CN107153737B - Method for determining optimal axle load period of road surface based on mechanics-experience method - Google Patents
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Abstract
The invention discloses a method for determining an optimal axle load period of a road surface based on a mechanics-experience method, which comprises the following steps: (10) acquiring basic data: collecting basic data; (20) data sorting and analyzing: determining a periodic value of an axle load spectrum test of the design of the asphalt pavement according to the classification results of the traffic volume, the vehicle load and the axle distribution data; (30) analysis of test condition parameters: predicting a disease value of a design year according to the climate data and the disease data; (40) design index prediction: obtaining different test period design indexes according to vehicle type distribution, average traffic volume, axle load space-time distribution corresponding to vehicle type classification, traffic volume relations corresponding to all levels of loads and climate data predicted values; (50) obtaining the optimal axle load period: and comparing the disease value in the design year with different test period design indexes, and selecting the test axle load period corresponding to the highest prediction precision as the optimal axle load period of the pavement design. The method of the invention has more accurate result and optimal design result.
Description
Technical Field
The invention belongs to the technical field of highway pavement design, and particularly relates to a pavement optimal axle load period determining method based on a mechanics-experience method, which is more accurate in result and optimal in design result.
Background
With the rapid development of economy in China, the number of road transport vehicles is increasing day by day, and the overload and overrun phenomenon generally exists. The road surface structure design of most provincial expressways in China is basically predicted based on the local average axle load level, the problem of fatigue damage of the road surface structure is more serious, and continuous innovation of a road surface design method and a load analysis method is urgently needed to promote the sustainable development of the road surface structure so as to meet the future technical requirements. Sustainability is the fundamental characteristic of future pavement structures, and is mainly represented by pavement structure design and maintenance based on a full life cycle, design life and pavement structure service life are prolonged.
For a distribution model of axle load characteristics, abundant research results are accumulated in the highway construction process, wherein typical models include probability density functions, normal distributions, lognormal distributions, polynomial distributions and the like, but the tests on the effectiveness of the models and the innovations on the models are lacked. In the design specification of road asphalt pavement (JTGD50-2006), corresponding regulations are made for calculating the axle load accumulated equivalent axle times, and the problems that the distribution differences of different axle load characteristics cannot be compared and the relation between the standard axle load and the accumulated equivalent axle times is lack of connection still exist.
At present, the axle load data acquisition means is single, mainly manual data recording is taken as a main part, and the data acquisition becomes a key difficulty in road design. The research on the axle load spectrum adopts years as a design period, the analysis of lane distribution, time distribution, monthly traffic variation coefficient and the like is used as an axle load basis in the design process of the asphalt pavement, and the data processing amount is small according to the traditional classification mode, so that the actual traffic condition of the road cannot be fully represented.
The existing axle load design is commonly used in AASHTO experiment, and axle load conversion is carried out according to the road surface use performance. However, with the development of economy, the road transport industry has undergone a great deal of change. The axle load size, the axle load type, the tire pressure and the wheel set form of the vehicle are greatly different from those of the AASHTO test, wherein the occurrence of a large number of medium-sized trucks ensures that the road surface is seriously damaged, the service performance of the road surface is greatly reduced, and the current traffic situation is in conflict with the original AASHTO axle load conversion formula.
The existing asphalt pavement design method mainly focuses on mechanical characteristic analysis, and has less consideration on actual traffic and environmental parameters, so that the theoretical and actual deviation is larger. The influence of environmental factors and economic factors on axle load distribution is not fully considered, the control index is single, and the requirement of modern highway design cannot be met. Because the asphalt pavement has different fatigue performances under the action of different temperature loads, the influence of environmental factors on the axle load effect cannot be ignored.
In the process of the combined design of the surface layer structure of the asphalt pavement, the prior art depends on foreign use experience and cannot completely meet the operation condition of the asphalt pavement in China. The existing standard is mostly established in a semi-rigid base layer compression model, the fatigue life of an asphalt surface layer on a flexible base layer is far longer than that of a pavement structure, fatigue cracking of the asphalt surface layer can not occur, and a pavement structure design is not consistent with the actual situation by taking a pavement design deflection value as a unique control index. The traffic load is characterized by equivalent axle load acting times in the traditional road surface structure design. The equivalent axle load acting times are converted into the acting times of the standard axle load according to the principle that the same damage is caused to the road surface structure. The equivalent axle load acting times cannot represent the tire contact area and the grounding pressure of vehicles with different load grades on a road, and the pavement damage change brought in the experimental simulation process is easy to ignore.
In summary, the prior art has the following problems: the optimal axle load period of the road surface is determined inaccurately, so that the design result of the highway road surface is unreliable.
Disclosure of Invention
The invention aims to provide a method for determining the optimal axle load period of a road surface based on a mechanics-experience method, which has more accurate result and enables the design result of the highway road surface to be optimal.
The technical solution for realizing the purpose of the invention is as follows:
a method for determining the optimal axle load period of a road surface based on a mechanics-experience method comprises the following steps:
(10) acquiring basic data: determining the installation position of a dynamic weighing device required by data acquisition, acquiring basic data including traffic flow, vehicle load and axle distribution, and acquiring climate data, disease and maintenance data thereof according to a road management and maintenance decision system;
(20) data sorting and analyzing: classifying traffic volume, vehicle load and axle distribution data into a load space-time distribution data set and an axle type space-time distribution data set, and determining a designed axle load test period value of the asphalt pavement according to a classification result;
(30) analysis of test condition parameters: respectively carrying out cluster analysis on the load space-time distribution data set and the axle type space-time distribution data set to obtain vehicle type distribution, average daily traffic, axle load space-time distribution corresponding to vehicle type classification and traffic relations corresponding to loads of all grades, predicting temperature, humidity and sunshine condition values according to climate data, and predicting disease values of a design year by using a weighted moving average method according to disease data in basic data;
(40) design index prediction: using a mechanical-empirical method road surface design tool, taking the vehicle type distribution, the average traffic volume, the axle load space-time distribution corresponding to the vehicle type classification, the traffic volume relation corresponding to each level of load and the climate data predicted value under different axle load test periods as input experiment conditions, and outputting design indexes in different test periods;
(50) obtaining the optimal axle load period: and comparing the disease value of the design year with design indexes in different axle load test periods, analyzing the disease prediction precision under different axle load test periods, and selecting the axle load test period corresponding to the highest prediction precision as the optimal axle load period of the pavement design.
Compared with the prior art, the invention has the following remarkable advantages:
(1) according to the invention, a traffic volume and load data classification model is designed according to the data required in the pavement design process and aiming at the data of a dynamic weighing system and a road management maintenance decision system, so that the problem of difficulty in data collection and classification is solved.
(2) The invention provides a method for determining the optimal axle load period according to the actually measured axle load data of a dynamic weighing system, which solves the contradiction between the actual axle load and the axle load conversion in the AASHTO experiment;
(3) the problem of single analysis of influence factors is solved by analyzing traffic data in the dynamic weighing system, disease data and climate data in the road management and maintenance decision system;
(4) the method can solve the problem that the contact area and the compressive stress are unbalanced in the traditional data accumulative equivalent load, provides the prediction of pavement diseases based on flatness, asphalt surface cracking and the like, and verifies the disease prediction precision by comparing the disease prediction with the disease data in a road management maintenance management decision system;
(5) the optimal axle load period configuration scheme for the road surface design based on the mechanics-experience method provided by the invention realizes the optimal axle load spectrum design period, can adjust parameters such as road surface contact area and the like for the road surface design under different environments, improves the reliability of the method under the condition of not changing the calculated amount, and ensures the reliability and the safety of the road surface design.
The invention is described in further detail below with reference to the figures and the detailed description.
Drawings
FIG. 1 is a flow chart of an optimal axle load period configuration scheme of the invention based on a mechanical-empirical method for road surface design.
Fig. 2 is a load-frequency distribution diagram of the present invention.
Fig. 3 is a traffic-time distribution histogram of the present invention.
FIG. 4 is a schematic representation of the present invention in determining a typical pavement structure.
FIG. 5 is a histogram of disease prediction errors determined for a certain axial load period according to the present invention.
Detailed Description
As shown in FIG. 1, the method for determining the optimal axle load period of the road surface based on the mechanics-experience method comprises the following steps:
(10) acquiring basic data: determining the installation position of a dynamic weighing device required by data acquisition, acquiring basic data including traffic flow, vehicle load and axle distribution, and acquiring climate data, disease and maintenance data thereof according to a road management maintenance decision system provided by a highway maintenance company;
and (10) acquiring basic data in the step of acquiring the basic data, wherein the basic data refer to continuous 3-year traffic flow, vehicle load, axle distribution, climate data, diseases and maintenance data thereof in a dynamic weighing system and a road management maintenance decision system.
By adopting the basic data acquisition method, the problems of difficult data acquisition and single data acquisition in the prior art are solved.
(20) Data sorting and analyzing: classifying traffic volume, vehicle load and axle distribution data into a load space-time distribution data set and an axle type space-time distribution data set, and determining a designed axle load test period value of the asphalt pavement according to a classification result;
in the step (20) of data sorting and analyzing, the axle load test period value is a set of experimental values determined according to seasons, temperatures and actual traffic volumes, and the climate data characteristics in the test period can be analyzed according to the axle load test period value.
The data sorting and analyzing process increases the data volume, expands the data dimension, analyzes the traffic volume and load data influencing the use performance of the asphalt pavement in detail, provides the concept of the axle load test period and lays a foundation for determining the optimal design axle load period.
(30) Analysis of test condition parameters: respectively carrying out cluster analysis on the load space-time distribution data set and the axle type space-time distribution data set to obtain vehicle type distribution, average daily traffic, axle load space-time distribution corresponding to vehicle type classification and traffic relations corresponding to loads of all grades, predicting temperature, humidity and sunshine condition values according to climate data, and predicting disease values of a design year by using a weighted moving average method according to disease data in basic data;
(40) design index prediction: predicting different axle load test period design indexes based on a mechanics-experience method;
and (40) the design indexes in different axial load test periods in the design index prediction step comprise fatigue cracking, longitudinal cracks and flatness prediction values.
The design index prediction step (40) is specifically as follows:
and (3) by utilizing a mechanical-empirical method road surface design tool, taking the vehicle type distribution, the average traffic volume, the axle load space-time distribution corresponding to vehicle type classification, the traffic volume relation corresponding to each level of load and the climate data predicted value under the axle load test period as input experiment conditions, and outputting different axle load test period design indexes.
The mechanical-empirical method road surface design tool comprises DARWin-ME software and MATLAB data analysis software for mechanical-empirical method design. The method is based on fatigue cracking, longitudinal cracks and flatness of the road surface, predicts the disease data through mechanics-experience design software based on actually measured traffic volume, load distribution and climate data, establishes the relation between traffic variables and environmental variables and the asphalt road surface, and plays a promoting role in promoting the subject penetration between road engineering and traffic engineering. (50) Obtaining the optimal axle load period: and comparing the disease value of the design year with design indexes of different axle load test periods, analyzing the disease prediction accuracy under different axle load test periods, and selecting the axle load test period corresponding to the highest prediction accuracy as the optimal axle load period of the pavement design.
The invention is further illustrated by the following specific examples.
The method comprises the steps of firstly, calculating and analyzing a certain 100km six-lane highway section, installing and deploying a dynamic weighing system device and a highway disease detection device on the highway section, respectively detecting main node data in the section, selecting collected complete data as road surface design guidance data, collecting climate data once every 1 hour, recording the data as 365 days all year round, and collecting 8760 points of climate data in an accumulated mode. Data are selected by adopting an equal-depth box dividing method, the consistency of the data is realized, and the collected data are shown in figures 2 and 3.
Aiming at areas with few monitoring points of the dynamic weighing system, selecting a large-range axle load spectrum establishment method based on cluster analysis, selecting existing monitoring point data, analyzing traffic flow data and traffic load distribution data, defining the axle load data as K clusters based on a K-Means algorithm, and iterating by taking each selected centroid as a cluster center of the K-th class of axle loads until a preset convergence standard is reached, namely stopping iteration. Different grades and axle types are selected for different regions to carry out cluster analysis, and the axle load of the uncertain axle type can be obtained by fitting other different axle loads, and the relation is shown as the following formula 1.
Wherein: LS (i) -represents the i-th level axle load distribution; l isk-number of kth traffic cluster; bk-class k traffic clustering weight; a-correction factor.
Classifying the shaft types by integrating a dynamic weighing system and induction characteristic data; determining the time length of an axle load spectrum clustering analysis experiment period, drawing a corresponding axle load spectrum, wherein a load time distribution histogram is shown in figure 3. Dynamic weighing system (WIM) and managed maintenance decision system (PMS) data were divided into 5 specific types of data. The method comprises the steps of respectively carrying out annual accumulated axle load spectrum (12 months), quarterly characteristic month axle load spectrum (4 months), quarterly characteristic week axle load spectrum (4 weeks), annual average month axle load spectrum, continuous 48h axle load spectrum and the accumulated occurrence frequency of each disease of a road maintenance decision system.
Climate data collected in meteorological systems (air temperature, rainfall, wind speed, insolation rate, relative humidity, etc.). Because the climate data required by design is too huge, the basic weather data is difficult to obtain, based on the obtained climate data of a typical region, the original weather data takes 3 hours as a basic time period, the linear interpolation mode is adopted to complement the climate data of the lacking time period, and the obtained original data are all international standard units, so that the weather data need to be converted into English system units required by EICM climate files.
The change of the temperature and the humidity of the pavement structure is predicted by utilizing an enhanced integrated environment model, and the climate data integration comprises the following steps: temperature (in degrees Fahrenheit) at 2m above the surface, peak every hour; taking an average value at a position 10m above the ground surface at the wind speed (mile/hour: mph); the rainfall is measured by the accumulated rainfall within one hour of the detection point; sunshine rate (0 is cloudy, 100 is sunny); relative humidity (the ratio of the absolute humidity in the air to the saturated absolute humidity at the same temperature).
And step three, determining main input parameters for predicting the road surface performance by a mechanical-empirical method according to the axle load spectrum data acquired in the step two. The parameters adopted by the mechanics-experience method in the pavement design process comprise key parameters such as average daily traffic volume, traffic volume change coefficient, axle load distribution coefficient and the like corresponding to various axle load spectrum analyses.
And determining accumulated diseases of the pavement structure within the design year by using the road maintenance and disease data of the design base year derived by the PMS, and correctly estimating the development trend of the pavement diseases in the service life. And (3) predicting the diseases by adopting an exponential smoothing prediction method, wherein the number of the diseases in the design year is as follows:
Nt＝N1(1+γ)t+1(formula 2)
Wherein N ist-number of diseases designed year; n is a radical of1-designing a total number of diseases for the base year; gamma-annual growth rate of disease number.
The method does not consider the influence of the number of diseases in the middle year, and the total number of the obtained diseases has larger error. The average growth rate in t years is adopted, and the disease number in a prospective year is designed as follows:
the design age limit is comprehensively determined according to factors such as economy, traffic volume, the position of a designed road in a road network, regional construction, project investment and the like.
And step four, predicting main performance indexes of the pavement based on a mechanical-empirical method, wherein the main performance indexes comprise fatigue cracking, longitudinal cracks, flatness and the like.
The fatigue crack prediction of the pavement structure is based on the Miner's failure rule, namely, under the action of repeated load, although the tensile stress and the shear stress of the pavement structure under the action of the load do not reach the failure critical value, the pavement structure can also generate fatigue damage under the action of the repeated load.
Wherein: d is the destruction index; t represents a design period; n isi-number of axle load events in cycle i; n is a radical ofi-the maximum number of axle loads allowed in the i-cycle.
The fatigue crack prediction model is based on an AI model (with a stress constant model):
C＝10M
wherein: n is a radical off-fatigue life; epsilont-strain; e-dynamic modulus; m-modulus of rupture; va-porosity; vb-effective binder amount.
The form of failure of the pavement structure (ruts, transverse cracks, potholes, web cracks, longitudinal cracks, etc.) can have an effect on the flatness. The flatness prediction model varies depending on the type of base layer. A typical road surface is shown in fig. 4.
In the M-E response model, corresponding climate conditions and load conditions are respectively input in each season according to the seasonal change characteristics of the design period. And (3) respectively analyzing the damage characteristics of the road surface structure under the action of the axle load spectrums corresponding to the set axle load periods, dividing the axle load spectrums into load groups with the gradient of 10kN, and performing response calculation for 40 times. And obtaining the disease characteristic index.
And step five, comparing the predicted values of the performance indexes, as shown in an error distribution histogram 5, and obtaining an experimental result. Reference characteristic indexes of different axle load periods predicted by the M-E mechanical response model are respectively compared with indexes predicted according to PMS system data, prediction errors of the different axle load periods can be obtained, and therefore the optimal design axle load spectrum period is selected.
Different damage types of the road surface structure have different decay processes in the using process, so that the road surface using performance has various attributes. And according to different evaluation indexes of the road surface performance, making corresponding decisions, and reasonably and effectively arranging the maintenance sequence of different projects. When the pavement structure performance is excellent, only daily cleaning is needed; when the pavement performance is good, simple maintenance is needed to ensure that the pavement maintains good service performance, and the further deterioration of the pavement service performance is avoided; when the pavement performance is medium, major repair is needed to ensure the service level of the pavement; when the service performance of the pavement structure is inferior or poor, the pavement structure needs to be overhauled or rebuilt.
Claims (6)
1. A method for determining the optimal axle load period of a road surface based on a mechanics-experience method is characterized by comprising the following steps:
(10) acquiring basic data: determining the installation position of a dynamic weighing device required by data acquisition, acquiring basic data including traffic flow, vehicle load and axle distribution, and acquiring climate data, disease and maintenance data thereof according to a road management and maintenance decision system;
(20) data sorting and analyzing: classifying traffic volume, vehicle load and axle distribution data into a load space-time distribution data set and an axle type space-time distribution data set, and determining a designed axle load test period value of the asphalt pavement according to a classification result;
(30) analysis of test condition parameters: respectively carrying out cluster analysis on the load space-time distribution data set and the axle type space-time distribution data set to obtain vehicle type distribution, average daily traffic, axle load space-time distribution corresponding to vehicle type classification and traffic relations corresponding to loads of all grades, predicting temperature, humidity and sunshine condition values according to climate data, and predicting disease values of a design year by using a weighted moving average method according to disease data in basic data;
(40) design index prediction: predicting different axle load test period design indexes based on a mechanics-experience method;
(50) obtaining the optimal axle load period: and comparing the disease value of the design year with design indexes of different axle load test periods, analyzing the disease prediction accuracy under different axle load test periods, and selecting the axle load test period corresponding to the highest prediction accuracy as the optimal axle load period of the pavement design.
2. The method for determining an optimal axle load period of a road surface according to claim 1, characterized in that:
and (10) acquiring basic data in the step of acquiring the basic data, wherein the basic data refer to continuous 3-year traffic flow, vehicle load, axle distribution, climate data, diseases and maintenance data thereof in a dynamic weighing system and a road management maintenance decision system.
3. The method for determining an optimal axle load period of a road surface according to claim 1, characterized in that:
in the step (20) of data sorting and analyzing, the axle load test period value is a set of experimental values determined according to seasons, temperatures and actual traffic volumes, and the climate data characteristics in the test period can be analyzed according to the axle load test period value.
4. The method for determining an optimal axle load period of a road surface according to claim 1, characterized in that:
and (40) the design indexes in different axial load test periods in the design index prediction step comprise fatigue cracking, longitudinal cracks and flatness prediction values.
5. The method for determining the optimal axle load period of a road surface according to claim 1, wherein the step (40) of predicting the design index is specifically:
and (3) by utilizing a mechanical-empirical method road surface design tool, taking the vehicle type distribution, the average traffic volume, the axle load space-time distribution corresponding to vehicle type classification, the traffic volume relation corresponding to each level of load and the climate data predicted value under the axle load test period as input experiment conditions, and outputting different axle load test period design indexes.
6. The method for determining the optimal axle load period of a road surface according to claim 5, wherein the mechanical-empirical road surface design tool comprises mechanical-empirical design DARWin-ME software and MATLAB data analysis software.
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| CN109696232B (en) * | 2017-10-20 | 2021-02-19 | 山东省交通科学研究院 | Automatic calibration method of road axle load spectrum detection system |
| CN107844671B (en) * | 2017-12-04 | 2021-03-23 | 中公高科养护科技股份有限公司 | Analysis method for residual life of semi-rigid base layer structure of asphalt pavement |
| CN109033714B (en) * | 2018-09-03 | 2023-02-28 | 李来宾 | Design method for controlling coordinated deformation of roadbed and pavement |
| CN111400955B (en) * | 2020-03-27 | 2023-06-23 | 武汉科技大学 | Evaluation method for fatigue cracking damage of asphalt pavement caused by temperature change |
| CN111882875B (en) * | 2020-07-31 | 2023-10-20 | 山东高速股份有限公司 | Primary and secondary road traffic data investigation and statistics method for asphalt pavement structural design |
| CN113030450A (en) * | 2021-03-15 | 2021-06-25 | 海南省交通工程检测中心 | Asphalt pavement full-period performance monitoring and evaluating method and system |
| CN113808414B (en) * | 2021-09-13 | 2022-11-15 | 杭州海康威视***技术有限公司 | Road load determination method, device and storage medium |
| CN115600443B (en) * | 2022-12-16 | 2023-03-17 | 湖南华城检测技术有限公司 | System and method for predicting fatigue damage of full-section pavement structure |
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