Method and system for predicting service life of semi-rigid base pavement structure

Abstract

The invention relates to the technical field of road engineering, in particular to a method and system for predicting the service life of a semi-rigid base pavement structure, and the method comprises the steps: obtaining crack related data of a target pavement and operation data reflecting the service conditions of the pavement; based on the crack related data, crack state parameters used for representing the reflection crack development state in the target pavement are determined; based on the crack state parameters and the operation data, a prediction model of the crack development state changing along with the pavement service condition is established; and setting an end-of-life criterion corresponding to the crack state parameter, and when a prediction result of the prediction model meets the end-of-life criterion, determining the structural service life of the target pavement. According to the invention, the problems of low crack development prediction precision and incapability of dynamically adapting to the change of service conditions in the prior art are effectively solved, and the accuracy and adaptability of pavement life prediction are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and in particular to a method and system for predicting the life of semi-rigid base pavement structures. Background Technology

[0002] During the use of semi-rigid base pavement structures, the development of cracks is often one of the key factors leading to pavement failure, especially reflective cracks. These cracks are usually caused by stress transfer between the base layer and the surface layer. As the service time increases, the cracks gradually expand and affect the pavement's load-bearing capacity and durability. Traditional pavement life prediction methods are usually based on static data, such as pavement age, traffic load, and climate conditions, but these methods lack dynamic tracking and accurate prediction of the crack development process.

[0003] Existing pavement life prediction methods, especially for crack development in semi-rigid base pavements, often fail to effectively combine the relationship between crack development state and actual service conditions. These methods usually rely on empirical rules or simplified physical models, while ignoring the evolution of crack state at different service stages and its complex interaction with service conditions. Therefore, pavement life prediction under existing technologies often has large errors, leading to inaccurate decisions on pavement maintenance and reinforcement. Summary of the Invention

[0004] This invention provides a method for predicting the life of semi-rigid base pavement structures, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for predicting the life of a semi-rigid base course pavement structure, the method comprising: Acquire relevant data on cracks in the target pavement and operational data reflecting the pavement's service conditions; Based on the crack-related data, crack state parameters are determined to characterize the development state of reflective cracks in the target pavement. Based on the crack state parameters and the operational data, a predictive model for the change of crack development state with pavement service conditions is established. A life end criterion corresponding to the crack state parameters is set. When the prediction result of the prediction model meets the life end criterion, the structural service life of the target pavement is determined.

[0006] Furthermore, based on the crack state parameters and the operational data, a predictive model is established to show how the crack development state changes with pavement service conditions, including: Obtain the crack state parameters and operational data of the target pavement corresponding to several years of service; Analyze the evolution trend of the crack state parameters as a function of the operational data; Based on the evolution trend, the correlation between the crack state parameters and pavement service conditions is analyzed, and the prediction model is obtained by fitting historical detection data.

[0007] Furthermore, a lifetime end-of-life criterion corresponding to the crack state parameters is set, including: Based on the evolution trend of the crack state parameters, the time point at which the crack reaches the failure critical value during the crack development process is determined. When the crack state parameters reach the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure has reached the end of its service life.

[0008] Furthermore, the time point at which the failure threshold is obtained is determined through mutation point detection.

[0009] Furthermore, the crack-related data includes at least the total number of transverse cracks in the target pavement over the years; the pavement service condition operation data includes at least the road section length, traffic opening time, and the cumulative number of equivalent standard axle loads.

[0010] Furthermore, the crack state parameter is a crack spacing parameter used to characterize the density of transverse reflective cracks on the target pavement.

[0011] Furthermore, the crack state parameters are determined based on the ratio of the number of transverse reflective cracks in the target pavement over the years to the corresponding road segment length.

[0012] Furthermore, when the prediction result of the prediction model satisfies the life end criterion, the structural service life of the target pavement is determined, including: Obtain the current road age and current cumulative axle load of the target road segment; Based on the prediction model, the future cumulative axle load of the target road segment is predicted, and combined with the changes in the service conditions of the target road segment, the future development trend of its crack state is predicted. When the crack condition reaches the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure of the target road section has reached the end of its service life. Based on the results of the prediction model, the remaining lifespan of the target road segment is calculated, which is the difference between the predicted total lifespan of the target road segment and its current age.

[0013] A life prediction system for semi-rigid base course pavement structures, the system comprising: The road surface data acquisition module acquires crack-related data of the target road surface and operational data reflecting the road surface service conditions; The state parameter determination module determines crack state parameters, based on the crack-related data, to characterize the development state of reflective cracks in the target pavement. The prediction model building module establishes a prediction model of how the crack development state changes with pavement service conditions, based on the crack state parameters and the operational data. The service life determination module sets a service life end criterion corresponding to the crack state parameters. When the prediction result of the prediction model meets the service life end criterion, the structural service life of the target pavement is determined.

[0014] Furthermore, the prediction model construction module includes: The service data acquisition unit acquires the crack state parameters and operational data of the target pavement corresponding to several years of service. An evolution trend analysis unit analyzes the evolution trend of the crack state parameters as the operational data changes; The prediction model fitting unit analyzes the correlation between the crack state parameters and pavement service conditions based on the evolution trend, and obtains the prediction model by fitting historical detection data.

[0015] The technical solution of this invention can achieve the following technical effects: It effectively solves the problems of low accuracy in crack development prediction and inability to dynamically adapt to changes in service conditions in existing technologies, and significantly improves the accuracy and adaptability of pavement life prediction.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for predicting the life of a semi-rigid base pavement structure. Figure 2 A flowchart illustrating the process of establishing a predictive model for crack development as pavement service conditions change. Figure 3 A flowchart illustrating the process of setting the lifetime end criteria corresponding to crack state parameters; Figure 4 A flowchart illustrating the process of determining the structural service life of a target pavement. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: like Figure 1 As shown, this application provides a method for predicting the life of a semi-rigid base pavement structure, the method comprising: S1: Acquire relevant data on cracks in the target pavement and operational data reflecting the pavement's service conditions; Specifically, the first step is to identify the target road segment of the semi-rigid base pavement to be analyzed. During the service life of the target road segment, data on pavement cracks are collected to obtain crack-related data reflecting the damage to the pavement structure. Crack-related data can be obtained through manual inspection records, image acquisition equipment, or pavement detection devices. The collected data includes the number of transverse cracks on the pavement and their distribution within the road segment. Preferably, the transverse cracks are caused by the shrinkage or deformation of the semi-rigid base layer and reflect off the surface layer. Simultaneously, operational data reflecting the service status of the target pavement is acquired. Operational data includes information on the service time of the pavement since it was put into use and the load operation related to traffic. Operational data can be obtained through historical archives, traffic surveys, or traffic monitoring systems, and the data is collected in a corresponding manner to the crack-related data to reflect the actual operational status of the target pavement at different service stages.

[0022] S2: Based on crack correlation data, determine the crack state parameters used to characterize the development state of reflective cracks in the target pavement; Specifically, after obtaining relevant crack data for the target pavement, the collected crack information is organized and analyzed to extract quantitative indicators that reflect the overall development level of reflective cracks in semi-rigid base pavement. Considering the distribution characteristics of reflective cracks in the pavement and their impact on structural performance, this embodiment preferably uses the spatial distribution characteristics of reflective cracks as crack state parameters. By comprehensively characterizing the relationship between the number of transverse reflective cracks and the length of the road segment, the dispersed crack information is transformed into a state quantity that reflects the density of reflective cracks. In practical implementation, based on the statistical count of transverse cracks, the portion belonging to reflective cracks can be screened or corrected in conjunction with crack identification results to avoid interference from non-structural cracks on the evaluation results. This yields crack state parameters that objectively reflect the development state of reflective cracks in the target pavement, allowing these parameters to dynamically change with the number and distribution of reflective cracks.

[0023] S3: Based on crack state parameters and operational data, establish a predictive model for the change of crack development state with pavement service conditions; Specifically, by analyzing the relationship between crack state parameters and operational data reflecting pavement service conditions, crack state information that was originally scattered across different service stages can be uniformly described. This transforms the crack state at a single point in time into a process expression that reflects changes with pavement service conditions. Through this step, an intermediate expression for characterizing the changing patterns of reflective crack development can be formed in the technical process. This ensures that the crack development state no longer exists only as static data, but reflects its correlation with service time and traffic conditions, providing a continuous and interpretable basic expression for further technical processing of the crack development process.

[0024] S4: Set the life end criterion corresponding to the crack state parameters. When the prediction result of the prediction model meets the life end criterion, determine the structural service life of the target pavement.

[0025] Specifically, after obtaining a predictive model that reflects the relationship between crack development state and pavement service conditions, a life end criterion corresponding to the crack state parameters is set based on the characterization significance of crack state parameters on pavement structural performance. This criterion serves as the basis for determining whether the pavement structure has reached its service life end. The life end criterion limits the critical level that crack development state can reach. When the crack state parameter result given by the predictive model reaches or exceeds this critical level, it is considered that the structural performance of the target pavement no longer meets the expected requirements, and thus the corresponding service state is determined as the structural service life of the target pavement. By mapping crack state parameters to life end criterion, the service life of the target pavement structure can be determined based on the crack development state.

[0026] This invention effectively solves the problems of low accuracy in predicting crack development and inability to dynamically adapt to changes in service conditions in existing technologies, and significantly improves the accuracy and adaptability of pavement life prediction.

[0027] As a preferred embodiment of the above, such as Figure 2 As shown, step S3, based on crack state parameters and operational data, establishes a predictive model for the change of crack development state with pavement service conditions, including: S31: Obtain crack state parameters and operational data of the target pavement for several years of service; S32: Analyze the evolution trend of crack state parameters with changes in operational data; S33: Based on the evolution trend, the correlation between crack state parameters and pavement service conditions is analyzed, and a prediction model is obtained by fitting historical detection data.

[0028] Specifically, the method first selects inspection records of the target pavement under multiple service years, and obtains the crack state parameters and operational data reflecting the current pavement service conditions for each service year. By correlating the crack state parameters with the operational data over time, the crack development state under different service stages becomes comparable. Based on this, the changes in crack state parameters with service time and traffic effects are systematically organized. By comparing the magnitude and direction of crack state changes at different service stages, the evolutionary characteristics of the reflective crack development process, from slow to significant changes, are identified, thus depicting the overall evolutionary trend of crack development state with changes in pavement service conditions. After clarifying the above evolutionary trend, the intrinsic correlation between crack state parameters and pavement service conditions is further analyzed, and this correlation is fitted based on historical inspection data of the target pavement, so that the relationship between crack development state and service conditions can be expressed in model form, thereby forming a predictive model for crack development state changes with pavement service conditions.

[0029] As a preferred embodiment of the above, such as Figure 3 As shown, the lifetime end criteria corresponding to the crack state parameters are set, including: A10: Based on the evolution trend of crack state parameters, determine the time point when the crack reaches the failure critical value during crack development; A20: When the crack state parameters reach the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure has reached the end of its service life.

[0030] Specifically, firstly, based on the evolution trend of crack state parameters as pavement service conditions change, the crack development process is continuously observed and analyzed. By comparing the crack state change characteristics at different service stages, the key nodes in the transition from a stable stage to a rapid development stage are identified, thereby determining the time point when the crack development process reaches the critical state of structural failure. This critical state of failure reflects that the degree of crack development has had a substantial impact on the overall service performance of the pavement structure. When the crack state parameters reach the corresponding critical failure value in the prediction results, it is determined that the target pavement has entered the structural failure stage, and the service state corresponding to this time point is determined as the end of the service life of the pavement structure. Thus, the service life of the target pavement structure is determined based on the crack development state.

[0031] As a preferred embodiment of the above, the time point at which the failure threshold is obtained is obtained through mutation point detection.

[0032] Specifically, the changes in crack state parameters under different service stages are arranged in chronological order to form a state sequence reflecting the entire crack development process. By analyzing the continuity of the rate and magnitude of change in this state sequence, the position where the crack state transitions from a relatively stable development stage to a rapid deterioration stage is identified. When the crack state parameters deviate significantly from their original trend near this position, the corresponding time node is identified as a mutation point. This mutation point reflects the moment when the crack development state undergoes a substantial change. This time node is determined as the time point when the crack reaches the failure threshold during the crack development process, thus providing an objective basis for the subsequent determination of the structural failure stage.

[0033] As a preferred embodiment of the above, the crack-related data shall include at least the total number of transverse cracks in the target pavement over the years; the pavement service condition operation data shall include at least the road section length, the time of opening to traffic, and the cumulative number of equivalent standard axle loads.

[0034] Specifically, when acquiring crack-related data, the crack-related data includes at least the total number of transverse cracks detected in the target pavement during different service years. By summarizing the detection records over the years, the cumulative situation and change characteristics of transverse cracks over the service time of the pavement can be reflected. The transverse cracks are preferably the type of cracks formed under the action of a semi-rigid base layer and reflected to the surface layer. At the same time, when acquiring operational data reflecting the service conditions of the pavement, the operational data includes at least the length of the target road segment, the time of opening to traffic, and the cumulative number of equivalent standard axle loads. The length of the road segment is used to characterize the spatial range of the crack data, the time of opening to traffic is used to reflect the service time of the pavement after it is actually put into use, and the cumulative number of equivalent standard axle loads is used to characterize the cumulative effect level of traffic loads during the service process of the pavement. The above operational data can be obtained through engineering archives, traffic survey data, or monitoring systems, and should be consistent with the crack-related data in the time dimension. In other embodiments, the crack-related data and operational data can also be expanded or replaced according to the actual engineering conditions and data acquisition conditions. As long as they can objectively reflect the development state of cracks in the target pavement and the changes in its service conditions, they can be used as effective parameters in the technical solution of this invention.

[0035] As a preferred embodiment of the above, the crack state parameter is a crack spacing parameter used to characterize the density of transverse reflective cracks on the target pavement.

[0036] As a preferred embodiment of the above, the crack state parameters are determined based on the ratio of the number of transverse reflective cracks in the target pavement over the years to the corresponding road segment length.

[0037] Specifically, the crack state parameter is selected as a crack spacing parameter to characterize the density of transverse reflective cracks on the target pavement. This parameter is determined by the ratio between the number of transverse reflective cracks on the target pavement and the corresponding road segment length, reflecting the density of transverse reflective cracks within a unit length. In practice, based on the number of transverse reflective cracks detected on the target pavement in different service years and the road segment length in the corresponding year, the crack spacing parameter is determined based on the ratio between the two. When the number of transverse reflective cracks within a unit length is small, the crack spacing level corresponding to this ratio is large, indicating that the crack distribution is relatively sparse. When the number of transverse reflective cracks within a unit length increases, the crack spacing level corresponding to this ratio decreases accordingly, indicating that the crack distribution tends to be denser. Thus, the crack spacing parameter can objectively reflect the change in the transverse reflective crack distribution of the target pavement from sparse to dense.

[0038] As a preferred embodiment of the above, such as Figure 4 As shown, when the prediction results of the prediction model meet the life end criterion, the structural service life of the target pavement is determined, including: B10: Obtain the current road age and current cumulative axle load of the target road segment; B20: Based on the prediction model, predict the future cumulative axle load of the target road section, and combine the changes in the service conditions of the target road section to predict the future development trend of its crack state. B30: When the crack condition reaches the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure of the target road section has reached the end of its service life. B40: Based on the results of the prediction model, calculate the remaining lifespan of the target road segment, which is the difference between the predicted total lifespan of the target road segment and its current age.

[0039] Specifically, the process begins by acquiring information on the current service status of the target road segment, including its current age and the cumulative axle load it has experienced up to the current moment, to reflect its actual service level at this stage. Based on this, and using an established prediction model, the traffic load on the target road segment during its subsequent service is estimated, predicting the potential changes in the cumulative axle load. Combined with changes in the service conditions of the target road segment, the development trend of crack state parameters in the future service stage is predicted, thus obtaining the prediction results of crack development state evolution over time. When the predicted crack state development results reach a predetermined failure threshold, the target pavement is determined to have entered the structural failure stage from the normal service stage, and the corresponding service state is defined as the end of the service life of the target road segment's pavement structure. Furthermore, based on the determined total service life, by comparing the current age of the target road segment, the remaining service life of the target road segment under its current service state is determined.

[0040] Example 2: Based on the same inventive concept as the semi-rigid base pavement structure life prediction method in the foregoing embodiments, the present invention also provides a semi-rigid base pavement structure life prediction system, comprising: The road surface data acquisition module acquires crack-related data of the target road surface and operational data reflecting the road surface service conditions; The state parameter determination module determines crack state parameters, based on crack-related data, to characterize the development state of reflective cracks in the target pavement. The prediction model building module establishes a prediction model of how the crack development state changes with pavement service conditions, based on crack state parameters and operational data. The service life determination module sets the service life end criteria corresponding to the crack state parameters. When the prediction results of the prediction model meet the service life end criteria, the structural service life of the target pavement is determined.

[0041] The prediction system described above in this invention can effectively realize the life prediction method for semi-rigid base pavement structures, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0042] As a preferred embodiment of the above, the prediction model construction module includes: Service data acquisition unit acquires crack state parameters and operational data of the target pavement for several years of service; The evolution trend analysis unit analyzes the evolution trend of crack state parameters as operational data changes; The prediction model fitting unit analyzes the correlation between crack state parameters and pavement service conditions based on evolution trends, and obtains the prediction model by fitting historical detection data.

[0043] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0044] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for predicting the service life of a semi-rigid base pavement structure, characterized by, The method includes: Acquire relevant data on cracks in the target pavement and operational data reflecting the pavement's service conditions; Based on the crack-related data, crack state parameters are determined to characterize the development state of reflective cracks in the target pavement. Based on the crack state parameters and the operational data, a predictive model for the change of crack development state with pavement service conditions is established. A life end criterion corresponding to the crack state parameters is set. When the prediction result of the prediction model meets the life end criterion, the structural service life of the target pavement is determined.

2. The method for predicting the service life of a semi-rigid base pavement structure according to claim 1, characterized by, Based on the crack state parameters and the operational data, a predictive model for crack development state changes with pavement service conditions is established, including: Obtain the crack state parameters and operational data of the target pavement corresponding to several years of service; Analyze the evolution trend of the crack state parameters as a function of the operational data; Based on the evolution trend, the correlation between the crack state parameters and pavement service conditions is analyzed, and the prediction model is obtained by fitting historical detection data.

3. The method for predicting the service life of a semi-rigid base pavement structure according to claim 1, characterized by, Define the lifetime end criterion corresponding to the crack state parameters, including: Based on the evolution trend of the crack state parameters, the time point at which the crack reaches the failure critical value during the crack development process is determined. When the crack state parameters reach the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure has reached the end of its service life.

4. The method for predicting the service life of a semi-rigid base pavement structure according to claim 3, characterized by, The time point at which the failure threshold is obtained through mutation point detection.

5. The method for predicting the service life of a semi-rigid base pavement structure according to claim 1, characterized by, The crack-related data includes at least the total number of transverse cracks in the target pavement over the years; the pavement service condition operation data includes at least the road section length, traffic opening time, and the cumulative number of equivalent standard axle loads.

6. The method for predicting the service life of a semi-rigid base course pavement structure according to claim 1, characterized by, The crack state parameters are crack spacing parameters used to characterize the density of transverse reflective cracks on the target pavement.

7. The method for predicting the service life of a semi-rigid base course pavement structure according to claim 1, characterized by, The crack condition parameters are determined based on the ratio of the number of transverse reflective cracks in the target pavement over the years to the corresponding road segment length.

8. The method for predicting the life of semi-rigid base pavement structures according to claim 1, characterized in that, When the prediction result of the prediction model meets the life end criterion, the structural service life of the target pavement is determined, including: Obtain the current road age and current cumulative axle load of the target road segment; Based on the prediction model, the future cumulative axle load of the target road segment is predicted, and combined with the changes in the service conditions of the target road segment, the future development trend of its crack state is predicted. When the crack condition reaches the failure threshold, the target pavement is determined to have entered the structural failure stage, that is, the pavement structure of the target road section has reached the end of its service life. Based on the results of the prediction model, the remaining lifespan of the target road segment is calculated, which is the difference between the predicted total lifespan of the target road segment and its current age.

9. A semi-rigid base course pavement structure life prediction system, characterized by, The system includes: The road surface data acquisition module acquires crack-related data of the target road surface and operational data reflecting the road surface service conditions; The state parameter determination module determines crack state parameters, based on the crack-related data, to characterize the development state of reflective cracks in the target pavement. The prediction model building module establishes a prediction model of how the crack development state changes with pavement service conditions, based on the crack state parameters and the operational data. The service life determination module sets a service life end criterion corresponding to the crack state parameters. When the prediction result of the prediction model meets the service life end criterion, the structural service life of the target pavement is determined.

10. The semi-rigid base course pavement structure life prediction system of claim 9, wherein, The prediction model construction module includes: The service data acquisition unit acquires the crack state parameters and operational data of the target pavement corresponding to several years of service. An evolution trend analysis unit analyzes the evolution trend of the crack state parameters as the operational data changes; The prediction model fitting unit analyzes the correlation between the crack state parameters and pavement service conditions based on the evolution trend, and obtains the prediction model by fitting historical detection data.

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