifies the external driving factors of crack expansion through comprehensive geological, hydrological and structural analysis, thereby reducing false alarms and missed alarms, ensuring the accuracy of crack prediction, improving the intelligent level of house structure safety management, predicting crack expansion risks in advance, and providing a scientific basis for house maintenance decisions.

[0128] Example 5

[0129] Please refer to Figure 1 and Figure 4 ,Specifically: the crack prediction and intelligent early warning module includes a crack ,prediction unit and an early warning unit;

[0130] The crack prediction unit is used to collect historical groundwater level related data and house crack related data, and use linear regression method to build a time series model based on the crack expansion rate. Acquisition methods and factors affecting water level changes Acquisition method: Get the crack expansion rate of the house in the past period of time and water level change factors , and use the time series model to obtain future time points The crack growth rate and future time points The water level change factors , where the future time point The crack growth rate and future time points The water level change factors The way to obtain is:

[0131]

[0132]

[0133] Where, and They represent the crack expansion rate and water level change influencing factor at the current data monitoring time point, J represents the time window size used for linear regression calculation, and j represents the index of historical data. and represents the regression coefficient, and Represents past time points The change in crack growth rate and the change in the water level change influencing factor;

[0134] Construct a crack change prediction model to predict future time points Crack width , where the future time point Crack width The calculation formula is:

[0135]

[0136] Where, represents the crack width at the current data monitoring time point t, Indicates a future time point The crack growth rate, represents the structural load influence coefficient, represents the groundwater level influence coefficient, Indicates a future time point The water level change factor at time represents the anomaly correction term, K represents the number of time steps in the prediction time range, k represents the index of the time step, , represents the predicted future time point, Indicates the time from the current data monitoring point to the future prediction point At any point in time between Represents the influence model of groundwater level on crack expansion, avoiding the groundwater influence factor taking a negative value, ∈(t, ).

[0137] Structural load influence coefficient Obtained through stress monitoring coefficient;

[0138] Groundwater level influence coefficient Obtained by using linear regression analysis on historical data;

[0139] Abnormal correction items It is set by experts based on historical experience, and the value range is usually 0.01~0.05mm;

[0140] The early warning unit is used to preset the first crack safety threshold and the second crack safety threshold , and the future time point Crack width and the first crack safety threshold and the second crack safety threshold Conduct comparative analysis and assess the future safety status of the house. The specific assessment contents are as follows:

[0141] If the future time Crack width Less than or equal to the first crack safety threshold , then it is determined that the future safety status of the house is in normal state and no treatment is required;

[0142] If the future time Crack width Greater than the first crack safety threshold , and is less than the second crack safety threshold , then the future safety status of the house is judged to be at risk. At this time, the frequency of house crack inspection is increased from once a month to once a week. The house foundation settlement is also inspected, groundwater level fluctuations are observed, and foundation drainage measures are implemented.

[0143] If the future time Crack width Greater than or equal to the second crack safety threshold , it is determined that the future safety status of the house is in a dangerous state. At this time, the people in the house are immediately notified to evacuate the room, and the alarm is immediately called to activate the emergency response mechanism to reinforce the structure of the house. Among them, the structural reinforcement of the house includes using high-strength epoxy resin and polymer mortar to seal cracks, adding steel plates and carbon fiber reinforcement layers in areas where cracks are concentrated, and performing high-pressure grouting to stabilize the foundation.

[0144] The following are some examples:

[0145] Assume that a residential building in a soft soil area has a property management system that has introduced a real-time dynamic monitoring and early warning system for house cracks and deformations. Data related to house cracks and groundwater levels has been collected over the past six months, as shown in Table 2:

[0146] Table 2

[0147] Use linear regression to predict crack growth rates over the next three months and the water level change influencing factor Ψ, the results are shown in Table 3:

[0148] Table 3

[0149] It is known that the width of the crack in the house in February 2025 is 4.1mm. =0.2, =0.8, =0.02, calculate the crack width in May 2025 based on Table 2 and Table 3 above :

[0150]

[0151] Setting the first crack safety threshold and the second crack safety threshold The crack widths are 5.0mm and 6.0mm respectively, which means the crack widths in May 2025 Greater than the first crack safety threshold , and is less than the second crack safety threshold , it is determined that the safety of the house is at risk in May 2025. At this time, it is necessary to immediately increase the frequency of crack monitoring to once a week, detect the foundation settlement, and carry out foundation drainage treatment.

[0152] In the embodiment, a time series model is constructed by linear regression method, and the historical groundwater level data and house crack expansion data are combined to accurately predict the future time point. The crack growth rate and future time points The water level change factors , dynamically analyze the long-term evolution of crack expansion, evaluate the future safety status of the house, improve the foresight and reliability of crack warning, and through the warning unit, the system presets the first crack safety threshold and the second crack safety threshold , and based on future time points Crack width Based on the prediction results, the system divides the house safety status into three types: normal, risky and dangerous, and provides scientific and reasonable response measures for different risk levels. For houses with cracks in a risky state, the system will automatically increase the frequency of crack detection, and combined with groundwater level monitoring and foundation settlement detection, timely take measures such as foundation drainage to slow down the expansion of cracks; when the cracks reach a dangerous state, the system can immediately alarm and notify the house occupants to evacuate, and initiate emergency reinforcement measures, such as using epoxy resin to fill cracks, carbon fiber reinforced structure reinforcement, and high-pressure grouting to stabilize the foundation, to ensure the long-term stability of the house structure. Through the crack change prediction model + dynamic early warning mechanism, accurate prediction, intelligent decision-making and active protection are achieved, which not only reduces sudden house safety accidents caused by crack expansion, but also reduces house maintenance costs and improves the service life and safety of buildings.

[0153] Example 6

[0154] Please refer to Figure 1 Specifically: the intelligent feedback module is used to collect crack change data in real time for a period of time in the future, and to compare the collected crack change data with the predicted future time points. Crack width Feedback is given to the crack change prediction model, the crack change prediction model is intelligently adjusted, and the crack change prediction model error is optimized.

[0155] In the embodiment, by collecting crack change data for a period of time in the future and comparing it 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 and can dynamically correct the prediction parameters according to the latest crack change trend, optimize the model accuracy, ensure the rationality of the reinforcement plan, avoid ineffective maintenance, and reduce resource waste. Ultimately, it improves the intelligence level of crack monitoring, enhances the scientific nature of housing safety management, and provides accurate decision-making support for structural maintenance.

[0156] Example 7

[0157] Please refer to Figure 2 Specifically: A real-time dynamic monitoring and early warning method for house crack deformation includes the following steps:

[0158] Step 1: Monitor the groundwater level fluctuations around the house in real time and obtain the groundwater level related data set H;

[0159] Step 2: Obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate , and conduct a preliminary assessment of the safety status of the house;

[0160] Step 3: Based on the acquired groundwater level related data set H, perform summary calculation to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, obtain the water level change impact factor Ψ;

[0161] Step 4: Build a crack change prediction model to predict future time points Crack width , assess the future safety status of the house and trigger the early warning mechanism;

[0162] Step 5: Collect crack change data for a period of time in the future in real time and feed it back to the crack change prediction model to optimize the model prediction results.

[0163] In the embodiment, through the collection of groundwater level data, the impact of groundwater level fluctuations on the foundation can be grasped in real time, the accuracy of crack expansion analysis can be ensured, and the crack expansion rate can be obtained. Combined with the house safety assessment, safety anomalies can be identified in advance, and the soil expansion factor Tr and the water level change influence factor Ψ can be calculated to provide scientific support for crack prediction. At the same time, the development trend of cracks can be predicted, risk assessment can be performed, the initiative of house maintenance can be improved, and future crack change data can be collected and fed back to the crack change prediction model to improve the accuracy of long-term monitoring, reduce errors, reduce house maintenance costs, and improve the scientificity and reliability of safety warnings.

[0164] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time dynamic monitoring and early warning system for house cracks and deformations, characterized by: It includes groundwater level data acquisition module, crack deformation monitoring module, data analysis module, crack prediction and intelligent early warning module and intelligent feedback module; The groundwater level data acquisition module is used to monitor the groundwater level fluctuation around the house in real time and obtain the groundwater level related data set H; The crack deformation monitoring module is used to obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate. , and conduct a preliminary assessment of the safety status of the house; The data analysis module is used to perform summary calculations based on the acquired groundwater level related data set H to obtain the soil expansion factor Tr, and to obtain the water level change impact 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 and predict future time points Crack width , assess the future safety status of the house and trigger the early warning mechanism; The intelligent feedback module is used to collect crack change data in real time over a period of time in the future and feed it back to the crack change prediction model to optimize the model prediction results.

2. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 1, characterized in that: The groundwater level data acquisition module is used to collect geological reports on the area where the target house is located, determine the soil type in the area where the target house is located, and set geological monitoring points. The intelligent sensor group is deployed at 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 meter, a capacitive soil moisture sensor, and a micro ground displacement sensor. Based on the intelligent sensor group deployed at the geological monitoring point, the groundwater level data in the area where the target house is located is collected in real time, and a groundwater level data set H is constructed, where the groundwater level data includes the groundwater level height W, soil moisture content and foundation settlement .

3. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 1, characterized in that: The crack deformation monitoring module includes a crack data acquisition unit and a preliminary evaluation unit; The crack data acquisition unit is used to set up data acquisition nodes in the target house and install smart sensors at the data acquisition nodes to obtain data related to house cracks. The data acquisition nodes include the load-bearing walls, beam-column nodes, door and window edges and the soft soil foundation area of ​​the house. The smart sensors include crack depth ultrasonic measuring instruments and laser rangefinders. The house crack related data include crack width and crack depth ; Based on the acquired house crack related data, feature extraction is performed to obtain the crack expansion rate and crack depth change rate , where the crack growth rate and crack depth change rate The way to obtain it is: Where, and Respectively represent the crack width of the house at the current data monitoring time point t and crack depth , and Respectively represent the last data monitoring time point The width of the crack in the house and crack depth , Indicates the time interval between two data monitoring time points.

4. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 3, characterized in that: The initial evaluation unit is used to determine the crack growth rate and crack depth change rate , perform summary calculation to obtain the crack risk index Lf, where the crack risk index Lf is obtained as follows: Where, and Represent the crack growth rate and crack depth change rate The weight coefficient, C represents the first correction constant; A crack risk threshold Lfyz is preset, and the crack risk threshold Lfyz and the crack risk index Lf are compared and analyzed to evaluate the safety of the house at the current data monitoring time point t. The specific evaluation content is as follows: If the crack risk index Lf is less than the crack risk threshold Lfyz, that is, Lf<Lfyz, then it is determined that the house safety is within the normal range and no treatment is required; If the crack risk index Lf is greater than or equal to the crack risk threshold Lfyz, that is, Lf ≥ Lfyz, the house safety is determined to be in a dangerous state at this time, the alarm system is triggered, an alarm message is automatically generated, and the alarm message is sent to the owner of the house for emergency reinforcement work on the house cracks.

5. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 4, characterized in that: The data analysis module includes a pre-processing unit and a groundwater level related data analysis unit; The pre-processing unit is used to perform denoising and data cleaning on the data in the groundwater level related data set H, and perform dimensionless processing on the cleaned data; The groundwater level related data analysis unit is used to extract features based on the pre-processed groundwater level related data set H when the house safety is within the normal range, and obtain the foundation material sensitivity coefficient and residual effect factors , where the foundation material sensitivity coefficient is The way to obtain it is: Where, represents the amount of foundation settlement, represents the change in groundwater level, Indicates soil moisture content; Residual effect factor The way to obtain it is: Where, represents the empirical attenuation coefficient, represents the groundwater level change at the i-th data monitoring time point, represents the soil moisture content at the i-th data monitoring time point, n represents the total number of data monitoring time points, .

6. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 5, characterized in that: Based on the sensitivity coefficient of foundation material and residual effect factors , combined with the groundwater level height W and soil moisture content in the groundwater level related data set H , perform summary calculations to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, further obtain the water level change impact factor Ψ. The soil expansion factor Tr is obtained as follows: Where, Indicates the groundwater level height of the area where the house is located at the current data monitoring time point t, Indicates the last data monitoring time point The height of the groundwater level in the area where the house is located; The water level change influencing factor Ψ is obtained as follows: Where, Indicates the crack response lag time period The cumulative effect of groundwater level change on crack expansion, i.e., the water level change factor, It represents the crack hysteresis response coefficient, which indicates the sensitivity of the crack to the expansion and contraction of the foundation. represents the independent variable of integration, .

7. A real-time dynamic monitoring and early warning system for house cracks and deformations 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 use linear regression method to build a time series model based on the crack expansion rate. Acquisition methods and factors affecting water level changes Acquisition method: Get the crack expansion rate of the house in the past period of time and water level change factors , and use the time series model to obtain future time points The crack growth rate and future time points The water level change factors , where the future time point The crack growth rate and future time points The water level change factors The way to obtain is: Where, and They represent the crack expansion rate and water level change influencing factor at the current data monitoring time point, J represents the time window size used for linear regression calculation, and j represents the index of historical data. and represents the regression coefficient, and Represents past time points The change in crack growth rate and the change in the water level change influencing factor; Construct a crack change prediction model to predict future time points Crack width , where the future time point Crack width The calculation formula is: Where, represents the crack width at the current data monitoring time point t, Indicates a future time point The crack growth rate, represents the structural load influence coefficient, represents the groundwater level influence coefficient, Indicates a future time point The water level change factor at time represents the anomaly correction term, K represents the number of time steps in the prediction time range, k represents the index of the time step, , represents the predicted future time point, Indicates the time from the current data monitoring point to the future prediction point At any point in time between Represents the influence model of groundwater level on crack expansion, avoiding the groundwater influence factor taking a negative value, ∈(t, ).

8. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 7, characterized in that: The early warning unit is used to preset the first crack safety threshold and the second crack safety threshold , and the future time point Crack width and the first crack safety threshold and the second crack safety threshold Conduct comparative analysis and assess the future safety status of the house. The specific assessment contents are as follows: If the future time Crack width Less than or equal to the first crack safety threshold , then it is determined that the future safety status of the house is in normal state and no treatment is required; If the future time Crack width Greater than the first crack safety threshold , and is less than the second crack safety threshold , then the future safety status of the house is judged to be at risk. At this time, the frequency of house crack inspection is increased from once a month to once a week. The house foundation settlement is also inspected, groundwater level fluctuations are observed, and foundation drainage measures are implemented. If the future time Crack width Greater than or equal to the second crack safety threshold , it is determined that the future safety status of the house is in a dangerous state. At this time, the people in the house are immediately notified to evacuate the room, and the alarm is immediately called to activate the emergency response mechanism to reinforce the structure of the house. Among them, the structural reinforcement of the house includes using high-strength epoxy resin and polymer mortar to seal cracks, adding steel plates and carbon fiber reinforcement layers in areas where cracks are concentrated, and performing high-pressure grouting to stabilize the foundation.

9. A real-time dynamic monitoring and early warning system for house cracks and deformations according to claim 7, characterized in that: The intelligent feedback module is used to collect crack change data in real time over a period of time in the future, and to compare the collected crack change data with the predicted future time points. Crack width Feedback is given to the crack change prediction model, the crack change prediction model is intelligently adjusted, and the crack change prediction model error is optimized.

10. A real-time dynamic monitoring and early warning method for house crack deformation, used to implement a real-time dynamic monitoring and early warning system for house crack deformation according to any one of claims 1 to 9, characterized in that: The following steps are included: Step 1: Monitor the groundwater level fluctuations around the house in real time and obtain the groundwater level related data set H; Step 2: Obtain data related to house cracks, perform feature extraction, and obtain the crack expansion rate , and conduct a preliminary assessment of the safety status of the house; Step 3: Based on the acquired groundwater level related data set H, perform summary calculation to obtain the soil expansion factor Tr, and based on the soil expansion factor Tr, obtain the water level change impact factor Ψ; Step 4: Build a crack change prediction model to predict future time points Crack width , assess the future safety status of the house and trigger the early warning mechanism; Step 5: Collect crack change data for a period of time in the future in real time and feed it back to the crack change prediction model to optimize the model prediction results.

Images