CN-122017213-A - Intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases

CN122017213ACN 122017213 ACN122017213 ACN 122017213ACN-122017213-A

Abstract

The invention relates to the technical field of road engineering operation and maintenance, and discloses an intelligent recognition and preventive maintenance analysis system for asphalt pavement diseases, which comprises a material modeling module, a multi-source perception module, a damping inversion module and a maintenance decision module. The system comprises a material modeling module, a multi-source perception module, a damping inversion module, a maintenance decision module, a chemical restoration or physical restoration scheme, wherein the material modeling module builds a heat-moisture transfer characteristic model based on material properties and outputs an initial performance state vector, the multi-source perception module collects on-site environment excitation, internal structure response and traffic load data, the damping inversion module calculates a functional attenuation index representing microscopic pore characteristics by comparing heat-moisture transfer lag differences of a theoretical model and actual measurement data, calculates a comprehensive health index by combining loads, and the maintenance decision module identifies that a pavement is in a functional hidden danger period or a structural fatigue period based on double-threshold decision logic. The quantitative identification of early hidden water damage is realized through a thermal-wet damping inversion mechanism, and the decoupling function attenuation and the structural damage are matched to realize preventive maintenance scheme.

Inventors

PENG AIHONG
ZHANG KAI
WANG CHAO
ZHU YAOTING
ZHANG YUQING
ZHANG DERUN
TAO JINGLIN
Zhong Kunzhi

Assignees

江西省交通投资集团有限责任公司
江西省交投养护科技集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. An intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases, which is characterized by comprising: the material modeling module is used for constructing a heat-moisture transfer characteristic model of the pavement structure based on material proportion data of the asphalt pavement and laboratory mechanical property indexes and outputting an initial performance state vector; The multi-source sensing module is deployed on a pavement service site and is used for collecting external environment excitation data, internal structure response data and traffic load data of the pavement in real time; The damping inversion module is respectively connected with the material modeling module and the multi-source perception module and is used for receiving the initial performance state vector and the dynamic data collected on site, calculating a functional attenuation index representing microscopic pore characteristic change of the pavement by comparing the difference between the theoretical heat-moisture transfer model and the actual response data, and calculating a comprehensive health index representing macroscopic mechanical property of the pavement by combining traffic load data; And the maintenance decision module is connected with the damping inversion module and is used for carrying out double-threshold judgment based on the functional damping index and the comprehensive health index, identifying the current disease evolution stage of the pavement and generating a corresponding preventive maintenance scheme instruction.
2. The intelligent identification and preventative maintenance analysis system of asphalt pavement damage of claim 1 wherein the material modeling module includes a component performance calibration unit that performs the following computational logic to generate the initial performance state vector: calculating a water repellency efficiency factor based on the mixture water absorption inhibition ratio; Calculating a compact efficiency factor based on the mapping relation between the water-gel ratio and the dynamic modulus; Calculating a water retention efficiency factor based on the limit shrinkage strain; And packaging the water repellent performance factor, the compact performance factor and the water retention performance factor into the initial performance state vector.
3. The intelligent identification and preventative maintenance analysis system of asphalt pavement diseases of claim 1, wherein the multi-source perception module comprises: The environment sensing unit is used for collecting the external environment excitation data including the atmospheric temperature, the atmospheric humidity and the rainfall; the internal response acquisition unit is used for acquiring the temperature and dielectric constant of a base layer in the pavement structure and taking the base layer temperature and the dielectric constant as the internal structure response data; and the load monitoring unit is used for collecting accumulated axle load times born by the road surface and taking the accumulated axle load times as the traffic load data.
4. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 1, wherein the damping inversion module comprises a theoretical reference value calculating unit and a dynamic parameter identifying unit: the theoretical reference value calculation unit is used for inputting the initial performance state vector into a heat-humidity transfer differential equation, and solving to obtain a theoretical environmental response lag time constant of the pavement structure to the external environmental excitation data under an ideal lossless state; The dynamic parameter identification unit is used for performing cross-correlation analysis on the synchronously acquired atmospheric temperature time sequence and the base layer temperature time sequence, and identifying and obtaining an actual lag time constant under the actual service state of the pavement.
5. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 4, wherein the damping inversion module further comprises a functional damping evaluation unit: the functional attenuation evaluation unit calculates a deviation value between the theoretical environmental response lag time constant and the actual lag time constant, and corrects the deviation value by utilizing the actually measured dielectric constant to obtain the functional attenuation index; the functional decay index is calculated according to the following formula: ; In the formula, Expressed as time of day The functional decay index is set to be, Representing the theoretical ambient response lag time constant, Indicating time of day Is used to determine the actual thermal response lag time constant of (c), Representing a time variable; The functional attenuation index is used for quantitatively representing the connectivity change of the micro-pore structure caused by water erosion inside the pavement material.
6. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 1, wherein the damping inversion module further comprises a comprehensive health evaluation unit: The comprehensive health evaluation unit adopts a gray correlation analysis method, selects accumulated axle load action times, dielectric constants and the functional attenuation indexes to construct a comparison sequence, takes the limit state of the pavement design life period as a reference sequence, calculates gray correlation coefficients of all parameters and performs weighted summation to obtain the comprehensive health index; the comprehensive health index is calculated according to the following formula: ; In the formula, The comprehensive health index is represented by the index, Represents the start value of the cyclic variable, calculated from the 1 st evaluation index, Represent the first The individual evaluation indexes are as follows The correlation coefficient of the time of day, A variable of the time is represented and, Is the first Weight coefficients of the evaluation indexes and satisfy 。
7. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 1, wherein the maintenance decision module is preset with a functional early warning threshold and a structural safety threshold, and executes the following decision logic: if the function attenuation index is smaller than the function early warning threshold value and the comprehensive health index is larger than the structural safety threshold value, judging that the pavement is in a stable service period; if the functional attenuation index is larger than or equal to the functional early warning threshold value and the comprehensive health index is larger than the structural safety threshold value, judging that the pavement is in a functional hidden danger period; And if the comprehensive health index is smaller than or equal to the structural safety threshold value, judging that the pavement is in the structural fatigue period.
8. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 7, wherein the maintenance decision module comprises a scheme generating unit for generating instructions according to the judging result: When the pavement is judged to be in the functional hidden trouble period, generating a chemical function restoration scheme instruction, wherein the chemical function restoration scheme instruction comprises construction parameters for spraying a permeable water repellent; And when the pavement is judged to be in the structural fatigue period, generating a physical structure repair scheme instruction, wherein the physical structure repair scheme instruction comprises construction parameters of pavement milling and repacking or high-pressure grouting.
9. The intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases according to claim 1, wherein the maintenance decision module further comprises a benefit evaluation unit: the benefit evaluation unit is used for calculating the dynamic input-output ratio of the maintenance scheme by combining the disease evolution rate; the dynamic input-output ratio is calculated according to the following formula: ; In the formula, Indicating the corrected dynamic input-output ratio, Represents economic benefit discount values brought by the expected prolonged service life of the pavement after maintenance, Represents the coefficient of the sensitivity of the evolution, Representing the direct economic cost of performing the current maintenance regimen, Represents annual average rainfall; Outputting the preventive maintenance scheme instruction only when the dynamic input-output ratio is larger than a preset benefit threshold value, and otherwise, putting the pavement into a continuous observation queue.
10. The intelligent recognition and preventive maintenance analysis system for asphalt pavement diseases according to claim 4, wherein the theoretical reference value calculation unit maps water-repellent efficiency factors in the initial performance state vector into wet diffusion coefficient parameters of equations, maps compact efficiency factors into heat conductivity coefficient parameters of the equations, and introduces the atmospheric humidity and rainfall data collected by the environment sensing unit as dynamic boundary conditions of the equations to construct a theoretical heat transfer model reflecting pavement specific material properties and real-time environment humidity.

Description

Intelligent identifying and preventive maintenance analysis system for asphalt pavement diseases Technical Field The invention relates to the technical field of road engineering operation and maintenance, in particular to an intelligent recognition and preventive maintenance analysis system for asphalt pavement diseases. Background Asphalt pavement is used as a main body form of road traffic infrastructure, and can be subjected to double coupling effects of traffic load and natural environment in a long-term service process. Among them, water damage is one of the main reasons for early degradation of pavement performance, and water intrusion into the pavement structure can cause asphalt film peeling and loosening of the mixture, thereby inducing macroscopic diseases such as pits, cracks and the like, and seriously shortening the service life of the road. Therefore, the implementation of scientific preventive maintenance has important significance for maintaining the pavement service level and reducing the maintenance cost of the whole life cycle. The existing asphalt pavement disease detection technology mainly depends on means such as manual inspection, vehicle-mounted camera image recognition, ground penetrating radar detection and the like. These conventional methods focus on capturing geometric imperfections in the surface appearance or obvious physical discontinuities such as cracks, voids, or obvious loose areas that have formed inside the pavement. However, the evolution of pavement water damage is a progressive process from microscopic to macroscopic, and the microscopic pore structure and hydrophilic-hydrophobic properties within the pavement material have been altered before visible cracking or structural strength dip occurs. The existing detection means lack sufficient sensitivity to the early and hidden material function attenuation, and are difficult to effectively identify in the latent period of the disease, so that the disease is often discovered in the stage of developing structural damage, and the optimal preventive treatment time is missed. In addition, the conventional road surface technical condition evaluation system generally adopts comprehensive indexes such as road surface condition index (PCI) to score the road surface health degree. Although the evaluation index of the aggregate can reflect the overall service level of the road surface, decoupling analysis of physical properties of diseases is difficult. Specifically, the existing evaluation method cannot accurately distinguish whether the pavement performance degradation is caused by functional attenuation (such as loss of hydrophobicity and microscopic leakage) of a material layer or mechanical fatigue (such as cracking of a base layer and reduction of bearing capacity) of a structural layer. Due to lack of accurate definition of disease evolution stage and essential attributes, maintenance decisions tend to adopt unified engineering measures, such as erroneously selecting milling and resurfacing when only surface function recovery is needed, or only performing surface sealing when a structure has been substantially damaged, resulting in waste of maintenance resources or incomplete engineering management. In the prior art, an analysis system capable of combining a material heat and humidity transfer mechanism, inverting microscopic disease characteristics through dynamic response differences and formulating a targeted maintenance strategy according to the dynamic response differences is lacking. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an intelligent recognition and preventive maintenance analysis system for asphalt pavement diseases, which solves the problems that in the prior art, early hidden water damage of an asphalt pavement is difficult to quantitatively recognize, and functional attenuation and structural fatigue are difficult to distinguish, so that maintenance time lag and measures are unmatched. In order to achieve the purpose, the intelligent identification and preventive maintenance analysis system for the asphalt pavement diseases is realized by the following technical scheme that the intelligent identification and preventive maintenance analysis system comprises: the material modeling module is used for constructing a heat-moisture transfer characteristic model of the pavement structure based on material proportion data of the asphalt pavement and laboratory mechanical property indexes and outputting an initial performance state vector; The multi-source sensing module is deployed on a pavement service site and is used for collecting external environment excitation data, internal structure response data and traffic load data of the pavement in real time; The damping inversion module is respectively connected with the material modeling module and the multi-source perception module and is used for receiving the initial performance state vector and the dynamic data collect