A method for early warning and identification of waterlogged roadbeds under repeated seepage and load.

Abstract

This invention relates to the field of road engineering technology, and in particular provides a method for early warning identification of waterlogged roadbeds under repeated seepage and load. It comprehensively considers the impact of repeated seepage and load on the waterlogged roadbed, enabling more accurate simulation of actual working conditions. It comprehensively monitors changes in seepage, stress, displacement, and moisture content of the waterlogged roadbed, providing more comprehensive data support for early warning. It establishes an early warning index system, improving the accuracy and reliability of early warning by considering multiple indicators. The method includes: establishing a roadbed mechanical model; deploying monitoring sensor groups in layers along the depth direction at typical cross-sections of the waterlogged roadbed; collecting monitoring data in real time at a preset frequency; establishing an early warning index system based on the roadbed mechanical model and monitoring data; establishing an early warning model based on the early warning index system; calculating a comprehensive value of the early warning index based on the monitoring data, and comparing the comprehensive value with an early warning threshold to issue an early warning.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and in particular to a method for early warning identification of waterlogged roadbeds under repeated seepage and load. Background Technology

[0002] The roadbed is a crucial component of road engineering, and its stability directly impacts the safe use of the road. In practical engineering, roadbeds are often subjected to the combined effects of repeated seepage and loading, especially in waterlogged environments, where their stability is more vulnerable. Repeated seepage leads to changes in the moisture content of the roadbed soil, affecting its mechanical properties; while loading induces stress and deformation in the roadbed. When these two factors act together, the roadbed may experience settlement, slippage, and other problems, even leading to serious safety accidents. Therefore, developing a method for accurately predicting the effects of repeated seepage and loading on waterlogged roadbeds is of significant practical importance.

[0003] Currently, although various methods exist for monitoring and early warning of flooded roadbeds, most focus on single factors, such as seepage or load. Research on early warning of flooded roadbeds under the combined effects of repeated seepage and load is limited. Existing technologies sometimes assess the stability of flooded roadbeds solely by monitoring surface displacement or settlement, but this method fails to reflect internal seepage and stress changes, resulting in insufficient accuracy and timeliness of early warning. Other methods, while considering seepage and load factors, lack consideration for repeated effects and cannot accurately simulate actual working conditions. Therefore, existing technologies are inadequate in providing early warning for flooded roadbeds under repeated seepage and load. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] This invention provides a method for early warning identification of waterlogged roadbeds under repeated seepage and load conditions, comprising:

[0006] A roadbed mechanics model was established to simulate the seepage, stress, and deformation changes of a flooded roadbed under different working conditions.

[0007] Monitoring sensor groups are deployed in layers along the depth direction at typical cross-sections of flooded roadbeds; the monitoring sensor groups include, but are not limited to, seepage monitoring sensors, stress monitoring sensors, displacement monitoring sensors and water content monitoring sensors.

[0008] The monitoring data of each monitoring sensor is collected in real time at a preset frequency.

[0009] Based on the roadbed mechanical model and the monitoring data, an early warning index system is established; the early warning index system includes, but is not limited to, seepage velocity index, stress index, displacement index and water content index.

[0010] An early warning model is established based on the aforementioned early warning indicator system.

[0011] The early warning model calculates a comprehensive value of the early warning indicators based on the monitoring data, and compares the comprehensive value of the early warning indicators with the early warning threshold:

[0012] When the comprehensive value of the warning indicator is less than the warning threshold, there is no need to issue a warning signal.

[0013] When the comprehensive value of the warning indicator is greater than or equal to the warning threshold, a warning signal is issued.

[0014] Furthermore, the aforementioned method for early warning identification of waterlogged roadbeds under repeated seepage and load conditions, after the early warning model calculates a comprehensive value of the early warning index based on the monitoring data and compares the comprehensive value of the early warning index with the early warning threshold, further includes:

[0015] Calculate the difference between the comprehensive value of the early warning indicator and the early warning threshold.

[0016] A graded early warning system is implemented based on the difference.

[0017] Furthermore, the aforementioned early warning identification method for waterlogged roadbeds under repeated seepage and load action includes graded early warning based on the difference, comprising:

[0018] When the difference is within the first-level warning threshold, a first-level warning is triggered.

[0019] If the difference is within the level 2 warning threshold, a level 2 warning is triggered.

[0020] If the difference is within the level 3 warning threshold, a level 3 warning is triggered.

[0021] Furthermore, the early warning identification method for waterlogged roadbeds under repeated seepage and load conditions, after classifying and issuing early warnings based on the difference, further includes:

[0022] The early warning results are fed back to the monitoring sensor group and the early warning model, so that the monitoring sensor group and the early warning model can be optimized and adjusted using the early warning results.

[0023] Furthermore, the early warning identification method for waterlogged roadbeds under repeated seepage and load conditions, after collecting monitoring data from each of the monitoring sensors in real time at a preset frequency, also includes:

[0024] The monitoring data is preprocessed; the preprocessing includes, but is not limited to, data filtering, data denoising, and data normalization.

[0025] Furthermore, in the aforementioned method for early warning and identification of waterlogged roadbeds under repeated seepage and load, the monitoring data includes:

[0026] The monitoring data includes, but is not limited to, seepage data, stress data, displacement data, and moisture content data of the flooded roadbed.

[0027] Furthermore, the aforementioned method for early warning and identification of waterlogged roadbeds under repeated seepage and load conditions, including the establishment of a roadbed mechanical model, comprises:

[0028] The roadbed mechanical model is established based on the finite element analysis method, that is, the flooded roadbed is divided into multiple elements to simulate the seepage, stress and deformation changes of the flooded roadbed under different working conditions.

[0029] Furthermore, in the aforementioned method for early warning identification of waterlogged roadbeds under repeated seepage and load, the comprehensive value of the early warning index includes:

[0030] The early warning model uses a weighted average method to calculate the comprehensive value of the early warning indicators.

[0031] Furthermore, in the aforementioned method for early warning identification of waterlogged roadbeds under repeated seepage and load, the early warning threshold includes:

[0032] The warning threshold is set based on historical data and empirical formulas.

[0033] Furthermore, in the aforementioned method for early warning identification of waterlogged roadbeds under repeated seepage and load, the early warning signal includes:

[0034] The warning signals include, but are not limited to, sound warnings, light warnings, SMS notifications, and system prompts.

[0035] This invention provides an early warning identification method for waterlogged roadbeds under repeated seepage and load conditions. It comprehensively considers the impact of repeated seepage and load on the waterlogged roadbed, establishes a mechanical model to more accurately simulate actual working conditions, and comprehensively monitors changes in seepage, stress, displacement, and moisture content of the waterlogged roadbed, providing more comprehensive data support for early warning. It establishes an early warning index system, improving the accuracy and reliability of early warning by comprehensively considering multiple indicators. The early warning results are fed back to the monitoring sensor group and early warning model, allowing for optimization and adjustment of these systems to better adapt to actual engineering needs. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0037] Figure 1This is a schematic diagram of the early warning identification method for a waterlogged roadbed under repeated seepage and load action provided in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the early warning identification method for a waterlogged roadbed under repeated seepage and load action provided in Embodiment 2 of the present invention. Detailed Implementation

[0039] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0040] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0041] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0042] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0043] The embodiments of the present invention can be used for monitoring and early warning of road and railway subgrades, especially in areas with complex geological conditions or high groundwater levels, to ensure road safety.

[0044] Example 1:

[0045] like Figure 1 As shown, this embodiment of the invention provides an early warning identification method for waterlogged roadbeds under repeated seepage and load conditions, comprising the following steps:

[0046] S101. Establish a roadbed mechanical model to simulate the seepage, stress, and deformation changes of a flooded roadbed under different working conditions.

[0047] The roadbed mechanics model is a mathematical model used to describe the mechanical behavior of a flooded roadbed under various external conditions (such as seepage and loads). It predicts the response of the flooded roadbed under different working conditions by establishing relationships between physical quantities (such as seepage, stress, strain, and displacement). The roadbed mechanics model can not only simulate the seepage within the flooded roadbed, including changes in groundwater level and the distribution of seepage velocity—crucial for understanding the stability of the roadbed in a flooded environment, as seepage alters the mechanical properties of the soil—but also calculate the stress distribution of the flooded roadbed under different loads. For example, vehicle loads and static loads have different stress effects on the flooded roadbed, and the model can predict the magnitude and location of these stresses. Furthermore, the roadbed mechanics model can predict the deformation of the flooded roadbed under seepage and loads, including settlement and displacement. These deformations are important indicators for assessing the stability of the flooded roadbed; excessive deformation may lead to roadbed failure.

[0048] The method for establishing a roadbed mechanical model is as follows: a mathematical model is established based on theories such as soil mechanics, seepage theory, and structural mechanics. For example, Darcy's law is used to describe seepage, and elasticity or plasticity theory is used to describe stress and deformation. The finite element analysis (FEA) method is typically used to solve the model. The flooded roadbed is divided into multiple elements, and the seepage, stress, and deformation of each element are obtained through numerical calculations. The parameters in the model are determined based on the soil properties (such as permeability coefficient, elastic modulus, Poisson's ratio, etc.), load characteristics (such as vehicle load magnitude and frequency, etc.), and boundary conditions (such as groundwater level, boundary constraints, etc.) in the actual engineering project.

[0049] Specifically, Darcy's law is the fundamental equation describing the seepage flow of fluid through porous media (such as roadbed soil). Its expression is:

[0050] ,

[0051] In the formula, Indicates the seepage rate. Indicates the permeability coefficient of the soil. Indicates the cross-sectional area of ​​seepage. This represents the pressure gradient per unit length.

[0052] In elasticity, the relationship between stress and deformation can be described by constitutive equations. For linear elastic materials, the constitutive equations can be expressed as:

[0053] ,

[0054] In the formula, Represents strain components, Indicates the elastic modulus. It is Poisson's ratio. Represents stress components, Represents the trajectory of stress. This represents the Kronecker symbol.

[0055] By establishing a roadbed mechanics model, the behavior of flooded roadbeds under different working conditions can be predicted in advance, providing theoretical support for the early warning index system; it can also help identify potential unstable factors, providing a basis for the deployment of monitoring sensor groups and the setting of early warning thresholds, thereby improving the accuracy and reliability of early warning.

[0056] S102. At typical cross-sections of flooded roadbeds, monitoring sensor groups are deployed in layers along the depth direction; the monitoring sensor groups include, but are not limited to, seepage monitoring sensors, stress monitoring sensors, displacement monitoring sensors, and water content monitoring sensors.

[0057] A typical cross-section refers to a section that represents the overall characteristics of a flooded roadbed. This cross-section is typically selected in areas where the roadbed experiences complex stress, exhibits significant geological variations, or is prone to defects. Examples include the centerline cross-section, slope cross-section, or cross-section where groundwater seepage paths exist. By deploying monitoring sensor arrays at typical cross-sections, the overall condition of the flooded roadbed can be more comprehensively reflected, avoiding the omission of crucial information due to localized monitoring.

[0058] The depth direction refers to the vertical direction from the surface of the submerged roadbed to its base. Since the seepage, stress, and deformation within the submerged roadbed vary with depth, monitoring at different depths is necessary.

[0059] Layered deployment refers to arranging monitoring sensor groups at certain depth intervals within different layers of the flooded roadbed. For example, sensors can be placed at the surface, middle layer, and bottom of the flooded roadbed to obtain monitoring data at different depths.

[0060] A sensor array refers to a group of different types of sensors used to monitor various physical quantities of a flooded roadbed. A sensor array typically includes:

[0061] (1) Seepage monitoring sensors: such as water level gauges and flow velocity sensors, used to monitor groundwater level and seepage velocity.

[0062] (2) Stress monitoring sensors: such as strain gauges and pressure sensors, used to monitor the stress distribution inside the submerged roadbed.

[0063] (3) Displacement monitoring sensors: such as total station, GPS positioning system, displacement meter, used to monitor the deformation of submerged roadbed.

[0064] (4) Moisture content monitoring sensors: such as soil moisture sensors and humidity sensors, used to monitor the moisture content of submerged roadbed soil.

[0065] In practical implementation, the specific placement of each sensor in the sensor array is determined based on the geological conditions, seepage path, and stress characteristics of the flooded roadbed. For example, displacement sensors and moisture content sensors are placed on the surface of the flooded roadbed, stress sensors and seepage sensors are placed in the middle layer, and water level gauges are placed at the bottom. The depth interval of the sensors can be determined according to the actual needs and monitoring accuracy requirements of the flooded roadbed. For example, for shallower flooded roadbeds, the interval can be 0.5 meters or 1 meter; for deeper flooded roadbeds, the interval can be 1 meter or 2 meters. Sensors can be installed inside the flooded roadbed through drilling, burying, or other methods. During installation, it is necessary to ensure the stability and reliability of the sensors to avoid affecting the accuracy of the monitoring data due to improper installation.

[0066] S103. Collect monitoring data from each of the monitoring sensors in real time according to a preset frequency.

[0067] The preset frequency refers to the data acquisition time interval pre-set during the design of the monitoring sensor group. This frequency can be determined based on actual monitoring needs and the stability requirements of the flooded roadbed. The preset frequency determines the timeliness and density of the monitoring data. A higher acquisition frequency can provide more timely and detailed data, but it will also increase the burden of data processing and energy consumption; a lower acquisition frequency can save resources, but may miss some key transient changes. For example, for flooded roadbeds that require high-precision monitoring, the preset frequency can be set to acquire data once per minute; for relatively stable flooded roadbeds, the preset frequency can be set to acquire data once per hour.

[0068] Real-time data acquisition refers to the continuous collection of output signals from various sensors at a preset frequency and their conversion into analyzable data. This acquisition method ensures data continuity and timeliness. Real-time acquisition can promptly capture changes in the condition of flooded roadbeds, especially in sudden situations (such as sudden changes in seepage caused by heavy rain or sudden changes in stress caused by vehicle overloading), quickly reflecting problems and triggering early warning mechanisms. Specifically, real-time acquisition typically relies on automated monitoring equipment and data transmission systems, such as wireless sensor networks (WSNs) and wired data transmission systems, to ensure that data can be transmitted quickly and accurately to the data processing center.

[0069] Monitoring data refers to the numerical values ​​of various physical quantities collected by sensors, such as seepage velocity, stress magnitude, displacement, and moisture content. These data are crucial for assessing the stability of flooded roadbeds. By analyzing this data, it can be determined whether the flooded roadbed is in a safe state and whether maintenance or repair measures are needed. Generally, the collected monitoring data usually requires preprocessing, such as filtering, noise reduction, and normalization, to improve data quality and usability.

[0070] S104. Based on the roadbed mechanical model and the monitoring data, establish an early warning index system; the early warning index system includes, but is not limited to, seepage velocity index, stress index, displacement index and water content index.

[0071] The early warning indicator system is a set of quantitative indicators used to assess the stability of flooded roadbeds. These indicators, based on roadbed mechanical models and monitoring data, reflect the state changes of flooded roadbeds in various aspects. By establishing this system, real-time monitoring and early warning of the flooded roadbed's condition can be achieved.

[0072] The seepage velocity index reflects the magnitude of seepage velocity within a flooded roadbed. Changes in seepage velocity can affect the mechanical properties of the soil. Excessively high seepage velocities can lead to erosion or softening of the flooded roadbed soil, thereby affecting its stability. The seepage velocity index is typically calculated from data collected by seepage monitoring sensors; for example, it is the ratio of the seepage velocity to the design allowable seepage velocity.

[0073] Stress indices reflect the stress level within the soil of a flooded roadbed. Excessive stress can lead to damage or deformation of the soil, affecting its bearing capacity. Stress indices are calculated from data collected by stress monitoring sensors; for example, the ratio of the maximum stress in the flooded roadbed soil to its compressive strength.

[0074] Displacement indices reflect the deformation of the surface or interior of a flooded roadbed. Excessive displacement may indicate settlement, slippage, or other instability in the flooded roadbed. Displacement indices are calculated from data collected by displacement monitoring sensors; for example, they are the ratio of the maximum displacement of the flooded roadbed surface to the design allowable displacement.

[0075] Moisture content indicators reflect changes in the moisture content of subgrade soil. These changes affect the mechanical properties of the soil; both excessively high and low moisture content can lead to stability problems in subgrades. Moisture content indicators are calculated from data collected by moisture content monitoring sensors, such as the ratio of the moisture content of subgrade soil to its saturated moisture content.

[0076] The establishment of an early warning indicator system requires the integration of a roadbed mechanics model and monitoring data. The roadbed mechanics model provides theoretical guidance, while monitoring data provides practical verification. The early warning indicator system not only includes the four indicators mentioned above, but can also add other relevant indicators, such as temperature and vibration indicators, according to actual needs, to more comprehensively assess the stability of a flooded roadbed. The early warning indicator system can be dynamically adjusted based on monitoring data and the verification results of the roadbed mechanics model to improve the accuracy and reliability of early warnings.

[0077] S105. Establish an early warning model based on the aforementioned early warning indicator system.

[0078] The early warning model is a comprehensive analytical tool used to assess the stability of flooded roadbeds based on various indicators in the early warning indicator system. The early warning model integrates multiple indicators and uses specific algorithms or rules to determine the overall state of the flooded roadbed, thereby achieving early identification and warning of potential risks.

[0079] Specifically, the input data for the early warning model comes from various indicators in the early warning indicator system. These indicators reflect the state of the flooded roadbed in terms of seepage, stress, displacement, and moisture content. Simple algorithms such as weighted average and linear combination can be used to combine multiple indicators into a single comprehensive indicator value. For example, weights can be assigned to each indicator based on its impact on the stability of the flooded roadbed to calculate the comprehensive indicator value. Alternatively, machine learning algorithms (such as support vector machines and neural networks) or data mining techniques can be used to learn from and analyze historical data to build more complex early warning models, thereby improving the accuracy and reliability of early warnings.

[0080] S106. The early warning model calculates a comprehensive value of the early warning indicators based on the monitoring data, and compares the comprehensive value of the early warning indicators with the early warning threshold:

[0081] When the comprehensive value of the warning indicator is less than the warning threshold, there is no need to issue a warning signal.

[0082] When the comprehensive value of the warning indicator is greater than or equal to the warning threshold, a warning signal is issued.

[0083] The comprehensive early warning index value is a numerical value that comprehensively considers multiple early warning indicators and is used to assess the overall stability of a flooded roadbed. Specifically, the comprehensive early warning index value can be calculated using a weighted average method, or a more complex algorithm, such as a machine learning algorithm (support vector machine, neural network, etc.), which automatically calculates the comprehensive early warning index value through learning and analysis of historical data. The weighted average method will be described in detail below:

[0084] Comprehensive value of early warning indicators = Seepage velocity index + Stress index + Displacement index + Moisture content index

[0085] In the formula, , , , These are the weights of the corresponding indicators.

[0086] The warning threshold is a preset critical value used to determine whether a flooded roadbed is in a safe state. Specifically, the warning threshold can be set based on historical data and engineering experience, or it can be set according to relevant design specifications and safety standards.

[0087] Specifically, the early warning model compares the calculated comprehensive value of the early warning indicators with the preset early warning threshold:

[0088] When the comprehensive value of the warning indicator is less than the warning threshold, it indicates that the flooded roadbed is in a safe state and no warning signal needs to be issued.

[0089] When the comprehensive value of the warning indicator is greater than or equal to the warning threshold, it indicates that there may be safety hazards in the flooded roadbed, and a warning signal needs to be issued.

[0090] This invention provides an early warning identification method for waterlogged roadbeds under repeated seepage and load conditions. It comprehensively considers the impact of repeated seepage and load on waterlogged roadbeds, establishes a mechanical model, and can more accurately simulate actual working conditions. It comprehensively monitors the seepage, stress, displacement, and water content changes of waterlogged roadbeds, providing more comprehensive data support for early warning. It establishes an early warning index system, and improves the accuracy and reliability of early warning by comprehensively considering multiple indicators.

[0091] Example 2:

[0092] like Figure 2 As shown, this embodiment of the invention provides an early warning identification method for waterlogged roadbeds under repeated seepage and load conditions, comprising the following steps:

[0093] S201. Establish a roadbed mechanical model to simulate the seepage, stress, and deformation changes of a flooded roadbed under different working conditions.

[0094] Specifically, a roadbed mechanical model is established based on the finite element method (FEM), which divides the flooded roadbed into multiple elements to simulate the seepage, stress, and deformation changes of the flooded roadbed under different working conditions. The FEM is a numerical analysis method used to solve complex systems in engineering and physics problems. It divides a continuous domain into a finite number of small elements, approximates the unknown function with a simple function within each element, and then combines the approximate solutions of all elements to obtain an approximate solution for the entire system. In the roadbed mechanical model, the FEM can be used to simulate the seepage, stress, and deformation changes of the flooded roadbed under different working conditions, providing numerical solutions for the stability analysis of the flooded roadbed.

[0095] S202. At typical cross-sections of flooded roadbeds, monitoring sensor groups are deployed in layers along the depth direction; the monitoring sensor groups include, but are not limited to, seepage monitoring sensors, stress monitoring sensors, displacement monitoring sensors, and water content monitoring sensors.

[0096] S203. Collect monitoring data from each of the monitoring sensors in real time according to a preset frequency.

[0097] Monitoring data refers to the numerical values ​​of various physical quantities collected through various sensors and monitoring equipment, reflecting the actual operating state of the flooded roadbed. These data are crucial for assessing the stability of the flooded roadbed, conducting early warning analysis, and implementing maintenance measures. Specifically, monitoring data includes, but is not limited to, seepage data, stress data, displacement data, and moisture content data of the flooded roadbed. The following will provide a detailed description of each monitoring data point:

[0098] Seepage data refers to data reflecting the seepage situation inside a flooded roadbed, including groundwater level, seepage velocity, and seepage direction. Seepage is one of the key factors affecting the stability of a flooded roadbed. Excessively high seepage velocities can lead to erosion, softening, or liquefaction of the soil, thereby affecting the bearing capacity and stability of the roadbed. Generally, groundwater level changes inside the flooded roadbed are monitored using water level gauges; seepage velocity is monitored using flow velocity sensors; and seepage paths are analyzed by combining numerical simulations with monitoring data.

[0099] Stress data refers to data reflecting the stress distribution within a flooded roadbed, including normal stress and shear stress in the soil. Stress is a crucial indicator for assessing the bearing capacity and stability of a flooded roadbed. Excessive stress can lead to soil failure or deformation, affecting the normal use of the flooded roadbed. Generally, normal stress in the flooded roadbed soil is monitored using strain gauges or pressure sensors; shear stress is monitored using shear sensors; and stress distribution is analyzed by combining finite element analysis with monitoring data.

[0100] Displacement data refers to data reflecting the surface or internal deformation of a flooded roadbed, including horizontal and vertical displacements. Displacement is an important indicator for assessing the deformation and stability of a flooded roadbed. Excessive displacement may indicate settlement, slippage, or other instability in the flooded roadbed. Generally, horizontal displacement of the flooded roadbed surface is monitored using a total station or GPS positioning system; vertical displacement within the flooded roadbed is monitored using displacement gauges; and the overall deformation of the flooded roadbed is analyzed by combining numerical simulations with monitoring data.

[0101] Moisture content data refers to data reflecting changes in the moisture content of subgrade soil, including volumetric water content and relative humidity. Changes in moisture content affect the mechanical properties of the soil; excessively high or low moisture content can lead to stability problems in subgrades. Generally, soil moisture sensors are used to monitor the volumetric water content of subgrade soil, and humidity sensors are used to monitor the relative humidity. Numerical simulations and monitoring data are combined to analyze the trend of moisture content changes.

[0102] S204. Preprocess the monitoring data; the preprocessing includes, but is not limited to, data filtering, data denoising, and data normalization.

[0103] Preprocessing refers to a series of preliminary steps taken before analyzing monitoring data. These steps aim to improve data quality and usability, ensuring that subsequent analysis more accurately reflects the actual situation. Preprocessing removes noise and outliers from the data, standardizes data format and dimensions, thereby providing a more reliable data foundation for subsequent data analysis and model calculations.

[0104] Specifically, preprocessing includes, but is not limited to, data filtering, data denoising, and data normalization, which will be described in detail below:

[0105] Data filtering is a method that uses mathematical algorithms to remove high-frequency noise or irregular fluctuations from data. The purpose of filtering is to preserve the main characteristics of the data while removing unnecessary interference. Data filtering can smooth data curves, reduce short-term fluctuations caused by sensor accuracy limitations or environmental interference, and make the data easier to analyze. For example, a moving average filter can be used to smooth monitoring data.

[0106] Data filtering typically uses methods such as moving average filters or low-pass filters. For example, the formula for a moving average filter is:

[0107]

[0108] In the formula, This represents the filtered data points. Represents the original data points. The window size represents the moving average.

[0109] Data denoising refers to removing random noise or interference signals from data using specific algorithms or techniques. The goal of denoising is to improve the signal-to-noise ratio of the data, making it closer to the true value. Denoising can reduce random errors in data, improving its accuracy and reliability. For example, wavelet transform can be used to decompose and reconstruct data, removing high-frequency noise; or dynamic estimation can be used to remove noise from data, which is suitable for time series data.

[0110] Data denoising can be achieved using methods such as wavelet transform and Kalman filtering. For example, the formula for wavelet transform is:

[0111]

[0112] In the formula, Represents wavelet coefficients, Represents the original data. Describing wavelet functions, Indicates the scale parameter. This represents the translation parameter.

[0113] Data normalization refers to transforming data into a uniform range (usually [0, 1] or [-1, 1]) to facilitate comparison and analysis. The purpose of normalization is to eliminate differences in units of measurement between different data points, making them comparable. Normalization can avoid analytical biases caused by differences in data units, improving the efficiency and accuracy of data processing. Examples include min-max normalization or Z-score standardization.

[0114] Data normalization typically uses methods such as min-max normalization or Z-score normalization. For example, the formula for min-max normalization is:

[0115]

[0116] In the formula, This represents the normalized data points. Represents the original data points. This represents the minimum value of the data. This indicates the maximum value of the data.

[0117] The formula for Z-score standardization is:

[0118]

[0119] In the formula, This represents the standardized data points. Represents the original data points. This represents the mean of the data. The standard deviation of the data.

[0120] S205. Based on the roadbed mechanical model and the monitoring data, establish an early warning index system; the early warning index system includes, but is not limited to, seepage velocity index, stress index, displacement index and water content index.

[0121] S206. Establish an early warning model based on the aforementioned early warning indicator system.

[0122] S207. The early warning model calculates a comprehensive value of the early warning indicators based on the monitoring data, and compares the comprehensive value of the early warning indicators with the early warning threshold:

[0123] When the comprehensive value of the warning indicator is less than the warning threshold, there is no need to issue a warning signal.

[0124] When the comprehensive value of the warning indicator is greater than or equal to the warning threshold, a warning signal is issued.

[0125] Warning signals, in particular, are alerts or warnings issued by the early warning system when monitoring data reaches or exceeds a preset warning threshold. These signals serve to remind relevant personnel of potential safety hazards posed by flooded roadbeds. The purpose of warning signals is to promptly notify relevant personnel to take necessary measures to prevent accidents or minimize losses.

[0126] Specifically, warning signals include, but are not limited to, audible warnings, visual warnings, SMS notifications, and system alerts. The following is a detailed description of each warning signal:

[0127] Sound alerts are used to remind relevant personnel by emitting specific sound signals (such as alarms or buzzers). Sound alerts can quickly attract the attention of on-site personnel and are suitable for scenarios requiring immediate response. For example, sound alarms can be installed at construction sites or monitoring centers to emit alarm sounds when monitoring data is abnormal, or voice prompts can be issued via loudspeakers or walkie-talkies to remind on-site personnel to pay attention.

[0128] Light warnings use visual signals (such as flashing red or yellow lights) to alert relevant personnel. Light warnings are visually salient and can attract attention from a distance or in noisy environments. For example, flashing red lights can be installed at monitoring points for flooded roadbeds; the lights flash when monitoring data is abnormal. Alternatively, different colors of light can be used to indicate different warning levels.

[0129] SMS notifications are sent to the mobile phones of relevant personnel to inform them of abnormal monitoring data for flooded roadbeds. SMS notifications can promptly deliver early warning signals to management or maintenance personnel who are not on-site, ensuring they can take timely action. For example, when the comprehensive value of the warning indicator is greater than or equal to the warning threshold, an SMS is automatically sent to a preset mobile phone number.

[0130] System prompts are warning messages or pop-up notifications displayed through the monitoring system interface (such as computer software, mobile applications, etc.). System prompts can display monitoring data and early warning signals in real time, allowing monitoring personnel to view and handle them at any time. For example, system prompts can include detailed information such as charts and data trend analysis to help monitoring personnel quickly determine the severity of the problem.

[0131] S208. Calculate the difference between the comprehensive value of the early warning indicator and the early warning threshold.

[0132] Calculate the difference between the comprehensive value of the early warning indicators and the early warning threshold, i.e.:

[0133] Difference = Comprehensive value of early warning indicators - Early warning threshold

[0134] The difference reflects the gap between the stability of a flooded roadbed and the safety standard. The magnitude of the difference can be used to determine the stability of the flooded roadbed and the urgency of taking corresponding measures.

[0135] S209. Based on the difference, a graded early warning is issued.

[0136] When the difference is within the first-level warning threshold, a first-level warning is triggered.

[0137] When the difference is within the level 2 warning threshold, a level 2 warning is triggered.

[0138] When the difference is within the level 3 warning threshold, a level 3 warning is triggered.

[0139] Among them, graded early warning is a mechanism that classifies early warning signals into different levels, namely Level 1, Level 2, and Level 3, based on the magnitude of the difference in warning values. Different levels of early warning signals correspond to different degrees of safety risks, thus allowing for corresponding measures to be taken. Graded early warning can more precisely reflect the stability state of flooded roadbeds, helping relevant personnel to take appropriate measures according to the risk level, thereby improving the practicality and effectiveness of the early warning system.

[0140] S210. Feedback the early warning results to the monitoring sensor group and the early warning model, so as to optimize and adjust the monitoring sensor group and the early warning model using the early warning results.

[0141] The early warning result refers to the final judgment of the early warning model, including whether an early warning signal is issued, the level of the early warning (such as Level 1, Level 2, and Level 3), and related recommended measures. The early warning result is an important output for assessing the stability of flooded roadbeds and is also the basis of the feedback mechanism.

[0142] Based on the early warning results, the monitoring sensor group should be optimized and adjusted, including the sensor placement, quantity, type, and sampling frequency. Optimizing the sensor group can improve the quality and representativeness of monitoring data, ensuring that the monitoring system can more accurately reflect the actual condition of the flooded roadbed. For example, if the early warning results show insufficient or abnormal monitoring data in certain areas, sensors can be added or rearranged; the sampling frequency of the sensors can be adjusted according to the frequency and importance of the early warning results to improve the timeliness and accuracy of the data; if certain types of sensors perform poorly under specific conditions, replacing them with more suitable sensor types can be considered.

[0143] The early warning model is optimized and adjusted based on the early warning results, including adjusting the model's parameters, algorithms, and thresholds. Optimizing the early warning model can improve the accuracy and reliability of early warnings, reduce false alarms and missed alarms, and ensure that the early warning system can more effectively identify potential safety hazards. For example, early warning thresholds can be dynamically adjusted based on the early warning results to better adapt to different operating conditions and environmental conditions; the algorithm of the early warning model can be adjusted based on the accuracy and response speed of the early warning results; and indicators in the early warning indicator system can be added or removed based on the effectiveness of the early warning results to improve the model's comprehensive evaluation capability.

[0144] This invention provides an early warning identification method for waterlogged roadbeds under repeated seepage and load conditions. It comprehensively considers the impact of repeated seepage and load on the waterlogged roadbed, establishes a mechanical model to more accurately simulate actual working conditions, and comprehensively monitors changes in seepage, stress, displacement, and moisture content of the waterlogged roadbed, providing more comprehensive data support for early warning. It establishes an early warning index system, improving the accuracy and reliability of early warning by comprehensively considering multiple indicators. The early warning results are fed back to the monitoring sensor group and early warning model, allowing for optimization and adjustment of these systems to better adapt to actual engineering needs.

[0145] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0149] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning and identification of waterlogged roadbeds under repeated seepage and load conditions, characterized in that, include: A roadbed mechanical model was established to simulate the seepage, stress, and deformation changes of a flooded roadbed under different working conditions. Monitoring sensor groups are deployed in layers along the depth direction at typical cross-sections of the flooded roadbed; the monitoring sensor groups include, but are not limited to, seepage monitoring sensors, stress monitoring sensors, displacement monitoring sensors, and moisture content monitoring sensors; The monitoring data of each monitoring sensor is collected in real time at a preset frequency; Based on the aforementioned roadbed mechanical model and the aforementioned monitoring data, an early warning index system is established; the early warning index system includes, but is not limited to, seepage velocity index, stress index, displacement index, and water content index. An early warning model is established based on the aforementioned early warning indicator system; The roadbed mechanical model is established based on the finite element analysis method, that is, the waterlogged roadbed is divided into multiple elements to simulate the seepage, stress and deformation changes of the waterlogged roadbed under different working conditions. The method for establishing the roadbed mechanical model is as follows: a mathematical model is established based on soil mechanics, seepage theory, and structural mechanics theory. Darcy's law is used to describe seepage, while the theories of elasticity or plasticity are used to describe stress and deformation. The model was solved using the finite element analysis method; the flooded roadbed was divided into multiple elements, and the seepage, stress and deformation of each element were obtained through numerical calculation; the parameters in the model were determined based on the soil properties, load characteristics and boundary conditions in the actual project. Specifically, Darcy's law is the fundamental equation describing the seepage flow of fluid through a porous medium, and its expression is: ； In the formula, Indicates the seepage rate. Indicates the permeability coefficient of the soil. Indicates the cross-sectional area of ​​seepage. This represents the pressure gradient per unit length; In elasticity, the relationship between stress and deformation is described by constitutive equations. For linear elastic materials, the constitutive equations are expressed as: ； In the formula, Represents strain components, Indicates the elastic modulus. It is Poisson's ratio. Represents stress components, Represents the trajectory of stress. Represents the Kronecker symbol; The early warning model calculates a comprehensive value of the early warning indicators based on the monitoring data, and compares the comprehensive value of the early warning indicators with the early warning threshold: When the comprehensive value of the warning indicator is less than the warning threshold, there is no need to issue a warning signal; When the comprehensive value of the warning indicator is greater than or equal to the warning threshold, a warning signal is issued.

2. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, After the early warning model calculates a comprehensive value of the early warning indicators based on the monitoring data and compares the comprehensive value of the early warning indicators with the early warning threshold, it further includes: Calculate the difference between the comprehensive value of the early warning indicator and the early warning threshold; A graded early warning system is implemented based on the difference.

3. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 2, characterized in that, Based on the difference, a tiered early warning system is implemented, including: If the difference is within the first-level warning threshold, a first-level warning is triggered; If the difference is within the level 2 warning threshold, a level 2 warning is triggered; If the difference is within the level 3 warning threshold, a level 3 warning is triggered.

4. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 2, characterized in that, After issuing graded warnings based on the aforementioned differences, the system also includes: The early warning results are fed back to the monitoring sensor group and the early warning model, so that the monitoring sensor group and the early warning model can be optimized and adjusted using the early warning results.

5. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, After collecting monitoring data from each of the monitoring sensors in real time at a preset frequency, the method further includes: The monitoring data is preprocessed; the preprocessing includes, but is not limited to, data filtering, data denoising, and data normalization.

6. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, The monitoring data includes: The monitoring data includes, but is not limited to, seepage data, stress data, displacement data, and moisture content data of the flooded roadbed.

7. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, The comprehensive value of the early warning indicators includes: The early warning model uses a weighted average method to calculate the comprehensive value of the early warning indicators.

8. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, The warning thresholds include: The warning threshold is set based on historical data and empirical formulas.

9. The early warning identification method for waterlogged roadbeds under repeated seepage and load as described in claim 1, characterized in that, The warning signals include: The warning signals include, but are not limited to, sound warnings, light warnings, SMS notifications, and system prompts.

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