A method and system for diagnosing the safety status of pile foundation structures

Abstract

The purpose of this application is to provide a method and system for diagnosing the safety status of pile foundation structures. Specifically, firstly, data sets from two sensors on the same cross-section of the pile foundation structure within a specific time period are acquired. These two sensor data sets are then normalized to obtain two sets of normalized data points. Next, the two sets of normalized data points are imported into a dynamic time warping algorithm for calculation to obtain a quantified similarity distance between the two sets of data sets. Then, one set of data sets is selected as a reference set, and the similarity percentage between the other set of data sets and the reference set is calculated. Based on the similarity percentage, it is determined whether the pile foundation structure is under horizontal load. This application fully utilizes the monitoring data collected by sensors, enabling efficient and accurate diagnosis of the safety status of pile foundation structures.

Description

Technical Field

[0001] This application relates to the field of engineering operation and maintenance technology, and in particular to a technology for diagnosing the safety status of pile foundation structures. Background Technology

[0002] In the operation and maintenance of pile foundation structures, the safety of the structure itself under external forces must be considered. While existing automated monitoring systems can collect a large amount of response data, including structural stress and strain, they only focus on collecting this data and fail to fully utilize it. Summary of the Invention

[0003] One object of this application is to provide a method and system for diagnosing the safety status of pile foundation structures.

[0004] According to one aspect of this application, a method for diagnosing the safety status of a pile foundation structure is provided, wherein the method includes:

[0005] Data sets from two sensors on the same cross section of the pile foundation structure are obtained within a specific time period. The data sets from the two sensors are then normalized to obtain two sets of normalized data points.

[0006] The two sets of data points after normalization are imported into the dynamic time warping algorithm for calculation to obtain the quantized similarity distance between the two sets of data points.

[0007] Based on these two datasets, one dataset is selected as the benchmark dataset, and the similarity percentage between the other dataset and the benchmark dataset is calculated.

[0008] The similarity percentage is used to determine whether the pile foundation structure is under horizontal load.

[0009] Furthermore, the method also includes:

[0010] Anomaly diagnosis was performed on the two datasets using an integrated moving average autoregressive model, yielding the ratio of the number of data points within the confidence interval to the total number of data points.

[0011] Furthermore, the datasets from the two sensors are normalized to obtain two sets of normalized data points, including:

[0012] The datasets from the two sensors are normalized using the following calculation formula;

[0013]

[0014] in, This represents the data points after normalization. This represents the data points before normalization. This represents the largest data point in the original dataset. This represents the smallest data point in the original dataset.

[0015] Furthermore, based on these two datasets, one dataset is selected as the benchmark dataset, and the similarity percentage between the other dataset and the benchmark dataset is calculated, including:

[0016] Based on these two datasets, one dataset is selected as the benchmark dataset, and the similarity percentage between the other dataset and the benchmark dataset is calculated using the following formula.

[0017]

[0018] in, This represents the percentage of similarity among the datasets. This represents the similarity distance value between the dataset containing all "0"s and the dataset containing all "1"s. This represents the similarity distance value between the benchmark dataset and the dataset to be compared.

[0019] Furthermore, the two sensors on the same cross-section of the pile foundation structure are arranged on opposite sides.

[0020] Furthermore, when installing the sensor, the sensor is welded to the longitudinal reinforcing bars of the pile foundation structure; wherein the sensor is a reinforcing bar stress gauge.

[0021] Furthermore, the sensor is a fiber optic grating type or a vibrating wire type steel stress gauge.

[0022] According to another aspect of this application, a system for diagnosing the safety status of a pile foundation structure is also provided, wherein the system comprises:

[0023] The normalization processing module is used to acquire the datasets of two sensors on the same cross section of the pile foundation structure within a specific time period, and to normalize the datasets of the two sensors to obtain two sets of normalized data points.

[0024] The dynamic time warping module is used to import the two sets of normalized data points into the dynamic time warping algorithm for calculation to obtain the similarity distance quantization value between the two sets of data.

[0025] The similarity calculation module is used to select one of the two datasets as the benchmark dataset and calculate the similarity percentage between the other dataset and the benchmark dataset.

[0026] The judgment module is used to determine whether the pile foundation structure is under horizontal load conditions based on the similarity percentage.

[0027] According to another aspect of this application, a computing device is also provided, wherein the device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to perform the method for diagnosing the safety status of the pile foundation structure.

[0028] According to another aspect of this application, a computer-readable medium is also provided, having stored thereon computer program instructions that can be executed by a processor to implement the method for diagnosing the safety status of a pile foundation structure.

[0029] The solution provided in this application first acquires datasets from two sensors on the same cross-section of the pile foundation structure within a specific time period. These datasets are then normalized to obtain two sets of normalized data points. Next, the two sets of normalized data points are imported into a dynamic time warping algorithm to calculate the quantified similarity distance between the two sets of datasets. Based on these two sets of datasets, one set is selected as a benchmark dataset, and the similarity percentage between the other set and the benchmark dataset is calculated. The similarity percentage is then used to determine whether the pile foundation structure is under horizontal load. Compared with existing technologies, this application fully utilizes the monitoring data collected by sensors, enabling real-time diagnosis of the correlation between pile foundation monitoring datasets within a specific time period, determining whether the pile foundation structure is under horizontal load, and performing anomaly diagnosis based on the monitoring datasets, thereby diagnosing the safety status of the pile foundation structure. Attached Figure Description

[0030] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0031] Figure 1 This is a flowchart of a method for diagnosing the safety status of a pile foundation structure according to an embodiment of this application;

[0032] Figure 2 This is a schematic diagram of a system for diagnosing the safety status of a pile foundation structure according to an embodiment of this application;

[0033] Figure 3 This is a schematic diagram showing the positions of two sensors on the same cross-section of a pile foundation structure according to an embodiment of this application.

[0034] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0035] The present application will now be described in further detail with reference to the accompanying drawings.

[0036] In a typical configuration of this application, the terminal, the device of the service network, and the trusted party all include one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0037] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0038] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer program instructions, data structures, program devices, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0039] This application provides a method for diagnosing the safety status of pile foundation structures, which can be used to diagnose the reliability and rationality of pile foundation stress monitoring data, and can meet the needs of port engineering and offshore wind power operation and maintenance.

[0040] In practical scenarios, the device implementing this method can be a user device, a network device, or a device composed of user devices and network devices integrated through a network. The user device includes, but is not limited to, terminal devices such as smartphones, tablets, and personal computers. The network device includes, but is not limited to, network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets. Here, the cloud consists of a large number of hosts or network servers based on cloud computing, where cloud computing is a type of distributed computing, a virtual computer composed of a group of loosely coupled computer sets.

[0041] Figure 1 This is a flowchart of a method for diagnosing the safety status of a pile foundation structure according to an embodiment of this application. The method includes steps S101, S102, S103 and S104.

[0042] Step S101: Obtain the datasets of two sensors on the same cross section of the pile foundation structure within a specific time period, and normalize the datasets of the two sensors to obtain two sets of normalized data points.

[0043] In some embodiments, such as Figure 3 As shown, two sensors are arranged on opposite sides of the same cross-section of the pile foundation structure.

[0044] In some embodiments, when installing the sensor, it is welded to the longitudinal reinforcement of the pile foundation structure (such as a PHC pile foundation); wherein the sensor is a reinforcement stress gauge. For example, the dataset obtained from the sensor includes the reinforcement stress or strain of the PHC pile foundation.

[0045] In some embodiments, the sensor is a fiber optic grating type or a vibrating wire type rebar stress gauge.

[0046] Here, monitoring data from both sides of the pile foundation section are obtained within the same specific time period, and the data is processed by introducing a linear function normalization method (Min-Max Scaling).

[0047] In some embodiments, step S101 includes: normalizing the datasets of the two sensors using the following calculation formula;

[0048]

[0049] in, This represents the data points after normalization. This represents the data points before normalization. This represents the largest data point in the original dataset. This represents the smallest data point in the original dataset.

[0050] Step S102: The two datasets, composed of the two sets of normalized data points, are imported into the dynamic time warping algorithm for calculation to obtain the quantized similarity distance between the two datasets.

[0051] For example, the two sets of data points after normalization The two datasets were imported into the Dynamic Time Warping algorithm for computation.

[0052] Step S103: Based on the two datasets, select one dataset as the benchmark dataset and calculate the similarity percentage between the other dataset and the benchmark dataset.

[0053] In some embodiments, step S103 includes: based on the two datasets, selecting one dataset as a benchmark dataset, and calculating the similarity percentage between the other dataset and the benchmark dataset using the following formula;

[0054]

[0055] in, This represents the percentage of similarity among the datasets. This represents the similarity distance value between the dataset containing all "0"s and the dataset containing all "1"s. This represents the similarity distance value between the benchmark dataset and the dataset to be compared.

[0056] Step S104: Determine whether the pile foundation structure is under horizontal load based on the similarity percentage.

[0057] Here, by using quantitative values ​​of similar percentages, we can grasp the synchronization of axial stress (strain) on both sides of the pile foundation, that is, to make a preliminary judgment on whether the pile is under horizontal load.

[0058] In some embodiments, the method for diagnosing the safety status of pile foundation structures further includes: using an autoregressive integrated moving average model to diagnose anomalies in the two datasets, and obtaining the ratio of the number of data points within the confidence interval to the total number of data points.

[0059] For example, after initially determining whether the foundation pile is under horizontal load, an autoregressive integrated moving average (AIM) model can be used to diagnose anomalies in the two datasets. Specifically, the AIM model can predict short-term data after a certain point in time based on long-term data before that point. By shifting the time point forward slightly, real-time anomaly diagnosis can be performed, using a 95% confidence interval as the evaluation criterion. The diagnostic quantification value is the ratio of the number of data points within the confidence interval to the total number of diagnostic data points. In this embodiment, customized requirements can be met, and the limits for anomaly diagnosis can be flexibly adjusted.

[0060] In some embodiments, a web interface can also be used to display real-time monitoring data and the calculated real-time similarity percentage.

[0061] Figure 2This is a schematic diagram of a system for diagnosing the safety status of a pile foundation structure according to an embodiment of this application. The system includes a normalization processing module 201, a dynamic time warping module 202, a similarity calculation module 203, and a judgment module 204.

[0062] The normalization processing module 201 acquires the datasets of two sensors on the same cross section of the pile foundation structure within a specific time period, performs normalization processing on the datasets of the two sensors, and obtains two sets of normalized data points respectively.

[0063] In some embodiments, such as Figure 3 As shown, two sensors are arranged on opposite sides of the same cross-section of the pile foundation structure.

[0064] In some embodiments, when installing the sensor, it is welded to the longitudinal reinforcement of the pile foundation structure (such as a PHC pile foundation); wherein the sensor is a reinforcement stress gauge. For example, the dataset obtained from the sensor includes the reinforcement stress or strain of the PHC pile foundation.

[0065] In some embodiments, the sensor is a fiber optic grating type or a vibrating wire type rebar stress gauge.

[0066] Here, monitoring data from both sides of the pile foundation section are obtained within the same specific time period, and the data is processed by introducing a linear function normalization method (Min-Max Scaling).

[0067] In some embodiments, the normalization processing module 201 is used to: normalize the datasets of the two sensors using the following calculation formula;

[0068]

[0069] in, This represents the data points after normalization. This represents the data points before normalization. This represents the largest data point in the original dataset. This represents the smallest data point in the original dataset.

[0070] The dynamic time warping module 202 imports the two sets of normalized data points into the dynamic time warping algorithm for calculation to obtain the quantized similarity distance between the two sets of data points.

[0071] For example, the two sets of data points after normalization The two datasets were imported into the Dynamic Time Warping algorithm for computation.

[0072] The similarity calculation module 203 selects one of the two datasets as the benchmark dataset and calculates the similarity percentage between the other dataset and the benchmark dataset.

[0073] In some embodiments, the similarity calculation module 203 is used to: select one of the two datasets as a benchmark dataset based on the two datasets, and calculate the similarity percentage between the other dataset and the benchmark dataset using the following formula;

[0074]

[0075] in, This represents the percentage of similarity among the datasets. This represents the similarity distance value between the dataset containing all "0"s and the dataset containing all "1"s. This represents the similarity distance value between the benchmark dataset and the dataset to be compared.

[0076] The judgment module 204 determines whether the pile foundation structure is under horizontal load based on the similarity percentage.

[0077] Here, by using quantitative values ​​of similar percentages, we can grasp the synchronization of axial stress (strain) on both sides of the pile foundation, that is, to make a preliminary judgment on whether the pile is under horizontal load.

[0078] In some embodiments, the system for diagnosing the safety status of pile foundation structures is further configured to: use an autoregressive integrated moving average model to diagnose anomalies in the two datasets and obtain the ratio of the number of data points within the confidence interval to the total number of data points.

[0079] For example, after initially determining whether the foundation pile is under horizontal load, an autoregressive integrated moving average (AIM) model can be used to diagnose anomalies in the two datasets. Specifically, the AIM model can predict short-term data after a certain point in time based on long-term data before that point. By shifting the time point forward slightly, real-time anomaly diagnosis can be performed, using a 95% confidence interval as the evaluation criterion. The diagnostic quantification value is the ratio of the number of data points within the confidence interval to the total number of diagnostic data points. In this embodiment, customized requirements can be met, and the limits for anomaly diagnosis can be flexibly adjusted.

[0080] In some embodiments, a web interface can also be used to display real-time monitoring data and the calculated real-time similarity percentage.

[0081] In summary, the embodiments of this application can diagnose the correlation between pile foundation monitoring stress (strain) datasets within a specific time period in real time, determine whether the pile foundation structure is under horizontal load, and perform anomaly diagnosis based on the monitoring dataset, thereby diagnosing the safety status of the pile foundation structure.

[0082] Furthermore, a portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. The program instructions invoking the methods of this application may be stored in a fixed or removable recording medium, and / or transmitted via data streams in broadcast or other signal carrying media, and / or stored in the working memory of a computer device operating according to the program instructions. Here, some embodiments of this application provide a computing device including a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to execute the methods and / or technical solutions of the aforementioned embodiments of this application.

[0083] Furthermore, some embodiments of this application also provide a computer-readable medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of the aforementioned embodiments of this application.

[0084] It should be noted that this application can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0085] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A method of diagnosing the safety condition of a pile structure, wherein, The method includes: Data sets from two sensors on the same cross section of the pile foundation structure are obtained within a specific time period. The data sets from the two sensors are then normalized to obtain two sets of normalized data points. The two sets of data points after normalization are imported into the dynamic time warping algorithm for calculation to obtain the quantized similarity distance between the two sets of data points. Based on these two datasets, one dataset is selected as the benchmark dataset, and the similarity percentage between the other dataset and the benchmark dataset is calculated. Determine whether the pile foundation structure is under horizontal load based on the similarity percentage; Based on these two datasets, one dataset is selected as the baseline dataset, and the similarity percentage between the other dataset and the baseline dataset is calculated, including: Based on these two datasets, one dataset is selected as the benchmark dataset, and the similarity percentage between the other dataset and the benchmark dataset is calculated using the following formula. wherein, represents the similarity percentage of the data set, represents the similarity distance value between the all "0" data set and the all "1" data set, represents the similarity distance value between the reference data set and the data set to be compared.

2. The method of claim 1, wherein, The method further includes: Anomaly diagnosis was performed on the two datasets using an integrated moving average autoregressive model, yielding the ratio of the number of data points within the confidence interval to the total number of data points.

3. The method of claim 1 or 2, wherein, The datasets from the two sensors were normalized to obtain two sets of normalized data points, including: The datasets from the two sensors are normalized using the following calculation formula; wherein, denotes the normalized data point, denotes the data point before normalization, denotes the maximum data point in the original data set, denotes the minimum data point in the original data set.

4. The method according to claim 1 or 2, wherein, The two sensors on the same cross section of the pile foundation structure are arranged on opposite sides.

5. The method according to claim 4, wherein, When installing the sensor, the sensor is welded to the longitudinal reinforcement of the pile foundation structure; The sensor in question is a steel bar stress gauge.

6. The method according to claim 5, wherein, The sensor is a fiber optic grating type or a vibrating wire type steel stress gauge.

7. A system for diagnosing the safety status of pile foundation structures, wherein, The system includes: The normalization processing module is used to acquire the datasets of two sensors on the same cross section of the pile foundation structure within a specific time period, and to normalize the datasets of the two sensors to obtain two sets of normalized data points. The dynamic time warping module is used to import the two sets of normalized data points into the dynamic time warping algorithm for calculation to obtain the similarity distance quantization value between the two sets of data. The similarity calculation module is used to select one of the two datasets as the benchmark dataset and calculate the similarity percentage between the other dataset and the benchmark dataset using the following formula. in, This represents the percentage of similarity among the datasets. This represents the similarity distance value between the dataset containing all "0"s and the dataset containing all "1"s. This represents the similarity distance between the benchmark dataset and the dataset to be compared. The judgment module is used to determine whether the pile foundation structure is under horizontal load conditions based on the similarity percentage.

8. A computing device, wherein, The device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to perform the method of any one of claims 1 to 7.

9. A computer-readable medium having stored thereon computer program instructions that can be executed by a processor to implement the method as described in any one of claims 1 to 7.

Images