CN116089891A - Method and system for diagnosing safety condition of pile foundation structure - Google Patents
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Abstract
The invention aims to provide a method and a system for diagnosing the safety condition of a pile foundation structure. Specifically, firstly acquiring data sets of two sensors on the same cross section of a pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of normalized data points; then, two groups of data sets formed by the two groups of data points after normalization processing are imported into a dynamic time warping algorithm for calculation, and a similar distance quantized value between the two groups of data sets is obtained; then selecting one group of data sets as a reference data set, and calculating to obtain the similarity percentage of the other group of data sets and the reference data set; and judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage. The method and the device fully utilize the monitoring data collected by the sensor, and can efficiently and accurately diagnose the safety condition of the pile foundation structure.
Description
Technical Field
The application relates to the technical field of engineering operation and maintenance, in particular to a technology for diagnosing the safety condition of a pile foundation structure.
Background
In the operation and maintenance of pile structures, the safety of the pile structure itself under external action is generally considered. Based on existing automated monitoring systems, a large amount of response data, including structural stress or strain, can be collected. However, the existing monitoring system only stays at the level of collecting response data, and monitoring data cannot be fully utilized.
Disclosure of Invention
It is an object of the present application to provide a method and system for diagnosing the safety condition of pile structures.
According to one aspect of the present application, there is provided a method of diagnosing a safety condition of a pile foundation structure, wherein the method comprises:
acquiring data sets of two sensors on the same cross section of a pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of normalized data points;
the two sets of data sets formed by the two sets of normalized data points are imported into a dynamic time warping algorithm for calculation, and a similar distance quantized value between the two sets of data sets is obtained;
based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating to obtain the similarity percentage of the other set of data set and the reference data set;
and judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
Further, the method further comprises:
and performing anomaly diagnosis on the two groups of data sets by using an integrated moving average autoregressive model to obtain the ratio of the number of data points to the total number of data points in the confidence interval.
Further, normalization processing is performed on the data sets of the two sensors, so as to obtain two groups of data points after normalization processing, including:
normalizing the data sets of the two sensors by using the following calculation formula;
wherein X is norm Represents the data points after normalization processing, X represents the data points before normalization processing, X max Representing the largest data point, X, in the original dataset min Representing the smallest data point in the original dataset.
Further, based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating to obtain the similarity percentage of the other set of data set and the reference data set, wherein the method comprises the following steps:
based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating the similarity percentage of the other set of data set and the reference data set by using the following formula;
where S represents a similar percentage of the dataset, DTW 0-1 Representing a value of similar distance between the all "0" dataset and the all "1" dataset, DTW To be compared with Representing a value of a similarity distance between the reference dataset and the dataset to be compared.
Further, two sensors are arranged on two opposite sides of the same cross section of the pile foundation structure.
Further, when the sensor is installed, the sensor is welded on a longitudinal steel bar of the pile foundation structure; wherein, the sensor is a steel bar stress meter.
Further, the sensor is a fiber bragg grating type or vibrating wire type steel bar stress meter.
According to another aspect of the present application, there is also provided a system for diagnosing a safety condition of a pile foundation structure, wherein the system comprises:
the normalization processing module is used for acquiring data sets of two sensors on the same cross section of the pile foundation structure in a specific time period, carrying out normalization processing on the data sets of the two sensors, and respectively obtaining two groups of data points after normalization processing;
the dynamic time normalization module is used for importing two groups of data sets formed by the two groups of normalized data points into a dynamic time normalization algorithm for calculation and obtaining a similar distance quantization value between the two groups of data sets;
the similarity calculation module is used for selecting one set of data set as a reference data set based on the two sets of data sets, and calculating to obtain the similarity percentage of the other set of data set and the reference data set;
and the judging module is used for judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
According to yet another aspect of the present application there is also provided a computing device, wherein the device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the method of diagnosing a safety condition of a pile foundation structure.
According to yet another aspect of the present application, there is also provided a computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of diagnosing a safety condition of a pile foundation structure.
In the scheme provided by the application, firstly, acquiring data sets of two sensors on the same cross section of a pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of data points after normalization processing; then, two groups of data sets formed by the two groups of data points after normalization processing are imported into a dynamic time warping algorithm for calculation, and a similar distance quantized value between the two groups of data sets is obtained; then, based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating to obtain the similarity percentage of the other set of data set and the reference data set; and judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage. Compared with the prior art, the method and the device fully utilize the monitoring data collected by the sensor, can diagnose the correlation between pile foundation monitoring data sets in a specific time period in real time, judge whether the pile foundation structure is in a horizontal load condition, and perform abnormal diagnosis based on the monitoring data sets, thereby diagnosing the safety condition of the pile foundation structure.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is a flow chart of a method of diagnosing safety conditions of a pile foundation structure according to an embodiment of the present application;
FIG. 2 is a system schematic diagram of diagnosing safety conditions of pile foundation structures according to an embodiment of the present application;
FIG. 3 is a schematic illustration of the location of two sensors on the same cross section of a pile structure according to an embodiment of the present application.
The same or similar reference numbers in the drawings refer to the same or similar parts.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In one typical configuration of the present application, the terminal, the device of the service network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, program devices, or other data. Examples of storage media for a computer 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 technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device.
The embodiment of the application provides a method for diagnosing the safety condition of a pile foundation structure, which can be used for diagnosing the reliability and rationality of pile foundation stress monitoring data and can meet the requirements of port engineering and offshore wind power operation and maintenance.
In a practical scenario, the device implementing the method may be a user device, a network device, or a device formed by integrating the user device and the network device through a network. The user equipment comprises terminal equipment such as a smart phone, a tablet personal computer, a personal computer and the like, and the network equipment comprises network hosts, a single network server, a plurality of network server sets or a computer set based on cloud computing and the like. Here, the Cloud is composed of a large number of hosts or web servers based on Cloud Computing (Cloud Computing), which is a kind of distributed Computing, one virtual computer composed of a group of loosely coupled computer sets.
Fig. 1 is a flowchart of a method for diagnosing safety conditions of a pile foundation structure according to an embodiment of the present application, the method including steps S101, S102, S103 and S104.
Step S101, acquiring data sets of two sensors on the same cross section of a pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of data points after normalization processing.
In some embodiments, as shown in fig. 3, two sensors are disposed on opposite sides of the same cross section of the pile structure.
In some embodiments, the sensor is welded to the longitudinal rebar of the pile structure (e.g., PHC pile foundation) while the sensor is installed; wherein, the sensor is a steel bar stress meter. For example, the data set acquired from the sensor includes the rebar stress or rebar strain of the PHC pile foundation.
In some embodiments, the sensor is a fiber bragg grating or vibrating wire rebar strain gauge.
The monitoring data of two sides of the pile foundation section in the same specific time period are obtained, and the data processing is carried out by introducing a linear function normalization method (Min-Max Scaling).
In some embodiments, the step S101 includes: normalizing the data sets of the two sensors by using the following calculation formula;
wherein X is norm Represents the data points after normalization processing, X represents the data points before normalization processing, X max Representing the largest data point, X, in the original dataset min Representing the smallest data point in the original dataset.
Step S102, two sets of data sets formed by the two sets of data points after normalization processing are imported into a dynamic time warping algorithm for calculation, and a similar distance quantized value between the two sets of data sets is obtained.
For example, the normalized two sets of data points X norm The two sets of data are imported into a dynamic time warping algorithm (Dynamic Time Warping) for computation.
Step S103, selecting one data set as a reference data set based on the two data sets, and calculating to obtain the similarity percentage of the other data set and the reference data set.
In some embodiments, the step S103 includes: based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating the similarity percentage of the other set of data set and the reference data set by using the following formula;
where S represents a similar percentage of the dataset, DTW 0-1 Representing a value of similar distance between the all "0" dataset and the all "1" dataset, DTW To be compared with Representing a value of a similarity distance between the reference dataset and the dataset to be compared.
And step S104, judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
The synchronous condition of the axial stress (strain) at two sides of the pile foundation can be mastered by the quantized value of the similar percentage, namely whether the foundation pile is in a horizontal load condition or not is primarily judged.
In some embodiments, the method of diagnosing a safety condition of a pile foundation structure further comprises: the two sets of data sets are anomaly diagnosed using an integrated moving average autoregressive model (Autoregressive Integrated Moving Average model) to obtain a ratio of the number of data points to the total number of data points within a confidence interval.
For example, after a preliminary determination of whether the foundation pile is in a horizontal loading condition, an integrated moving average autoregressive model (Autoregressive Integrated Moving Average model) may be used to diagnose anomalies in both sets of data. Specifically, by using the integrated moving average autoregressive model, a short-term data after a certain time point can be predicted based on a long-term data before the certain time point, the time point is advanced for a short period of time, so that abnormal diagnosis of the real-time data can be performed, a 95% confidence interval can be used as a judgment standard, and a diagnosis quantization value is a ratio of the number of data points in the confidence interval to the total number of diagnosis data points. In this embodiment, the customized requirements can be achieved, and the limit value of the abnormality diagnosis can be flexibly adjusted.
In some embodiments, a web-side interface may also be utilized to present the real-time monitoring data and the calculated real-time similarity percentages.
Fig. 2 is a schematic diagram of a system for diagnosing safety conditions of pile structures according to an embodiment of the present application, which includes a normalization processing module 201, a dynamic time normalization module 202, a similarity calculation module 203, and a determination module 204.
And the normalization processing module 201 is used for acquiring data sets of two sensors on the same cross section of the pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of data points after normalization processing.
In some embodiments, as shown in fig. 3, two sensors are disposed on opposite sides of the same cross section of the pile structure.
In some embodiments, the sensor is welded to the longitudinal rebar of the pile structure (e.g., PHC pile foundation) while the sensor is installed; wherein, the sensor is a steel bar stress meter. For example, the data set acquired from the sensor includes the rebar stress or rebar strain of the PHC pile foundation.
In some embodiments, the sensor is a fiber bragg grating or vibrating wire rebar strain gauge.
The monitoring data of two sides of the pile foundation section in the same specific time period are obtained, and the data processing is carried out by introducing a linear function normalization method (Min-Max Scaling).
In some embodiments, the normalization processing module 201 is configured to: normalizing the data sets of the two sensors by using the following calculation formula;
wherein X is norm Represents the data points after normalization processing, X represents the data points before normalization processing, X max Representing the largest data point, X, in the original dataset min Representing the smallest data point in the original dataset.
The dynamic time normalization module 202 imports the two sets of data sets formed by the two sets of normalized data points into a dynamic time normalization algorithm for calculation, and obtains the quantized value of the similar distance between the two sets of data sets.
For example, the normalized two sets of data points X norm The two sets of data are imported into a dynamic time warping algorithm (Dynamic Time Warping) for computation.
The similarity calculation module 203 selects one set of data sets as a reference data set based on the two sets of data sets, and calculates a similarity percentage between the other set of data sets and the reference data set.
In some embodiments, the similarity calculation module 203 is configured to: based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating the similarity percentage of the other set of data set and the reference data set by using the following formula;
where S represents a similar percentage of the dataset, DTW 0-1 Representing a value of similar distance between the all "0" dataset and the all "1" dataset, DTW To be compared with Representing a value of a similarity distance between the reference dataset and the dataset to be compared.
And a judging module 204, configured to judge whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
The synchronous condition of the axial stress (strain) at two sides of the pile foundation can be mastered by the quantized value of the similar percentage, namely whether the foundation pile is in a horizontal load condition or not is primarily judged.
In some embodiments, the system for diagnosing a safety condition of a pile foundation structure is further configured to: the two sets of data sets are anomaly diagnosed using an integrated moving average autoregressive model (Autoregressive Integrated Moving Average model) to obtain a ratio of the number of data points to the total number of data points within a confidence interval.
For example, after a preliminary determination of whether the foundation pile is in a horizontal loading condition, an integrated moving average autoregressive model (Autoregressive Integrated Moving Average model) may be used to diagnose anomalies in both sets of data. Specifically, by using the integrated moving average autoregressive model, a short-term data after a certain time point can be predicted based on a long-term data before the certain time point, the time point is advanced for a short period of time, so that abnormal diagnosis of the real-time data can be performed, a 95% confidence interval can be used as a judgment standard, and a diagnosis quantization value is a ratio of the number of data points in the confidence interval to the total number of diagnosis data points. In this embodiment, the customized requirements can be achieved, and the limit value of the abnormality diagnosis can be flexibly adjusted.
In some embodiments, a web-side interface may also be utilized to present the real-time monitoring data and the calculated real-time similarity percentages.
In summary, the embodiment of the present application may diagnose the correlation between pile foundation monitoring stress (strain) data sets in a specific period of time in real time, determine whether the pile foundation structure is in a horizontal load condition, and perform an abnormal diagnosis based on the monitoring data sets, thereby diagnosing the safety condition of the pile foundation structure.
Furthermore, portions of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application by way of operation of the computer. Program instructions for invoking the methods of the present application may be stored in fixed or removable recording media and/or transmitted via a data stream in a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating according to the program instructions. Some embodiments of the present application provide a computing device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the methods and/or aspects of the various embodiments of the present application described previously.
Furthermore, some embodiments of the present application provide a computer readable medium having stored thereon computer program instructions executable by a processor to implement the methods and/or aspects of the various embodiments of the present application described above.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, using Application Specific Integrated Circuits (ASIC), a general purpose computer or any other similar hardware device. In some embodiments, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs of the present application (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the apparatus claims can also be implemented by means of one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (10)
1. A method of diagnosing a safety condition of a pile foundation structure, wherein the method comprises:
acquiring data sets of two sensors on the same cross section of a pile foundation structure in a specific time period, and carrying out normalization processing on the data sets of the two sensors to respectively obtain two groups of normalized data points;
the two sets of data sets formed by the two sets of normalized data points are imported into a dynamic time warping algorithm for calculation, and a similar distance quantized value between the two sets of data sets is obtained;
based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating to obtain the similarity percentage of the other set of data set and the reference data set;
and judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
2. The method of claim 1, wherein the method further comprises:
and performing anomaly diagnosis on the two groups of data sets by using an integrated moving average autoregressive model to obtain the ratio of the number of data points to the total number of data points in the confidence interval.
3. The method according to claim 1 or 2, wherein normalizing the data sets of the two sensors results in two sets of normalized data points, respectively, comprising:
normalizing the data sets of the two sensors by using the following calculation formula;
wherein X is norm Represents the data points after normalization processing, X represents the data points before normalization processing, X max Representing the largest data point, X, in the original dataset min Representing the smallest data point in the original dataset.
4. The method according to claim 1 or 2, wherein, based on the two sets of data sets, one set of data sets is selected as a reference data set, and the percentage of similarity between the other set of data sets and the reference data set is calculated, including:
based on the two sets of data sets, selecting one set of data set as a reference data set, and calculating the similarity percentage of the other set of data set and the reference data set by using the following formula;
where S represents a similar percentage of the dataset, DTW 0-1 Representing a value of similar distance between the all "0" dataset and the all "1" dataset, DTW To be compared with Representing a value of a similarity distance between the reference dataset and the dataset to be compared.
5. A method according to any one of claims 1 to 4, wherein two sensors are arranged on opposite sides of the same cross section of the pile foundation structure.
6. The method of claim 5, wherein the sensor is welded to longitudinal rebar of the pile foundation structure while the sensor is installed;
wherein, the sensor is a steel bar stress meter.
7. The method of claim 6, wherein the sensor is a fiber bragg grating or vibrating wire bar strain gauge.
8. A system for diagnosing a safety condition of a pile foundation structure, wherein the system comprises:
the normalization processing module is used for acquiring data sets of two sensors on the same cross section of the pile foundation structure in a specific time period, carrying out normalization processing on the data sets of the two sensors, and respectively obtaining two groups of data points after normalization processing;
the dynamic time normalization module is used for importing two groups of data sets formed by the two groups of normalized data points into a dynamic time normalization algorithm for calculation and obtaining a similar distance quantization value between the two groups of data sets;
the similarity calculation module is used for selecting one set of data set as a reference data set based on the two sets of data sets, and calculating to obtain the similarity percentage of the other set of data set and the reference data set;
and the judging module is used for judging whether the pile foundation structure is in a horizontal load condition according to the similarity percentage.
9. A computing device, wherein the device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the method of any one of claims 1 to 7.
10. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116695801A (en) * | 2023-08-04 | 2023-09-05 | 启业建设有限公司 | Pile foundation safety monitoring method and system based on big data |
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| CN116695801A (en) * | 2023-08-04 | 2023-09-05 | 启业建设有限公司 | Pile foundation safety monitoring method and system based on big data |
| CN116695801B (en) * | 2023-08-04 | 2023-10-03 | 启业建设有限公司 | Pile foundation safety monitoring method and system based on big data |
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