Asphalt pavement evaluation system based on rainwater analysis

Abstract

The invention relates to the technical field of road engineering monitoring, in particular to an asphalt pavement evaluation system based on rainwater analysis, which comprises the following steps of: determining an inertia pressure bearing value of a monitored road section by analyzing traffic flow information and vehicle information of the monitored road section, calculating a real-time rainwater influence value according to rainwater components and rainfall collected in real time, and evaluating the asphalt pavement according to the real-time rainwater influence value. According to the method, the vehicle inertia pressure bearing value and the rainwater influence value are comprehensively considered, the road surface state value of the monitored road section can be comprehensively and accurately determined, and then the road surface abnormal signal is generated in real time based on the road surface state value. Therefore, a worker can make a targeted maintenance plan in time and reasonably arrange maintenance resources, so that the safety and smoothness of a road are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering monitoring, and particularly relates to an asphalt pavement evaluation system based on rainwater analysis. Background Technique

[0002] With the acceleration of the urbanization process, asphalt pavements are widely used in various road construction projects due to their good driving comfort and low construction costs.

[0003] The prior art CN114896663A discloses a method for evaluating the hydrological performance of a permeable pavement structure, including evaluating the hydrological performance of the permeable pavement structure through three indicators. Among them, the permeability coefficient of the permeable asphalt surface layer reflects the ability of external rainwater and surface runoff to enter the permeable pavement structure. If its value is too small, it will be difficult for rainwater and surface runoff to enter the permeable pavement structure, and water will accumulate on the ground surface even when the precipitation is very small, making it difficult to play the role of regulating surface runoff of the permeable pavement structure; the water storage capacity reflects the ability of the permeable pavement structure to store rainwater and surface runoff under ideal conditions. If the water storage capacity is too small, it indicates that the design of the permeable pavement structure is unreasonable and it is difficult to achieve good urban flood prevention effects. However, during rainfall, rainwater easily penetrates into the interior of the asphalt pavement. When the pavement is in a saturated state, the repeated action of the impact and hydrodynamic pressure generated by heavy vehicles during driving will accelerate the peeling of the asphalt film from the aggregates, and even cause the aggregates to fly away, thereby forming loose pits. If not repaired in time, these pits will gradually expand into potholes, seriously affecting the integrity of the pavement structure and driving safety. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems in the background technique, and to propose an asphalt pavement evaluation system based on rainwater analysis.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: An asphalt pavement evaluation system based on rainwater analysis, comprising: A pavement monitoring module, used to collect the operation information of the monitored section and transmit it to the pressure analysis module; A pressure analysis module, used to obtain historical operation information, and based on the historical operation information, identify and process the interval flow value and vehicle type in each information interval to obtain the inertial traffic flow in the information interval and the inertial flow ratio of the vehicle type. At the same time, the inertial traffic flow and the inertial flow ratio are comprehensively calculated to obtain the inertial pressure bearing value; A rainwater analysis module, used to process the components of rainwater information, and calculate the rainwater influence value based on the influencing components in the rainwater components and the real-time rainfall; An integrated analysis module is used to comprehensively process the inertial pressure bearing value and the rainwater influence value to determine the road surface state value of the monitored section, and then generate a road surface abnormal signal based on the road surface state value; An evaluation and output module is used to display the road surface state value on the terminal device, and at the same time generate an audible and visual reminder message based on the road surface abnormal signal and give a real-time reminder to the management personnel.

[0006] As a further solution of the present invention, the method for obtaining the inertial traffic flow in the information interval includes: S1: Taking the current time as a node, obtain the historical operation information within the effective time. The effective time is set to 3 months, and the operation information refers to the usage state and traffic operation state of the asphalt road surface in the monitored section; Set the unit time, and divide the operation information within the effective time according to the unit time to obtain information intervals. The unit time is set to 1 day; S2: Based on the image recognition technology, identify and count the vehicles in each information interval respectively to obtain the interval flow value Li, where i represents different information intervals, and i ∈ [1, I], and I represents the total number of existing information intervals; Based on the normal distribution algorithm, calculate the mean value of the interval flow value Li respectively and the standard deviation , according to the mean value and the standard deviation , set the normal interval Q( , ), and the value of k is set to 2; Compare the interval flow value Li with the normal interval Q. If Li ∈ Q, mark the corresponding interval flow value Li as normal data. On the contrary, if Li ∉ Q, mark the corresponding interval flow value Li as abnormal data. After all the interval flow values Li are compared, delete the abnormal data in the interval flow value Li, so that all the data in the interval flow value Li are normal data; Obtain the interval flow value Li under normal data and recalculate the mean value to obtain the inertial traffic flow Ga of the monitored section.

[0007] As a further solution of the present invention, the vehicle types include small vehicles, medium-sized vehicles and large vehicles. The method for identifying vehicle types includes: Based on image recognition technology, the contour of vehicles in the information interval is recognized. Based on the contour of the vehicles, the vehicles in the information interval are classified by vehicle type. The volume of the vehicles is estimated according to the contour of the vehicles. Then, the volume of the vehicles is compared with volume thresholds X1 and X2. If the volume of the vehicle is less than or equal to volume threshold X1, the corresponding vehicle is marked as a small vehicle. If the volume of the vehicle is greater than X1 and less than or equal to X2, the corresponding vehicle is marked as a medium vehicle. If the volume of the vehicle is greater than X2, the corresponding vehicle is marked as a large vehicle.

[0008] As a further solution of the present invention, the method for determining the inertial flow ratio of vehicle types includes: Count the vehicle types of the vehicles in each information interval to obtain the number of vehicles of each vehicle type. Then, divide the number of vehicles of each vehicle type by the interval flow value in the corresponding information interval to obtain the vehicle type ratio. Arbitrarily select a vehicle type and mark it as the target vehicle type. Extract the vehicle type ratios of the target vehicle type in all information intervals under normal data and perform mean calculation. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle type. Then, sequentially set the remaining vehicle types as the target vehicle type and process according to the above method to obtain the inertial flow ratios Cj of all vehicle types, where j represents different vehicle types.

[0009] As a further solution of the present invention, the method for obtaining the inertial pressure bearing value includes: Use the formula to obtain the inertial pressure bearing value Fs of the monitored section per unit time, where Zj represents the reference mass corresponding to different vehicle types, and J represents the total number of vehicle types present.

[0010] As a further solution of the present invention, the method for calculating the rain influence value includes: SS1: Identify the influencing components in the rain components, and at the same time obtain the proportion of the influencing components in the rain components and mark this proportion as the influence proportion value Ym, where m represents different influencing components, and m ∈ [1, M], indicating that there are a total of M influencing components. Among them, the influencing components refer to the components that have a negative impact on the asphalt pavement. SS2: Based on the formula calculate the component influence value CF, where R represents the rainfall in the monitored section, represents the influence coefficient of the influencing component m on the asphalt pavement per unit rainfall; Then use the formula to calculate the rain influence value WS, where is the porosity of the asphalt pavement, and k is the water stability of the asphalt.

[0011] As a further solution of the present invention, the method for obtaining the road surface state value and the road surface abnormal signal includes: Using the formula calculate the road surface state value HZ, where b1 is the base coefficient and 0 < b1 < 1, t is the rainfall duration, and are the proportionality coefficients respectively, and , Fs is the inertial pressure bearing value, and WS is the rainwater influence value; Compare the obtained road surface state value HZ with the state threshold X1. If HZ < X1, a normal operation signal is generated. Conversely, if HZ ≥ X1, a road surface anomaly signal is generated.

[0012] As a further solution of the present invention, the rainwater information is collected by the rainwater collection module and transmitted to the rainwater analysis module, and the rainwater information includes rainwater composition, rainfall, and continuous rainfall time.

[0013] As a further solution of the present invention, it further includes a road surface information collection module for collecting the road surface information of the asphalt road surface, where the road surface information includes the construction structure and construction parameters of the asphalt road surface and the monitored section of the asphalt road surface. Then, the road surface information collection module transmits the collected road surface information to the road surface monitoring module and the pressure analysis module respectively.

[0014] Compared with the existing technology, the advantages of the present invention are as follows: By analyzing the traffic flow information and vehicle information of the monitored section, the present invention determines the inertial pressure bearing value of the monitored section. At the same time, according to the rainwater composition and rainfall collected in real time, it calculates the real-time rainwater influence value, and comprehensively considers the vehicle inertial pressure bearing value and the rainwater influence value, which can comprehensively and accurately determine the road surface state value of the monitored section, thereby helping the management personnel to deeply understand the damage situation of the road surface under the action of vehicle load and rainwater. Then, based on the road surface state value, a road surface anomaly signal is generated in real time, enabling the staff to timely formulate targeted maintenance plans and reasonably arrange maintenance resources, further ensuring the safe and unobstructed operation of the road. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic structural diagram of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0017] Refer to Figure 1, an asphalt pavement evaluation system based on rainwater analysis, comprising a pavement information collection module, a pavement monitoring module, a pressure analysis module, a rainwater collection module, a rainwater analysis module, an integrated analysis module, and an evaluation output module; The pavement information collection module is used to collect the pavement information of the asphalt pavement. Among them, the pavement information includes the construction structure and parameters of the asphalt pavement and the monitored sections of the asphalt pavement. After that, the pavement information collection module transmits the collected pavement information to the pavement monitoring module and the pressure analysis module respectively; The pavement monitoring module is used to set monitoring devices in the monitored sections according to the monitored sections, and based on the monitoring devices, collect the operation information of the monitored sections and transmit it to the pressure analysis module. Among them, the monitoring device in this embodiment is a camera, and the operation information of the monitored section refers to the use state and traffic operation state of the asphalt pavement in the monitored section, specifically including traffic flow information and vehicle load information; The pressure analysis module is used to obtain the operation information and determine the inertial pressure bearing value of the monitored section based on the operation information. The specific method for determining the inertial pressure bearing value includes: S1: Taking the current time as a node, obtain the historical operation information within the effective time. Among them, the specific value of the effective time is set by those skilled in the art according to big data experience. Further, the effective time in this embodiment is set to 3 months; Set the unit time, and divide the operation information within the effective time according to the unit time to obtain information intervals. Among them, the unit time in this embodiment is set to 1 day; S2: Based on image recognition technology, identify and count the vehicles in each information interval respectively to obtain the interval flow value Li, where i represents different information intervals, and i ∈ [1, I], and I represents the total number of existing information intervals; After that, based on the normal distribution algorithm, calculate the mean value of the interval flow value Li respectively and the standard deviation , according to the mean value and the standard deviation , set the normal interval Q ( , ), where k belongs to the threshold. In this embodiment, the value of k is set to 2; Compare the interval flow value Li with the normal interval Q. If Li ∈ Q, mark the corresponding interval flow value Li as normal data. On the contrary, if Li ∉ Q, mark the corresponding interval flow value Li as abnormal data. After all the interval flow values Li are compared, delete the abnormal data in the interval flow value Li, so that all the data in the interval flow value Li are normal data; S3: Based on image recognition technology, identify the contours of vehicles in the information interval. Further, when identifying the contours of vehicles in the information interval, only identify the vehicles in the information interval corresponding to normal data; After that, based on the contours of the vehicles, classify the vehicles in the information interval. Among them, the vehicle types include small vehicles, medium-sized vehicles, and large vehicles; Further, when classifying based on the contours of the vehicles, it is necessary to estimate the volume of the vehicles according to the contours of the vehicles, and then compare the volume of the vehicles with volume thresholds X1 and X2. If the volume of the vehicle is less than or equal to the volume threshold X1, the corresponding vehicle is marked as a small vehicle. If the volume of the vehicle is greater than X1 and less than or equal to X2, the corresponding vehicle is marked as a medium-sized vehicle. If the volume of the vehicle is greater than X2, the corresponding vehicle is marked as a large vehicle. Among them, the specific values of the volume thresholds X1 and X2 are set by those skilled in the art according to big data experience; S4: Count the vehicle types of the vehicles in each information interval to obtain the number of vehicle types for each vehicle type. Then, divide the number of vehicle types by the interval flow value in the corresponding information interval to obtain the vehicle type ratio; Arbitrarily select a vehicle type and mark it as the target vehicle type. Extract the vehicle type ratios of the target vehicle type in all information intervals under normal data and perform mean calculation. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle type. Then, sequentially set the remaining vehicle types as the target vehicle type and process them according to the above method to obtain the inertial flow ratios Cj of all vehicle types, where j represents different vehicle types; Obtain the interval flow value Li under normal data and perform mean calculation again to obtain the inertial traffic flow Ga of the monitored section; S5: Use the formula to obtain the inertial pressure bearing value Fs of the monitored section per unit time. Among them, Zj represents the reference mass corresponding to different vehicle types, and J represents the total number of vehicle types. Further, the reference mass is a threshold, and the specific values of the reference masses of different vehicle types are set by those skilled in the art according to big data experience; After that, the pressure analysis module transmits the inertial pressure bearing value Fs of the monitored section per unit time to the integrated analysis module; The rainwater collection module is used to collect the rainwater information of the monitored section in real time and transmit it to the rainwater analysis module. Among them, the rainwater information includes rainwater components, rainfall, and continuous rainfall time. Further, the rainwater components include dissolved oxygen and acidic substances, etc.; The rainwater analysis module is used to obtain the rainwater information and perform environmental analysis on the rainwater information to determine the rainwater influence value. The specific method for determining the rainwater influence value includes: SS1: Identify the influencing components in the rainwater composition, and simultaneously obtain the proportion of the influencing components in the rainwater composition, and mark this proportion as the influence proportion value Ym, where m represents different influencing components, and m ∈ [1, M], indicating that there are a total of M influencing components. Among them, the influencing components refer to the components that have a negative impact on the asphalt pavement. For example, dissolved oxygen and acidic substances. Dissolved oxygen will accelerate the oxidation process of asphalt with the participation of water, and acidic substances will react with the alkaline substances in the asphalt pavement to neutralize, thereby destroying the chemical structure of asphalt and accelerating the damage of the pavement. SS2: Based on the formula Calculate the component influence value CF, where R represents the rainfall in the monitored section, represents the influence coefficient of the influencing component m on the asphalt pavement under unit rainfall, The specific values are respectively obtained by those skilled in the art through big data operations; After that, use the formula Calculate the rainwater influence value WS, where, is the porosity of the asphalt pavement, k is the water stability of the asphalt, and 0 < k < 1. Further, and the specific values of k are respectively determined by the actual construction parameters of the monitored section. At the same time, when the rainwater influence value WS is larger, it indicates that the negative impact of rainwater on the monitored section is greater. On the contrary, when the rainwater influence value WS is smaller, it indicates that the negative impact of rainwater on the monitored section is smaller; It should be further noted that the above calculations belong to a dimensionless calculation process, which can eliminate the complexity caused by different units between different physical quantities, and is more convenient for induction, summary and analysis, so as to more easily discover the laws and trends in the data; After that, the rainwater analysis module transmits the rainwater influence value to the integrated analysis module; The integrated analysis module is used to obtain the inertial pressure bearing value Fs of the monitored section and the rainwater influence value WS, and based on the inertial pressure bearing value Fs and the rainwater influence value WS, evaluate the road surface state value HZ of the monitored section. The specific method for evaluating the road surface state value HZ includes: Use the formula Calculate the road surface state value HZ, where b1 is the base coefficient, and 0 < b1 < 1, t is the rainfall duration, and are respectively proportionality coefficients, and , further, b1, and The specific values are obtained by those skilled in the art through big data operations; Compare the obtained road surface state value HZ with the state threshold X1. If HZ < X1, a normal operation signal is generated; otherwise, if HZ ≥ X1, a road surface abnormality signal is generated. Then, set up a one-way communication connection between the integrated analysis module and the evaluation output module. The evaluation output module is used to obtain the road surface state value HZ and display the road surface state value HZ on the terminal device in real time. At the same time, when the output evaluation module detects a road surface abnormality signal, it generates an audible and visual reminder message in real time to remind the management personnel.

[0018] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An asphalt pavement evaluation system based on rainwater analysis, characterized in that Including: A road surface monitoring module, which is used to collect the operation information of the monitored section and transmit it to the pressure analysis module; A pressure analysis module, which is used to obtain historical operation information, identify and process the interval traffic volume value and vehicle type in each information interval based on the historical operation information, obtain the inertial traffic flow of the information interval and the inertial flow ratio of the vehicle type, and at the same time comprehensively calculate the inertial traffic flow and the inertial flow ratio to obtain the inertial pressure bearing value; A rainwater analysis module, which is used to process the components of rainwater information and calculate the rainwater influence value based on the influencing components in the rainwater components and the real-time rainfall; An integrated analysis module, which is used to comprehensively process the inertial pressure bearing value and the rainwater influence value, determine the road surface state value of the monitored section, and generate a road surface abnormal signal based on the road surface state value; An evaluation and output module, which is used to display the road surface state value on the terminal device, and at the same time generate an audible and visual reminder message based on the road surface abnormal signal and give a real-time reminder to the management personnel.

2. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, wherein, The method for obtaining the inertial traffic flow of the information interval includes: S1: Taking the current time as a node, obtain the historical operation information within the effective time. The effective time is set to 3 months, and the operation information refers to the use state and traffic operation state of the asphalt road surface in the monitored section; Set the unit time, and divide the operation information within the effective time according to the unit time to obtain information intervals. The unit time is set to 1 day; S2: Based on image recognition technology, identify and count the vehicles in each information interval respectively to obtain the interval traffic volume value Li, where i represents different information intervals, and i ∈ [1, I], and I represents the total number of existing information intervals; Based on the normal distribution algorithm, calculate the mean value of the interval flow value Li respectively and the standard deviation , according to the mean value and the standard deviation , set the normal interval Q( , ), and set the value of k to 2; Compare the interval traffic volume value Li with the normal interval Q. If Li ∈ Q, mark the corresponding interval traffic volume value Li as normal data. On the contrary, if Li ∉ Q, mark the corresponding interval traffic volume value Li as abnormal data. After all the interval traffic volume values Li are compared, delete the abnormal data in the interval traffic volume value Li, so that all the data in the interval traffic volume value Li are normal data; Obtain the interval traffic volume value Li under normal data and recalculate the mean value to obtain the inertial traffic flow Ga of the monitored section.

3. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, The vehicle types include small vehicles, medium-sized vehicles and large vehicles. The method for identifying vehicle types includes: Based on image recognition technology, identify the contour of the vehicle in the information interval. Based on the contour of the vehicle, divide the vehicles in the information interval into vehicle types, estimate the volume of the vehicle according to the contour of the vehicle, and then compare the volume of the vehicle with the volume thresholds X1 and X2. If the volume of the vehicle is less than or equal to the volume threshold X1, mark the corresponding vehicle as a small vehicle. If the volume of the vehicle is greater than X1 and less than or equal to X2, mark the corresponding vehicle as a medium-sized vehicle. If the volume of the vehicle is greater than X2, mark the corresponding vehicle as a large vehicle.

4. The asphalt pavement evaluation system based on rainwater analysis according to claim 2, wherein, The method for determining the inertial flow ratio of vehicle types includes: Count the vehicle types of the vehicles in each information interval to obtain the number of vehicles of each vehicle type, and then divide the number of vehicles of each vehicle type by the interval traffic volume value in the corresponding information interval to obtain the vehicle type ratio; Arbitrarily select a vehicle model and mark it as the target vehicle model. Extract the vehicle model ratio of the target vehicle model in all information intervals under normal data, and perform mean calculation. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle model. Then, sequentially set the remaining vehicle models as the target vehicle model and process them according to the above method to obtain the inertial flow ratios Cj of all vehicle models, where j represents different vehicle models.

5. The asphalt pavement evaluation system based on rainwater analysis according to claim 4, wherein The method for obtaining the inertial pressure bearing value includes: Using the formula obtain the inertial pressure bearing value Fs of the monitored section within a unit time, where Zj represents the reference mass corresponding to different vehicle types, and J represents the total number of existing vehicle types.

6. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, wherein The method for calculating the rainwater influence value includes: SS1: Identify the influencing components in the rainwater components, and at the same time obtain the proportion of the influencing components in the rainwater components, and mark this proportion as the influence proportion value Ym, where m represents different influencing components, and m ∈ [1, M], indicating that there are a total of M influencing components. Among them, the influencing components refer to the components that have a negative impact on the asphalt pavement. SS2: Based on the formula calculate the component influence value CF, where R represents the rainfall in the monitored section represents the influence coefficient of the influencing component m on the asphalt pavement under unit rainfall; Reuse the formula to calculate the rainwater influence value WS, where is the porosity of the asphalt pavement, and k is the water stability of the asphalt.

7. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, wherein, The method for obtaining the road surface state value and the road surface abnormal signal includes: Using the formula the road surface state value HZ is calculated, where b1 is the base coefficient and 0 < b1 < 1, t is the rainfall duration, and are the proportionality coefficients respectively, and , Fs is the inertial pressure bearing value, and WS is the rainwater influence value; Compare the obtained road surface state value HZ with the state threshold X1. If HZ < X1, then generate a normal operation signal; otherwise, if HZ ≥ X1, then generate a road surface abnormal signal.

8. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, The rainwater information is collected by the rainwater collection module and transmitted to the rainwater analysis module. The rainwater information includes rainwater components, rainfall, and continuous rainfall time.

9. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, It also includes a road surface information collection module for collecting the road surface information of the asphalt pavement. Among them, the road surface information includes the construction structure and construction parameters of the asphalt pavement and the monitored section of the asphalt pavement. Then, the road surface information collection module transmits the collected road surface information to the road surface monitoring module and the pressure analysis module respectively.

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