CN-122024962-A - Asphalt pavement modulus back calculation method and system based on traffic speed deflection meter detection data

CN122024962ACN 122024962 ACN122024962 ACN 122024962ACN-122024962-A

Abstract

The invention discloses an asphalt pavement modulus back calculation method and system based on traffic speed deflection meter detection data, and relates to the technical field of road engineering nondestructive testing. Through the simulation of different combined working conditions, a direct physical relation between deflection and the thicknesses of the soil foundation and the upper structure is constructed to determine the unique soil foundation modulus, and a simulated annealing-particle swarm optimization matching method is introduced to realize the dynamic modulus back calculation of the upper structure by taking the unique soil foundation modulus as a physical boundary condition, so that a back calculation system based on the coupling of a physical rule and intelligent optimization is constructed. The method can obviously improve the back calculation precision and stability, reduce the sensitivity to initial values, enhance the multi-working condition adaptability and provide reliable technical support for the application of TSD in road network level structure evaluation and intelligent maintenance.

Inventors

TONG XIAOYING
XIE YI
WEI XUEJIAN
JING LEI
Cui Kangxin
LI MENGHUI
ZHENG XING
ZHOU LEI

Assignees

上海城建养护管理有限公司
上海城建城市运营(集团)有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. The utility model provides a bituminous pavement modulus back calculation method based on traffic speed deflection appearance detection data which characterized in that includes: Collecting data through a traffic speed deflectometer, and extracting mechanical response characteristic parameters; Constructing an analytic relationship between the soil base modulus and the mechanical response characteristic parameter, and inverting the soil base modulus; Under the constraint condition of known soil base modulus, an error minimization optimization model of actual measurement response and theoretical response is constructed, and inversion is carried out on the base layer modulus and the surface layer modulus through an SA-PSO algorithm.
2. The method for back calculation of the modulus of an asphalt pavement based on detection data of a traffic speed deflectometer according to claim 1, wherein the mechanical response characteristic parameters comprise structural layer parameters, structural layer modulus and initial range, TSD load parameters and TSD test data.
3. The method for back calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 1, the method is characterized in that the performing of the inversion of the earth base modulus comprises the following steps: Determining corresponding TSD inertia point parameters under the condition of given soil base modulus and overall thickness of the upper structure; At the offset distance, comparing theoretical calculation deflection with actual measurement deflection according to TSD inertia point parameters, and performing convergence judgment; And if the convergence condition is met, receiving the soil base modulus corresponding to the current inertia point position as a unique solution.
4. A method of reverse calculation of the modulus of an asphalt pavement based on traffic speed deflectometer detection data as defined in claim 3, wherein said determining the corresponding TSD inertia point parameters comprises: Under the condition of given soil base modulus Es and upper structure total thickness H, respectively calculating dynamic deflection response of the pavement under the action of TSD double-wheel moving load based on a multilayer elastic theory and a finite element simulation model to obtain the differential pressure ) Theoretical deflection basin data of combined lower total L groups of upper structures and load working conditions ; For each measuring point offset distance i, firstly, calculating the average value of all working condition deflection at the position Calculating the root mean square error of the position, obtaining discrete data of the RMSE changing along with the offset distance i, and smoothing the RMSE (i) curve by adopting a spline interpolation method; searching a first local minimum point on the smoothed RMSE (i) curve, and recording the corresponding offset distance as Namely (a) the whole plant is ) The horizontal position of the inert point under the combination; then at the same offset position Average deflection of the part Is marked as the deflection value of the inert point Obtaining the inertia point parameter [ ] , )。
5. The method for reverse calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 4, wherein comparing theoretical calculation deflectometry with actual measurement deflectometry according to TSD inertia point parameters at offset distance comprises: S11, at offset distance At the point, the theoretically calculated inertia point deflection value And actually measured deflection Comparing; s12, if the convergence condition is satisfied, namely At the time, the current inert point position is accepted Modulus of soil base corresponding to the position Is the only solution; If the convergence condition is not satisfied, i.e. when When (1): a. if it is Initial modulus The size is smaller; ; Repeating the step S1 until the convergence condition is met; b. if it is Initial modulus The size is larger; ; S1 is repeated until the convergence condition is satisfied.
6. The method for back calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 1, wherein the inversion of modulus of base layer and modulus of surface layer by SA-PSO algorithm comprises: S21, in a given search interval 、 Modulus of soil base The conditions are as follows: a. Generating initial particle group position and speed, and recording the two-dimensional position of each particle as% ); B. Setting the particle number N and learning factors , Initial temperature Annealing coefficient alpha, maximum iteration number M and iteration index k; s22, in each iteration, for a given # - ) Theoretical deflection of each measuring point based on multilayer elastic theory or finite element model Calculating an objective function F; S23, each particle in the current particle swarm ) Calculating an objective function value F, and taking the objective function value F as a particle fitness to update the individual and group optima; s24, updating the speed and the position of each particle according to a standard PSO updating formula; s25, for the new solution updated by PSO, local optimization is avoided through simulating an annealing SA criterion; s26, iterating according to preset conditions, and outputting the optimal position of the group after the iteration is finished The modulus of the surface layer and the modulus of the base layer obtained as final inversion.
7. The method for back calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 6, wherein the objective function is: 。
8. The method for back-calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 6, the method is characterized in that the avoiding of local optimization by simulating an annealing SA criterion comprises the following steps: Calculating the change quantity of the objective function for the new solution after PSO updating ; A. if delta F is less than or equal to 0, unconditionally accepting the new solution; b. If Δf >0, accepting the worse solution according to the acceptance probability p probability, skipping out the local optimum: ; ; wherein, p is the probability of acceptance, For the amount of change in the objective function, For the temperature of the kth iteration, As a result of the initial temperature being set, Is the cooling coefficient.
9. The method for back calculation of modulus of asphalt pavement based on traffic speed deflectometer detection data according to claim 6, wherein after the iteration is finished, the corresponding RMSE is output as the matching accuracy index.
10. The asphalt pavement modulus back calculation system based on the traffic speed deflectometer detection data is characterized by comprising an acquisition module, a calculation module and a calculation module, wherein the acquisition module is used for acquiring data through the traffic speed deflectometer and extracting mechanical response characteristic parameters; the soil base modulus inversion module is used for constructing an analysis relation between the soil base modulus and the mechanical response characteristic parameter and carrying out soil base modulus inversion; The identification module is used for constructing an error minimization optimization model of actual measurement response and theoretical response under the constraint condition of known soil base modulus, and inverting the base layer modulus and the surface layer modulus through an SA-PSO algorithm.

Description

Asphalt pavement modulus back calculation method and system based on traffic speed deflection meter detection data Technical Field The invention relates to the technical field of road engineering and nondestructive testing, in particular to an asphalt pavement modulus back calculation method and system based on traffic speed deflection meter detection data. Background The dynamic modulus is a core parameter for scientifically evaluating the bearing capacity and the damage development rule of the asphalt pavement, and the main mode for acquiring the parameter in the current engineering field is to carry out structural layer modulus back calculation by relying on the pavement mechanical response actually measured on site. The drop hammer type deflection meter (FALLING WEIGHT deflectometer, FWD) is used as the most commonly used field response test equipment at present, has the advantages of high test precision, convenient operation and the like, but has the problems of easy influence of traffic and climate, low detection efficiency and limited operation safety. In order to meet the rapid evaluation requirement of road network level, a traffic speed deflection meter (TRAFFIC SPEED Deflectometer, TSD) is developed, the device continuously collects the longitudinal deformation speed of the road surface through a Doppler laser sensor at the conventional running speed, and the device has the advantages of high efficiency, small interference, high automation degree and the like, and has great potential in road asset management and intelligent maintenance. In recent years, a modulus inversion method based on TSD test data is continuously developed, and mainly comprises a trial algorithm, a matching method, an empirical formula method and the like. Compared with a trial algorithm and an empirical formula method, the matching method is widely applied due to flexible modeling and strong applicability, and can be further subdivided into an iterative optimization method, a database searching method, an artificial neural network matching method and the like. The above-mentioned several matching methods are obviously different from 1) iterative method, i.e. by means of real-time calculation of theoretical deformation response of road surface and combining with optimization algorithm the gradual regulation of structural parameters so as to minimize the difference of theoretical value and actual measured value. The iterative method may also be combined with various Optimization algorithms, such as genetic algorithm (Genetic Algorithm, GA), particle swarm Optimization (PARTICLE SWARM Optimization, PSO), simulated annealing (Simulated Annealing, SA), and ant colony algorithm (Ant Colony Algorithm, ACA), among others. These methods are well established in the back calculation of modulus based on FWD data, but remain in the exploration phase in TSD-oriented applications. The iterative method has the characteristics of higher inversion precision and good physical consistency, but the convergence process has stronger dependence on initial parameters, and simultaneously has the problems of non-unique solution, high calculation cost and the like, so the method has obvious limitation in large-scale and multi-working-condition rapid inversion. 2) The database searching method is that a reasonable structural parameter value range is set before inversion, a structural parameter-road surface response database is built in advance based on theoretical calculation or numerical simulation, and an optimal solution is quickly positioned through table lookup or interpolation searching during inversion. The method has the advantages of high stability, simple realization and the like, is suitable for rapid analysis of large-batch working conditions, but the inversion accuracy and solution uniqueness are highly dependent on the coverage range and parameter granularity of the database, and the prediction capability is obviously reduced when the method faces to new working conditions beyond a preset range. 3) Similar to the database search method, the artificial neural network (ARTIFICIAL NEURAL NETWORK, ANN) matching method also needs to generate multiple-working-condition response samples in advance, but the inversion stage does not depend on physical calculation any more, and a nonlinear mapping relation between mechanical response and structural parameters is established by training the neural network. The method has the advantages of high back calculation speed, capability of processing complex nonlinear relations and the like, but the prediction precision depends on the diversity and quality of training samples, and the uniqueness of the solution cannot be guaranteed. Therefore, it can be seen that although the matching method shows better adaptability under different working conditions, the core limitation is non-uniqueness of the solution, which is particularly prominent in practical application. Aiming at the problem, the academy provides a