A method and system for detecting water accumulation in a road subgrade structure layer

Abstract

The application provides a kind of road subgrade structure layer water accumulation detection method and system, comprising: obtaining the acoustic resonance frequency of current position subgrade, then the acoustic resonance frequency is inverted to subgrade structure density;Using difference elimination method to calculate the density variation, then according to the reference water content of subgrade structure density and density variation;Constructing multiple linear regression model, then based on the reference water content, using multiple linear regression model to calculate the water content of subgrade;According to the water content of subgrade, using kriging interpolation algorithm to locate the subgrade water accumulation section, then calculate the maximum water depth of subgrade water accumulation section.The application solves the problem that the existing technology cannot detect the water accumulation state inside the subgrade structure layer in real time and effectively under the condition of continuous traffic.

Description

Technical Field

[0001] This invention relates to the field of non-destructive testing and maintenance technology for road engineering, and in particular to a method and system for detecting water accumulation in the roadbed structure layer. Background Technology

[0002] Water accumulation in the roadbed structure layer is one of the important reasons for the decline in roadbed and pavement durability. Road sections with water accumulation are prone to serious defects such as mud pumping, base layer voids, pavement cracking, and roadbed collapse under long-term traffic dynamic loads.

[0003] Traditional methods for detecting roadbed moisture have many limitations. For example, borehole sampling damages the road structure, is inefficient, and affects traffic; ground-penetrating radar requires road closures and is difficult to accurately quantify the moisture content and changes in the structural layers.

[0004] In recent years, some new technologies have been applied to the field of road inspection, such as deep learning image recognition technology for identifying water accumulation areas on the road. However, most of these technologies are limited to road surface water detection and are easily affected by the environment, making it difficult to judge the roadbed condition and effectively detect the water accumulation state inside the roadbed structure layer. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for detecting water accumulation in road subgrade structural layers, which solves the problem that existing technologies cannot detect the water accumulation state inside the subgrade structural layers in real time and effectively under continuous traffic conditions.

[0006] According to an embodiment of the present invention, a method for detecting water accumulation in a road subgrade structural layer includes: Obtain the acoustic resonance frequency of the roadbed at the current location, and then invert the acoustic resonance frequency into the roadbed structure density; The density change was calculated using the differential elimination method, and then the reference moisture content was calculated based on the subgrade structure density and the density change. A multiple linear regression model was constructed, and then the subgrade moisture content was calculated using the multiple linear regression model based on the baseline moisture content. Based on the subgrade moisture content, the Kriging interpolation algorithm is used to locate the water-rich sections of the subgrade, and then the maximum water depth of the water-rich sections is calculated.

[0007] Preferably, after acquiring the acoustic resonance frequency, the acoustic resonance frequency is subjected to noise reduction filtering, and then effectiveness correction is performed.

[0008] Preferably, the calculation formula for effectiveness correction is as follows: in, The effective acoustic resonance frequency. Let be the acoustic resonance frequency at the k-th measurement. denoted as , where is the signal-to-noise ratio of the acoustic resonance frequency at the k-th measurement.

[0009] Preferably, the method for calculating density change using the difference elimination method includes: Measure the current ambient temperature of the roadbed at the current location and calculate the temperature compensation coefficient based on the current ambient temperature; Obtain the reference frequency of the roadbed at the current location, and then calculate the density change based on the temperature compensation coefficient and the reference frequency.

[0010] Preferably, the formula for calculating the density change is as follows: Where Δρ is the density change in the current state relative to the dry state. This refers to the density of the roadbed structure layers under dry conditions. As the reference frequency, This represents the frequency shift under dry conditions. This is the temperature compensation coefficient. The change in temperature This is the frequency-density conversion coefficient.

[0011] Preferably, after locating the water-rich section, the first resonance frequency of the water-rich section is obtained when it is in a dry state, and then the second resonance frequency of the water-rich section at the current moment is measured. Then, the maximum water depth of the water-rich section of the roadbed is calculated based on the first resonance frequency and the second resonance frequency.

[0012] Preferably, the formula for calculating the depth of water accumulation in the roadbed is as follows: in, This refers to the depth of water accumulation in the roadbed. The first resonant frequency, This is the second resonant frequency. Here, is the frequency-to-wave velocity conversion coefficient, a and b are empirical coefficients, dimensionless, and c is the reference wave velocity.

[0013] On the other hand, according to embodiments of the present invention, a road subgrade structural layer water-rich detection system is also provided. This system uses the aforementioned method for detecting water-rich areas in a road subgrade structural layer, including: An acoustic resonant cavity array is used to obtain the acoustic resonant frequency of the current roadbed and to measure the second resonant frequency of the water-rich section at the current moment. An inversion module, which is used to invert acoustic resonance frequencies into roadbed structure density; The model derivation module is used to construct a multiple linear regression model, calculate the density change using the difference elimination method, calculate the benchmark moisture content based on the subgrade structure density and density change, and then calculate the subgrade moisture content using the multiple linear regression model. The positioning module is used to locate the water-rich section based on the subgrade moisture content using the Kriging interpolation algorithm. A water accumulation depth calculation module is used to calculate the maximum water accumulation depth in water-rich sections of the roadbed.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes vehicle vibration energy to excite acoustic resonance. Through an acoustic resonant cavity array, resonance frequency data under multiple working conditions are collected to avoid the influence of environmental factors and obtain more accurate data. Then, a hybrid model combining differential elimination method and multiple linear regression is used to calculate the water content of each structural layer of the roadbed through frequency-density-moisture content relationship inversion. Based on the Kriging interpolation algorithm, three-dimensional positioning of water-rich areas is achieved, accurately and timely locating the location and depth of water-rich state in the roadbed. Attached Figure Description

[0015] Figure 1 This is a diagram of a water accumulation detection method according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for detecting water accumulation in the roadbed structure layer, including: Obtain the acoustic resonance frequency of the roadbed at the current location, and then invert the acoustic resonance frequency into the roadbed structure density; This invention determines the condition of the roadbed based on acoustic frequencies. Therefore, it is necessary to install appropriate acoustic detection instruments at suitable locations around the roadbed. However, the roadbed itself has a complex structure, and directly detecting the acoustic characteristics of the roadbed itself has a large error. Therefore, this invention uses an acoustic resonant cavity array to convert vehicle vibration energy into measurable acoustic resonant frequencies, thereby indirectly determining the condition of the roadbed. Through the acoustic resonant cavity array, resonant frequency data under multiple working conditions are collected, avoiding the influence of environmental factors, so as to collect more accurate data.

[0018] The acoustic resonant cavity array is the core sensor of this system, responsible for converting vehicle vibration energy into measurable acoustic resonant frequencies. The acoustic resonant cavity array is installed inside the roadbed drainage pipe, arranged in an array at a spacing of 5-10m in the Φ100mm transverse drainage pipe, and fixed to the inner wall of the existing road transverse drainage pipe by a ring-shaped cross brace to form a sensor array. Its resonant cavity is a Helmholtz type stainless steel cavity with a specific natural frequency. The resonant cavity adopts a waterproof and sealed design and uses a high-sensitivity sound pressure sensor MEMS microphone (sensitivity −42±3dB) to receive vehicle vibration energy and capture acoustic resonant frequencies through mechanical coupling.

[0019] After the acoustic resonant cavity array is installed, all parameters of the roadbed in a dry state must be measured immediately, including acoustic resonant frequency, temperature and density, and saved in the database for subsequent calculation and judgment of the water-rich state of the roadbed.

[0020] At the current moment, the acoustic resonant frequency at its installation location is obtained using an acoustic resonant cavity array, and then cleaned and optimized to ensure data quality.

[0021] (1) Noise reduction filtering: Adaptive filtering algorithm is used to eliminate environmental vibration noise interference.

[0022] (2) Validity correction: The formula for calculating the validity correction is as follows: in, The effective acoustic resonance frequency. Let be the acoustic resonance frequency at the k-th measurement. denoted as , where is the signal-to-noise ratio of the acoustic resonance frequency at the k-th measurement.

[0023] Effectiveness correction can correct the acquired acoustic resonant frequencies affected by the acoustic resonant cavity array device itself to an accurate signal.

[0024] Then, the optimized acoustic resonance frequency (f) is inverted into the density (ρ) of the subgrade structural layer: in, The density of the subgrade structure layer calculated during the i-th measurement, in units of k is the dynamic density calibration coefficient, determined by the cavity geometry and material properties, and its unit is... , The acoustic resonant frequency obtained from the i-th measurement is in Hz. n is the energy dissipation index, which needs to be determined by fitting through dry / wet condition experiments. The theoretical value is 2. C is the background density offset, which is used to compensate for the systematic offset of the resonant frequency caused by the mass of the acoustic resonant cavity array itself and the installation preload. This value is determined by installing the acoustic resonant cavity array in a standard medium of known density and then using the acoustic resonant cavity array for benchmark testing.

[0025] The density change was calculated using the differential elimination method, and then the reference moisture content was calculated based on the subgrade structure density and the density change. The formula for calculating the density change using the difference elimination method is as follows: Where Δρ is the density change in the current state relative to the dry state. The density of the roadbed structural layer under dry conditions (g / cm³) 3 ), β is the temperature compensation coefficient ( / ℃), and Δf is the frequency offset (Hz) under dry conditions. The reference frequency (Hz) The change in temperature This is the frequency-density conversion coefficient.

[0026] The density change is the density of the subgrade structure layer between the initial moment (dry state) and the current moment. However, the temperature change of the subgrade will also affect the calculation of the density change. Therefore, when the differential elimination method is used to calculate the density change of the subgrade structure layer in this invention, temperature compensation is required, that is, the calculation of the temperature compensation coefficient.

[0027] The formula for calculating the temperature compensation coefficient is as follows: in This is the baseline value for stainless steel (1.8 × 10−4 / ℃). This is the soil correction factor for the roadbed at the current location (0.12 for sand and 0.25 for clay). This is the real-time temperature (°C) of the roadbed at the current location. The reference temperature is (°C). This represents the highest ambient temperature of the roadbed at the current location during the measurement period, expressed in °C.

[0028] A multiple linear regression model was constructed, and then the subgrade moisture content was calculated using the multiple linear regression model based on the baseline moisture content. The reference moisture content of the roadbed is the moisture content under specific temperature changes and noise conditions at the time of the first measurement.

[0029] Based on the change, the reference moisture content of the roadbed is calculated. ).

[0030] Where ρs is the soil particle density (g / cm³) 3 ), representing the density of the solid particles in the roadbed structure layer itself, which is a constant.

[0031] Next, a multiple linear regression model was constructed to calculate the subgrade moisture content. Using data such as temperature (T), soil type, and auxiliary sensor data as input features, a multiple linear regression model is used for training and correction. The model then predicts the outcome using a linear fitting formula. : in, The subgrade moisture content (%) For the intercept term, The contribution rate of the baseline moisture content (%) Temperature sensitivity (%·℃⁻¹). Auxiliary density coefficient (%·cm) 3 / g), For auxiliary density (g / cm) 3 ), This is the random error corrected for noise.

[0032] Based on the subgrade moisture content, the Kriging interpolation algorithm is used to locate the water-rich section, and then the maximum water depth of the water-rich section is calculated.

[0033] Kriging interpolation is an algorithm for solving spatial interpolation problems. Spatial interpolation is a problem of estimating the attribute value of any point in space given the observed values ​​of a certain attribute (such as temperature or altitude) at several discrete points in space. Since geographical attributes have spatial correlations, things that are close to each other will be more similar. Therefore, this invention uses Kriging interpolation to estimate the subgrade moisture content at other locations based on the subgrade moisture content at the current location.

[0034] The mutation function model of the Kriging interpolation algorithm is as follows: Where h is the straight-line distance between the current position and the current position, the IDW algorithm is automatically switched when the interpolation error is greater than 10%.

[0035] For the same location, a roadbed moisture content is measured beforehand when the roadbed is dry. Then, after rainfall or other environmental factors cause water accumulation in the roadbed, a new roadbed moisture content is measured. Therefore, the difference between the current roadbed moisture content under dry conditions and after rainfall is calculated. As the change in water content at the current location and time, a threshold criterion is set, and the water-rich level is automatically identified and classified according to the change in water content, as shown in Table 1, and a graded early warning is generated.

[0036] Table 1: Standards for Classifying Water Abundance Levels 5%-10% normal green No processing required 10%-15% higher yellow monitor 15%-20% high orange color drain More than 20% Extremely high red Emergency treatment Then, based on the changes in water content of the waterlogged sections and the classification criteria in Table 1, each waterlogged location is marked, and its urgency of treatment is marked. In this way, the water content of the roadbed at the adjacent locations is determined by the Kriging interpolation algorithm. Then, based on Table 1, it is determined whether all locations are waterlogged sections, realizing three-dimensional positioning of waterlogged areas and accurately and timely locating the locations of roadbeds that may be in a waterlogged state.

[0037] After identifying all waterlogged areas, the first resonance frequency of the waterlogged section is obtained when it is dry. Then, the second resonance frequency of the waterlogged section at the current moment is measured. Finally, the maximum water depth of the waterlogged section of the roadbed is calculated based on the first and second resonance frequencies.

[0038] in, This refers to the depth of water accumulation in the roadbed. The first resonant frequency, This is the second resonant frequency. Here, is the frequency-to-wave velocity conversion coefficient, a and b are empirical coefficients, dimensionless, and c is the reference wave velocity.

[0039] On the other hand, embodiments of the present invention also provide a road subgrade structural layer water-rich detection system, which uses the above-mentioned road subgrade structural layer water-rich detection method, including: An acoustic resonant cavity array is used to obtain the acoustic resonant frequency of the roadbed at the current location and to measure the second resonant frequency of the water-rich section at the current moment. An inversion module, which is used to invert acoustic resonance frequencies into roadbed structure density; The model derivation module is used to construct a multiple linear regression model, calculate the density change using the difference elimination method, calculate the benchmark moisture content based on the subgrade structure density and density change, and then calculate the subgrade moisture content using the multiple linear regression model. The positioning module is used to locate the water-rich section based on the subgrade moisture content using the Kriging interpolation algorithm. A water accumulation depth calculation module is used to calculate the maximum water accumulation depth in water-rich sections of the roadbed.

[0040] Finally, it should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting water accumulation in the roadbed structure layer, characterized in that: include: An acoustic resonant cavity array is installed inside the roadbed drainage pipe, arranged in an array at a spacing of 5-10m in the Φ100mm transverse drainage pipe. It is fixed to the inner wall of the existing road transverse drainage pipe by a ring cross brace to form a sensor array. Its resonant cavity is a Helmholtz type stainless steel cavity. It receives vehicle vibration energy through mechanical coupling, captures the acoustic resonant frequency, and then inverts the acoustic resonant frequency into the roadbed structure density. After the acoustic resonant cavity array is installed, all parameters of the roadbed in a dry state must be measured immediately, including acoustic resonant frequency, temperature and density, and saved in the database. Measure the current ambient temperature of the roadbed at the current location and calculate the temperature compensation coefficient based on the current ambient temperature. Obtain the reference frequency of the roadbed at the current location, and then calculate the density change based on the temperature compensation coefficient and the reference frequency. Use the differential elimination method to calculate the density change, and then calculate the reference moisture content based on the roadbed structure density and the density change. A multiple linear regression model is constructed, with benchmark moisture content, temperature, soil type, and auxiliary sensor data as input features. The model is trained and corrected using the multiple linear regression model, and prediction is made through a linear fitting formula. Then, based on the benchmark moisture content, the multiple linear regression model is used to calculate the subgrade moisture content. Based on the subgrade moisture content, the Kriging interpolation algorithm is used to locate the water-rich section of the subgrade. The first resonant frequency of the water-rich section is obtained when it is dry. Then, the second resonant frequency of the water-rich section at the current moment is measured. Finally, the maximum water depth of the water-rich section of the subgrade is calculated based on the first and second resonant frequencies.

2. The method for detecting water accumulation in the road subgrade structure layer as described in claim 1, characterized in that: After acquiring the acoustic resonance frequency, noise reduction filtering is applied to the acoustic resonance frequency, followed by effectiveness correction.

3. The method for detecting water accumulation in the road subgrade structure layer as described in claim 2, characterized in that: The formula for calculating the validity correction is as follows: in, The effective acoustic resonance frequency. Let be the acoustic resonance frequency at the k-th measurement. denoted as , where is the signal-to-noise ratio of the acoustic resonance frequency at the k-th measurement.

4. The method for detecting water accumulation in the road subgrade structure layer as described in claim 1, characterized in that: The formula for calculating the density change is as follows: Where Δρ is the density change in the current state relative to the dry state. This refers to the density of the roadbed structure layers under dry conditions. As the reference frequency, This represents the frequency shift under dry conditions. This is the temperature compensation coefficient. The change in temperature This is the frequency-density conversion coefficient.

5. The method for detecting water accumulation in the road subgrade structure layer as described in claim 1, characterized in that: The formula for calculating the depth of water accumulation in the roadbed is as follows: in, This refers to the depth of water accumulation in the roadbed. The first resonant frequency, This is the second resonant frequency. Here, is the frequency-to-wave velocity conversion coefficient, a and b are empirical coefficients, dimensionless, and c is the reference wave velocity.

6. A system for detecting water accumulation in road subgrade structural layers, characterized in that: The system uses a method for detecting water accumulation in the road subgrade structure layer as described in any one of claims 1-5, comprising: An acoustic resonant cavity array is used to obtain the acoustic resonant frequency of the roadbed at the current location and to measure the second resonant frequency of the water-rich section at the current moment. An inversion module, which is used to invert acoustic resonance frequencies into roadbed structure density; The model derivation module is used to construct a multiple linear regression model, calculate the density change using the difference elimination method, calculate the benchmark moisture content based on the subgrade structure density and density change, and then calculate the subgrade moisture content using the multiple linear regression model. The positioning module is used to locate the water-rich section based on the subgrade moisture content using the Kriging interpolation algorithm. A water accumulation depth calculation module is used to calculate the maximum water accumulation depth in water-rich sections of the roadbed.

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