Multi-index railway slope retaining wall stability early warning method based on Internet of Things

Abstract

The invention discloses a multi-index railway slope retaining wall stability early warning method based on the Internet of Things, and relates to the technical field of the Internet of Things. Comprising the following steps of data collection, wherein MEMS acceleration sensors are evenly distributed at the top and the bottom of a retaining wall, and the MEMS acceleration sensors are used for sampling to obtain acceleration data; data transmission: uploading the acceleration data to a server; performing data processing on the acceleration data by using a built-in algorithm of the server to obtain a velocity amplitude ratio, an inherent frequency and an attenuation constant; and in combination with the velocity amplitude ratio, the first threshold value and the second threshold value of the attenuation constant and the inherent frequency, the stability of the retaining wall is evaluated and early warned based on the results. According to the method, various dynamic indexes such as the velocity amplitude ratio, the inherent frequency and the attenuation constant are comprehensively considered, the recognition precision and integrity of the structural stability change are remarkably improved, risk omission caused by parameter weighting imbalance is avoided, and the robustness of an evaluation system and the timeliness of field intervention are ensured.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a multi-index early warning method for railway slope retaining wall stability based on IoT. Background Technology

[0002] As the scale of railway engineering in mountainous areas continues to expand, the role of slopes and retaining walls in ensuring the safe operation of railway lines is becoming increasingly prominent. To prevent slope instability or retaining wall damage caused by geological disasters, rainfall infiltration, long-term loads, and other factors, long-term monitoring of their structural stability has become a crucial task in railway infrastructure management. Currently, conventional monitoring methods for railway retaining walls mainly include manual inspections, static deformation monitoring, and video recognition. However, these methods generally suffer from long monitoring cycles, are greatly affected by environmental factors, and struggle to provide real-time early warnings. Their response capabilities are particularly limited in addressing gradual or sudden damage such as wall stiffness degradation and foundation displacement.

[0003] In recent years, vibration signal analysis technology has been increasingly applied to structural health monitoring. It can reflect changes in structural stiffness and damage characteristics through the dynamic response under train excitation, offering advantages such as non-contact operation, strong continuity, and high sensitivity. However, relying solely on vibration parameters is insufficient to fully reveal the stability evolution trend of a structure during long-term operation. Meanwhile, existing technologies typically assess retaining wall stability by introducing static deformation indices such as tilt angle and horizontal displacement. However, when these static indices change, the retaining wall is usually on the verge of instability, and the reaction time for on-site personnel is limited, making early warning difficult.

[0004] Therefore, there is an urgent need to develop a slope retaining wall stability monitoring method that integrates multiple types of monitoring indicators and has remote communication and automatic evaluation capabilities, so as to improve the level of structural risk identification and early warning response efficiency along railway lines. Summary of the Invention

[0005] To achieve at least one of the above objectives, this application provides a multi-index railway slope retaining wall stability early warning method based on the Internet of Things, the method comprising the following steps: S1. Data acquisition: MEMS accelerometers are installed at the top and bottom of the retaining wall, and acceleration data is obtained by sampling using the MEMS accelerometers. S2. Data transmission: Acceleration data is transmitted and uploaded to the server; S3. Process the acceleration data using the server's built-in algorithm: Based on the acceleration data and the sampling time, obtain the natural frequency of the retaining wall through fast Fourier transform; perform integral calculation on the acceleration data to obtain velocity data, and obtain the velocity amplitude ratio based on the velocity data at the top and bottom of the retaining wall; perform frequency domain analysis on the acceleration sequence to obtain its half-power point, and then calculate the attenuation constant. S4. Output evaluation results: Combining the velocity amplitude ratio and the first and second thresholds of the attenuation constant, and the natural frequency, the stability of the retaining wall is evaluated and an early warning is given based on the results of S3.

[0006] The beneficial effects of this invention are as follows: This invention comprehensively considers multiple dynamic indicators such as velocity-amplitude ratio, natural frequency, and attenuation constant, significantly improving the accuracy and completeness of identifying changes in structural stability. Dynamic parameters are highly sensitive to early-stage latent damage such as stiffness degradation and connection slack, enabling early warning identification before changes in traditional static indicators, thus gaining crucial time for engineering early warning and disaster intervention. Utilizing the natural excitation during train operation as a power source eliminates the need for external vibration equipment, reducing system deployment costs and improving on-site adaptability, making it suitable for large-scale application in practical engineering. The system adopts a safety judgment strategy of "triggering an early warning mechanism when any parameter enters a high-risk zone," avoiding risk omissions due to parameter weighting imbalances and ensuring the robustness of the assessment system and the timeliness of on-site intervention. Attached Figure Description

[0007] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 The figure shows the variation of the natural frequency with the monitoring date; Figure 2 This is a graph showing the relationship between the natural frequency and velocity amplitude ratio of the retaining wall in May of this embodiment; Figure 3 This is a graph showing the relationship between the natural frequency and velocity amplitude ratio of the retaining wall in June of this embodiment; Figure 4 This is a graph showing the relationship between the natural frequency and velocity amplitude ratio before and after the retaining wall reinforcement in July of this embodiment; Figure 5 This is a graph showing the relationship between the attenuation constant of the retaining wall and the velocity amplitude ratio in May of this embodiment; Figure 6 This is a graph showing the relationship between the attenuation constant of the retaining wall and the velocity amplitude ratio in June of this embodiment; Figure 7 This is a graph showing the relationship between the attenuation constant and the velocity amplitude ratio before and after the retaining wall reinforcement in July in this embodiment; Figure 8 This is a diagram showing the change of the natural frequency of the retaining wall at four different stages in the supplementary example; Figure 9 This is a graph showing the change in the velocity amplitude ratio of the retaining wall at four different stages in the supplementary example; Figure 10 This is a graph showing the changes in the attenuation constant of the retaining wall at four different stages in the supplementary example. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are all within the protection scope of this application.

[0010] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0011] A multi-index railway slope retaining wall stability early warning method based on the Internet of Things includes: S1. Data acquisition: MEMS accelerometers are installed at the top and bottom of the retaining wall. Acceleration data is obtained by sampling using the MEMS accelerometers at regular intervals. In this embodiment, since the purpose is to provide early warning of the stability of retaining walls, data can be collected and subsequent operations can be performed on all retaining walls; alternatively, potential unstable retaining walls can be identified through on-site surveys and analysis of historical data, and then data can be collected and subsequent operations can be performed on these unstable retaining walls.

[0012] When deploying MEMS accelerometers, one MEMS accelerometer is placed at the top and bottom of the retaining wall at the same location. The Z-axis of the MEMS accelerometer is vertically upward, and the X-axis and Y-axis are positioned in the east-west and north-south directions, respectively. For the same retaining wall, if its length is long, multiple sets of MEMS accelerometers can be placed at intervals, such as one set every 20 meters. Of course, those skilled in the art can adjust the density of the MEMS accelerometers according to the actual situation.

[0013] Regarding the sampling timing of the MEMS accelerometer, this embodiment uses a preset acceleration amplitude excitation threshold for determination: when the vibration acceleration amplitude of the retaining wall exceeds the excitation threshold, the MEMS accelerometer is activated and sampling is performed. Typically, when a train passes through the section of road to which the retaining wall belongs, the acceleration amplitude of the retaining wall changes rapidly due to the train's influence. This setting makes the sampling timing more controllable. The excitation threshold is twice the background amplitude detected by the MEMS accelerometer in that area; however, those skilled in the art can set it according to the specific circumstances of this block.

[0014] For the MEMS accelerometer in this embodiment, since its sampling data is used to calculate acceleration and velocity, its sampling frequency is usually high, such as 20 to 200 times / s, or even 100 times / s; the sampling time for a single sampling can be set to several seconds to tens of seconds, such as 10s.

[0015] In this step, the final result is an acceleration sequence: In the formula, a N This represents the acceleration of the Nth sample, where N represents the maximum number of samples within a sampling period.

[0016] S2. Data transmission: Acceleration data is transmitted and uploaded to the server; The collected data needs to be uploaded to a server. Therefore, in this embodiment, the MEMS accelerometer is also equipped with a GPS positioning module and a data transceiver module. The GPS positioning module can determine the location corresponding to the data, and the data transceiver module can transmit the data to the server. The GPS positioning module and the data transceiver module are common devices in the field and can be purchased from current mainstream shopping platforms or stores.

[0017] In addition, a power supply module is needed to power the MEMS accelerometer, GPS positioning module and data transceiver module. The power supply module can use conventional power supply wires or solar panels.

[0018] S3. Process the acceleration data using the server's built-in algorithm: Based on the acceleration data and the sampling time, obtain the natural frequency of the retaining wall through fast Fourier transform; perform integral calculation on the acceleration data to obtain velocity data, and obtain the velocity amplitude ratio based on the velocity data at the top and bottom of the retaining wall; perform frequency domain analysis on the acceleration sequence to obtain its half-power point, and then calculate the attenuation constant. This step is the key point of this embodiment; different data processing methods can yield different parameters.

[0019] Specifically, the natural frequency of a retaining wall can be obtained as follows: Perform a Fast Fourier Transform (FFT) on the acceleration data to convert it into a frequency-amplitude response spectrum. Find the first major peak frequency in the spectrum; this is the natural frequency of the retaining wall. The natural frequency reflects the overall stiffness level of the wall structure. If it decreases over time, it usually indicates stiffness degradation or internal structural failure. In particular, to ensure recognition accuracy, the system performs window function processing and spectral smoothing on the original data before the FFT, ultimately achieving automatic extraction and trend tracking of the structure's natural frequency.

[0020] The natural frequency can reflect the overall stiffness level of the wall structure, but the decrease in the natural frequency alone is not enough to indicate its risk. Therefore, based on a large number of experiments, the inventors also provided two parameters, the velocity amplitude ratio and the attenuation constant, to provide a comprehensive early warning of the risk of retaining walls.

[0021] The velocity-amplitude ratio reflects the degree of swaying of the top of a retaining wall relative to its bottom, as well as the overall coordination and transmission efficiency of its structural response. When the velocity-amplitude ratio increases, it indicates that the relative swaying between the top and bottom of the retaining wall is more severe, suggesting uneven stiffness of the retaining wall or loosening of the top constraint, which is a precursor signal of decreased structural stability.

[0022] The velocity amplitude ratio is calculated as follows: First, the velocity sequence is calculated based on the acceleration sequence: , , In the formula, v 1 represents the initial velocity. v i Indicates the first i The speed of each sample a i Indicates the first i The acceleration of each sampling point; , The sampling frequency is used. Subsequently, the RMS value of the velocity sequence is calculated: In the formula, v RMS This represents the RMS value. Finally, based on the RMS value, the velocity-amplitude ratio is calculated: In the formula, , These represent the RMS values ​​at the top and bottom of the retaining wall, respectively.

[0023] The attenuation constant is used to characterize the energy dissipation capacity of a retaining wall under dynamic excitation and is closely related to the damping characteristics of the system. A larger attenuation constant indicates that the retaining wall structure has good energy dissipation capacity, while a smaller attenuation constant or a continuous decrease may indicate the accumulation of internal damage, prolonged vibration duration, and increased stability risk.

[0024] The method for calculating the attenuation constant is as follows: Perform a fast Fourier transform on the acceleration data to convert the acceleration data into a frequency-amplitude response spectrum A( f ), representing the structure's response intensity at various frequencies; calculate the peak amplitude. The two frequencies corresponding to the multiple: the low-frequency side frequency f 1 and high-frequency side frequency f 2. Calculate bandwidth Then calculate the attenuation constant. .

[0025] S4. Output evaluation results: Combining the velocity amplitude ratio and the first and second thresholds of the attenuation constant, and the natural frequency, the stability of the retaining wall is evaluated and an early warning is given based on the results of S3.

[0026] In this step, the velocity amplitude ratio between the first threshold and the second threshold is set as slightly risky, the velocity amplitude ratio not greater than the first threshold is set as safe, and the velocity amplitude ratio not less than the second threshold is set as high risk; the attenuation constant between the first threshold and the second threshold is set as slightly risky, the attenuation constant not less than the first threshold is set as safe, and the attenuation constant not greater than the second threshold is set as high risk.

[0027] After extensive engineering experiments, the inventors found that the first and second threshold values ​​for the velocity-amplitude ratio were set to 1 and 2, respectively; and the first and second threshold values ​​for the attenuation constant were set to 0.2 and 0.1, respectively. Of course, those skilled in the art can adjust these values ​​according to the specific region and geological conditions.

[0028] When conducting specific assessments and early warnings, the following principles are followed: If the natural frequency decreases, or if either the velocity amplitude ratio or the attenuation constant indicates a slight risk, the retaining wall is considered to have a safety risk, and the server marks the retaining wall as a key inspection target; if the natural frequency decreases, and at least one of the velocity amplitude ratio or the attenuation constant indicates a slight risk, the retaining wall is considered to have a high safety risk, and the server marks the retaining wall as an object to be inspected immediately; if at least one of the velocity amplitude ratio or the attenuation constant indicates a high risk, the retaining wall is considered to have an extremely high safety risk, the server marks the retaining wall as a high-risk object, and notifies the railway management unit.

[0029] To further illustrate the effects of the embodiments of the present invention, specific examples are given below.

[0030] Taking a cracked retaining wall on a railway slope in a mountainous area as the research object, monitoring data from four consecutive months after the deployment of the monitoring system were selected as the basis for analysis to construct the response relationship between monitoring parameters and structural deformation. The natural frequency changes with monitoring dates as follows: Figure 1 As shown. By Figure 1 It can be seen that from April to August, the natural frequencies of the retaining wall in both the NS and EW directions were concentrated around 12.5 Hz, with the natural frequency in the EW direction being slightly higher than that in the NS direction. After more than three months of monitoring, it was found that the natural frequency of the retaining wall did not show a significant decrease. Evaluation based on a single natural frequency index can only conclude that the retaining wall may not have continued to develop in an unstable direction.

[0031] Then, using the method described in this embodiment, a risk level early warning assessment chart was constructed, such as... Figures 2-7 As shown.

[0032] The relationship between the natural frequency and the RMS velocity amplitude ratio from May to July is as follows: Figures 2-4 As shown in the figure, in May and June, the RMS velocity amplitude ratio was generally less than or equal to 1.0, which was in a relatively safe range. However, in July, although the natural frequency did not change significantly, the RMS velocity amplitude ratio was gradually moving towards the high-risk area and had entered the mild-risk range (1.0~2.0).

[0033] The relationship between the decay constant and the RMS velocity amplitude ratio from May to July is as follows: Figures 5-7 As shown in the figure, the combined early warning patterns of the attenuation constant and RMS velocity amplitude ratio are almost identical to those of the natural frequency and RMS velocity amplitude ratio. This indicates that the retaining wall poses a high safety risk, and the server has marked it as an object requiring immediate inspection.

[0034] The joint early warning indicated that the railway retaining wall had entered a low-risk zone in July. Combined with on-site investigation, it was found that the retaining wall had a significant and deep crack due to cyclic freeze-thaw cycles. Therefore, we used anchor bolts to reinforce the damaged area of ​​the retaining wall. Then, we monitored the RMS velocity amplitude ratio, natural frequency, and attenuation constant of the reinforced retaining wall again, as shown below. Figure 4 and 7 As shown, the RMS velocity amplitude of the reinforced retaining wall immediately decreased, and both the natural frequency and attenuation constant increased, while the retaining wall monitoring and early warning indicators returned to the safe zone.

[0035] This case demonstrates that dynamic indicators can effectively achieve early warning functions and have the ability to identify early signs of disasters.

[0036] To further verify the effectiveness and necessity of the joint evaluation of three dynamic indicators (natural frequency, decay constant, and RMS velocity-amplitude ratio), this invention adds a supplementary example, using another railway retaining wall as the monitoring object. Vibration response signals at different stages under train excitation are collected, and the changing trends of each indicator are extracted. The results are as follows: Figure 8-10 As shown, the degree of deterioration is based on the degree of crack development and is identified manually. Specifically, it is related to the number of cracks, crack length, crack width, and crack depth.

[0037] The retaining wall exhibits relatively small changes in its natural frequency and its RMS velocity-amplitude ratio remains largely stable or fluctuates slightly, but its attenuation constant decreases significantly from 0.35 to 0.09. This trend clearly demonstrates that relying solely on natural frequency and / or RMS velocity-amplitude ratio as early warning criteria may fail to identify early deterioration states in a timely manner, leading to the risk of delayed warnings and missed diagnoses. Introducing the attenuation constant as a supplementary dynamic indicator allows for more sensitive detection of early anomalies such as structural stiffness degradation or loosening of connections, effectively improving the monitoring system's ability to identify precursors and the overall accuracy of its assessment.

[0038] It should be noted that, upon considering the specification and practicing the application disclosed herein, those skilled in the art will readily conceive of other embodiments of this application. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0039] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The true scope is indicated by this application.

Claims

1. A multi-index railway slope retaining wall stability early warning method based on the Internet of Things, characterized in that, include: S1. Data acquisition: MEMS accelerometers are installed at the top and bottom of the retaining wall, and acceleration data is obtained by sampling using the MEMS accelerometers. S2. Data transmission: Acceleration data is transmitted and uploaded to the server; S3. Process the acceleration data using the server's built-in algorithm: Based on the acceleration data and the sampling time, obtain the natural frequency of the retaining wall through fast Fourier transform. Acceleration data is integrated to obtain velocity data, and the velocity amplitude ratio is obtained based on the velocity data at the top and bottom of the retaining wall. Frequency domain analysis of the acceleration sequence is performed to obtain its half-power point, and then the decay constant is calculated. S4. Output evaluation results: Combining the velocity amplitude ratio and the first and second thresholds of the attenuation constant, and the natural frequency, the stability of the retaining wall is evaluated and an early warning is given based on the results of S3.

2. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, In S1, the Z-axis of the MEMS accelerometer is set vertically upward, and the X-axis and Y-axis are set in the east-west and north-south directions, respectively.

3. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, When the vibration acceleration amplitude of the retaining wall exceeds the excitation threshold, the MEMS accelerometer is activated and samples are taken.

4. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, In S2, the GPS positioning module and the data transceiver module upload the acceleration data to the server.

5. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, In S3, the formula for calculating the speed data is as follows: , , In the formula, v 1 represents the initial velocity. v i Indicates the first i The rate of a sample, where N represents the maximum number of samples within a sampling period. a i Indicates the first i The acceleration of each sampling point; , The sampling frequency.

6. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, In S3, the natural frequency is calculated by performing a fast Fourier transform on the acceleration data to convert the acceleration data into a frequency-amplitude response spectrum, and finding the first major peak frequency in the spectrum, which is the natural frequency of the retaining wall.

7. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, In S3, the velocity amplitude ratio is calculated using the following formula: , In the formula, R v Indicates the velocity-amplitude ratio; , These represent the RMS values ​​at the top and bottom of the retaining wall, respectively. v RMS Represents the RMS value; N represents the maximum number of samples within a sampling period; v i Indicates the first i The speed of each sample.

8. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, The attenuation constant is calculated as follows: A fast Fourier transform is performed on the acceleration data to convert it into a frequency-amplitude response spectrum A( f ), representing the structure's response intensity at various frequencies; calculate the peak amplitude. The two frequencies corresponding to the multiple: the low-frequency side frequency f 1 and high-frequency side frequency f 2. Calculate bandwidth Then calculate the attenuation constant. , For A( f The frequency corresponding to the peak value.

9. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, The first threshold values ​​for the velocity amplitude ratio and the attenuation constant are 1 and 2, respectively, and the second threshold values ​​for the velocity amplitude ratio and the attenuation constant are 0.2 and 0.1, respectively.

10. The method for early warning of stability of railway slope retaining walls based on the Internet of Things according to claim 1, characterized in that, S4 includes the following sub-steps: setting the velocity amplitude ratio between the first threshold and the second threshold as slightly risky, setting the velocity amplitude ratio not greater than the first threshold as safe, and setting the velocity amplitude ratio not less than the second threshold as high risk; setting the attenuation constant between the first threshold and the second threshold as slightly risky, setting the attenuation constant not less than the first threshold as safe, and setting the attenuation constant not greater than the second threshold as high risk; If the natural frequency decreases, or if either the velocity amplitude ratio or the attenuation constant indicates a slight risk, it means that the retaining wall has a safety risk, and the server marks the retaining wall as a key inspection target. If the natural frequency decreases, and at least one of the velocity amplitude ratio and attenuation constant is at slight risk, the server marks the retaining wall as an object to be inspected immediately. If at least one of the velocity amplitude ratio and attenuation constant is of high risk, the server marks the retaining wall as a high-risk object and notifies the railway management unit.

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