Strong earthquake station permanent displacement identification method and system based on strong earthquake record
Patent Information
- Application Number
- CN202611203892.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-10
- Publication Date
- 2026-11-03
AI Technical Summary
When identifying permanent displacements of engineering structures such as embankments, slopes, and building foundations under seismic loading, existing technologies rely heavily on manual judgment and are difficult to adapt to the changing characteristics of different stages of strong earthquake time histories, resulting in inaccurate and unstable permanent displacement identification results.
The three-dimensional particle swarm optimization algorithm (3D-PSO) is used to automatically determine the feature time, and a hybrid baseline correction is performed by combining quadratic and linear functions. By constructing a velocity baseline drift term in the form of a three-segment function, the flatness evaluation index of the candidate displacement time history is optimized, thereby improving the accuracy and stability of the identification results.
It has achieved automated and stable identification of permanent displacements at strong earthquake stations, reduced manual intervention, and improved the accuracy and stability of the identification results, achieving an identification effect close to that of GPS measured values.
Smart Images
Figure CN123028778A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of earthquake engineering technology, specifically relating to a method and system for identifying permanent displacement of strong earthquake stations based on strong earthquake records. Background Technology
[0002] The permanent displacement of engineering structures such as embankments, slopes, and building foundations under seismic loading is a key parameter for assessing the degree of earthquake damage and structural safety. Acceleration time histories from strong-motion seismic stations are the fundamental data for obtaining this parameter; however, they are inevitably affected by baseline offset interference introduced by factors such as the instrument itself, environmental noise, and complex structural responses. This interference is severely amplified during displacement integration calculations, leading to significant distortion of the obtained permanent displacement and affecting the accuracy of engineering assessments.
[0003] Traditional baseline correction methods primarily employ a two-stage correction approach. For example, earthquake records are divided into pre-seismic stages (0 to 10). ), Vibration phase ( to ) and the post-earthquake phase ( Up to the end of the recording, the velocity time histories of both the main shock phase and the post-strong earthquake phase were corrected using a linear function, but this method had limited correction effectiveness. Therefore, Wu and Wu (2007) introduced the concept of flatness to modify the two-stage correction method of Iwan et al. (1985), leading to... This represents the start time of the ground displacement. To reach the moment of permanent displacement, for The method has significant drawbacks: (1) Key characteristic times usually need to be determined by manual judgment, empirical thresholds, or trial calculations. The stability of values under different records and different station conditions is insufficient, making it difficult to form a unified and repeatable automatic processing flow; (2) Existing methods mostly use a single function form for baseline drift fitting, which is difficult to adapt to the changing characteristics of different stages of strong earthquake time history at the same time. Specifically, it is difficult to fit the nonlinear changes in velocity during the main shaking stage using a single linear function; while in the end interval of strong earthquake time history, i.e. Towards the end of the time series, the relationship between velocity and time typically stabilizes and exhibits a linear characteristic. Using a quadratic function for fitting can lead to overfitting, which in turn affects the convergence of the displacement. Summary of the Invention
[0004] This invention addresses the shortcomings of existing technologies by providing a method and system for identifying permanent displacements at strong-motion stations based on strong-motion records, capable of automatically determining characteristic times. , and Furthermore, it can rationally select different functions for fitting based on the characteristics of strong earthquake time history stages, so as to achieve hybrid baseline correction of velocity time history and improve the accuracy and stability of permanent displacement identification results.
[0005] This invention provides the following technical solution:
[0006] Firstly, a method for identifying permanent displacement of strong-motion stations based on strong-motion records is provided, comprising the following steps: Obtain the acceleration time history and original velocity time history of strong-motion stations under seismic loading; Determine the characteristic time respectively , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. For characteristic time To the end of the timeline At any time between; A three-dimensional particle swarm optimization algorithm is used to generate candidate feature time combinations within the search range. For each candidate feature time combination, a velocity baseline drift term in the form of a three-segment function is constructed. After baseline correction and integration of the original velocity time history, candidate displacement time histories are obtained. The velocity baseline drift term... The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function; Using candidate displacement time history in The optimization objective is to maximize the flatness within a segment. The optimal feature time combination is determined, and the candidate displacement time histories corresponding to the optimal feature time combination are then analyzed. The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
[0007] Optionally, after obtaining the acceleration time history, preprocessing is performed, including removing the initial zero line offset, and then the preprocessed acceleration time history is integrated once to obtain the original velocity time history.
[0008] Optionally, characteristic time , and The search range is determined based on the time when the normalized cumulative acceleration energy of the acceleration time history reaches different preset values.
[0009] Optionally, the normalized cumulative acceleration energy is expressed as: ; in, for Time-normalized cumulative acceleration energy This is the preprocessed acceleration time history; Characteristic Time The search range is the time interval corresponding to the normalized cumulative acceleration energy of 0.001% to 5%, and the characteristic time... The search range is the time interval corresponding to 50%–95% of the normalized cumulative acceleration energy, and the characteristic time. The search is conducted within a feasible range that satisfies the time sequence constraint, with the moment when the normalized cumulative acceleration energy reaches 99% serving as the upper bound for the search.
[0010] Optionally, the velocity baseline drift term in the form of the three-segment function The specific form is as follows: ; in, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, , and They are respectively The constant term, the coefficient of the linear term, and the coefficient of the quadratic term, and They are respectively The constant term and the coefficient of the linear term.
[0011] Optionally, when constructing the velocity baseline drift term in three-segment function form, the intersection points of adjacent segments are constrained to be continuous. The specific constraint conditions are as follows: ; ; ; in, express At characteristic time The derivative at point .
[0012] Optionally, flatness The calculation formula is: ; ; ; ; in, The linear correlation coefficient, Yes The slope of the candidate displacement time history was obtained by fitting using the least squares method. yes The standard deviation of the time history of the candidate displacement segment yes The number of data points in the segment and They are the first The time and displacement value corresponding to each data point and They are The arithmetic mean of time and displacement within a segment.
[0013] Secondly, a permanent displacement identification system for strong-motion stations based on strong-motion records is provided, including: The data acquisition module is used to acquire the acceleration time history and the original velocity time history of strong-motion stations under seismic action; The search range determination module is used to determine the feature time separately. , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. for To the end of the timeline At any time between; The automatic optimization module uses a three-dimensional particle swarm optimization algorithm to generate candidate feature time combinations within the search range. For each candidate feature time combination, a three-segment function of velocity baseline drift term is constructed. After baseline correction and integration of the original velocity time history, candidate displacement time histories are obtained. Maximizing the flatness within a segment is the optimization objective, determining the optimal feature time combination; among which, the velocity baseline drift term... The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function; This is the last moment of the timeline; The permanent displacement determination module is used to determine the candidate displacement time history corresponding to the optimal feature time combination. The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
[0014] Thirdly, a computer device is provided, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the steps of the method for identifying permanent displacement of strong earthquake stations based on strong earthquake records as described in any one of the first aspects.
[0015] Fourthly, a computer-readable storage medium is provided for storing a computer program; when the computer program is executed by a processor, it implements the steps of the method for identifying permanent displacement of strong earthquake stations based on strong earthquake records as described in any one of the first aspects.
[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention is based on the determination of normalized cumulative acceleration energy. , and The search range is expanded, and a three-dimensional particle swarm optimization algorithm (3D-PSO) is used to simultaneously optimize three feature times, reducing the subjectivity caused by manual input, fixed thresholds, and step-by-step trial calculations, and improving the automation and stability of feature time identification. Furthermore, this invention uses the maximum tail-segment flatness evaluation index as the optimization objective, directly evaluating the stability of candidate displacement time histories within the tail-segment interval, making the optimal feature time combination more consistent with the physical characteristic that displacement time histories tend to stabilize after permanent displacement formation. This invention employs a velocity domain hybrid baseline correction method combining quadratic and linear functions, which can simultaneously adapt to nonlinear baseline drift and near-linear tail-segment drift during strong earthquakes, improving the accuracy and stability of permanent displacement identification results. Attached Figure Description
[0017] Figure 1 This is a flowchart of the steps of the permanent displacement identification method for strong earthquake stations based on strong earthquake records of the present invention; Figure 2 This is a segmented schematic diagram of the hybrid velocity domain baseline correction of the present invention; Figure 3 This is the acceleration time history diagram of AKT0061103111446.EW in Embodiment 2 of the present invention; Figure 4 This is a diagram showing the hybrid baseline fitting and correction effect of the velocity time history in Embodiment 2 of the present invention; Figure 5 This is a comparison and verification diagram of the permanent displacement identification results and GPS measured values in Embodiment 2 of the present invention; Figure 6 This is a structural block diagram of the permanent displacement identification system for strong earthquake stations based on strong earthquake records of the present invention; Figure 7 This is a comparison chart of permanent displacements obtained by three methods and GPS measured values in Embodiment 3 of the present invention; Figure 8 This is a displacement time history comparison diagram obtained by processing the component data of IWT0191103111446.NS in Embodiment 3 of the present invention; Figure 9 This is a displacement time history comparison diagram obtained by processing the component data of AKT0061103111446.NS in Embodiment 3 of the present invention; Figure 10 This is a displacement time history comparison diagram obtained by processing the component data of AKT0061103111446.EW in Embodiment 3 of the present invention. Detailed Implementation
[0018] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the scope of protection of the present invention. It should be noted that the term "comprising" and any variations thereof in the specification, claims and the above-mentioned drawings of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or devices.
[0019] Example 1: like Figure 1 and Figure 2 As shown, a method for identifying permanent displacement of strong-motion stations based on strong-motion records includes the following steps: Step S1: Obtain the acceleration time history and original velocity time history of strong-motion stations under seismic action.
[0020] Step S1 specifically includes: Step S1.1: Obtain the acceleration time history at at least one measuring point of the strong earthquake station.
[0021] In this embodiment, a strong-motion seismograph is deployed on the ground to obtain the acceleration time history under seismic action. The strong-motion seismograph uses a triaxial accelerometer that can simultaneously record acceleration in the east-west (EW), north-south (NS), and vertical (UD) directions.
[0022] Step S1.2: Preprocess the acquired acceleration time history.
[0023] Preprocessing includes removing the initial zero-line offset. For example, the average value of the first 0.05 seconds of the acceleration time history is calculated and subtracted from the entire acceleration time history to eliminate the effect of the initial zero-line offset.
[0024] Step S1.3: Obtain the original velocity time history.
[0025] Integrate the preprocessed acceleration time history once to obtain the original velocity time history.
[0026] Step S2: Determine the characteristic time respectively , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. for To the end of the timeline At any time between.
[0027] Specifically, the characteristic time is determined based on the time when the normalized cumulative acceleration energy of the acceleration time history reaches different preset values. , and The search scope.
[0028] The normalized cumulative acceleration energy is expressed as: ; in, for Time-normalized cumulative acceleration energy (%) This is the preprocessed acceleration time history; Characteristic Time The search range is the time interval corresponding to the normalized cumulative acceleration energy of 0.001% to 5%, and the characteristic time... The search range is the time interval corresponding to 50%–95% of the normalized cumulative acceleration energy, and the characteristic time. The search is conducted within a feasible range that satisfies the time sequence constraint, with the moment when the normalized cumulative acceleration energy reaches 99% serving as the upper bound for the search.
[0029] Step S3: Use the three-dimensional particle swarm optimization algorithm to generate candidate feature time combinations within the search range and automatically optimize them.
[0030] Step S3 is as follows: Step S3.1: Generate candidate feature time combinations.
[0031] Each particle represents a set of candidate feature time combinations, and its position vector can be expressed as: ; in, For the first The particle in the first The position vector at the next iteration; For the first The particle in the first The feature time corresponding to the next iteration This represents the start time of ground displacement; For the first The particle in the first The feature time corresponding to the next iteration , characterization To the end of the timeline At any time between; For the first The particle in the first The feature time corresponding to the next iteration This represents the moment when the ground reaches its permanent displacement.
[0032] Step S3.2: For each candidate feature time combination, construct a velocity baseline drift term in the form of a three-segment function, and obtain the candidate displacement time history after baseline correction and integration of the original velocity time history.
[0033] The velocity baseline drift term, in the form of a three-segment function, is divided into three segments. The first segment is... The second paragraph is The third paragraph is Segment. Among them, the velocity baseline drift term The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function.
[0034] The velocity baseline drift term is in the form of a three-segment function. The specific form is as follows: ; in, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, , and They are respectively The constant term, the coefficient of the linear term, and the coefficient of the quadratic term, and They are respectively The constant term and the coefficient of the linear term are determined by the least squares method.
[0035] When constructing the velocity baseline drift term in the form of a three-segment function, the intersection points of adjacent segments are constrained to be continuous. The specific constraint conditions are as follows: ; ; ; in, express At characteristic time The derivative at point .
[0036] Solve based on the above constraints: ; ; .
[0037] For each candidate feature time combination, first construct the velocity baseline drift term by combining the aforementioned quadratic and linear functions, and subtract the velocity baseline drift term from the original velocity time history to obtain the candidate corrected velocity time history; then, perform a first integration on the candidate corrected velocity time history to obtain the candidate displacement time history.
[0038] Step S3.3: Using the candidate displacement time history in The goal is to maximize the flatness within a segment, and then determine the optimal combination of feature times.
[0039] by The segment is used as the flatness evaluation interval, and the flatness evaluation index of the candidate displacement time history within this interval is calculated. The larger the flatness evaluation index, the weaker the linear drift and the smaller the dispersion of the tail segment of the candidate displacement time history, the closer the displacement time history is to the horizontal stable state, and the better the correction effect of the current candidate feature time combination.
[0040] In the 3D particle swarm optimization process, the optimal positions of individual particles and the global optimal positions are updated with the goal of maximizing the flatness evaluation index. In each iteration, each particle calculates its evaluation index value according to the process of "constructing a velocity baseline drift term - obtaining candidate corrected velocity time histories - integrating to obtain candidate displacement time histories - calculating the flatness evaluation index". When the flatness evaluation index of a particle increases, its individual optimal position is updated, and the global optimal position of the particle swarm is further updated. After the iteration is completed, the global optimal position with the largest flatness evaluation index is determined as the optimal feature time combination.
[0041] Each particle iterates according to the velocity update formula and the position update formula. The velocity update formula is: ; The position update formula is: ; After each position update, the particle positions are subject to temporal order constraints to ensure that: ; in, For the number of iterations, For the first The particle in the first The velocity vector at the next iteration For the first The particle in the first The velocity vector at the next iteration For inertial weights, and These are individual learning factors and social learning factors, respectively. and Given two random numbers in the interval [0,1] For the first The optimal position of each individual particle. This represents the global optimal position of the particle swarm.
[0042] flatness The calculation formula is: ; ; ; ; in, The linear correlation coefficient, Yes The slope of the candidate displacement time history was obtained by fitting using the least squares method. Yes The standard deviation of the time history of the candidate displacement segment yes The number of data points in the segment and They are the first The time and displacement value corresponding to each data point and They are The arithmetic mean of time and displacement within a segment.
[0043] Step S4: Combine the candidate displacement time histories corresponding to the optimal feature time combination The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
[0044] Example 2: Specific examples of the permanent displacement identification method of the present invention are provided, including: (1) Engineering background and data acquisition.
[0045] This embodiment uses the AKT0061103111446.EW (AKT0061103111446 represents the station record number, and EW represents the east-west direction) acceleration record within 5km of the GPS station during the "3.11" earthquake in Japan as an example to illustrate the specific implementation process of the method of the present invention. This record contains 30,000 valid points, a sampling frequency of 100Hz, and a recording duration of 300s.
[0046] Figure 3 The acceleration time history of AKT0061103111446.EW after removing constant zero bias is given. Significant divergence occurs when integrating the uncorrected velocity, therefore hybrid velocity domain baseline correction is required for the velocity time history.
[0047] (2) Preprocessing and original velocity time history calculation.
[0048] The initial zero-line offset was calculated by selecting a time interval of 0.05 seconds after the start of the acceleration time history, and this offset was subtracted from the entire time history. Then, the preprocessed acceleration time history was integrated once to obtain the original velocity time history.
[0049] (3) Determine the search range based on normalized cumulative acceleration energy.
[0050] Characteristic Time , and The search range is determined based on the times when the normalized cumulative acceleration energy reaches different preset values; among them, The search range is the time interval corresponding to 0.001% to 5% of the normalized cumulative acceleration energy. The search range is the time interval corresponding to 50% to 95% of the normalized cumulative acceleration energy. The search is conducted within a feasible range that satisfies the time sequence constraint, with the moment when the normalized cumulative acceleration energy reaches 99% serving as the upper bound for the search.
[0051] (4) Construction of velocity baseline drift term under candidate feature time combination.
[0052] The characteristic time combination is determined based on the normalized cumulative acceleration energy. , and After determining the search range, a three-dimensional particle swarm optimization algorithm (3D-PSO) is used to generate candidate feature time combinations within the search range; for each candidate feature time combination, a velocity baseline drift term in the form of a three-segment function is constructed.
[0053] In 0~ part, ;exist to Time period ;exist to Time period .
[0054] Meanwhile, the specific constraints are as follows: ; ; ; Solve for the coefficients of the quadratic function based on the above constraints. , , The velocity baseline drift term corresponding to the current candidate feature time combination is constructed by combining a quadratic function and a linear function.
[0055] (5) Calculation of candidate displacement time history, flatness evaluation and 3D-PSO optimization.
[0056] like Figure 4 As shown, for each candidate feature time combination, a velocity baseline drift term is first constructed using the aforementioned combination of quadratic and linear functions, and this velocity baseline drift term is subtracted from the original velocity time history to obtain the candidate corrected velocity time history; subsequently, the candidate corrected velocity time history is integrated once to obtain the candidate displacement time history; to The time period is used as the flatness evaluation interval, and the flatness evaluation index of the candidate displacement time history within this interval is calculated. The larger the flatness evaluation index, the weaker the linear drift and the smaller the dispersion of the candidate displacement time history tail segment, the closer the displacement time history is to a horizontal stable state, and the better the correction effect of the current candidate feature time combination.
[0057] In the 3D particle swarm optimization process, the optimal positions of individual particles and the global optimal positions are updated with the goal of maximizing the flatness evaluation index. In each iteration, each particle calculates its evaluation index value according to the process of "constructing a velocity baseline drift term—obtaining candidate corrected velocity time histories—integrating to obtain candidate displacement time histories—calculating the flatness evaluation index." When the flatness evaluation index of a particle increases, its individual optimal position is updated, and the global optimal position of the particle swarm is further updated. After the iteration is completed, the global optimal position with the largest flatness evaluation index is determined as the optimal feature time combination.
[0058] In this embodiment, the three-dimensional particle swarm optimization algorithm uses the following parameters: random seed is 176, number of particles is 80, maximum number of iterations is 120, inertia weight decreases linearly from 0.9 to 0.4 with the number of iterations, and individual learning factor and social learning factor are both 1.6.
[0059] Determine the optimal combination of feature times: .
[0060] (6) Determination of permanent displacement.
[0061] After three-dimensional particle swarm optimization, the candidate feature time combination with the largest flatness evaluation index is determined as the optimal feature time combination; the candidate displacement time history corresponding to the optimal feature time combination is the corrected displacement time history of this embodiment, and its tail platform stability value is the permanent displacement identification result of the strong earthquake station.
[0062] In this embodiment, as Figure 5 As shown, the maximum flatness evaluation index Tail-end correlation coefficient tail segment displacement slope Standard deviation of tail segment displacement The above results indicate that the corrected displacement time history exhibits weak linear drift and low dispersion in the tail segment, suggesting that the tail segment has essentially entered a stable plateau state.
[0063] The final calculated permanent displacement of the strong-motion station was 48.42 cm. Comparison showed that the GPS measured value was 50.21 cm. The absolute error between the method's identification result and the GPS measured value was approximately 1.79 cm, indicating that the method of this invention can accurately identify the permanent displacement of the strong-motion station.
[0064] Example 3: A comparative example for determining the permanent displacement of a strong-motion seismic station is given. This example is based on the IWT0191103111446.NS (IWT0191103111446 represents the station record number, NS represents the north-south direction) component data, the AKT0061103111446.NS (AKT0061103111446 represents the station record number, NS represents the north-south direction) component data, and the AKT0061103111446.EW (AKT0061103111446 represents the station record number, EW represents the east-west direction) component data. The method for determining the permanent displacement of the station is: Method 1: Two-stage linear function correction ( part, (segment); Method 2: One-stage quadratic function correction ( (segment); This invention: quadratic function + linear function ( part, (Section). This invention, along with methods 1 and 2, all employ 3D-PSO for optimization; the main difference lies in the baseline correction function.
[0065] Table 1 Comparison of results for identifying permanent displacement using three methods
[0066] According to Table 1, Figure 7 , Figure 8 , Figure 9 and Figure 10As shown, in the directions IWT0191103111446.NS (result in this application: -77.12cm), AKT0061103111446.NS (result in this application: -61.01cm), and AKT0061103111446.EW (result in this application: 48.42cm), this application's results are close to the GPS measured values. For the AKT0061103111446.EW (east-west) component data: the two-stage linear function correction result is 96.66cm, and the one-stage quadratic function correction result is 93.63cm, both of which deviate significantly from the GPS measured value by 50.21cm. This indicates that existing methods have resulted in severe over-correction or under-correction under this complex baseline drift condition. In contrast, the permanent displacement calculated in this application is 48.42cm, with an error of only 1.79cm (approximately 4%) compared to the GPS measured value.
[0067] Example 4: like Figure 6 As shown, a permanent displacement identification system for strong-motion stations based on strong-motion records includes: The data acquisition module is used to acquire the acceleration time history and the original velocity time history of strong-motion stations under seismic action; The search range determination module is used to determine the feature time separately. , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. for To the end of the timeline At any time between; The automatic optimization module uses a three-dimensional particle swarm optimization algorithm to generate candidate feature time combinations within the search range. For each candidate feature time combination, a three-segment function of velocity baseline drift term is constructed. After baseline correction and integration of the original velocity time history, candidate displacement time histories are obtained. Maximizing the flatness within a segment is the optimization objective, determining the optimal feature time combination; among which, the velocity baseline drift term... The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function; This is the last moment of the timeline; The permanent displacement determination module is used to determine the candidate displacement time history corresponding to the optimal feature time combination. The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
[0068] For more detailed information about the system described above, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.
[0069] Example 5: The present invention provides a computer device, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the above-described method for identifying permanent displacement of strong earthquake stations based on strong earthquake records.
[0070] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.
[0071] Example 6: The present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the above-described method for identifying permanent displacement of strong earthquake stations based on strong earthquake records.
[0072] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.
[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The systems, devices, and storage media disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant details can be found in the method section.
[0074] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.
[0075] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.
Claims
1. A method for identifying permanent displacement of strong-motion stations based on strong-motion records, characterized in that, Includes the following steps: Obtain the acceleration time history and original velocity time history of strong-motion stations under seismic loading; Determine the characteristic time respectively , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. For characteristic time To the end of the timeline At any time between; A three-dimensional particle swarm optimization algorithm is used to generate candidate feature time combinations within the search range. For each candidate feature time combination, a velocity baseline drift term in the form of a three-segment function is constructed. After baseline correction and integration of the original velocity time history, candidate displacement time histories are obtained. The velocity baseline drift term... The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function; Using candidate displacement time history in The optimization objective is to maximize the flatness within a segment. The optimal feature time combination is determined, and the candidate displacement time histories corresponding to the optimal feature time combination are then analyzed. The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
2. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 1, characterized in that, After obtaining the acceleration time history, preprocessing is performed, including removing the initial zero line offset. Then, the preprocessed acceleration time history is integrated once to obtain the original velocity time history.
3. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 1, characterized in that, Characteristic Time , and The search range is determined based on the time when the normalized cumulative acceleration energy of the acceleration time history reaches different preset values.
4. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 3, characterized in that, The normalized cumulative acceleration energy is expressed as: ； in, for Time-normalized cumulative acceleration energy This is the preprocessed acceleration time history; Characteristic Time The search range is the time interval corresponding to the normalized cumulative acceleration energy of 0.001% to 5%, and the characteristic time... The search range is the time interval corresponding to 50%–95% of the normalized cumulative acceleration energy, and the characteristic time. The search is conducted within a feasible range that satisfies the time sequence constraint, with the moment when the normalized cumulative acceleration energy reaches 99% serving as the upper bound for the search.
5. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 1, characterized in that, The velocity baseline drift term in the form of the three-segment function The specific form is as follows: ； in, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, For velocity baseline drift term The fitting function of the segment, , and They are respectively The constant term, the coefficient of the linear term, and the coefficient of the quadratic term, and They are respectively The constant term and the coefficient of the linear term.
6. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 5, characterized in that, When constructing the velocity baseline drift term in the form of a three-segment function, the intersection points of adjacent segments are constrained to be continuous. The specific constraint conditions are as follows: ； ； ； in, express At characteristic time The derivative at point .
7. The method for identifying permanent displacement of strong-motion stations based on strong-motion records according to claim 1, characterized in that, flatness The calculation formula is: ； ； ； ； in, The linear correlation coefficient, Yes The slope of the candidate displacement time history was obtained by fitting using the least squares method. yes The standard deviation of the time history of the candidate displacement segment yes The number of data points in the segment and They are the first The time and displacement value corresponding to each data point and They are The arithmetic mean of time and displacement within a segment.
8. A permanent displacement identification system for strong-motion stations based on strong-motion records, characterized in that, include: The data acquisition module is used to acquire the acceleration time history and the original velocity time history of strong-motion stations under seismic action; The search range determination module is used to determine the feature time separately. , and The search scope; where, This represents the start time of the ground displacement. The moment when the ground reaches permanent displacement. for To the end of the timeline At any time between; The automatic optimization module uses a three-dimensional particle swarm optimization algorithm to generate candidate feature time combinations within the search range. For each candidate feature time combination, a three-segment function of velocity baseline drift term is constructed. After baseline correction and integration of the original velocity time history, candidate displacement time histories are obtained. Maximizing the flatness within a segment is the optimization objective, determining the optimal feature time combination; among which, the velocity baseline drift term... The segment is zero. The segment is constructed using a quadratic function. The segment is constructed using a linear function; This is the last moment of the timeline; The permanent displacement determination module is used to determine the candidate displacement time history corresponding to the optimal feature time combination. The stable value of the segment was determined as the permanent displacement of the strong earthquake station.
9. A computer device, characterized in that, It includes a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the permanent displacement identification method for strong earthquake stations based on strong earthquake records as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, Used to store computer programs; when executed by a processor, the computer programs implement the steps of the permanent displacement identification method for strong earthquake stations based on strong earthquake records as described in any one of claims 1-7.