Building structure health monitoring method and system based on big data

Abstract

The present invention discloses a method and system for monitoring the health of building structures based on big data, comprising: arranging sensors, acquiring sensor data in real time, calculating the health correlation between sensors based on the acquired sensor data and establishing a health graph model; calculating the harmonic path weights between sensors based on the health correlation; calculating the entropy weight of each sensor based on the harmonic path weights between sensors, and obtaining the connectivity index of the health graph model based on the entropy weight calculation; calculating the building structure health index using a building structure health index assessment algorithm based on the harmonic path weights between sensors and the connectivity index of the health graph model, and comparing the building structure health index with a health threshold to achieve building structure health monitoring. The present invention comprehensively assesses the health status of a building structure, is sensitive to subtle changes, enhances the stability of abnormal values, and improves the accuracy of the assessment of the health status of the building structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent buildings, and specifically relates to a building structure health monitoring method and system based on big data. Background Art

[0002] With the acceleration of urbanization and the continuous development of construction technology, the number of infrastructure is increasing. The health status of building structures is directly related to public safety, economic interests and social stability. It is necessary to monitor the health of building structures, discover potential problems in a timely manner, and take preventive and repair measures. However, traditional building structure health monitoring methods rely on manual inspections and regular inspections, which have limitations. With the rapid development of information technology and sensing technology, building structure health monitoring methods based on big data have emerged. By utilizing the advantages of big data technology, real-time, comprehensive and accurate monitoring of building structures can be achieved, providing strong support for building structure health assessment, which has important theoretical significance and practical application value.

[0003] my country's patent application number: CN201910227557.1, published on July 12, 2019, discloses a building performance assessment method and system based on structural health monitoring data, including obtaining building structural response parameters; obtaining the numerical range of the building structural response parameters corresponding to the building performance; and comparing the building structural response parameters with the numerical range of the building structural response parameters corresponding to the building performance to calibrate the building performance. The present invention utilizes the building structural response parameters detected by existing building health monitoring systems in the prior art, and obtains the numerical range information of the building structural response parameters corresponding to the building performance through existing specifications and guidance documents. By determining the numerical range of the obtained building structural response parameters, the building performance, i.e., the building damage level, can be calibrated, thereby achieving a comprehensive evaluation of the health status of the building structure.

[0004] However, the above technologies have at least the following technical problems: traditional building structure health monitoring methods rely on periodic manual inspections or simple static monitoring, which cannot achieve real-time monitoring and dynamic analysis of building structures; data processing capabilities are limited, making it difficult to fully mine and utilize the complex information in sensor data; existing technologies lack sensitivity to subtle changes when assessing the health status of building structures, and are unable to capture potential problems in the structure in a timely manner, resulting in low accuracy in health status assessments. Summary of the Invention

[0005] The purpose of the present invention is to address the shortcomings of the existing technology and provide a building structure health monitoring method based on big data. The method can solve the problems that the existing technology is difficult to fully mine and utilize the complex information in sensor data, and the existing technology lacks sensitivity to subtle changes when assessing the health status of building structures, and is unable to capture potential problems in the structure in a timely manner, resulting in low accuracy of health status assessment.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A building structure health monitoring method based on big data includes the following steps:

[0008] Step 1: Arrange sensors on the building structure and acquire sensor data in real time. Calculate the health correlation between sensors based on the acquired sensor data and establish a health graph model based on the health correlation.

[0009] Step 2: Based on the health correlation between sensors, the adaptive harmonic cycle analysis algorithm is used to calculate the harmonic path weights between sensors;

[0010] Step 3: Based on the harmonic path weights between sensors, the entropy weight connectivity analysis algorithm is used to calculate the entropy weight of each sensor, and the connectivity index of the health graph model is obtained according to the entropy weight calculation;

[0011] Step 4: Based on the harmonic path weights between sensors and the connectivity index of the health graph model, the building structure health index is calculated using the building structure health index assessment algorithm, and the building structure health index is compared with the health threshold to achieve building structure health monitoring.

[0012] Furthermore, the method for calculating the health association between sensors in step 1 is specifically as follows:

[0013] Nonlinear transformation and dynamic adjustment mechanisms are used on sensor data to capture the complex relationships between sensor data. The hyperbolic tangent function is introduced to calculate the differences between different sensor data. A dynamic adjustment mechanism for the standard deviation and the square of the standard deviation is then introduced to adapt to the volatility of sensor data. Finally, an exponential function is used to convert the health correlation between sensors to a range of (0, 1).

[0014] The specific calculation formula for the health correlation between sensors is:

[0015]

[0016] Among them, w ij(t) represents the health correlation between the i-th sensor and the j-th sensor at time t; exp represents the exponential function; |tanh(·)| represents the absolute value of the hyperbolic tangent function, and the processing data difference D i (t)-D j (t); D i (t) and D j (t) represents the i-th sensor data and the j-th sensor data at time t; σ ij (t) represents the standard deviation between the i-th sensor data and the j-th sensor data at time t; κ represents the adjustment parameter.

[0017] Furthermore, the health graph model is constructed as follows: using sensors as nodes and the health correlation between sensors as edges to establish a health graph model. The health graph model is expressed as: G(y) = (V(t), E(t)), where V(t) represents the node set at time t and E(t) represents the edge set at time t.

[0018] Furthermore, the calculation method for calculating the harmonic path weight in step 2 includes:

[0019] Phase offset is used to capture the periodic characteristics of the sensor, and the sliding window mechanism is used to achieve adaptive period adjustment, and the harmonic path weight is calculated in combination with the health correlation degree;

[0020] Phase offset is to calculate the error between the harmonic component and the actual sensor data for the current phase offset, and iteratively update the phase offset value through the gradient descent method until the error is minimized, thereby matching the actual fluctuation of the sensor data;

[0021] The method of using the sliding window mechanism to achieve adaptive period adjustment is to use fast Fourier transform to perform spectral analysis on sensor data, preliminarily calculate the period of sensor data, and determine the size and step size of the sliding window based on the period. The least squares fitting method is applied to the sensor data in each sliding window to fit a sine or cosine function, and the local period is determined according to the frequency of the sine or cosine function. Finally, the weighted average method is used to integrate the local periods into an adaptive period.

[0022] Furthermore, the specific calculation formula for the harmonic path weight between sensors is:

[0023]

[0024] Among them, h ij (t) represents the harmonic path weight between the u-th sensor and the j-th sensor at time t; Represents the summation symbol for n from 1 to N; α n Represents the sine coefficient of the nth harmonic component; Represents a period T(t) and a phase offset φ n sine function; 2π is a constant, representing one cycle; T(t) represents the adaptive cycle at time t; φ n represents the phase shift of the nth harmonic component; γ n Represents the cosine coefficient of the nth harmonic component; represents a cosine function with period T(t); w ij (t) represents the health correlation between the i-th sensor and the j-th sensor at time t.

[0025] Furthermore, in step 3, the entropy weight of each sensor is calculated by combining the harmonic path weights between sensors and the influence of the sensors in the sensor network. The calculation formula of the entropy weight is:

[0026]

[0027] Among them, e i (t) represents the entropy weight of the i-th sensor at time t, which measures the connectivity between sensors; represents the accumulation symbol, |V(t)|-1 represents the number of remaining nodes, that is, the number of remaining sensors except the i-th sensor; represents the modified probability weight, which calculates the connectivity weight between the i-th sensor and the j-th sensor at time t; represents the normalized modified probability weight; h ij (t) represents the harmonic path weight between the i-th sensor and the j-th sensor at time t, reflecting the periodic weight; W represents the logarithm of the modified probability weight, which is used to calculate the information entropy and reflects the uncertainty between the i-th sensor and the j-th sensor; j (t) represents the influence weight of the jth sensor at time t, and the specific calculation formula is:

[0028]

[0029] Among them, d ij (t) represents the actual path distance between the i-th sensor and the j-th sensor at time t.

[0030] Furthermore, in step 3, based on the entropy weight of the sensor, a mathematical statistical method is used to first sum and then average the values ​​to obtain the connectivity index of the health graph model. The specific calculation formula is:

[0031]

[0032] Among them, C G (t) represents the connectivity index of the health graph model at time t, reflecting the health status of the overall structure; represents the average entropy weight of all sensors, and V(t) represents the node set at time t.

[0033] Furthermore, the connectivity index and harmonic path weight of the health graph model are combined to consider the impact of harmonic paths on the fatigue life of building structures. The fatigue sensitivity factor and frequency sensitivity factor are used to quantify the impact and calculate the building structure health index. The calculation formula of the building structure health index is:

[0034]

[0035] Among them, H G (t) represents the building structure health index at time t; C G (t) represents the connectivity index of the health graph model at time t; log(1+C G (t)) represents the nonlinear transformation of connectivity index; (C G (t)) m represents the high power of the connectivity index; S(t) represents the stiffness coefficient; q represents the exponent of the strength adjustment stiffness; p represents the exponent of the stiffness suppression term; exp represents the exponential function; L f (t) represents the predicted value of fatigue life of the building structure at time t.

[0036] Furthermore, in step 4, the health threshold θ(t) is set, and the building structure health index H G (t) is compared with the health threshold θ(t), when H G When (t)≥θ(t), an alarm is issued, indicating that there may be an abnormality in the building structure; when H G When (t)<θ(t), it indicates that the monitoring status of the building structure is normal.

[0037] Another object of the present invention is to provide a system for implementing the above-mentioned building structure health monitoring method based on big data, comprising:

[0038] A data acquisition module, used to acquire real-time data from sensors arranged on the building structure;

[0039] A health correlation establishment module is used to calculate the health correlation between sensors based on the sensor data obtained by the data acquisition module and establish a health graph model based on the health correlation;

[0040] A harmonic path weight calculation module is used to calculate the harmonic path weights between sensors based on the health correlation between the sensors using an adaptive harmonic period analysis algorithm;

[0041] The entropy weight and connectivity index calculation module is used to calculate the entropy weight of each sensor based on the harmonic path weight between sensors using the entropy weight connectivity analysis algorithm, and obtain the connectivity index of the health graph model based on the entropy weight calculation;

[0042] A building structure health index calculation module is used to calculate the building structure health index using a building structure health index evaluation algorithm based on the harmonic path weights between sensors and the connectivity index of the health graph model;

[0043] The building structure health judgment module is used to judge the health status of the building structure by comparing the building structure health index with the health threshold.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This invention establishes a dynamic health graph model to convert the health associations between sensors into nodes and edges, allowing complex sensor data relationships to be presented intuitively. The construction of the health graph model helps to comprehensively capture the health status of building structures and their changing trends, providing an effective graphical analysis tool that makes structural health monitoring more intuitive and easy to understand.

[0046] 2. The dynamic health correlation calculation algorithm used in the present invention adopts nonlinear transformation and dynamic adjustment mechanism. It processes sensor data differences through hyperbolic tangent function, and combines the standard deviation and standard deviation square dynamic adjustment mechanism to enhance the nonlinear characteristics of data differences, adapt to data volatility, and improve the accuracy of health correlation between sensor data, making the health status assessment of building structures accurate and reliable.

[0047] 3. The adaptive harmonic period analysis algorithm employed in this paper captures the periodic characteristics of sensor data and calculates the harmonic path weights between sensors. By introducing a phase offset and sliding window mechanism and adaptively adjusting the periodic parameters, the algorithm improves the accuracy of path weights in sensor networks, reflects the periodic relationships between sensor data, and helps identify periodic changes and potential hazards in building structures.

[0048] 4. The entropy-weighted connectivity analysis algorithm used in this invention combines the harmonic path weights and the influence of sensors in the network to calculate the entropy weight of each sensor, reflecting the connectivity of the sensor network. By calculating the entropy weight, the connectivity and influence of sensors in the network are quantified, effectively assessing the overall health of the building structure.

[0049] 5. The building structure health index assessment algorithm calculated by the present invention combines the connectivity index, stiffness coefficient and fatigue life prediction value of the building structure to comprehensively assess the health status of the building structure. It is also sensitive to subtle changes, enhances the stability against outliers, and improves the accuracy of the assessment of the health status of the building structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flowchart of a building structure health monitoring method based on big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0053] The present invention will be further described below with reference to specific examples, but they are not intended to limit the present invention.

[0054] Refer to the attached Figure 1 , which shows a flow chart of a building structure health monitoring method based on big data provided by an embodiment of the present invention, the method comprising the following steps:

[0055] Step 1: Arrange sensors on the building structure and acquire sensor data in real time. Calculate the health correlation between sensors based on the acquired sensor data and establish a health graph model based on the health correlation.

[0056] A variety of sensors are arranged on the building structure to obtain sensor data in real time. The sensor data is used to evaluate the real-time status of the building structure, identify potential anomalies or damage, and ensure the safety and stability of the building. In this embodiment, the arranged sensors include acceleration sensors, displacement sensors, and strain sensors, which are distributed to obtain acceleration data, displacement data, and strain data.

[0057] Since different sensor data have different physical quantities, units, and magnitudes, they need to be normalized to make the sensor data comparable in subsequent calculations. Normalization methods are well known to those skilled in the art and will not be described in detail here.

[0058] Based on the normalized sensor data, a dynamic health correlation calculation algorithm is used to calculate the health correlation between sensors, identify the complex relationships between sensor data, and provide an accurate description of the building structure status. Specifically, the dynamic health correlation calculation algorithm uses nonlinear transformation and dynamic adjustment mechanisms on the normalized sensor data to capture the complex relationships between sensor data. It then calculates the differences between different sensor data to reflect the different responses of different sensors at specific time points. By introducing a hyperbolic tangent function to convert input values ​​into outputs between -1 and 1, it effectively captures small differences and processes the differences between different sensor data. This can enhance the nonlinear characteristics of sensor data differences, thereby accurately reflecting the health correlation between different sensors. Finally, a dynamic adjustment mechanism for the standard deviation and the square of the standard deviation is introduced. For sensor data, the standard deviation can reflect the volatility or instability of sensor data. When the volatility of sensor data is particularly large, the contribution of the square of the standard deviation to the denominator will increase significantly. The standard deviation and its square ensure sensitivity to the volatility of sensor data, making the adjustment of health correlation more obvious in high volatility or abnormal situations. By combining the standard deviation and the square of the standard deviation, it can adapt to the volatility of sensor data. The exponential function is used to convert the range of health correlation between sensors to (0,1).

[0059] The specific calculation formula for the health correlation between sensors is:

[0060]

[0061] Among them, w ij (t) represents the health correlation between the i-th sensor and the j-th sensor at time t; exp represents the exponential function, which converts the calculated difference into the health correlation to ensure that the health correlation result is in the range of (0,1); |tanh(·)| represents the absolute value of the hyperbolic tangent function, which processes the data difference D i (t)-D j (t), enhance the nonlinear characteristics of sensor data through nonlinear transformation; D i (t) and D j (t) represents the i-th sensor data and the j-th sensor data at time t; σ ij (t) represents the standard deviation of the i-th sensor data and the j-th sensor data at time t; κ represents the adjustment parameter, which adjusts the square term of the standard deviation to adapt to the dynamic changes of sensor data.

[0062] After calculating the health associations between sensors, a health graph model of the building structure is established, using sensors as nodes and the health associations between sensors as edges. The health graph model is represented as: G(t) = (V(t), E(t)), where V(t) represents the node set at time t and E(t) represents the edge set at time t.

[0063] Step 2: Based on the health correlation between sensors, the adaptive harmonic cycle analysis algorithm is used to calculate the harmonic path weights between sensors;

[0064] Based on the health correlation between sensors, an adaptive harmonic cycle analysis algorithm is used to calculate the harmonic path weights between sensors. Sensor data represents the dynamic response of a building structure at different time points, such as acceleration, displacement, and strain. Frequency domain analysis (e.g., Fourier transform) of the sensor data converts the time domain signal into a frequency domain signal and decomposes it into multiple frequency components, each corresponding to a harmonic component. The amplitude and phase of the harmonic components describe the periodic characteristics of the signal, reflecting the vibration and stress distribution of the building structure. The adaptive harmonic cycle analysis algorithm improves the accuracy of path calculation by capturing the periodic characteristics of the data and incorporating the health correlation. This allows the harmonic path weights to account not only for periodic changes but also for the health correlation between sensors, more accurately reflecting the periodic changes and potential fatigue issues of the building structure. The adaptive harmonic cycle analysis algorithm introduces phase offsets to capture periodic characteristics and utilizes a sliding window mechanism to achieve adaptive period adjustment, thus reflecting the periodicity and interrelationships of the sensor data.

[0065] Phase offset is adjusted for each harmonic component. For the current phase offset, the error between the harmonic component and the actual sensor data is calculated. The phase offset value is iteratively updated using gradient descent until the error is minimized. Adjusting the phase offset changes the starting point or peak position of the sine wave to match the actual fluctuations in the sensor data, allowing each harmonic component to accurately reflect the periodic correlation between the sensor data.

[0066] Adaptive period adjustment is achieved by using a sliding window mechanism. Fast Fourier transform is used to perform spectrum analysis on sensor data, preliminarily calculate the period of the sensor data, and determine the size and step size of the sliding window based on the period. The sliding window mechanism is used to perform segmented analysis on the sensor data to capture the dynamic changes of the period. Specifically, the least squares fitting method is applied to the sensor data in each sliding window to fit a sine or cosine function. The local period is determined based on the frequency of the sine or cosine function, and the weighted average method is used to further integrate the local periods into an adaptive period.

[0067] The specific calculation formula for the harmonic path weight between sensors is:

[0068]

[0069] Among them, h ij (t) represents the harmonic path weight between the i-th sensor and the j-th sensor at time t, reflecting the periodic weight; Represents the summation symbol for n from 1 to N, which is used to accumulate the contribution of all harmonic components; α n Represents the sine coefficient of the nth harmonic component, which is used to adjust the amplitude of the sine wave; Represents a period T(t) and a phase offset φ n sine function; 2π is a constant, representing one cycle; T(t) represents the adaptive cycle at time t; φ n represents the phase shift of the nth harmonic component, which is used to capture the periodic characteristics; γ n Represents the cosine coefficient of the nth harmonic component, which is used to adjust the amplitude of the cosine wave; represents a cosine function with period T(t); w ij (t) represents the health correlation between the i-th sensor and the j-th sensor at time t;

[0070] Harmonic path weights reflect the periodic correlations between sensor data and help identify periodic changes and potential fatigue issues in building structures.

[0071] Step 3: Based on the harmonic path weights between sensors, the entropy weight connectivity analysis algorithm is used to calculate the entropy weight of each sensor, and the connectivity index of the health graph model is obtained according to the entropy weight calculation;

[0072] Based on the harmonic path weights between sensors, the entropy weight of each sensor is calculated using the entropy-weighted connectivity analysis algorithm. This algorithm combines the harmonic path weights between sensors with the sensor's influence within the sensor network to calculate the entropy weight of each sensor. This reflects the connectivity of the entire sensor network, helps identify potential structural weaknesses and failure points, and improves the ability to comprehensively assess the health of building structures.

[0073] The calculation formula of entropy weight is:

[0074]

[0075] Among them, e i (t) represents the entropy weight of the i-th sensor at time t, which measures the connectivity between sensors; represents the accumulation symbol, |V(t)|-1 represents the number of remaining nodes, that is, the number of remaining sensors except the i-th sensor; represents the modified probability weight, which calculates the connectivity weight between the i-th sensor and the j-th sensor at time t; represents the normalized modified probability weight; h ij (t) represents the harmonic path weight between the i-th sensor and the j-th sensor at time t, reflecting the periodic weight; W represents the logarithm of the modified probability weight, which is used to calculate the information entropy and reflects the uncertainty between the i-th sensor and the j-th sensor; j (t) represents the influence weight of the jth sensor at time t, and its specific calculation formula is:

[0076]

[0077] Among them, d ij (t) represents the actual path distance between the i-th sensor and the j-th sensor at time t;

[0078] Based on the entropy weight of the sensor, mathematical statistics methods are used to first sum and then average the data to obtain the connectivity index of the health graph model. The specific calculation formula is:

[0079]

[0080] Among them, C G (t) represents the connectivity index of the health graph model at time t, reflecting the health status of the overall structure; represents the average value of the entropy weights of all sensors;

[0081] Step 4: Based on the harmonic path weights between sensors and the connectivity index of the health graph model, the building structure health index is calculated using the building structure health index assessment algorithm, and the building structure health index is compared with the health threshold to achieve building structure health monitoring;

[0082] The building health index (SHI) is calculated using a building health index assessment algorithm based on the harmonic path weights between sensors and the connectivity index of the health graph model. This algorithm combines connectivity indicators, stiffness coefficients, and fatigue life predictions for the building structure to comprehensively assess the health of the building structure. It is also sensitive to subtle changes, improving the accuracy of structural health assessments.

[0083] The connectivity index of the health graph model is nonlinearly transformed using logarithmic and high-power functions to further smooth the connectivity index. The nonlinear processing of the stiffness coefficient reflects the building structure's ability to resist deformation. Exponential functions and reciprocals are used to process the fatigue life prediction values ​​of the building structure. By exponentially inverting the fatigue life prediction values, significant distinctions in fatigue life can be made. The nonlinear processing of the stiffness coefficient requires a balance between the numerator and denominator. The key lies in the choice of exponent: the exponent of the stiffness coefficient in the denominator is selected to be greater than the exponent of the stiffness coefficient in the numerator to ensure that the denominator effectively suppresses the amplification effect of the numerator.

[0084] The fatigue life prediction value of the building structure combines the connectivity index and harmonic path weight of the health graph model, taking into account the impact of the harmonic path on the fatigue life of the building structure and quantifying the impact through the fatigue sensitivity factor and frequency sensitivity factor. The fatigue sensitivity factor reflects the fatigue characteristics of the building materials and structures, while the frequency sensitivity factor measures the impact of the vibration frequency on the fatigue life of the building structure. Therefore, the calculation formula of the building structure health index is:

[0085]

[0086] Among them, H G (t) represents the building structure health index at time t, reflecting the overall health status of the building structure; C G (t) represents the connectivity index of the health graph model at time t, reflecting the health status of the overall structure; log(1+C G (t)) represents the nonlinear transformation of the connectivity index, and uses the logarithmic function to process the connectivity index to enhance the stability against outliers; (C G (t)) m Represents the high power of the connectivity index, which enhances the smoothing effect of outliers; S(t) represents the stiffness coefficient, which reflects the ability of the building structure to resist deformation. The calculation method of the stiffness coefficient is a technical means well known to those skilled in the art and will not be described in detail here; q represents the index for adjusting the influence of stiffness on strength, and the value is required to be greater than 1 to enhance the effect of stiffness on the health index; p represents the exponent for controlling the stiffness suppression term, which ensures that the growth of the health index tends to be gentle; exp represents an exponential function, which is used to enhance the response capability to extreme situations and helps to accurately assess the health status of the building structure when it is close to the fatigue limit; L f (t) represents the predicted value of fatigue life of the building structure at time t, reflecting the remaining fatigue life of the building structure under existing conditions. The specific calculation formula is:

[0087]

[0088] Among them, h l (t) represents the weight of the lth harmonic path at time t; Indicates the accumulation of contributions from M harmonic paths; I l (t) represents the fatigue sensitivity factor, which reflects the sensitivity of the lth harmonic path to the fatigue life of the building structure. It is obtained from experimental data considering the fatigue characteristics of the building materials and structures. represents the frequency sensitivity factor, considering the influence of the vibration frequency of the lth harmonic path on the fatigue life of the building structure; f l (t) represents the vibration frequency of the lth harmonic path at time t, which is collected by the sensor; f0 represents the reference frequency, which represents the frequency of the building structure under normal working conditions. It can be set according to the specific implementation scenario and is not limited here; β represents the frequency adjustment factor, which reflects the impact of vibration on the fatigue life of the building structure; B f (l) represents the allowable number of cycles of the lth harmonic path at the current stress level, which is the basis for calculating the fatigue life of building structures and is obtained from experimental data.

[0089] The building structure health index can intuitively reflect the overall health status of the building structure, helping management and maintenance personnel to timely understand the health status of the building structure, make scientific maintenance decisions, and ensure the long-term safety and stability of the building structure.

[0090] When judging the health status of a building, a health threshold θ(t) is set in advance. It can be set according to the specific implementation scenario and is not limited here. The building structure health index is compared with the health threshold θ(t). When H G When (t)≥θ(t), an alarm is issued, indicating that there may be an abnormality in the building structure; when H G When (t) < θ(t), it indicates that the building structure monitoring status is normal. By setting the health threshold, an alarm can be issued in time to prevent structural damage or accidents.

[0091] Examples

[0092] 1. Building Description

[0093] The experimental subject is a 12-story reinforced concrete frame building with a floor area of ​​12,000 square meters. The building is 15 years old and located in a busy traffic area with significant external vibration. This experiment focuses on evaluating the health of the building under dynamic loads and the influence of the natural environment.

[0094] 2. Sensor equipment

[0095] Accelerometer (MPU6050): measurement range ±16g, accuracy ±0.001g, arranged at the connection point between the main beam and the column.

[0096] Displacement sensor (LVDT type): measurement accuracy 0.01mm, arranged at the floor support point.

[0097] Strain sensor (HBM strain gauge): measuring range ±5000με, accuracy ±1με, placed on the building foundation column.

[0098] 3. Sensor layout and monitoring points

[0099] Position 1: Foundation column (strain sensor), monitoring the stress and deformation of the column.

[0100] Position 2: Third-layer main beam (acceleration sensor), monitoring the vibration response of the main beam.

[0101] Position 3: Sixth-floor slab support node (displacement sensor), monitoring floor slab deformation and displacement.

[0102] Data sampling frequency: 100 Hz (100 data points per second), continuous 24-hour monitoring.

[0103] Steps and data analysis

[0104] 1. Sensor data collection

[0105] Position 1 (base column):

[0106] Strain data: [100,101,99,...] (unit: με).

[0107] Data description: The column data fluctuates slightly, indicating that the column is relatively stable in terms of force.

[0108] Position 2 (main beam):

[0109] Acceleration data: [0.01, 0.02, 0.015, ...] (unit: m / s 2 ).

[0110] Data description: The acceleration of the main beam is small, reflecting that the vibration amplitude of the building structure is low and the overall stability is good.

[0111] Position 3 (floor support node):

[0112] Displacement data: [0.1, 0.12, 0.09, ...] (unit: mm).

[0113] Data description: The displacement of the floor support is within the normal range, with no obvious abnormal deformation.

[0114] 2. Calculation of health relevance

[0115] Through the dynamic health correlation calculation algorithm, the correlation between different sensors is quantified, revealing the linkage relationship between different parts of the building structure.

[0116] Calculation formula:

[0117]

[0118] Among them, D i (t) and D j (t) represents the i-th sensor data and the j-th sensor data at time t; σ ij (t) represents the standard deviation between the i-th sensor data and the j-th sensor data at time t; κ represents the adjustment parameter, which is set to 0.1.

[0119] Experimental data analysis results:

[0120] 1. Health correlation between position 1 and position 2:

[0121] Strain data vs acceleration data: vs.

[0122] Standard deviation σ 12 (t) = 0.005;

[0123] Calculation result w 12 (t)=0.85.

[0124] 2. Health correlation between position 2 and position 3:

[0125] Acceleration data vs displacement data: vs.

[0126] Standard deviation σ 23 (t) = 0.004;

[0127] Calculation result w 23 (t)=0.90.

[0128] 3. Harmonic path weight calculation

[0129] An adaptive harmonic period analysis algorithm is used to calculate the harmonic path weights between sensors, capturing the periodic vibration characteristics of the building structure and reflecting the impact of periodic loads in the building.

[0130] Calculation formula:

[0131]

[0132] Parameter settings:

[0133] N = 3 (number of harmonic components);

[0134] Adaptation period T(t) = 2 seconds;

[0135] Harmonic adjustment coefficient α n =0.7,γ n =0.3, phase shift φ n =0.1.

[0136] Calculation results:

[0137] 1. Harmonic path weights for position 1 and position 2:

[0138] Calculation result h 12 (t)=0.72.

[0139] 2. Harmonic path weights for positions 2 and 3:

[0140] Calculation result h 23 (t)=0.75.

[0141] The harmonic path weights show that the periodic response of the building structure is more obvious, especially between the main beams and floor supports, where there may be a certain amount of fatigue accumulation.

[0142] 4. Calculation of entropy weight and connectivity index

[0143] Entropy weights are used to assess the relative influence of each sensor in the network. By calculating entropy weights, the overall connectivity of the building structure can be measured and potential weaknesses in the building structure can be revealed.

[0144] Entropy weight calculation formula:

[0145]

[0146] Among them, W j (t) represents the influence weight of the jth sensor at time t, and the specific calculation formula is:

[0147]

[0148] Connectivity index calculation formula:

[0149]

[0150] C G (t): The overall connectivity index of the building structure, reflecting the health status of the building.

[0151] result:

[0152] 1. Entropy weight of position 1 (base cylinder): e1(t) = 0.65.

[0153] 2. Entropy weight of position 2 (main beam): e2(t) = 0.70.

[0154] 3. Entropy weight of position 3 (floor): e3(t) = 0.68.

[0155] Overall connectivity index C G (t)=0.68.

[0156] The connectivity of the building structure is good, the network correlation of each sensor is strong, the building as a whole is in a healthy state, and no obvious structural weaknesses are found.

[0157] 5. Building structure health index assessment

[0158] The building structural health index provides a quantitative health assessment tool by comprehensively considering connectivity indicators, stiffness and fatigue life of building structures.

[0159] Health index calculation formula:

[0160]

[0161] Parameter settings:

[0162] m = 2, q = 1.5, p = 1.2;

[0163] Stiffness coefficient S(t) = 0.85;

[0164] Fatigue life prediction formula:

[0165]

[0166] Parameter settings:

[0167] I l (t)=0.8,f l (t)=200,f0=1000,B f (l) = 100, β = 0.5, M = 3;

[0168] L f (t) = 10,000 hours

[0169] Calculation results:

[0170] Building structure health index H G (t)=1.25

[0171] Threshold settings:

[0172] To evaluate the health status of the building, the threshold θ(t) is set to 1, and the building structure health index is compared with the threshold. G When (t)≥θ(t), an alarm is issued, indicating that there may be an abnormality in the building structure; when H G When (t)<θ(t), it indicates that the monitoring status of the building structure is normal.

[0173] If the health index is higher than the threshold, it indicates that the building structure is in an unsafe state and requires maintenance or repair. The fatigue condition of the main beams and floor support points should be monitored regularly to prevent the accumulation of potential problems.

[0174] This experiment validated that the big data-based building structural health monitoring method can accurately reflect the dynamic health of a building. The results showed that the building's health index under current operating conditions was 1.25, exceeding the threshold of 1, indicating an overall structural abnormality. Maintenance or repair is recommended. Furthermore, more detailed fatigue monitoring of the main beams and floor support points is recommended to ensure the building's long-term safety.

[0175] An embodiment of the present invention further provides a system for implementing the above-mentioned building structure health monitoring method based on big data, comprising:

[0176] A data acquisition module, used to acquire real-time data from sensors arranged on the building structure;

[0177] A health correlation establishment module is used to calculate the health correlation between sensors based on the sensor data obtained by the data acquisition module and establish a health graph model based on the health correlation;

[0178] A harmonic path weight calculation module is used to calculate the harmonic path weights between sensors based on the health correlation between the sensors using an adaptive harmonic period analysis algorithm;

[0179] The entropy weight and connectivity index calculation module is used to calculate the entropy weight of each sensor based on the harmonic path weight between sensors using the entropy weight connectivity analysis algorithm, and obtain the connectivity index of the health graph model based on the entropy weight calculation;

[0180] A building structure health index calculation module is used to calculate the building structure health index using a building structure health index evaluation algorithm based on the harmonic path weights between sensors and the connectivity index of the health graph model;

[0181] The building structure health judgment module is used to judge the health status of the building structure by comparing the building structure health index with the health threshold.

[0182] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the present invention specification should be included in the protection scope of the present invention.

Claims

1. A building structure health monitoring method based on big data, characterized in that: The following steps are involved: Step 1: Arrange sensors on the building structure and acquire sensor data in real time. Calculate the health correlation between sensors based on the acquired sensor data and establish a health graph model based on the health correlation. Step 2: Based on the health correlation between sensors, the adaptive harmonic cycle analysis algorithm is used to calculate the harmonic path weights between sensors; Step 3: Based on the harmonic path weights between sensors, the entropy weight connectivity analysis algorithm is used to calculate the entropy weight of each sensor, and the connectivity index of the health graph model is obtained according to the entropy weight calculation; Step 4: Based on the harmonic path weights between sensors and the connectivity index of the health graph model, the building structure health index is calculated using the building structure health index assessment algorithm, and the building structure health index is compared with the health threshold to achieve building structure health monitoring; The calculation method for calculating the harmonic path weight in step 2 includes: Phase offset is used to capture the periodic characteristics of the sensor, and the sliding window mechanism is used to achieve adaptive period adjustment, and the harmonic path weight is calculated in combination with the health correlation degree; Phase offset is to calculate the error between the harmonic component and the actual sensor data for the current phase offset, and iteratively update the phase offset value through the gradient descent method until the error is minimized, thereby matching the actual fluctuation of the sensor data; The method of using the sliding window mechanism to achieve adaptive period adjustment is to use fast Fourier transform to perform spectral analysis on sensor data, preliminarily calculate the period of sensor data, and determine the size and step size of the sliding window based on the period. The least squares fitting method is applied to the sensor data in each sliding window to fit a sine or cosine function, and the local period is determined according to the frequency of the sine or cosine function. Finally, the weighted average method is used to integrate the local period into an adaptive period.

2. The building structure health monitoring method based on big data according to claim 1 is characterized in that: The specific method for calculating the health association between sensors in step 1 is: Nonlinear transformation and dynamic adjustment mechanisms are used on sensor data to capture the complex relationships between sensor data. The hyperbolic tangent function is introduced to calculate the differences between different sensor data. A dynamic adjustment mechanism for the standard deviation and the square of the standard deviation is then introduced to adapt to the volatility of sensor data. Finally, an exponential function is used to convert the health correlation between sensors to a range of (0, 1). The specific calculation formula for the health correlation between sensors is: in, Indicates the The sensor and Sensors at time health relevance; represents the exponential function; Represents the absolute value of the hyperbolic tangent function and handles data differences ; and Indicates time No. sensor data and the Sensor data; Indicates the sensor data and the Sensor data at time The standard deviation of Indicates adjustment parameters.

3. The building structure health monitoring method based on big data according to claim 1 is characterized in that: The construction method of the health graph model is as follows: sensors are used as nodes and the health association between sensors is used as edges to establish a health graph model. The health graph model is expressed as: ,in, Indicates time The node set, Indicates time The edge set of .

4. The building structure health monitoring method based on big data according to claim 1 is characterized in that: The specific calculation formula for the harmonic path weight between sensors is: in, Indicates time No. The sensor and Harmonic path weights between sensors; Express From 1 to The summation symbol; Indicates the Sine coefficients of the harmonic components; Indicates that it contains a period and phase shift The sine function of is a constant, representing one cycle; Indicates time Adaptive cycle of 1 hour; Indicates the Phase shift of each harmonic component; Indicates the The cosine coefficients of the harmonic components; Indicates that it contains a period The cosine function of Indicates the The sensor and Sensors at time health relevance.

5. The building structure health monitoring method based on big data according to claim 1 is characterized in that: In step 3, the entropy weight of each sensor is calculated by combining the harmonic path weights between sensors and the influence of the sensor in the sensor network. The entropy weight calculation formula is: ； in, Indicates the Sensors at time The entropy weight of , which measures the connectivity between sensors; Indicates the accumulation symbol, Indicates the number of remaining nodes, that is, except for the The number of sensors remaining; Represents the normalized modified probability weight, calculated at time No. The sensor and The connectivity weights between sensors; represents the modified probability weight; Indicates time No. The sensor and The harmonic path weights between sensors reflect the periodic weights; Represents the logarithm of the normalized corrected probability weight, which is used to calculate the information entropy and reflects the The sensor and Uncertainty between sensors; Indicates the Sensors at time The specific calculation formula for the influence weight is: ； in, Indicates time No. The sensor and The actual path distance between the sensors.

6. The building structure health monitoring method based on big data according to claim 5 is characterized in that: In step 3, based on the entropy weight of the sensor, mathematical statistics methods are used to first sum and then average the data to obtain the connectivity index of the health graph model. The specific calculation formula is: ； in, Indicates time The connectivity index of the health graph model reflects the health status of the overall structure; represents the average entropy weight of all sensors, Indicates time The node set.

7. The building structure health monitoring method based on big data according to claim 1 is characterized in that: Combining the connectivity index and harmonic path weight of the health graph model, the impact of harmonic paths on the fatigue life of building structures is considered, and the impact is quantified through fatigue sensitivity factors and frequency sensitivity factors to calculate the building structure health index. The calculation formula for the building structure health index is: ； in, Indicates time Building structural health index; Indicates time Connectivity metrics for health graph models; represents a nonlinear transformation of the connectivity index; Represents a high power of the connectivity index; represents the stiffness coefficient; An index indicating how adjusting stiffness affects strength; represents the index of the control stiffness suppression term; represents the exponential function; Indicates time Prediction of fatigue life of building structures.

8. The building structure health monitoring method based on big data according to claim 1 is characterized in that: Set the health threshold in step 4 , the building structure health index and health thresholds For comparison, when When the alarm is sounded, it indicates that there may be abnormality in the building structure; when When , it indicates that the building structure monitoring status is normal.

9. A system for implementing the building structure health monitoring method based on big data according to any one of claims 1 to 8, characterized in that: include: A data acquisition module, used to acquire real-time data from sensors arranged on the building structure; A health correlation establishment module is used to calculate the health correlation between sensors based on the sensor data obtained by the data acquisition module and establish a health graph model based on the health correlation; A harmonic path weight calculation module is used to calculate the harmonic path weights between sensors based on the health correlation between the sensors using an adaptive harmonic period analysis algorithm; The entropy weight and connectivity index calculation module is used to calculate the entropy weight of each sensor based on the harmonic path weight between sensors using the entropy weight connectivity analysis algorithm, and obtain the connectivity index of the health graph model based on the entropy weight calculation; A building structure health index calculation module is used to calculate the building structure health index using a building structure health index evaluation algorithm based on the harmonic path weights between sensors and the connectivity index of the health graph model; The building structure health judgment module is used to judge the health status of the building structure by comparing the building structure health index with the health threshold.

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