System for monitoring performance of wall type damper under earthquake action

Abstract

The invention provides a system for monitoring the performance of wall-type dampers under the action of an earthquake, and the system comprises a dynamic force sensor which is used for collecting the output parameters of each wall-type damper under the action of the earthquake; the displacement sensor is used for collecting relative displacement of all the wall type dampers under the earthquake action; the accelerometer is arranged on a target floor and used for collecting the floor movement acceleration under the earthquake action; the seismograph is arranged on the ground and is used for acquiring seismic parameters; the performance monitoring device is connected with the dynamic force sensor, the displacement sensor, the accelerometer and the seismograph and used for obtaining performance monitoring results of all the wall type dampers under the earthquake action according to the output parameters of all the wall type dampers under the earthquake action, the relative displacement of all the dampers, the floor movement acceleration and the earthquake parameters. By implementing the method, performance degradation of the damper and normal stress fluctuation under strong earthquake excitation can be effectively distinguished, and misjudgment and missed judgment are reduced.

Description

Technical Field

[0001] This invention belongs to the field of damper performance monitoring technology, specifically relating to a system for monitoring the performance of wall-type dampers under seismic loading. Background Technology

[0002] As a core energy-dissipating component of a building's seismic resistance system, wall dampers directly determine the building's overall collapse resistance and post-disaster repairability based on their mechanical performance under seismic loading. With modern buildings becoming increasingly tall and complex, the number of wall dampers installed has increased significantly. Furthermore, dampers on different floors and in different stress zones need to undertake differentiated seismic energy dissipation tasks, which places higher demands on the accuracy, real-time performance, and correlation of their performance monitoring.

[0003] Currently, the main technical solution for performance monitoring of wall dampers in the construction field is the local parameter monitoring scheme. This involves using sensors to collect only a single parameter of the damper itself, such as the output force or displacement. For example, a dynamic force sensor is used to record the axial force of the damper, or a displacement meter is used to measure the relative displacement. The above scheme can only reflect the local working state of the damper and cannot be correlated with external environmental factors such as the intensity of seismic excitation. This makes it difficult to distinguish between the performance degradation of the damper and the normal stress fluctuations under strong seismic excitation, which can easily lead to misjudgment or omission. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a system for monitoring the performance of wall dampers under seismic loading, so as to meet the needs of reducing misjudgment or omission in the performance monitoring of dampers and improving the accuracy of performance monitoring.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a system for monitoring the performance of wall dampers under seismic loading, comprising: a dynamic force sensor for acquiring the output force parameters of each wall damper under seismic loading; a displacement sensor for acquiring the relative displacement of each wall damper under seismic loading; an accelerometer installed on the target floor for acquiring the floor motion acceleration under seismic loading; a seismograph installed on the ground for acquiring seismic parameters; and a performance monitoring device connected to the dynamic force sensor, displacement sensor, accelerometer, and seismograph, for obtaining the performance monitoring results of each wall damper under seismic loading based on the output force parameters of each wall damper under seismic loading, the relative displacement of each damper, the floor motion acceleration, and the seismic parameters.

[0006] This invention provides a system for monitoring the performance of wall-type dampers under seismic loading. It simultaneously acquires the core parameters of the damper itself and the parameters related to the external environment, achieving full data coverage of the damper, floor, and seismic excitation. This system can comprehensively evaluate the performance of the damper in real seismic scenarios. By collecting the parameters related to the external environment as an indicator for performance monitoring, it can effectively distinguish between damper performance degradation and normal stress fluctuations under strong seismic excitation, reducing misjudgments and omissions.

[0007] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0008] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a schematic diagram of an applicable wall damper for a system used in this invention for monitoring the performance of wall dampers under seismic loading. Figure 2 This is a specific example diagram of a system for monitoring the performance of wall-type dampers under seismic loading, as described in this invention. Detailed Implementation The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0010] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0011] Wall-type dampers can be classified into viscous dampers, metal yield dampers, and friction dampers. For ease of understanding, this embodiment provides a schematic diagram of the overall structure of a damper, as shown below. Figure 1 As shown, Figure 1 This is an existing type of wall-mounted viscous damper, the core of which includes: a housing and an upper connecting plate. The housing, as the outer shell of the damper, is a rectangular closed structure, hollow inside to accommodate the damping viscous fluid and shear steel plates, and serves as the installation reference for all internal components. The housing has openings at both ends along its width, integrally connected to the openings of the fluid storage area, forming a "housing-fluid storage" interconnected structure. The upper connecting plate, located at the top of the housing, is a rectangular steel plate structure, serving as the connection interface between the damper and the building structure (such as floor slabs or hidden beams), and is fixed to the building by bolts; its bottom surface is welded and fixed to two shear steel plates, transferring the vibration load transmitted from the building to the shear steel plates, triggering energy dissipation action.

[0012] To monitor the performance of wall-type dampers under seismic loading, this invention provides a system for monitoring the performance of wall-type dampers under seismic loading, such as... Figure 2 As shown, it includes: Dynamic force sensor 101 is used to collect the output force parameters of each wall damper under seismic action; Displacement sensor 102 is used to collect the relative displacement of each wall damper under seismic action; Accelerometer 103 is installed on the target floor to collect the floor motion acceleration under seismic action; Seismograph 104, set on the ground, is used to collect seismic parameters; The performance monitoring device 105 is connected to the dynamic force sensor 101, displacement sensor 102, accelerometer 103 and seismograph 104 respectively, and is used to obtain the performance monitoring results of the wall dampers under seismic action based on the output parameters of each wall damper under seismic action, the relative displacement of each damper, the floor motion acceleration and seismic parameters.

[0013] For example, the dynamic force sensor 101 can be a piezoelectric dynamic force sensor, a strain gauge dynamic force sensor, etc. The dynamic force sensor is installed on the top surface of the connecting plate on each wall damper. Specifically, one dynamic force sensor is installed at the connection flange between each wall damper and the floor slab to collect the output force parameters of the wall damper under seismic loading, including axial force and shear force. The displacement sensor 102 can be a laser displacement sensor. A transmitter can be installed at the top edge of the top surface of each wall damper housing, and a reflector plate can be installed at the bottom edge of the upper connecting plate to emit and reflect the laser light, thereby collecting the relative displacement of the wall damper. The placement of the dynamic force sensor 101 and displacement sensor 102 varies depending on the wall sensor structure and can be adjusted according to the actual situation.

[0014] Accelerometer 103 can be installed on target floors, such as the 1st, 5th, and 10th floors, specifically in the middle of the load-bearing wall or floor slab of each target floor. Four accelerometers are installed at each installation point to comprehensively measure the vibration response in the four cardinal directions. Seismograph 104 is set up in non-building areas, such as open areas outside construction sites. The seismograph is fixed in a pre-excavated concrete pit and secured to a steel plate at the bottom of the pit, ensuring that the seismograph is level with the ground.

[0015] The performance monitoring device 105 can be an industrial computer equipped with a data acquisition card and a bus module to connect with dynamic force sensors, displacement sensors, accelerometers and seismographs. Specifically, the dynamic force sensors are converted by a signal conditioner and then connected to the analog input terminal of the data acquisition card. The displacement sensors and accelerometers are connected to the industrial computer through the bus module. The seismograph is connected to the industrial computer through an Ethernet interface.

[0016] Next, the applicable algorithm of this system will be explained: For the performance monitoring device 105, which is the most important performance evaluation hardware, this embodiment embeds a target algorithm within it. This algorithm obtains the performance monitoring results of the wall dampers under seismic loading based on the output parameters of each wall damper, the relative displacement of each damper, the floor acceleration, and the seismic parameters. The following is an example of one implementation process: First, the data collected by various sensors are aligned in time. Then, based on the output parameters of each wall damper under seismic loading, the relative displacement of each damper, the floor acceleration, and seismic parameters, a fusion judgment is performed. Specifically, the seismic parameters measured during the earthquake are substituted into a pre-established model to obtain the model's predicted relative displacement and output parameters of each damper, as well as the target floor's acceleration. Then, based on the changes in the relative displacement and output parameters of each damper over time, the predicted hysteresis curve is obtained, and the predicted hysteresis curve area is calculated. The percentage of energy consumption efficiency loss is determined by comparing the predicted hysteresis curve area with the actual hysteresis curve area obtained from the collected relative displacement and output parameters of each damper, thus determining whether the damper's performance has degraded. Furthermore, the predicted target floor's acceleration is compared with the actual collected floor acceleration. If the deviation is significant, an overall warning is issued; if the deviation is not significant, it indicates that only a local damper has a problem. The pre-established model can be a digital twin model created in finite element analysis software. The digital twin model was established using existing technology, which will not be elaborated upon here.

[0017] This invention provides a system for monitoring the performance of wall-type dampers under seismic loading. It simultaneously acquires the core parameters of the damper itself and the parameters related to the external environment, achieving full data coverage of the damper, floor, and seismic excitation. This system can comprehensively evaluate the performance of the damper in real seismic scenarios. By collecting the parameters related to the external environment as an indicator for performance monitoring, it can effectively distinguish between damper performance degradation and normal stress fluctuations under strong seismic excitation, reducing misjudgments and omissions.

[0018] The above embodiments provide a performance monitoring method that directly substitutes the measured seismic parameters during an earthquake into a pre-established model to obtain the simulated relative displacement and output parameters of each damper, as well as the acceleration of the target floor. These data are then processed with the collected data, and the performance status of the dampers is determined based on the percentage of energy loss. This simple performance determination method heavily relies on the accuracy of the model and the precision of data alignment. If this step fails, the entire judgment will fail. Furthermore, not all failure modes of dampers during actual earthquakes can be pre-modeled, leading to low accuracy and poor stability in practical applications. Therefore, this embodiment proposes a new method for performance monitoring.

[0019] As an optional implementation, the performance monitoring device includes: The feature vector creation module is used to construct the feature vector of each wall damper based on the location of each wall damper, the output parameters under seismic action, and the relative displacement of each damper. The clustering module is used to cluster the dampers based on the feature vectors of each wall damper, dividing the damper group into different clusters. The cluster analysis module is used to analyze the parameters of the clusters and determine the preliminary monitoring labels of the wall dampers represented by the clusters. These labels characterize the common behavior patterns of the dampers within the clusters. The environmental excitation parameter determination module is used to determine the environmental excitation parameters based on the seismic parameters. The performance monitoring submodule performs a secondary evaluation based on environmental excitation parameters, floor motion acceleration, preliminary monitoring tags and their distribution, and obtains the secondary monitoring results of the wall damper.

[0020] For example, feature vectors for each wall damper are constructed based on their location, output parameters under seismic loading, and relative displacement. The process of constructing feature vectors specifically includes: obtaining the design maximum output, design maximum displacement, and installation location of each wall damper; normalizing the output parameters and relative displacement based on the design maximum output and design maximum displacement to generate normalized indices that eliminate model differences, including output coefficients and displacement coefficients; then, grouping the dampers based on their installation locations and calculating statistical indices within each group. Grouping can be done by grouping dampers from one floor together. Statistical indices include output statistics and displacement statistics. The calculation formula is as follows: in, This represents the output power statistics of damper i. This represents the output force parameter of damper i. This represents the average output force of all dampers in the same layer. This represents the standard deviation of the output power of all dampers in the same layer.

[0021] The formula for calculating displacement statistics is as follows: in, This represents the displacement statistics of damper i. This represents the relative displacement of damper i. This represents the average relative displacement of all dampers in the same layer. This represents the standard deviation of the displacement of all dampers in the same layer.

[0022] A feature vector is constructed by combining the output coefficient, displacement coefficient, output statistics, and displacement statistics.

[0023] The clustering module can use DBSCAN. First, initialize the module by marking all data points as unvisited, and calculate the distance between each sample point and its k-th nearest neighbor. Sort these distances in ascending order and plot them as a line graph. Select the distance value corresponding to the inflection point in the graph as the neighborhood radius. The minimum number of points can be determined empirically; this embodiment does not impose a limit on this.

[0024] Then, treating [output coefficient; displacement coefficient; output statistics; displacement statistics] as a point in four-dimensional space, the system traverses each unvisited point, calculating the number of points within its neighborhood radius. If the number of points is less than the minimum required number, the point is temporarily marked as noise; if the minimum number is reached or exceeded, the point is determined to be a core point, and a new cluster is created using this core. Subsequently, the algorithm recursively explores the neighborhoods of all neighbors of the core point, incorporating all density-reachable core points and boundary points into the current cluster, thus expanding the cluster. This process is repeated until all points have been visited. Finally, the output divides the damper population into several density-connected clusters and potential noise points. Each cluster represents a common behavior pattern, and these outliers are highly likely to correspond to dampers with abnormal performance or malfunctions. This results in multiple clusters.

[0025] The clustering analysis module analyzes each cluster to determine its actual meaning. Specifically, it calculates the average, median, and other statistical values ​​of the features of all points within each cluster. Then, it maps these statistical values ​​to pre-established mapping rules. For example, if the average output coefficient is greater than 0.8 and the average displacement coefficient is greater than 0.7, and both the output and displacement statistics are greater than 0, the corresponding cluster is labeled as an overload cluster. If the average output coefficient is less than 0.3 and the average displacement coefficient is less than 0.3, and both the output and displacement statistics are less than 0, the corresponding cluster is a low-response cluster. If the average output coefficient is less than 0.2 and the average displacement coefficient is greater than 0.6, or the output statistics are less than -0.2 and the displacement statistics are greater than 0.4, or the output statistics are greater than 0.2 and the displacement statistics are less than 0.3, the cluster is labeled as a performance degradation cluster. The mapping rules given in this embodiment are not intended to be limiting; specific mapping rules can be set according to actual needs. The cluster labels obtained from the mapping rules are used as preliminary monitoring labels.

[0026] To further validate the initial monitoring tags and determine whether a damper's poor performance is due to its own inherent characteristics or environmental factors caused by the earthquake, an environmental excitation parameter determination module is also needed to obtain the peak ground acceleration based on the seismic parameters. As an optional implementation, the performance monitoring submodule is configured to perform the following steps: classify environmental excitation levels (strong, medium, weak) based on environmental excitation parameters; statistically analyze the number and proportion of preliminary monitoring tags for dampers by floor group based on preliminary monitoring tags and their distribution, and calculate the cluster consistency index. Preliminary monitoring tags include overload operation, performance degradation, and anomalies; determine the maximum inter-story drift angle based on floor acceleration, and compare the maximum inter-story drift angle with the expected response range based on historical data; determine whether it is a strong excitation response based on the cluster consistency index and the maximum inter-story drift angle; if the environmental excitation level is weak, the preliminary monitoring tag for the damper is overload operation or performance degradation, and it is not a strong excitation response, then the damper is confirmed to be truly abnormal; if the environmental excitation level is strong, the preliminary monitoring tag for the damper is overload operation, and it is a strong excitation response, then the damper is confirmed to be responding normally; if the environmental excitation level is strong or medium, the preliminary monitoring tag for the damper is abnormal, and it is not a strong excitation response, then the damper is confirmed to be faulty.

[0027] First, the performance monitoring submodule receives seismic parameters from the environmental excitation parameter determination module, floor motion acceleration data, and preliminary monitoring tags and distribution information of wall dampers from the cluster analysis module. The environmental excitation parameter is peak ground acceleration (PGA). Then, using PGA as the primary indicator, the excitation levels are classified as weak, moderate, and strong according to seismic design codes. Next, the number and proportion of preliminary monitoring tags for dampers are statistically analyzed by floor group, and a cluster consistency index is calculated to measure the degree of response uniformity. An example of the method for calculating the cluster consistency index is as follows: Using the physical floor of the building as a unit, all wall dampers within the same floor are considered as an analysis cluster. For example, a building with 3 floors and 8 dampers might have the following preliminary monitoring tags: overload, normal, overload, performance degradation, overload, overload, normal, overload. The tags for each floor are categorized and counted: 5 overload, 2 normal, and 1 performance degradation. Tag proportion = number of tags of this type / total number of dampers on the floor. Overload percentage: 5 / 8 = 0.625, Normal percentage: 2 / 8 = 0.25, Performance degradation percentage: 1 / 8 = 0.125. The highest percentage is the overload percentage. Therefore, the cluster consistency is obtained by dividing the number of tags with the highest percentage by the total number of floor dampers. The cluster consistency is 0.625.

[0028] Simultaneously, based on the motion acceleration time histories of adjacent floors, the maximum inter-story drift angle of each floor is determined through two integration and difference calculations. The maximum inter-story drift angle is then compared with the expected response range under strong earthquakes, which can be derived from statistical analysis of historical data. If the cluster consistency index is higher than the first threshold and the inter-story drift angle exceeds expectations, it is determined to be a systemic strong excitation response. If the consistency is lower than the second threshold but the inter-story drift angle is normal, there may be individual faults, and it is not considered a strong excitation response, i.e., a non-strong excitation response. The first threshold is higher than the second threshold; the first threshold can be 0.7, and the second threshold can be 0.5. It should be noted that a strong excitation response refers to the abnormal operating state exhibited by the damper group, a general structural response that meets expectations and is caused by strong seismic excitation, rather than a fault or performance degradation of the individual damper itself. Finally, if the environmental excitation level is weak, the initial monitoring label of the damper is overloaded or degraded, and the damper has a non-strong excitation response, then it is confirmed as a true anomaly. If the environmental excitation level is strong, the initial monitoring label of the damper is overloaded, and the damper has a strong excitation response, then the damper is confirmed as having a normal response. If the environmental excitation level is strong or medium, the initial monitoring label of the damper is abnormal, and the damper has a non-strong excitation response, then the damper is confirmed as having failed, thus obtaining the secondary monitoring results of the damper.

[0029] This invention provides a system for monitoring the performance of wall-type dampers under seismic loading. A clustering module divides the damper population into different clusters, enabling the discovery of common behavioral patterns among the damper population and avoiding the limitations of isolated analysis of individual dampers. A clustering analysis module determines preliminary monitoring labels by analyzing cluster parameters, quickly identifying damper populations with similar behavioral characteristics, thus improving the efficiency of preliminary performance anomaly identification. An environmental excitation parameter determination module incorporates external environmental dimensions based on seismic parameters, compensating for the difficulty in correlating external excitation effects by relying solely on damper data. A performance monitoring submodule performs secondary evaluation based on environmental excitation parameters, floor motion acceleration, and preliminary monitoring labels. This not only reduces individual misjudgments by utilizing population clustering results but also eliminates interference from strong excitations on monitoring results through external environmental and floor response data. Furthermore, this scheme requires no modeling and is unaffected by modeling accuracy, further improving the accuracy and stability of damper performance monitoring.

[0030] The above embodiments provide a specific method for implementing the clustering module. However, the above process sets a fixed neighborhood radius and a minimum number of points. In reality, a damper installed at the bottom layer and a damper installed at the top layer, even if they are of the same model, may have different displacement and output amplitudes and ranges during an earthquake. Their normal behavior will naturally occupy different area densities in the four-dimensional feature space. A fixed neighborhood radius will lead to errors in the clustering results. Based on this, this embodiment, as an optional implementation method, sets a dynamic cutoff distance, which is a non-fixed parameter, and improves the above process. Specifically, the clustering module is configured to perform the following steps: Determine the peak ground acceleration based on earthquake parameters; Obtain the installation location and model type of each wall damper; Based on the installation location, model attributes, and peak ground acceleration of each wall damper, the dynamic cutoff distance is calculated for each wall damper. Based on the preset feature weight vector and feature vector, calculate the weighted Euclidean distance between any two feature vectors of wall dampers, and construct the distance matrix; Based on the dynamic cutoff distance and distance matrix, calculate the local density of each wall damper and the relative distance between each wall damper and the target damper. The target damper is all dampers with a local density higher than the current damper. Based on the local density of each wall damper and its relative distance to higher-density dampers, multiple cluster centers and outliers are identified, and clusters are generated.

[0031] For example, in this embodiment, a dynamic cutoff distance is introduced. Based on the installation location, model attributes, and peak ground acceleration (PGA) of each wall damper, the dynamic cutoff distance is calculated for each wall damper. The calculation process is as follows: using PGA as the benchmark for input excitation intensity, the base cutoff distance is determined through a linear regression model. , where the coefficient , Calibration needs to be based on historical datasets, for example, =0.2, =0.05. After obtaining the basic cutoff distance, a position correction factor is introduced based on the installation location of the damper. For example, 0.8 is used for the bottom floor, 1.0 for the middle floor, and 1.2 for the top floor to reflect the difference in response amplitude caused by the dynamic amplification effect of each floor. Then, a model correction factor is introduced according to the model of the damper. The model correction factor is assigned different values ​​according to the type of damper, such as 1.0 for viscous dampers, 1.1 for metal yield dampers, and 0.9 for friction dampers, to reflect the inherent differences in the characteristics of different energy dissipation mechanisms. The three factors are then multiplied to obtain the cutoff distance of each damper. It should be noted that both the position correction factor and the model correction factor are obtained by calibrating the statistical results of structural dynamic time history analysis, individual damper test data, and engineering experience.

[0032] Then, based on domain knowledge or Monte Carlo cross-validation, a feature weight vector is set, for example, [0.3, 0.3, 0.2, 0.2]. The corresponding feature vectors for this feature weight vector are [output coefficient; displacement coefficient; output statistics; displacement statistics]. Therefore, this set feature weight vector emphasizes the dominant role of output and displacement coefficients, while weakening the influence of statistics. Subsequently, the weighted Euclidean distance between any two damper feature vectors is calculated, ultimately forming a... N×N The symmetric distance matrix quantifies the behavioral similarity among all damper pairs.

[0033] Then, the Gaussian kernel function is used to calculate the local density. The calculation method is as follows: for all other points, calculate the square of the ratio of its distance to point i to the cutoff distance of point i, take the negative exponent, and sum the results. This density value reflects the degree of clustering of sample points around point i. To make effective comparisons, all local densities are z-score normalized to obtain the normalized local density. For each point... i Iterate through all points with a density higher than its own, and take the relative distance between these points and the given point. i The minimum distance, if point i If the point is the point of highest density, then the relative distance is the maximum distance between it and all other points. The relative distance reflects the point's density. i Points with high relative distance values ​​may be potential cluster centers, depending on their degree of separation from the nearest higher-density points.

[0034] Finally, the composite value for each damper data point is obtained based on the product of standardized local density and relative distance. This composite value simultaneously measures two necessary conditions for a data point to be a cluster center; that is, an ideal cluster center should simultaneously possess high density and high relative distance, i.e., a high composite value. Then, the following is employed... Kneedle The algorithm automatically detects the inflection point of the comprehensive value ranking curve to determine the number of clusters. K and select the first KEach data point is used as a cluster center. An outlier criterion is set, and data points meeting this criterion are marked as performance anomalies. Finally, non-center points are assigned cluster membership. Each non-center point is processed in descending density order and assigned to the cluster of its nearest neighbor with a higher density. This completes the clustering process, resulting in multiple clusters.

[0035] This invention provides a system for monitoring the performance of wall-type dampers under seismic loading. By combining peak ground acceleration, damper installation location, and model attributes to calculate a dynamic cutoff distance, unlike traditional clustering algorithms that use a fixed cutoff distance, this system fully considers the differences in normal behavior characteristics of dampers under different floors, different models, and different seismic excitation intensities. This allows the cutoff distance to dynamically adapt to various monitoring scenarios, avoiding clustering bias caused by fixed parameters. At the same time, by calculating a weighted Euclidean distance through a preset feature weight vector, the system can highlight the influence of key features (such as output and displacement coefficient) according to actual monitoring needs, thereby improving the accuracy of distance calculation.

[0036] In addition to the performance monitoring submodule algorithm flow proposed in the above embodiments, this embodiment, as an optional implementation, also provides a new algorithm flow to complete the function of the performance monitoring submodule. Specifically, the performance monitoring submodule is configured to perform the following steps: Based on the environmental excitation parameters, determine the health baseline values ​​of the damper response to the environmental excitation parameters for each floor. The health baseline values ​​include the relative displacement and output parameters of the wall damper, as well as the hysteresis loop area and cumulative energy consumption obtained from the relative displacement and output parameters. The comprehensive response deviation value is determined based on the actual response value of the dampers on each floor under seismic action and the healthy benchmark value. The actual response value includes relative displacement and output parameters. Based on historical data, the average value and standard deviation of environmental excitation intensity for each floor under different seismic actions were determined. Based on the environmental excitation parameters, average environmental excitation intensity, and standard deviation of each floor, the standardized deviation of the environmental excitation intensity is determined. Based on the target parameters in the cluster, determine the initial monitoring tag confidence level of each wall damper represented by the cluster; The secondary monitoring results of the wall damper are obtained based on the comprehensive response deviation value, the standardized deviation value of the environmental excitation intensity, and the confidence level of the preliminary monitoring label.

[0037] For example, when performing secondary monitoring of wall-type dampers, the health baseline value is first obtained. This can be done by looking up environmental excitation parameters in a table, where the environmental excitation parameter is the peak ground acceleration. Alternatively, the environmental excitation parameters for each floor can be input into a pre-built damper performance baseline model. After inputting the environmental excitation parameters, the model outputs the health baseline value of the dampers on each floor. The hysteresis loop area baseline value is calculated by integrating the health output-displacement curve according to the cycle period. The cumulative energy consumption baseline value is the sum of the areas of all cyclic hysteresis loops during the earthquake duration.

[0038] It should be noted that, for each wall damper, the process of obtaining the hysteresis loop area and cumulative energy dissipation based on relative displacement and output parameters includes: constructing a relationship curve between output parameters and relative displacement based on the output parameters and relative displacement of the wall damper under seismic loading; calculating the hysteresis loop area of ​​the wall damper in each cycle based on the relationship curve; and integrating and accumulating the hysteresis loop area in each cycle for each wall damper to obtain the cumulative energy dissipation of the wall damper.

[0039] Next, the response deviation value is calculated. First, the actual relative displacement under seismic loading is collected using displacement sensors. During the collection process, the raw data needs to be preprocessed, and for each damper on each floor, the individual response deviation values ​​of relative displacement and output parameters are calculated separately, i.e.: For example, if the average relative displacement of a certain bottom-layer viscous damper is 5mm and the actual relative displacement is 8mm, then the displacement single-item response deviation is 60%. If the average output force of its health benchmark is 8kN and the actual output force is 12kN, then the output single-item response deviation is 50%. Finally, according to preset weights, for example, relative displacement and output force parameters each account for 30%, and subsequent hysteresis loop area and cumulative energy consumption each account for 20%, the single-item response deviation values ​​are weighted and averaged to obtain the comprehensive response deviation value of the damper. For example, if the single-item deviation value of the hysteresis loop area of ​​the above damper is 40% and the single-item deviation value of the cumulative energy consumption is 35%, then the comprehensive response deviation value = 30%×60%+30%×50%+20%×40%+20%×35%=48%.

[0040] Subsequently, statistical parameters for environmental excitation intensity were determined. Based on historical seismic data collected over many years by seismic stations at the building's location, earthquake levels were classified into weak, moderate, and strong excitations according to peak ground acceleration (PGA). For example, PGA greater than 0.3g was considered strong excitation, 0.3-0.1g moderate excitation, and less than 0.1g weak excitation. For each category, the environmental excitation intensity of valid seismic events needed to be statistically analyzed. The average and standard deviation of environmental excitation intensity for each floor under different earthquake levels were calculated. Then, based on the average and standard deviation of environmental excitation intensity, the standardized deviation of environmental excitation intensity for each floor was calculated using the z-score standardization method. The z-score standardization method is existing technology and will not be elaborated upon further.

[0041] Next, the initial monitoring tag confidence level is determined. Based on the target parameters in the clusters, the initial monitoring tag confidence level of each wall damper represented by the cluster is determined, including: where the target parameters include: the location of the damper data point within the current cluster and the location of each cluster center. Based on the location of the damper data point within the current cluster and the location of each cluster center, the individual uncertainty, group uncertainty, and inter-cluster separation of the damper data points in the current cluster are calculated. The individual uncertainty is characterized by the distance between each damper data point and its respective cluster center, and the group uncertainty is characterized by the distance between each damper data point and its cluster center. The standard deviation of the distance from the cluster center of the current cluster to each damper data point within the cluster is used to characterize the inter-cluster separation. The inter-cluster separation is characterized by the minimum sum of the distances between each damper data point in the current cluster and the center of the target cluster, which is any cluster other than the current cluster. Based on the individual uncertainty, group uncertainty, and inter-cluster separation of each damper data point in the current cluster, the initial confidence level of the label for each damper data point is determined. The initial confidence level of the label for each damper data point is then normalized to obtain the preliminary monitoring label confidence level for each wall damper.

[0042] First, based on the positions of the damper data points within the current cluster and the centers of each cluster, a weighted Euclidean distance is calculated using a preset feature weight vector. This yields the individual uncertainty, population uncertainty, and inter-cluster separation of the damper data points in the current cluster. Individual uncertainty is characterized by the weighted Euclidean distance between each damper data point and its respective cluster center; the larger the distance, the higher the individual uncertainty. Population uncertainty is characterized by the standard deviation of the weighted Euclidean distance from the cluster center to each damper data point within the cluster; the larger the standard deviation, the higher the population uncertainty. The higher the uncertainty, the higher the group uncertainty. For example, if the distances of 5 data points in a cluster are 0.1, 0.12, 0.08, 0.11, and 0.09, with an average of 0.1 and a standard deviation of 0.0166, this value is the group uncertainty. The inter-cluster separation is characterized by the minimum value of the sum of weighted Euclidean distances between each damper data point in the current cluster and the center of the target cluster (other clusters besides the current cluster). That is, the larger the minimum value, the higher the inter-cluster separation. For example, if a data point is 0.4 from the center of cluster B and 0.5 from the center of cluster C, then the inter-cluster separation is 0.4.

[0043] Then, based on the individual uncertainty, group uncertainty, and inter-cluster separation of each damper data point within the current cluster, according to the preset weighting formula: in, This indicates the initial confidence level of the label. Indicates individual uncertainty, This represents the maximum value of individual uncertainty. Indicates group uncertainty. This represents the maximum value of the group uncertainty. Indicates the inter-cluster separation degree. This represents the maximum inter-cluster separation. , , These are weighting coefficients, for example, a=0.4, b=0.3, c=0.3. The maximum separation degree between individuals / groups / clusters is the maximum value of the corresponding index for all dampers. For example, if the maximum individual uncertainty is 0.3, and the individual uncertainty of a certain data point is 0.15, then the proportion of this item is 0.15 / 0.3=0.5.

[0044] Then, the initial confidence level of the labels for each damper data point is normalized using the min-max normalization method to obtain the initial monitoring label confidence level for each wall damper. The value range is 0-1, and the closer it is to 1, the more reliable the initial monitoring label is.

[0045] Finally, for each wall damper, the secondary monitoring results are obtained based on the comprehensive response deviation, the standardized deviation of the environmental excitation intensity, and the initial monitoring tag confidence level. Specifically, when any damper has a comprehensive response deviation ≤ 20%, |standardized deviation| ≤ 1, and a confidence level ≥ 0.8, it is judged to be in normal performance, indicating that the actual response of the damper is consistent with the healthy state, the excitation has no extreme fluctuations, and the tag is reliable; when 20% < comprehensive response deviation ≤ 50%, 1 < |standardized deviation| ≤ 2, or 0.5 ≤ confidence level < 0.8, it is judged to be... Performance degradation is suspected and further verification is needed based on subsequent small-earthquake monitoring data. When the overall response deviation is >50%, the standardized deviation is ≤1, and the confidence level is ≥0.6, it is determined to be performance degradation, indicating that the deviation is caused by the damper's own performance decay and is unrelated to the excitation. When the overall response deviation is >50%, the standardized deviation is >2, and the confidence level is ≥0.6, it is determined to be a normal response under strong excitation, and the deviation is caused by extreme excitation, not a damper failure. When the confidence level is <0.5, the data is determined to be invalid and needs to be retested, possibly due to sensor failure or clustering error causing the label to be unreliable.

[0046] This invention provides a system for monitoring the performance of wall-type dampers under seismic loading. By introducing hysteresis loop area and cumulative energy dissipation, it supplements the analysis from the perspective of energy dissipation performance, providing a more comprehensive reflection of the damper's core seismic resistance performance. It calculates the comprehensive response deviation value, integrating the deviations of multiple parameters through weighted averaging, avoiding biased judgments caused by deviations of a single parameter. Based on historical data, it determines the average and standard deviation of environmental excitation intensity, and then calculates the standardized deviation value, which quantifies the difference between the current seismic excitation and historical normal excitation levels, clarifying the impact of external excitation on the damper's response. The introduction of preliminary monitoring label confidence scores assesses the reliability of the clustering results. Finally, through a multi-dimensional combination of comprehensive response deviation value, standardized deviation value, and confidence score, it considers both the actual changes in the damper's own performance and the influence of external excitation and the reliability of the clustering results, improving the accuracy of monitoring.

[0047] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A system for monitoring the performance of wall-type dampers under seismic loading, characterized in that, include: Dynamic force sensors are used to collect the output force parameters of each wall damper under seismic loading. Displacement sensors are used to collect the relative displacement of each wall damper under seismic loading. An accelerometer, installed on the target floor, is used to collect the floor's motion acceleration under seismic action; A seismograph is set on the ground to collect seismic parameters; The performance monitoring device is connected to a dynamic force sensor, a displacement sensor, an accelerometer, and a seismograph, respectively. It is used to obtain the performance monitoring results of each wall damper under seismic loading based on the output parameters of each wall damper, the relative displacement of each damper, the floor motion acceleration, and the seismic parameters.

2. The system for monitoring the performance of wall-type dampers under seismic loading according to claim 1, characterized in that, Performance monitoring device, including: The feature vector creation module is used to construct the feature vector of each wall damper based on the location of each wall damper, the output parameters under seismic action, and the relative displacement of each damper. The clustering module is used to cluster the dampers based on the feature vectors of each wall damper, dividing the damper group into different clusters. The cluster analysis module is used to analyze the parameters of the clusters and determine the preliminary monitoring labels of the wall dampers represented by the clusters. These labels characterize the common behavior patterns of the dampers within the clusters. The environmental excitation parameter determination module is used to determine the environmental excitation parameters based on the seismic parameters. The performance monitoring submodule performs a secondary evaluation based on environmental excitation parameters, floor motion acceleration, preliminary monitoring tags and their distribution, and obtains the secondary monitoring results of the wall damper.

3. A system for monitoring the performance of wall-type dampers under seismic loading according to claim 2, characterized in that, The performance monitoring submodule is configured to perform the following steps: Based on environmental incentive parameters, environmental incentive levels are classified as strong, medium, and weak. Based on the preliminary monitoring tags and their distribution, the number and proportion of preliminary monitoring tags for dampers are counted by floor group, and the cluster consistency index is calculated. The preliminary monitoring tags include overload operation, performance degradation, and anomalies. The maximum inter-story drift angle is determined based on the floor's motion acceleration, and then compared with the expected response range based on historical data statistics. Determine whether it is a strong stimulus response based on the cluster consistency index and the maximum inter-floor displacement angle. When the environmental excitation level is weak, the initial monitoring label of the damper is overloaded or degraded, and it is a non-strong excitation response, then the damper is confirmed to be truly abnormal. When the environmental excitation level is strong, the damper's initial monitoring label indicates overload operation and a strong excitation response, then the damper is confirmed to be responding normally. If the environmental excitation level is strong or medium, and the initial monitoring label of the damper is abnormal and the response is not strong excitation, then the damper is confirmed to be faulty.

4. A system for monitoring the performance of wall-type dampers under seismic loading according to claim 2, characterized in that, The clustering module is configured to perform the following steps: Determine the peak ground acceleration based on earthquake parameters; Obtain the installation location and model type of each wall damper; Based on the installation location, model attributes, and peak ground acceleration of each wall damper, the dynamic cutoff distance is calculated for each wall damper. Based on the preset feature weight vector and feature vector, calculate the weighted Euclidean distance between any two feature vectors of wall dampers, and construct the distance matrix; Based on the dynamic cutoff distance and distance matrix, calculate the local density of each wall damper and the relative distance between each wall damper and the target damper. The target damper is all dampers with a local density higher than the current damper. Based on the local density of each wall damper and its relative distance to higher-density dampers, multiple cluster centers and outliers are identified, and clusters are generated.

5. A system for monitoring the performance of wall-type dampers under seismic loading according to claim 2, characterized in that, The performance monitoring submodule is configured to perform the following steps: Based on the environmental excitation parameters, determine the health baseline values ​​of the damper response to the environmental excitation parameters for each floor. The health baseline values ​​include the relative displacement and output parameters of the wall damper, as well as the hysteresis loop area and cumulative energy consumption obtained from the relative displacement and output parameters. The comprehensive response deviation value is determined based on the actual response value of the dampers on each floor under seismic action and the healthy benchmark value. The actual response value includes relative displacement and output parameters. Based on historical data, the average value and standard deviation of environmental excitation intensity for each floor under different seismic actions were determined. Based on the environmental excitation parameters, average environmental excitation intensity, and standard deviation of each floor, the standardized deviation of the environmental excitation intensity is determined. Based on the target parameters in the cluster, determine the initial monitoring tag confidence level of each wall damper represented by the cluster; The secondary monitoring results of the wall damper are obtained based on the comprehensive response deviation value, the standardized deviation value of the environmental excitation intensity, and the confidence level of the preliminary monitoring label.

6. A system for monitoring the performance of wall-type dampers under seismic loading according to claim 5, characterized in that, The target parameters include: the location of damper data points within the current cluster, and the location of the center of each cluster. Based on the target parameters within the clusters, the preliminary monitoring tag confidence level of each wall damper represented by the cluster is determined, including: Based on the location of damper data points within the current cluster and the location of each cluster center, calculate the individual uncertainty, population uncertainty, and inter-cluster separation of the damper data points in the current cluster. The individual uncertainty is characterized by the distance between each damper data point and the center of its respective cluster. The population uncertainty is characterized by the standard deviation of the distance from the cluster center of the current cluster to each damper data point within the cluster. The inter-cluster separation is characterized by the minimum sum of the distances between each damper data point in the current cluster and the center of the target cluster. The target cluster is any cluster other than the current cluster. Based on the individual uncertainty, group uncertainty, and inter-cluster separation of each damper data point within the current cluster, determine the initial confidence level of the label for each damper data point; The initial confidence scores of the labels for each damper data point are normalized to obtain the preliminary monitoring label confidence scores for each wall damper.

7. A system for monitoring the performance of wall-type dampers under seismic loading according to claim 5, characterized in that, Based on relative displacement and output parameters, the hysteresis loop area and cumulative energy consumption are obtained, including: For each wall damper, a relationship curve between the output parameters and relative displacement is constructed based on the output parameters and relative displacement of the wall damper under seismic loading. Based on the relationship curve, the hysteresis loop area of ​​the wall damper in each cycle is calculated. For each wall damper, the hysteresis loop area within each cycle is integrated and accumulated to obtain the cumulative energy consumption of the wall damper.

Images