A method and system for evaluating service performance based on a cement concrete pavement state

Abstract

The application discloses a cement concrete pavement state service performance evaluation method and system, and the method comprises the following steps: obtaining disease data of the cement concrete pavement based on a nondestructive testing technology; dividing disease road sections based on the disease data; obtaining temperature data, humidity data and strain data of the disease road sections based on sensor detection; judging the health conditions of the disease road sections based on the temperature data, the humidity data and the strain data, and obtaining pavement health evaluation results; judging the structural integrity of the disease road sections based on the disease data, and obtaining pavement structure evaluation results; and combining the health evaluation results and the structure evaluation results to complete the service performance evaluation of the cement concrete pavement state. The application realizes comprehensive evaluation of the pavement service performance by combining health evaluation and structure evaluation, avoids insufficient evaluation caused by a single variable, and influences the prediction of the pavement service life.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pavement service performance evaluation, and particularly relates to a service performance evaluation method and system based on the state of cement concrete pavement. BACKGROUND

[0002] With the influence of vehicle load and environmental changes, the pavement structure and use performance will gradually deteriorate, thereby affecting the vehicle driving quality and road service level. Scientifically evaluating the health condition of the highway pavement is an important basis for decision-making of pavement maintenance, repair and reconstruction measures. If the pavement is not repaired in time, the service life of the cement concrete pavement will be greatly reduced. In order to improve the service performance of the cement concrete pavement, the structures of the built cement concrete pavement are comprehensively checked, then the service performance is evaluated according to the checking condition, and finally reasonable measures are proposed to maintain the cement concrete pavement so that the service life of the cement concrete pavement is prolonged. Therefore, it is an important work to carry out the service performance evaluation research on the cement concrete pavement. SUMMARY

[0003] The application aims to solve the problems of the prior art, and provides a service performance evaluation method and system based on the state of cement concrete pavement, which realizes the service performance evaluation through comprehensive evaluation of the pavement health and structure.

[0004] To achieve the above object, the application provides the following scheme:

[0005] A service performance evaluation method based on the state of cement concrete pavement, comprising the following steps:

[0006] S1, obtaining disease data of the cement concrete pavement based on non-destructive testing technology;

[0007] S2, dividing a disease section based on the disease data;

[0008] S3, obtaining temperature data, humidity data and strain data of the disease section based on sensor detection;

[0009] S4, judging the health condition of the disease section based on the temperature data, the humidity data and the strain data, and obtaining a pavement health evaluation result;

[0010] S5, judging the structural integrity of the disease section based on the disease data, and obtaining a pavement structure evaluation result;

[0011] S6, combining the health evaluation result and the structure evaluation result to complete the service performance evaluation of the state of the cement concrete pavement.

[0012] Preferably, in step S1, the method for obtaining the disease data of the cement concrete pavement is:

[0013] Obtaining original laser point cloud data of the cement concrete pavement based on a UAV laser radar;

[0014] Preprocessing the original laser point cloud data to obtain laser point cloud data;

[0015] Obtaining roughness features and Gaussian curvature features based on the elevation information of the laser point cloud data;

[0016] Obtaining intensity images by interpolation based on the reflection intensity of the laser point cloud data;

[0017] Obtaining regional features and shape features of the pavement disease target based on the intensity images;

[0018] Constructing a pavement disease identification model based on the roughness features, the Gaussian curvature features, the regional features, and the shape features by using a random forest algorithm;

[0019] Identifying the pavement disease type based on the disease identification model to obtain the disease data.

[0020] Preferably, the disease data includes disease type, region, area, perimeter, extension, main direction, roughness, and Gaussian curvature.

[0021] Preferably, in step S3:

[0022] Obtaining the strain data based on a vibrating wire sensor;

[0023] Obtaining the temperature data based on a temperature sensor;

[0024] Obtaining the humidity data based on a humidity sensor.

[0025] Preferably, in step S4, the method for obtaining the pavement health evaluation result is:

[0026] Obtaining temperature, humidity, and strain data of the cement concrete pavement in existing materials to establish evaluation indexes;

[0027] Taking the temperature data, the humidity data, and the strain data of the disease section obtained by the sensor as sample data to construct a sample data set;

[0028] Establishing an index matrix based on the evaluation indexes and the sample data set;

[0029] Performing normalization processing on the sample data set to obtain a dimensionless matrix based on the index matrix;

[0030] Based on the dimensionless matrix, a fuzzy equivalence matrix is obtained;

[0031] A preset clustering matrix threshold is set, and the fuzzy equivalence matrix is processed by cutting, to obtain a clustering matrix;

[0032] Based on the clustering matrix threshold and the clustering matrix, a road surface health level classification is completed;

[0033] Based on the road surface health level classification, a road surface health evaluation result is obtained.

[0034] Preferably, in step S5, the method for obtaining the road surface structure evaluation result is:

[0035] Based on the disease data, a cement concrete road surface disease data sample under the medium intelligence number is established;

[0036] Based on the existing road surface technical standard, a cement concrete road surface evaluation grade example under the medium intelligence number is established;

[0037] The similarity measure value of the disease data sample and the grade example is calculated;

[0038] Based on the grade corresponding to the maximum value of the similarity measure value, a calculation grade of the disease sample data is obtained;

[0039] Based on the calculation grade, a road surface structure evaluation result is obtained.

[0040] Preferably, in step S6, the method for completing the service performance evaluation of the cement concrete road surface state is:

[0041] The health evaluation result and the structure evaluation result are standardized;

[0042] Based on the information entropy of the health evaluation result and the structure evaluation result;

[0043] Based on the information entropy, the information utility of the health evaluation result and the structure evaluation result is calculated;

[0044] Based on the information utility, the weight of the health evaluation result and the structure evaluation result is calculated;

[0045] Based on the weight and the self-defined total score, the service performance evaluation of the cement concrete road surface state is completed.

[0046] The application also provides a cement concrete road surface state service performance evaluation system, which comprises a disease data acquisition module, a division module, a sensor module, a health evaluation module, a structure evaluation module and a performance evaluation module.

[0047] The disease data acquisition module is configured to acquire disease data of the cement concrete pavement based on a nondestructive testing technology.

[0048] The division module is configured to divide the disease road section based on the disease data.

[0049] The sensor module is configured to acquire temperature data, humidity data and strain data of the disease road section based on sensor detection.

[0050] The health evaluation module is configured to determine the health condition of the disease road section based on the temperature data, the humidity data and the strain data, and acquire a pavement health evaluation result.

[0051] The structure evaluation module is configured to determine the structural integrity of the disease road section based on the disease data, and acquire a pavement structure evaluation result.

[0052] The performance evaluation module is configured to complete the service performance evaluation of the cement concrete pavement state by combining the health evaluation result and the structure evaluation result.

[0053] Compared with the prior art, the present application has the beneficial effects that the health condition of the disease road section is determined based on the temperature data, the humidity data and the strain data, and the pavement health evaluation result is acquired; the structural integrity of the disease road section is determined based on the disease data, and the pavement structure evaluation result is acquired; and the service performance evaluation of the cement concrete pavement state is completed by combining the health evaluation result and the structure evaluation result. The present application realizes comprehensive evaluation of the pavement service performance by combining the health evaluation and the structure evaluation, avoids insufficient evaluation caused by a single variable, and influences the prediction of the pavement service life. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the present application, the drawings used in the embodiments are briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 is a service performance evaluation method flowchart of the cement concrete pavement state based on the embodiments of the present application;

[0056] Figure 2 is a service performance evaluation system structure schematic diagram of the cement concrete pavement state based on the embodiments of the present application. DETAILED DESCRIPTION

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Example 1

[0060] like Figure 1 As shown, a service performance evaluation method based on the condition of cement concrete pavement includes the following steps:

[0061] S1. Based on non-destructive testing technology, obtain the damage data of cement concrete pavement; the damage data includes damage type, region, area, perimeter, extensibility, main direction, roughness and Gaussian curvature.

[0062] Specifically, in step S1, the method for obtaining distress data for cement concrete pavement is as follows:

[0063] Based on UAV lidar, raw laser point cloud data of cement concrete pavement is obtained; the raw laser point cloud data includes the target point XYZ coordinates based on the centroid coordinate system, scanning angle, number of echoes, selected echo beam for sampling, data acquisition time, echo reflection intensity and other attributes.

[0064] The raw laser point cloud data is preprocessed to obtain laser point cloud data; the preprocessing includes filtering.

[0065] Based on the elevation information of laser point cloud data, pavement roughness and Gaussian curvature characteristics are obtained. Specifically, the elevation information of the laser point cloud data is obtained based on an irregular triangular network. The roughness of each point cloud is equal to the distance between that point and the optimal two-dimensional plane obtained by fitting the point and its surrounding point sets within a certain neighborhood. The error constraint of the optimal fitting plane is based on the least squares estimation method. In the three-dimensional point cloud data of the pavement, since the occurrence of defects is often a gradual erosion process of healthy pavement, its elevation changes often show a gradual characteristic under certain scale measurements. Therefore, defect areas are extracted by statistically analyzing the curvature characteristics in three-dimensional space.

[0066] Based on the reflection intensity of laser point cloud data, an intensity image is obtained by interpolation; specifically, the inverse distance weighting method is used to interpolate the radiation intensity values ​​of laser point cloud data to establish a reflection intensity image.

[0067] Obtain the area feature and shape feature of the road disease target based on the intensity image; the shape feature includes the shape factor, compactness, circularity and rectangularity of the object.

[0068] Based on the roughness feature, Gaussian curvature feature, area feature and shape feature, a random forest algorithm is used to construct a road disease identification model.

[0069] Based on the disease identification model, the road disease type is identified to obtain the disease data.

[0070] The road disease type includes crack type, deformation type and joint type; specifically, the crack type includes three types of cracks, linear cracks and broken cracks. Linear cracks include transverse cracks, longitudinal cracks and cracks around inspection wells. Broken cracks include corner cracks, edge cracks, well edge cracks and plate surface cracks. The deformation type mainly refers to two phenomena of fault and plate arching. Fault refers to the existence of steps on both sides of the road surface at the joint or crack of the concrete road surface, which causes the vehicle to jump when passing through, affecting the comfort and safety of driving. Plate arching refers to the arching of two plates at the joint of the cement road surface, and the concrete may be broken in severe cases. The joint type mainly includes two phenomena of joint breaking and joint filler loss.

[0071] S2, based on the disease data, the disease road section is divided;

[0072] S3, based on sensor detection, obtain temperature data, humidity data and strain data inside the disease road section; in step S3:

[0073] Based on the vibrating wire sensor, the strain data is obtained;

[0074] Based on the temperature sensor, the temperature data is obtained;

[0075] Based on the humidity sensor, the humidity data is obtained.

[0076] S4, based on the temperature data, humidity data and strain data, the health status of the disease road section is determined, and the road health evaluation result is obtained;

[0077] In step S4, the method for obtaining the road health evaluation result is:

[0078] Obtain the temperature, humidity and strain data of the cement concrete road in the existing data to establish evaluation indexes;

[0079] The temperature data, humidity data and strain data of the disease road section obtained by the sensor are used as sample data to construct a sample data set;

[0080] Based on m evaluation indexes p i =(p i1 ,p i2 ,....,pim ) and a sample data set P = {p1, p2,...., p n} to establish an index matrix A = (p ij )n x m.

[0081] The sample data set is normalized, and a dimensionless matrix is obtained based on the index matrix. The specific calculation formula is:

[0082]

[0083] In the formula, a1, a2,..., a n ∈ [0, 1] and are dimensionless constants, max is the maximum value of the sample data, and min is the minimum value of the sample data.

[0084] Based on the fuzzy similarity relationship of the sample data set and the dimensionless matrix, a fuzzy similarity matrix is obtained:

[0085]

[0086] In the formula, “∧” represents the AND operation (taking the minimum value);

[0087] “∨” represents the OR operation (taking the maximum value).

[0088] Based on the fuzzy similarity matrix, a fuzzy transitive matrix containing the fuzzy similarity relationship is obtained;

[0089] Based on the fuzzy transitive matrix, a transitive closure TR of the fuzzy similarity relationship is obtained;

[0090] Based on the transitive closure, a fuzzy equivalence matrix is obtained;

[0091] A clustering matrix threshold λ (λ ∈ [0, 1]) is preset, and the fuzzy equivalence matrix is cut off to obtain a clustering matrix Wherein,

[0092]

[0093] Based on the clustering matrix threshold and the clustering matrix, the road surface health level classification is completed;

[0094] Based on the road surface health level classification, the road surface health evaluation result is obtained.

[0095] S5, based on the disease data, the structural integrity of the disease section is determined, and the road surface structure evaluation result is obtained;

[0096] The form of the mean intelligence number is: N = a + bI, a and b are all real numbers, I is an uncertainty coefficient, a represents the determined part, and bI represents the uncertain part.

[0097] In step S5, the method for obtaining the road surface structure evaluation result is:

[0098] Based on the disease data, the cement concrete pavement disease data sample under the medium intelligence number is established;

[0099] Based on the existing pavement technical standard, the cement concrete pavement evaluation grade example under the medium intelligence number is established;

[0100] The similarity measure value of the disease data sample and the grade example is calculated; specifically, the cosine similarity measure is used, and the vector C=(c1, c2, c3, …, c n ), the vector B=(b1, b2, b3, …, b n ), and the cosine similarity measure between the vector C and the vector B can be expressed as:

[0101]

[0102] The weight vector W is introduced, wherein

[0103] The medium intelligence theory cosine similarity measure of the vector C and the vector B can be expressed as:

[0104]

[0105] The similarity between two vectors is measured by calculating the cosine value of the space included angle between the inner products of the two vectors. If the directions of the two vectors are consistent, the cosine similarity is 1, and if the directions of the two vectors form a 90° included angle, the cosine similarity is 0.

[0106] Based on the maximum value of the similarity measure value, the grade of the grade example corresponding to the grade is obtained, and the calculation grade of the disease sample data is obtained;

[0107] Based on the calculation grade, the pavement structure evaluation result is obtained.

[0108] S6, combined with the health evaluation result and the structure evaluation result, the service performance evaluation of the cement concrete pavement state is completed.

[0109] In step S6, the method for completing the service performance evaluation of the cement concrete pavement state is:

[0110] The health evaluation result and the structure evaluation result are standardized;

[0111] Based on the information entropy of the health evaluation result and the structure evaluation result;

[0112] Based on the information entropy, the information utility of the health evaluation result and the structure evaluation result is calculated;

[0113] Based on the information utility, the weight of the health evaluation result and the structure evaluation result is calculated;

[0114] Based on the weight and the custom total score, the service performance evaluation of the cement concrete pavement state is completed.

[0115] Based on the service performance evaluation result, the life prediction of the concrete pavement is performed by using a statistical model and a prediction method of multiple regression analysis.

[0116] Embodiment two

[0117] As shown in Figure 2 The application further provides a cement concrete pavement state service performance evaluation system, which comprises a disease data acquisition module, a division module, a sensor module, a health evaluation module, a structure evaluation module and a performance evaluation module.

[0118] The disease data acquisition module is used for acquiring disease data of the cement concrete pavement based on a nondestructive testing technology.

[0119] The division module is used for dividing the disease road section based on the disease data.

[0120] The sensor module is used for acquiring temperature data, humidity data and strain data of the disease road section based on sensor detection.

[0121] The health evaluation module is used for judging the health condition of the disease road section based on the temperature data, the humidity data and the strain data, and obtaining a pavement health evaluation result.

[0122] The structure evaluation module is used for judging the structural integrity of the disease road section based on the disease data, and obtaining a pavement structure evaluation result.

[0123] The performance evaluation module is used for combining the health evaluation result and the structure evaluation result to complete the service performance evaluation of the cement concrete pavement state.

[0124] The disease data acquisition module comprises a point cloud acquisition unit, a first feature acquisition unit, a second feature acquisition unit, an identification model construction unit and a disease data acquisition unit.

[0125] The point cloud acquisition unit is used for acquiring original laser point cloud data of the cement concrete pavement based on a UAV laser radar; and the original laser point cloud data is preprocessed to obtain laser point cloud data.

[0126] The first feature acquisition unit is used for acquiring pavement roughness features and Gaussian curvature features based on elevation information of the laser point cloud data.

[0127] The second feature acquisition unit is used for obtaining intensity images by interpolation based on reflection intensity of the laser point cloud data; and regional features and shape features of a pavement disease target are obtained based on the intensity images.

[0128] The identification model construction unit is configured to construct a road surface disease identification model based on the roughness feature, the Gaussian curvature feature, the region feature, and the shape feature by using a random forest algorithm.

[0129] The disease data acquisition unit is configured to identify a road surface disease type based on the disease identification model and acquire the disease data.

[0130] The above-described embodiments are merely used to describe the preferred modes of the present application, and are not used to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope of the present application as defined by the claims.

Claims

1. A method for evaluating service performance based on a state of a cement concrete pavement, characterized by, The method comprises the following steps: S1, obtaining disease data of the cement concrete pavement based on non-destructive testing technology; S2, dividing the disease road section based on the disease data; S3, obtaining temperature data, humidity data and strain data of the disease road section based on sensor detection; S4, judging the health condition of the disease road section based on the temperature data, humidity data and strain data, and obtaining pavement health evaluation results; S5, judging the structural integrity of the disease road section based on the disease data, and obtaining pavement structure evaluation results; S6, combining the health evaluation results and the structure evaluation results to complete the service performance evaluation of the cement concrete pavement state; In step S4, the method for obtaining the pavement health evaluation results is: Obtain the temperature, humidity and strain data of the cement concrete pavement in the existing data to establish evaluation indexes; The temperature data, humidity data and strain data of the disease road section obtained by the sensor are used as sample data to construct a sample data set; Based on the evaluation indexes and the sample data set, an index matrix is established; The sample data set is normalized, and based on the index matrix, a dimensionless matrix is obtained; Based on the dimensionless matrix, a fuzzy equivalence matrix is obtained; A clustering matrix threshold is preset, and the fuzzy equivalence matrix is processed by cutting to obtain a clustering matrix; Based on the clustering matrix threshold and the clustering matrix, pavement health level classification is completed; Based on the pavement health level classification, the pavement health evaluation results are obtained; In step S5, the method for obtaining the pavement structure evaluation results is: Based on the disease data, a disease data sample of the cement concrete pavement under the medium intelligence number is established; Based on the existing pavement technical standard, an evaluation grade example of the cement concrete pavement under the medium intelligence number is established; The similarity measure value of the disease data sample and the grade example is calculated; Based on the grade of the grade example corresponding to the maximum value of the similarity measure value, the calculation grade of the disease data sample is obtained; Based on the calculation grade, the pavement structure evaluation results are obtained; In step S6, the method for completing the service performance evaluation of the cement concrete pavement state is: The health evaluation results and the structure evaluation results are standardized; Based on the information entropy of the health evaluation results and the structure evaluation results; Based on the information entropy, the information utility of the health evaluation results and the structure evaluation results is calculated; Based on the information utility, the weight of the health evaluation results and the structure evaluation results is calculated; Based on the weight and the self-defined total score, the service performance evaluation of the cement concrete pavement state is completed.

2. The method for evaluating the service performance of a cement concrete pavement based on the state thereof according to claim 1, characterized by, In step S1, the method for obtaining the disease data of the cement concrete pavement is: Based on the unmanned aerial vehicle laser radar, the original laser point cloud data of the cement concrete pavement is obtained; The original laser point cloud data is preprocessed to obtain laser point cloud data; Based on the elevation information of the laser point cloud data, the pavement roughness feature and the Gaussian curvature feature are obtained; Based on the reflection intensity of the laser point cloud data, the intensity image is obtained by interpolation; Obtaining regional features and shape features of the pavement disease target based on the intensity image; Based on the roughness features, the Gaussian curvature features, the regional features and the shape features, a random forest algorithm is used to construct a pavement disease identification model; Based on the disease identification model, the type of pavement disease is identified to obtain the disease data.

3. The method for evaluating the service performance of a cement concrete pavement based on the state thereof according to claim 2, characterized by, The disease data includes disease type, area, area, perimeter, extension, main direction, roughness and Gaussian curvature.

4. The method for evaluating the service performance of a cement concrete pavement based on the pavement condition according to claim 1, characterized in that, In step S3: Based on the vibrating wire sensor, the strain data is obtained; Based on the temperature sensor, the temperature data is obtained; Based on the humidity sensor, the humidity data is obtained.

5. A system for evaluating the service performance of cement concrete pavement based on its state, which is used to realize the method of any one of claims 1-4, characterized in that, It includes disease data acquisition module, division module, sensor module, health evaluation module, structure evaluation module and performance evaluation module; The disease data acquisition module is used to obtain the disease data of the cement concrete pavement based on nondestructive testing technology; The division module is used to divide the disease road section based on the disease data; The sensor module is used to obtain the temperature data, humidity data and strain data of the disease road section based on sensor detection; The health evaluation module is used to judge the health condition of the disease road section based on the temperature data, humidity data and strain data, and obtain the pavement health evaluation result; The structure evaluation module is used to judge the structural integrity of the disease road section based on the disease data, and obtain the pavement structure evaluation result; The performance evaluation module is used to complete the service performance evaluation of the cement concrete pavement state by combining the health evaluation result and the structure evaluation result.

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