Asphalt pavement structure dynamic response acquisition and traffic load assessment analysis method

Abstract

The invention provides an asphalt pavement structure dynamic response acquisition and traffic load assessment analysis method, and relates to the technical field of building design, and high-density and low-deviation data acquisition is realized by uniformly arranging distributed optical fiber sensor arrays coated with low-modulus polyurethane in the longitudinal and transverse directions of an asphalt pavement structure; meanwhile, a matching error correction model is established, strain and temperature data deviation are effectively corrected, and the accuracy of traffic load intensity calculation is improved; a non-linear inversion technology and dynamic load grading judgment are combined, static threshold limitation is broken through, a threshold optimization scheme based on continuous monitoring is provided, traffic signal regulation and traffic flow induction measures are integrated, dynamic load limiting processing of a road section and an adjacent road section is achieved, and the intelligent level of safety monitoring and traffic management of an asphalt pavement structure is improved.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, specifically to a method for collecting dynamic response data of asphalt pavement structures and analyzing traffic load. Background Technology

[0002] In urban areas with high traffic density and significant road surface temperature differences, particularly on major trunk asphalt roads frequently used by heavy-duty vehicles, real-time dynamic monitoring of structural strain and temperature distribution is crucial. A distributed fiber optic sensor array coated with low-modulus polyurethane, effectively balancing flexibility and sensing accuracy, enables multi-point longitudinal and transverse strain and temperature data acquisition from the asphalt pavement, providing robust technical support for dynamic response analysis of the pavement structure. This enhances the ability of road management units to perceive and control traffic load conditions.

[0003] The existing technology, with publication number CN116910875B and titled "A Method for Life-Cycle Maintenance Planning of Asphalt Pavement Considering Ecological Benefits," includes: acquiring asphalt pavement parameters and simulating asphalt pavement service performance to obtain pavement service life indicators; simulating asphalt pavement technical condition evaluation indicators to obtain asphalt pavement technical condition evaluation indicators; judging the asphalt pavement technical condition evaluation indicators to obtain different maintenance engineering schemes; acquiring quantitative data on environmental emissions from each maintenance project and the asphalt pavement operation stage; conducting an environmental impact assessment of the asphalt pavement operation and maintenance stage; obtaining and comparing the normalized environmental impact assessment results of each maintenance project; and determining the asphalt pavement maintenance planning scheme with optimal ecological benefits. This method can reasonably quantify and predict the service performance of asphalt pavement during the operation and maintenance stage, thus realizing life-cycle maintenance planning of asphalt pavement considering ecological benefits.

[0004] However, several technical bottlenecks and shortcomings still exist:

[0005] 1. The mismatch in elastic modulus between the sensor coating material and the asphalt layer has not been effectively resolved, resulting in systematic deviations in strain and temperature data, which affects the accuracy of load inversion;

[0006] 2. Traditional load intensity assessments rely heavily on static thresholds and lack dynamic adaptive adjustment mechanisms, failing to effectively reflect the time-varying characteristics of traffic flow and road surface conditions.

[0007] 3. The synchronous processing of monitoring data and the nonlinear load inversion algorithm have not been fully optimized, resulting in insufficient calculation accuracy and real-time performance. This limits the effectiveness of data-driven dynamic load control strategies, lacks high-resolution dynamic response data and real-time feedback control capabilities, and makes it difficult to meet the needs of dynamic adjustment of urban traffic load.

[0008] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to provide a method for collecting dynamic response data of asphalt pavement structures and for assessing and analyzing traffic load, so as to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] The method for collecting dynamic response data and analyzing traffic load on asphalt pavement structures includes the following steps:

[0012] A method for collecting dynamic response data and assessing traffic load on asphalt pavement structures, characterized by the following specific steps:

[0013] Step S1: Distributed fiber optic sensor arrays coated with low-modulus polyurethane are uniformly deployed in the longitudinal and transverse directions of the urban asphalt pavement structure to collect strain and temperature data of the target road segment and adjacent road segments during the monitoring period.

[0014] Step S2: Obtain the initial matching error ΔE between the elastic modulus of the low modulus polyurethane coating material and the asphalt layer, and establish a correlation correction model between the initial matching error ΔE and the strain data deviation and temperature data deviation, which is used to correct the strain data and temperature data collected during the monitoring period.

[0015] Step S3: Simultaneously process the corrected strain and temperature data of the target road segment and adjacent road segments respectively, and use the nonlinear inversion model to calculate the traffic load intensity of the target road segment and adjacent road segments respectively;

[0016] Step S4: Compare the calculated traffic load intensity with the preset threshold to perform dynamic vehicle load classification and generate load classification results for the target road segment and adjacent road segments.

[0017] Step S5: Conduct a comprehensive analysis of the load classification results of the target road segment and adjacent road segments, and implement corresponding dynamic load control through traffic signals and traffic flow guidance.

[0018] Step S6: During the next monitoring period, continuously collect strain data and temperature data of the target road segment, calculate the traffic load intensity of the target road segment after traffic control, and correct and optimize the preset threshold.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: By uniformly deploying distributed fiber optic sensor arrays coated with low-modulus polyurethane in both longitudinal and transverse directions of the asphalt pavement structure, high-density, low-bias data acquisition is achieved; simultaneously, a matching error correction model is established to effectively correct for strain and temperature data deviations, improving the accuracy of traffic load intensity calculation; combining nonlinear inversion technology and dynamic load classification judgment, the static threshold limitation is overcome, and a threshold optimization scheme based on continuous monitoring is proposed, significantly enhancing the dynamic adaptability of load assessment. Furthermore, by integrating traffic signal control and traffic flow guidance measures, dynamic load limiting processing of road segments and adjacent road segments is achieved, effectively solving the problems of insufficient data integrity, lack of error correction, and lag in load judgment in existing technologies, significantly improving the intelligent level of asphalt pavement structure safety monitoring and traffic management. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0022] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0023] Example 1:

[0024] Please see Figure 1 The present invention provides a technical solution:

[0025] The method for collecting dynamic response data and analyzing traffic load on asphalt pavement structures includes the following steps:

[0026] Step S1: Distributed fiber optic sensor arrays coated with low-modulus polyurethane are uniformly deployed in the longitudinal and transverse directions of the urban asphalt pavement structure to collect strain and temperature data of the target road segment and adjacent road segments during the monitoring period.

[0027] Further explanation: The distributed optical fiber sensor array is based on the scattering effect within the optical fiber; the scattering effect includes Rayleigh scattering and Brillouin scattering;

[0028] Rayleigh scattering is used for high-sensitivity strain detection, while Brillouin scattering is used for accurate temperature decoupling and measurement.

[0029] Low-modulus polyurethane-coated optical fibers are symmetrically and evenly laid out along the longitudinal and transverse directions of the asphalt pavement structure according to the preset positioning drawings, and fixed between the layers of the target road section and adjacent road sections.

[0030] The strain data at spatial location (x,y) at time t is denoted as ε(x,y,t), and the temperature data is denoted as T(x,y,t);

[0031] The target road segment is represented by index h1, and the adjacent road segments are represented by index h2;

[0032] In this embodiment, the layers represent the relationship between the surface layer and the base layer, for the following reasons:

[0033] The surface layer is the uppermost layer of asphalt mixture paving on the road surface; the base layer is the structural load-bearing layer below the surface layer, which is composed of coarser asphalt mixture or non-asphalt materials.

[0034] Mechanical protection and signal stabilization: Maintain a certain depth to reduce damage caused by direct vehicle crushing and ensure that the sensor can maintain stable operation when the structural load changes.

[0035] Strain and temperature sensing: The surface layer bears the direct load, while the base layer provides structural support. Sensors at this location can simultaneously sense the relative deformation and temperature changes of the pavement structure under the influence of vehicles and the environment, providing data support for comprehensive assessment.

[0036] Elastic modulus matching: Ensure that the elastic properties of the sensor match those of the two layers of material, which can improve sensing accuracy and long-term durability.

[0037] Ensure that the elastic modulus matches that of the asphalt layer, and connect to the optical time domain reflectometer (OTDR) and Brillouin scattering analyzer via a dedicated fiber optic connector to achieve synchronous data acquisition.

[0038] The specific implementation steps are as follows:

[0039] 1.1) Sensor deployment planning:

[0040] The monitoring scope is defined to include the target road segment and adjacent road segments, and the total length of fiber optic sensors is planned to be 5 to 10 kilometers to ensure complete coverage of the entire route.

[0041] Initially, based on a 5-meter resolution standard, the total number of nodes was calculated, and uniform spacing in both the longitudinal and transverse directions was designed to form a grid sensing unit;

[0042] The resolution standards in this embodiment also include 20 meters, 100 meters, 200 meters and 300 meters;

[0043] 1.2) Fiber optic sensor material selection and coating process:

[0044] Polyurethane material with an elastic modulus of 0.5MPa to 1.5MPa was selected as the optical fiber cladding layer to achieve matching with the elastic modulus of the asphalt layer and reduce interference error.

[0045] After polyurethane coating, the mechanical strength and sensing sensitivity of the test optical fiber are improved to ensure monitoring stability under traffic load.

[0046] 1.3) Installation and deployment:

[0047] A prefabricated distributed optical fiber sensor array is laid between the surface layer and the base layer of the asphalt pavement structure, and special anchors are used to maintain the tension and position of the optical fibers.

[0048] Ensure that the fiber optic cables are arranged uniformly in both the longitudinal and transverse directions, and that the density meets the specified spacing to achieve continuous spatial sensing.

[0049] 1.4) Data Acquisition System Configuration:

[0050] The optical fiber is connected to the distributed sensing instrument, and the strain data based on Rayleigh scattering is collected by the OTDR module and the temperature data is collected by the Brillouin analysis module.

[0051] Configure a synchronous acquisition system with a sampling frequency of 10Hz to acquire strain data ε(x,y,t) and temperature data T(x,y,t) in real time, and ensure accurate and synchronized timestamp marking.

[0052] 1.5) Data Processing:

[0053] Using the built-in algorithm of the sensor, strain data ε(x,y,t) can be calculated based on the drift of Rayleigh scattering spectral lines. The calculation formula is as follows:

[0054]

[0055] In the formula, Rayleigh frequency shift, in Hz;

[0056] The strain sensitivity coefficient, in Hz / με, is determined based on calibration experiments;

[0057] The temperature data T(x,y,t) is calculated using the Brillouin scattering frequency drift. The calculation formula is as follows:

[0058]

[0059] in, Brillouin frequency shift, in Hz; Temperature sensitivity coefficient, in Hz / °C;

[0060] Strain interference was eliminated by decoupling using Rayleigh scattering data;

[0061] This step, through deterministic fiber optic deployment length, spatial resolution, and sampling frequency, combined with polyurethane soft-coating material to accurately match the asphalt elastic modulus, and explicitly employs Rayleigh and Brillouin scattering dual-channel technology to simultaneously acquire road surface strain and temperature, ensuring high sensor sensitivity and independent signal decoupling; compared to traditional single-scattering or high-modulus coated fiber optic sensing schemes, this embodiment achieves higher measurement accuracy and durability, meeting the dynamic real-time monitoring requirements under complex urban traffic loads and temperature variation environments.

[0062] Example 2:

[0063] Step S2: Obtain the initial matching error ΔE between the elastic modulus of the low modulus polyurethane coating material and the asphalt layer, and establish a correlation correction model between the initial matching error ΔE and the strain data deviation and temperature data deviation, which is used to correct the strain data and temperature data collected during the monitoring period.

[0064] Further explanation: The initial matching error ΔE is defined as the elastic modulus of the low-modulus polyurethane coating material. With the elastic modulus of the asphalt layer The difference, that is ;

[0065] in, and The units are all megapascals (MPa);

[0066] It should be noted that: In this embodiment, the elastic modulus was tested using a standard material testing instrument at room temperature (20±2)℃. The elastic modulus of the polyurethane coating material and the asphalt layer were tested at three points for bending or compression, and the elastic modulus values ​​were recorded.

[0067] The initial matching error ΔE is set to the range of [-E1, E1].

[0068] In this embodiment, E1 is set to 3 MPa. This value is determined based on material performance test data and actual engineering tolerance range, and can cover the modulus error caused by normal manufacturing and environmental influences.

[0069] The correlation correction model includes strain correction coefficients. and temperature correction factor ;

[0070] This embodiment determines the strain correction coefficient through preliminary experiments. and temperature correction factor ;

[0071] Regression analysis was performed on the strain data ε(x,y,t) acquired by the polyurethane-coated fiber optic sensor and the corresponding standard strain gauge data to fit the strain correction coefficient. ;

[0072] strain correction factor Defined as the relative strain correction ratio caused by modulus matching error per unit MPa, with units of 1 / MPa, this embodiment is set based on experimental results. The range is 0.01 to 0.051 MPa;

[0073] Similarly, sensitivity analysis is performed on the temperature data T(x,y,t) to determine the temperature correction coefficient. It is defined as the temperature reading correction range corresponding to the modulus error per unit MPa, with the unit being °C / MPa, and the experimental values ​​are limited to 0.1~0.5°C / MPa;

[0074] The effectiveness of the coefficients was verified by using a linear fitting model to ensure that the fitting correlation coefficient R² ≥ 0.9, thus guaranteeing the accuracy of the model.

[0075] The correlation correction model uses strain correction coefficients. Linear analysis was performed on the collected strain data ε(x,y,t) to obtain the corrected strain data. ;

[0076] The correlation correction model uses a temperature correction factor. Linear analysis was performed on the collected temperature data T(x,y,t) to obtain the corrected temperature data. .

[0077] The formula for correcting strain data in the correlation correction model is:

[0078]

[0079] The temperature data correction formula for the correlation correction model is:

[0080]

[0081] parameters , The initial matching error ΔE is entered into the data processing system as an input variable.

[0082] The above correction algorithm is applied in batches to the real-time strain and temperature data collected by monitoring to generate a corrected dataset.

[0083] Error tolerance judgment module: If |ΔE|>3MPa, the system will automatically pause the data correction function and issue a structural anomaly prompt for engineers to review.

[0084] This embodiment is applicable to the compensation of strain and temperature data measurement errors caused by the initial mismatch between the elastic modulus of the polyurethane coating layer and the asphalt layer material in asphalt pavement structural health monitoring. Under long-term dynamic monitoring and multivariate conditions, it promptly corrects the impact of material modulus changes on sensor sensitivity, ensuring the continuous accuracy of the data and facilitating subsequent pavement damage identification and traffic load inversion.

[0085] The experiment clarified the range of correction parameters and the calculation model, and integrated automatic parameter correction and dynamic calculation functions, avoiding the arbitrariness and inefficiency of traditional experience-based adjustments. It achieved quantitative data compensation based on material physical property errors, significantly improving the engineering applicability and accuracy of monitoring data.

[0086] It should be noted that the following experimental content is explained based on the above:

[0087] To verify the effectiveness of constructing a strain and temperature correction model using the initial matching error ΔE between the elastic modulus of low-modulus polyurethane coating material and the asphalt layer, a series of indoor simulation monitoring experiments were designed and conducted. First, three sets of polyurethane coating material samples with different elastic moduli were prepared, representing elastic moduli of 5 MPa, 7 MPa, and 9 MPa, respectively. The corresponding elastic modulus of the asphalt layer was 7 MPa, thus generating different degrees of modulus matching error ΔE. The elastic modulus of the materials was accurately determined using a standard three-point bending test. The testing instrument was an Instron 5944 universal testing machine, with the loading speed set to 2 mm / min and the temperature controlled at 20 ± 1℃.

[0088] Based on the above samples, composite test plates were prepared, and low-modulus polyurethane-coated fiber optic sensors were deployed at different locations. A distributed fiber optic sensing system was used to collect strain and temperature data in real time. To simulate actual working conditions, a servo hydraulic loader was used to apply a 50kN cyclic load for 20 minutes, during which monitoring data was continuously collected. The raw strain data (με) obtained showed deviations compared to the data collected by the standard strain gauge; simultaneously, changes in ambient temperature introduced temperature data deviations. Based on a pre-designed correction model, the following correction formula was used for calculation:

[0089] The formula for correcting strain data in the correlation correction model is:

[0090]

[0091] The temperature data correction formula for the correlation correction model is:

[0092]

[0093] in, The default value is 0.031 MPa. Take 0.3°C / MPa.

[0094] The study focused on testing the compensation effect of different initial matching errors ΔE on strain and temperature data deviations to verify the accuracy and practicality of the correction formula. Data acquisition was conducted at a frequency of 1 Hz, with acquisition points distributed at three locations along the fiber optic sensor array: near, middle, and far. After the experiment, the data before and after correction were compared with standard sensor data to calculate the error rate and correlation, generating a data table to demonstrate the effective improvement of the model.

[0095] Table 1. Validity study of the data correction formula for the correlation correction model:

[0096]

[0097] The above data shows that, with different initial matching errors ΔE, the original strain data deviates significantly from the standard value, while the original strain data deviates significantly from the standard value through correction coefficients. and After correction, the strain data error rate decreased significantly from a maximum of 13.95% to less than 1.77%, and the temperature error also decreased significantly. The error between the corrected temperature and the standard value was controlled within ±0.3°C.

[0098] Based on the ΔE values ​​of the six sample test plates in the table, the test plates are divided into three main intervals:

[0099] Interval 1: Negative matching error, ΔE∈[-3,-1]MPa (test plates A and D);

[0100] Interval 2: Zero or extremely small matching error, ΔE≈0MPa (Test plate B);

[0101] Interval 3: Positive matching error, ΔE∈[1,3]MPa (test plates C, E and F);

[0102] The negative ΔE results in a lower elastic modulus of the coating material compared to the asphalt layer, leading to an underestimation of the original strain measurement. This needs to be addressed in the corrected model. The negative correction factor, multiplied by the original data, increases the correction value, adjusting the underestimation towards the standard value. The error rate in this interval is significantly reduced, indicating that the formula exhibits a strong compensation effect when the modulus is insufficient, reducing measurement deviation by approximately 50% to 70%.

[0103] When ΔE=0, the correction coefficient term is zero, and the correction formula automatically maintains the original value, ensuring that no offset occurs, thus verifying the inherent consistency and accuracy of the correction model.

[0104] A positive ΔE indicates that the elastic modulus of the coating material is higher than that of the asphalt layer, leading to increased sensor sensitivity to local strain and thus higher data acquisition. The correction is achieved by adding a correction factor. This reduces the original strain and corrects overestimation errors. The precise linear adjustment significantly reduces the discrepancy between sensor data and standard measurements, demonstrating the correction formula's ability to manage scenarios with excessive modulus.

[0105] As ΔE changes sign, temperature correction is achieved through addition and subtraction. Adjusting the temperature deviation reveals a linear dependence, and the correction coefficient... The selection of this value ensures that the temperature compensation amount is reasonable and avoids over-correction.

[0106] Negative ΔE reduces the temperature reading, slightly lowering the deviation, while positive ΔE increases the temperature reading, adjusting the original temperature upwards if it is too low, ensuring that the temperature data correction direction is consistent with the strain correction logic.

[0107] The increase in ΔE leads to a correction multiplier. The strain data is adjusted from less than 1 to greater than 1.

[0108] Temperature correction is performed using an addition / subtraction method: a negative ΔE decreases the temperature reading, while a positive ΔE increases the temperature reading, ensuring that the temperature data correction is consistent with the direction of material performance deviation.

[0109] The error rate of strain data is reduced by more than 50%-85%, significantly improving data reliability.

[0110] The peak deviation of temperature data is controlled within ±0.3°C, improving the sensing error caused by the influence of ambient temperature. By systematically quantifying the strain and temperature data errors through a trajectory-controllable correction coefficient, the blindness and arbitrariness of traditional empirical correction are avoided.

[0111] In summary, the strain and temperature correction formulas constructed based on the steps of this invention exhibit good adjustment performance in the load range, zero load range, and positive load range, and effectively reduce the data deviation between the original data and the standard reference. This demonstrates the inherent linear synergistic relationship and high adaptability between the parameters, and achieves accurate data compensation function, fully verifying the effectiveness and superiority of the invention's technical solution.

[0112] Example 3:

[0113] Step S3: Simultaneously process the corrected strain and temperature data of the target road segment and adjacent road segments respectively, and use the nonlinear inversion model to calculate the traffic load intensity of the target road segment and adjacent road segments respectively;

[0114] Further explanation: The corrected strain data and temperature data Data identified as target road segment h1 and adjacent road segment h2, respectively;

[0115] It is necessary to ensure that the sampling frequency of these data is consistent with the spatial resolution of the sampling point location, complete the time and space alignment, and ensure the synchronization of subsequent model inputs.

[0116] A thermo-structural nonlinear inversion model based on multi-parameter coupling is selected, and the model form is defined as follows:

[0117]

[0118] in, Denotes the correlation function, i∈{1,2}; Indicates the first Traffic load intensity at location (x,y) and time t of road segment; These are the corrected strain data; This is the corrected temperature data. It includes, but is not limited to, elastic modulus, coefficient of thermal expansion, and sensor sensitivity parameters.

[0119] It should be noted that the calculation formula uses a uniform dimensional treatment to account for differences in calculation units with different effects;

[0120] The nonlinear model is chosen to be a hybrid model combining empirical fitting and physical coupling equations. The correlation function is specifically expressed as follows:

[0121]

[0122] in, The coefficient adjusts the strength of the relationship between strain and load. The power exponent of strain reflects the nonlinear sensitivity and has a value range of 1 to 2. Reflects the degree of attenuation or enhancement of the effect of temperature; This is the model bias term, used to correct for systematic errors.

[0123] Using a pre-set field calibration test dataset, a nonlinear least squares method was employed for fitting. , , , Parameters are set to ensure that the fitting error is below 5%;

[0124] The data of the target road segment h1 is used first to fit the model to ensure the accuracy of the core area. Then, the fitting parameters are used to verify the model's universality against the data of the adjacent road segment h2.

[0125] For each spatial coordinate point (x, y) and time point t of the target road segment h1 and the adjacent road segment h2, input the corresponding corrected strain data. and temperature data Calculate traffic load intensity .

[0126] With strain value The increase in traffic load intensity It exhibits a non-linear, rapid upward trend;

[0127] In nonlinear inversion models The rules for determining the value of are explained in detail below:

[0128] Initial settings If the value is negative, then the exponential function The decrease with increasing temperature reflects the trend that the load estimate decreases with rising temperature, which is more consistent with the mechanical behavior of typical asphalt materials.

[0129] Strain and actual load data at different temperatures are collected, and obtained through nonlinear fitting. The parameters are compared using positive and negative signs to determine the fitting error.

[0130] For validation of the experimental data fit:

[0131] Fitting was performed using synchronous strain-temperature-load data collected under multiple temperature conditions in the field or laboratory:

[0132] Determining traffic load intensity from fitted curves Does it show a decreasing trend as temperature increases? The correlation coefficient is negative and the fitting correlation coefficient is high, which verifies... Negative values ​​are reasonable;

[0133] If the fitting results show that the load increases with increasing temperature, and physical tests support this conclusion—that is, the load increases slightly after the low-temperature brittle material softens—then... Optional positive sign value;

[0134] Furthermore, the sign of the exponential function affects the model's sensitivity to temperature perturbations; an excessively large positive sign... This will cause a slight increase in temperature to cause an explosion in the load index, making it difficult to fit actual engineering data and reducing the robustness of the model;

[0135] Moderate negative This can mitigate the drastic impact of temperature on load estimation, resulting in smoother model output that aligns with engineering experience; the model in this embodiment is limited. The range is To prevent numerical divergence.

[0136] The calculated traffic load intensity It is stored in real time on the data management platform and supports multi-dimensional visualization analysis.

[0137] Data synchronization processing ensures load comparison of target road segments and adjacent road segments at the same time point, enabling dynamic monitoring of regional traffic load distribution.

[0138] This step is applicable to the dynamic monitoring of structural health and traffic load of urban arterial roads and adjacent auxiliary roads. It is especially suitable for monitoring traffic flow intersections and load spillover effects. By using a nonlinear coupling model to quantitatively invert multi-factor data, it improves the accuracy of traffic load identification and meets the refined needs of road maintenance and traffic management.

[0139] This solution addresses the issues of synchronization and inconsistency in data processing during multi-segment collaborative monitoring of roads, improving the accuracy and stability of traffic load identification. Furthermore, parameters can be flexibly adjusted based on on-site calibration, effectively meeting the engineering feasibility requirements for various application scenarios and achieving a combination of innovation and practicality.

[0140] Example 4:

[0141] Step S4: Compare the calculated traffic load intensity with the preset threshold to perform dynamic vehicle load classification and generate load classification results for the target road segment and adjacent road segments.

[0142] Further explanation: Both the target road segment h1 and the adjacent road segment h2 are divided into several grid cells; the specific implementation steps are as follows:

[0143] Mesh cell generation principles and size settings:

[0144] Based on the asphalt road planning and construction, the actual lengths of the target road segment h1 and the adjacent road segment h2 are determined through pre-measurement, and the matching grid unit specifications are set according to the optimal sensing orientation of the fiber optic sensor array.

[0145] Each grid cell is set to have a length of no more than 5 meters along the road segment and a width of strictly maintained at 1 meter to ensure effective coverage of the fiber optic sensors and data accuracy.

[0146] If the length of the target road segment h1 or the adjacent road segment h2 is greater than 5 meters, an incremental configuration method should be adopted to divide it into a number of equal-length grid units for segment-by-segment perception and data processing.

[0147] The location of each grid cell is identified by coordinates (x, y) to ensure that it corresponds to the collected strain and temperature data, thus achieving accurate spatial mapping.

[0148] The traffic load intensity and the preset threshold are set as low load thresholds. and high load threshold ;

[0149] Based on the bearing capacity and fatigue limit of asphalt pavement, a low load threshold for traffic load intensity is set. The low load threshold in this embodiment The value is 40 in this embodiment. The value ranges from [0, 100], and the larger the value, the higher the traffic load intensity.

[0150] High load threshold in this embodiment It is 80.

[0151] For each grid cell (x, y) of the target road segment h1 and the adjacent road segment h2 at time t, the traffic load intensity is... Load levels are classified according to the following rules:

[0152] when The mesh element was determined to be a low-load element.

[0153] when The mesh element is determined to be a medium-load element.

[0154] when The mesh element was determined to be a high-load element.

[0155] During real-time monitoring, the load level information of all grid cells is continuously updated based on synchronous sensor data.

[0156] The update results include grid index (x, y), time label t, and corresponding hierarchical results. The data structure is saved as a two-dimensional matrix or database table.

[0157] The grading results are used as input for pavement structure health monitoring and traffic management decisions through a data management platform.

[0158] The steps in this embodiment are applicable to time-sharing and zone-based dynamic load management of urban main roads and adjacent auxiliary roads, assisting in maintenance decisions and traffic flow optimization, realizing effective load level classification based on sensor data and physical thresholds, and supporting rapid identification and early warning management of high-load areas.

[0159] It enables spatially refined and timely updates of load levels; by clarifying threshold limits, it forms a practically implementable hierarchical judgment standard, which significantly improves the scientific nature of the judgment and its applicability to engineering applications, and solves the problems of mismatch between hierarchical scales and response lag in traditional methods.

[0160] Example 5:

[0161] Step S5: Conduct a comprehensive analysis of the load classification results of the target road segment and adjacent road segments, and implement corresponding dynamic load control through traffic signals and traffic flow guidance.

[0162] Further explanation: Implementing dynamic load control measures based on traffic signals and traffic flow guidance, specifically including:

[0163] Real-time scanning of load classification results within all grid cells of target road segment h1 to detect whether target road segment h1 meets the requirements. High-load unit;

[0164] When there are m1 high-load units in the target road segment h1, and the interval between the m1 high-load units is lower than the preset interval. When the alarm signal is generated, the alarm content includes the specific coordinates (x, y) and the time t of the timestamp.

[0165] Set preset interval Constraints ; It is the preset lower limit of the interval. This is the preset interval limit.

[0166] Further explanation: The preset interval is based on the spatial distribution characteristics of high-load units in the historical monitoring data of the target road segment h1. The baseline value of the preset interval is determined by statistically analyzing the expected value and variance of the Euclidean distance between adjacent high-load units over multiple periods. The preset interval baseline value Based on the preset upper and lower limit ranges, and taking into account the real-time monitoring of the number and spacing dispersion of high-load units, the preset interval is dynamically adjusted. To adapt to changes in load distribution;

[0167] Set the desired number of mesh cells that meet the high load requirements to be [value]. ;

[0168]

[0169] in, This represents the total number of grid cells that meet the high load criteria detected within the r-th statistical period; R is the total number of statistical periods.

[0170] Set the desired mesh spacing between high-load elements ;

[0171] For adjacent high-load elements in the r-th cycle, calculate the Euclidean distance between the two points:

[0172]

[0173] in, Let be the coordinates of the j-th high-load element in the r-th period, in meters; ;

[0174] Further define the expected value of grid spacing The calculation formula is:

[0175]

[0176] Calculate the variance of the high-load element spacing To measure the dispersion of the spacing between high-load elements, the calculation method is as follows:

[0177]

[0178] Set the preset interval baseline value ; Expected grid spacing obtained from historical measurements As the preset interval baseline value , ;

[0179] Set the upper and lower limits of the preset interval to

[0180]

[0181]

[0182] in, It is the preset lower limit of the interval. This is the preset interval limit; and These are the adjustment factors for the corresponding upper and lower limits;

[0183] Based on real-time data at the current time t, set This represents the current number of high-load units;

[0184] The variance of the current high-load element spacing;

[0185] The preset interval is dynamically adjusted using the following formula. :

[0186]

[0187] in, The high load quantity influence coefficient ranges from 0.1 to 0.5, reflecting the degree of influence of quantity changes on the interval; The spacing distribution dispersion influence coefficient, with a value ranging from 0.05 to 0.2, characterizes the influence of distribution density on the spacing.

[0188] When in real time When the score is greater than the historical average, If positive, multiply by a positive coefficient. This causes the overall brackets to increase in size, and the increase in size acts on the... The corresponding intervals are increased, with a moderate degree of leniency; if

[0189] Current Spacing Variance Below the historical average, score Less than 1, subtract from the parentheses. Multiplying by this ratio results in a decrease in the overall value, and the preset interval is reduced accordingly, reflecting that the high-load units are more concentrated.

[0190] The product of the two parts determines the dynamic preset interval, and the value is adjusted based on actual engineering feedback. and; To obtain an appropriate sensitivity value.

[0191] Further explanation: The alarm signal is pushed to the city traffic management platform interface in real time through a high-speed communication interface to trigger traffic control actions;

[0192] The green light duration of traffic lights is dynamically adjusted, and the adjusted green light duration is denoted as G(t). The specific implementation steps are as follows:

[0193] Set a baseline green light duration for traffic lights. The unit is seconds, set according to the intersection traffic design standards.

[0194] The adjustment coefficient is defined based on the degree of exceeding limits in the Level 3 high-load zone. , The coefficients are calculated based on:

[0195]

[0196] Will and A combined analysis was performed to calculate the adjusted green light duration G(t);

[0197]

[0198] Where G(t) is the adjusted green light duration in seconds; the adjustment coefficient is... The severity of overloading on a road segment is positively correlated with the length of the green light; the more severe the overloading, the shorter the green light duration.

[0199] The control system sends G(t) to the traffic light control terminal in real time, enabling dynamic adjustment of the green light duration.

[0200] When an alarm signal is generated, the traffic flow guidance system dynamically guides vehicles to divert to an adjacent road segment h2 with a load level lower than the target road segment h1. The specific implementation steps are as follows:

[0201] Induction coefficient Defined as:

[0202]

[0203] in, It is the number of vehicles that receive guidance signals and change routes within time period t. It is the total number of vehicles passing through the target road segment h1 during the same period;

[0204] The traffic management platform dynamically adjusts the guidance coefficient based on real-time traffic flow and guidance effectiveness. Control the proportion of vehicles diverted to maintain a reasonable range and avoid overloading of adjacent road sections.

[0205] Traffic guidance instructions are pushed through the navigation vehicle system, including route adjustment instructions such as lane changing and avoidance.

[0206] The unified data management platform monitors the load classification results, traffic light parameters, and traffic flow guidance instruction execution status.

[0207] A feedback mechanism is set up for the traffic signal control and traffic flow guidance system to collect execution results in real time, which is used to dynamically adjust and optimize parameters. , .

[0208] By controlling the green light duration and the proportion of guided vehicles through quantitative adjustment coefficients, it is ensured that the load level classification results of the previous step can directly trigger specific traffic management response measures, realize the closed loop of the one-way technical link, and can finely control the shortening ratio of the green light and the traffic diversion ratio according to the degree of overload, so as to achieve the precision and intelligence of traffic scheduling.

[0209] Example 6:

[0210] Step S6: During the next monitoring period, continuously collect strain data and temperature data of the target road segment, calculate the traffic load intensity of the target road segment after traffic control, and correct and optimize the preset threshold.

[0211] Further explanation: During the next monitoring period following the implementation of traffic control measures, continuous strain data will be collected for the target road segment h1. and temperature data Complete collection;

[0212] In this embodiment, the next monitoring period is 1 week, that is, 7 days of continuous monitoring;

[0213] By combining the pre-defined correlation correction model and nonlinear inversion model, the spatiotemporal distribution of traffic load intensity of the target road segment h1 during the entire monitoring period is calculated. ;

[0214] For the already calculated For the dataset, calculate the following peak and median metrics:

[0215] Peak metrics: ;

[0216] Median indicator: ;

[0217] Combined with the safety margin parameter S, S takes values ​​in the range of [0.05, 0.15]; S is determined based on engineering experience and is used for sensitivity control of threshold adjustment.

[0218] The revised low load threshold and high load threshold are set as follows;

[0219]

[0220]

[0221] in, It is the corrected low load threshold. It is the corrected high load threshold.

[0222] When the monitoring data reflects that the actual intensity is generally lower than the benchmark, the low load threshold and high load threshold are reduced, and vice versa. The threshold adjustment has a linear response and is regulated by the safety margin parameter S to control the adjustment range and prevent excessive fluctuations from causing misjudgment.

[0223] After each monitoring period of data collection and analysis is completed, the threshold adjustment calculations in steps 3 and 4 are performed.

[0224] Adjusted threshold , The data is uploaded to the control system corresponding to the dynamic load limit adjustment, and used for load classification and traffic light green light duration adjustment in the next cycle.

[0225] This step is applicable to long-term urban road surface health monitoring and load management systems. It utilizes feedback data to continuously optimize threshold settings, improve the accuracy of load determination, adapt to dynamic changes in traffic flow and road surface conditions, and achieve intelligent adaptive adjustment of threshold parameters.

[0226] This enables the orderly tracking of threshold judgment criteria and actual road load changes, ensuring the unidirectional forward technical constraints and logical coherence of the entire data acquisition, processing, and control process.

[0227] Further explanation: To verify the effectiveness of the threshold correction and optimization method after traffic control described in this invention, strain and temperature data were continuously collected for one week on the target section of Ningqiao Avenue, a major arterial road in a certain city. Traffic load intensity was calculated based on the collected data, and the preset threshold was further corrected and optimized. Specific experimental preparations included the installation and calibration of fiber optic strain sensors and high-precision temperature sensors, ensuring a spatial resolution of 1 meter and a temporal resolution of sampling once per hour; the green light duration of the benchmark traffic signal was also included. Set to 60 seconds, the baseline low load threshold. Set to 40kN, high load threshold It is set to 80kN.

[0228] During the implementation process, the corrected strain data collected over seven consecutive days were first processed. With corrected temperature data Perform spatial-temporal synchronization processing to calculate the traffic load intensity at the corresponding spatiotemporal points using a nonlinear inversion model:

[0229] based on ,Sure =1.1, =1.5, =0.02, =2.0; In the formula, the increase in strain leads to a superlinear increase in load amplitude, and the increase in temperature causes the load index to be sensitively adjusted.

[0230] After the calculation is completed, the peak load and median load for that period are calculated and recorded as peak load indicators. and median index Peak index and median index The values ​​are 88kN and 43kN, respectively. Based on a safety margin parameter S=0.10, the following modified model is used to dynamically adjust the threshold:

[0231] The following is a typical archive of data for various time periods and spatial points of the target road segment over 7 days, illustrating the calculation and statistical process in the steps;

[0232] Table 2. Research on modifying and optimizing preset thresholds:

[0233]

[0234] This embodiment calculates traffic load by continuously collecting actual data and combining it with a nonlinear hybrid model, dynamically adjusting threshold parameters, which is more flexible and reasonable than static thresholds. Data shows that traffic load intensity changes with strain and temperature. The method of this invention can promptly correct load thresholds to adapt to changes in road conditions, providing a scientific basis for dynamic load limits and signal control.

[0235] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, Min-Max Normalization and Z-Score standardization.

[0236] The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0237] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0238] 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.

[0239] 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 collecting dynamic response data and assessing traffic load on asphalt pavement structures, characterized in that, The specific steps include: Step S1: Distributed fiber optic sensor arrays coated with low-modulus polyurethane are uniformly deployed in the longitudinal and transverse directions of the urban asphalt pavement structure to collect strain and temperature data of the target road segment and adjacent road segments during the monitoring period. Step S2: Obtain the initial matching error ΔE between the elastic modulus of the low modulus polyurethane coating material and the asphalt layer, and establish a correlation correction model between the initial matching error ΔE and the strain data deviation and temperature data deviation, which is used to correct the strain data and temperature data collected during the monitoring period. Step S3: Simultaneously process the corrected strain and temperature data of the target road segment and adjacent road segments respectively, and use the nonlinear inversion model to calculate the traffic load intensity of the target road segment and adjacent road segments respectively; Step S4: Compare the calculated traffic load intensity with the preset threshold to perform dynamic vehicle load classification and generate load classification results for the target road segment and adjacent road segments. Step S5: Conduct a comprehensive analysis of the load classification results of the target road segment and adjacent road segments, and implement corresponding dynamic load control through traffic signals and traffic flow guidance. Step S6: During the next monitoring period, continuously collect strain data and temperature data of the target road segment, calculate the traffic load intensity of the target road segment after traffic control, and correct and optimize the preset threshold.

2. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 1, characterized in that: The distributed optical fiber sensor array is based on scattering effects within the optical fiber; these scattering effects include Rayleigh scattering and Brillouin scattering. Low-modulus polyurethane-coated optical fibers are symmetrically and evenly laid out along the longitudinal and transverse directions of the asphalt pavement structure according to the preset positioning drawings, and fixed between the layers of the target road section and adjacent road sections. The strain data at spatial location (x,y) at time t is denoted as ε(x,y,t), and the temperature data is denoted as T(x,y,t); The target road segment is represented by index h1, and the adjacent road segments are represented by index h2.

3. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 2, characterized in that: Define the initial matching error ΔE as the elastic modulus of the low-modulus polyurethane coating material. With the elastic modulus of the asphalt layer The difference, that is ; The initial matching error ΔE is set to the range of [-E1, E1]. The correlation correction model includes strain correction coefficients. and temperature correction factor ; Regression analysis was performed on the strain data ε(x,y,t) acquired by the polyurethane-coated fiber optic sensor and the corresponding standard strain gauge data to fit the strain correction coefficient. ; The correlation correction model uses strain correction coefficients. Linear analysis was performed on the collected strain data ε(x,y,t) to obtain the corrected strain data. ; The correlation correction model uses a temperature correction factor. Linear analysis was performed on the collected temperature data T(x,y,t) to obtain the corrected temperature data. .

4. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 3, characterized in that: Corrected strain data and temperature data Data identified as target road segment h1 and adjacent road segment h2, respectively; A thermo-structural nonlinear inversion model based on multi-parameter coupling is selected, and the model form is defined as follows: in, Denotes the correlation function, i∈{1,2}; Indicates the first Traffic load intensity at location (x,y) and time t of road segment; It is a section of road Corrected strain data; It is a section of road Corrected temperature data.

5. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 4, characterized in that: The target road segment h1 and its adjacent road segment h2 are divided into several grid units; The traffic load intensity and the preset threshold are set as low load thresholds. and high load threshold ; For each grid cell (x, y) of the target road segment h1 and the adjacent road segment h2 at time t, the traffic load intensity is... Load levels are classified according to the following rules: when The mesh element was determined to be a low-load element. when The mesh element is determined to be a medium-load element. when The mesh element was determined to be a high-load element.

6. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 5, characterized in that: Implement dynamic load control measures based on traffic signals and traffic flow guidance, specifically including: Detect whether the target road segment h1 meets the requirements High-load unit; When there are m1 high-load units in the target road segment h1, and the interval between the m1 high-load units is lower than the preset interval. When the alarm signal is generated, the alarm content includes the specific coordinates (x, y) and the time t of the timestamp. Set preset interval Constraints ; It is the preset lower limit of the interval. This is the preset interval limit.

7. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 6, characterized in that: The preset interval is determined based on the spatial distribution characteristics of high-load units in the historical monitoring data of the target road segment h1. The baseline value of the preset interval is determined by statistically analyzing the expected value and variance of the Euclidean distance between adjacent high-load units over multiple periods. The preset interval baseline value Based on the preset upper and lower limit ranges, and taking into account the real-time monitoring of the number and spacing dispersion of high-load units, the preset interval is dynamically adjusted. To adapt to changes in load distribution.

8. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 7, characterized in that: Set a baseline green light duration for traffic lights. Define the adjustment coefficient , ,Will and A combined analysis is performed to calculate the adjusted green light duration G(t). ; When generating an alarm signal, select an adjacent road segment h2 with a load level lower than the target road segment h1 to dynamically guide vehicle diversion.

9. The method for collecting dynamic response data and assessing traffic load of asphalt pavement structure according to claim 8, characterized in that: By combining the pre-defined correlation correction model and nonlinear inversion model, the spatiotemporal distribution of traffic load intensity of the target road segment h1 during the entire monitoring period is calculated. ; For the already calculated For the dataset, calculate the following peak and median metrics: Peak metrics: ; Median indicator: ; Combined with the safety margin parameter S, the value of S ranges from [0.05, 0.15]; The revised low load threshold and high load threshold are set as follows; in, It is the corrected low load threshold. It is the corrected high load threshold.

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