An asphalt pavement evaluation system based on rainwater analysis

Abstract

The present application relates to the technical field of road engineering monitoring, and more particularly to an asphalt pavement evaluation system based on rainwater analysis, comprising: determining the inertial pressure bearing value of the monitored road section by analyzing the traffic information and vehicle information of the monitored road section, calculating the real-time rainwater influence value according to the real-time collected rainwater composition and rainfall, comprehensively considering the vehicle inertial pressure bearing value and the rainwater influence value, and comprehensively and accurately determining the pavement state value of the monitored road section, and then generating a pavement abnormal signal in real time based on the pavement state value, the present application helps the management personnel to deeply understand the damage of the pavement under the action of vehicle load and rainwater, so that the staff can timely formulate a targeted maintenance plan and reasonably arrange maintenance resources, thereby ensuring the safety and smoothness of the road.

Description

Technical Field

[0001] This invention relates to the field of road engineering monitoring technology, and in particular to an asphalt pavement evaluation system based on rainwater analysis. Background Technology

[0002] With the acceleration of urbanization, asphalt pavement has been widely used in various types of road construction due to its good driving comfort and low construction cost.

[0003] The prior art CN114896663A discloses a method for evaluating the hydrological performance of permeable pavement structures. This method uses three indicators to evaluate the hydrological performance of permeable pavement structures. The permeability coefficient of the permeable asphalt surface layer reflects the ability of external rainwater and surface runoff to enter the permeable pavement structure. A value that is too small will make it difficult for rainwater and surface runoff to enter the permeable pavement structure, resulting in water accumulation on the surface during periods of low rainfall, thus hindering the permeable pavement structure's function of regulating surface runoff. The water storage capacity reflects the ability of the permeable pavement structure to store rainwater and surface runoff under ideal conditions. A water storage capacity that is too small indicates an unreasonable design of the permeable pavement structure, making it difficult to achieve effective urban flood control.

[0004] However, during rainfall, rainwater can easily seep into the interior of the asphalt pavement. When the pavement is saturated with water, the impact and dynamic water pressure generated by heavy vehicles during driving will accelerate the peeling of the asphalt film from the aggregate, and may even cause the aggregate to scatter, thus 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

[0005] The purpose of this invention is to solve the problems in the background art by proposing an asphalt pavement evaluation system based on rainwater analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An asphalt pavement evaluation system based on rainwater analysis includes:

[0008] The road surface monitoring module is used to collect operational information of the monitored road section and transmit it to the pressure analysis module;

[0009] The pressure analysis module is used to acquire historical operational information. Based on the historical operational information, it identifies and processes the interval traffic flow value and vehicle type in each information interval to obtain the inertial traffic flow of the information interval and the inertial traffic flow ratio of the vehicle type. At the same time, it calculates the inertial traffic flow and the inertial traffic flow ratio together to obtain the inertial pressure withstand value.

[0010] The rainwater analysis module is used to process the composition of rainwater information and calculate the rainwater impact value based on the influencing components in the rainwater composition and the real-time rainfall.

[0011] The integrated analysis module is used to comprehensively process the inertial pressure bearing value and the rainwater impact value to determine the road surface condition value of the monitored road section, and then generate road surface anomaly signals based on the road surface condition value.

[0012] The evaluation output module is used to display the road surface status value on the terminal device, and at the same time, it generates sound and light reminder information based on the road surface abnormality signal, and provides real-time reminders to the management personnel.

[0013] As a further aspect of the present invention, the method for obtaining the inertial traffic flow within the information interval includes:

[0014] S1: Using the current time as the node, obtain historical operation information within the valid time period. The valid time period is set to 3 months. The operation information refers to the usage status of the asphalt pavement and the traffic operation status in the monitored road section.

[0015] Set the unit time and divide the operational information within the effective time into information intervals according to the unit time, where the unit time is set to 1 day;

[0016] S2: Based on image recognition technology, the vehicles in each information interval are identified and counted to obtain the interval flow value Li, where i represents different information intervals and i∈[1,I], and I represents the total number of information intervals.

[0017] Based on the normal distribution algorithm, the mean value of the flow rate Li in each interval is calculated. and standard deviation According to the mean and standard deviation Set the normal range Q k is set to 2;

[0018] The interval flow value Li is compared with the normal interval Q. If Li∈Q, the corresponding interval flow value Li is marked as normal data. Otherwise, if Li∉Q, the corresponding interval flow value Li is marked as abnormal data. After all interval flow values ​​Li have been compared, the abnormal data in the interval flow values ​​Li is deleted, so that all the data in the interval flow values ​​Li are normal data.

[0019] Obtain the interval traffic flow value Li under normal data, and recalculate the mean to obtain the inertial traffic flow Ga of the monitored road segment.

[0020] As a further aspect of the present invention, the vehicle type includes small vehicles, medium-sized vehicles, and large vehicles, and the method for identifying the vehicle type includes:

[0021] Based on image recognition technology, the outlines of vehicles in the information range are identified. Based on the vehicle outlines, the vehicles in the information range are classified into different vehicle types. The vehicle volume is estimated based on the vehicle outlines. Then, the vehicle volume is compared with volume thresholds X1 and X2. If the vehicle volume is less than or equal to the volume threshold X1, the corresponding vehicle is marked as a small vehicle. If the vehicle volume is greater than X1 and less than or equal to X2, the corresponding vehicle is marked as a medium-sized vehicle. If the vehicle volume is greater than X2, the corresponding vehicle is marked as a large vehicle.

[0022] As a further aspect of the present invention, the method for determining the inertial flow ratio of vehicle models includes:

[0023] The vehicle types in each information interval are counted to obtain the number of each vehicle type. Then, the number of vehicle types is divided by the interval flow value in the corresponding information interval to obtain the vehicle type ratio.

[0024] Arbitrarily select a vehicle model and mark it as the target vehicle model. Extract the vehicle model ratio of the target vehicle model from all information intervals under normal data and calculate the mean. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle model. Then, set the remaining vehicle models as the target vehicle models in turn and process them in the same way to obtain the inertial flow ratio Cj of all vehicle models, where j represents different vehicle models.

[0025] As a further aspect of the present invention, the method for obtaining the inertial pressure withstand value includes:

[0026] Using formula The inertial pressure bearing value Fs of the monitored road segment per unit time is obtained, where Zj represents the baseline mass corresponding to different vehicle types, and J represents the total number of vehicle types.

[0027] As a further aspect of the present invention, the method for calculating the impact value of rainwater includes:

[0028] SS1: Identify the influencing components in rainwater composition, and obtain the proportion of the influencing components in the rainwater composition. Mark this proportion as the influence ratio value Ym, where m represents different influencing components, and m∈[1,M], indicating that there are M kinds of influencing components. Influencing components refer to components that have a negative impact on asphalt pavement.

[0029] SS2: Formula-based Calculate the component impact value CF, where R represents the rainfall in the monitored road section. This represents the influence coefficient of the influencing component m on the asphalt pavement under unit rainfall.

[0030] Reuse formula The rainfall impact value WS was calculated, where, Let be the porosity of the asphalt pavement, and k be the water stability of the asphalt.

[0031] As a further aspect of the present invention, the method for obtaining road surface state values ​​and road surface anomaly signals includes:

[0032] Using formula The road surface condition value HZ was calculated, where b1 is the base coefficient, and 0 < b1 < 1, and t is the duration of rainfall. and These are the proportionality coefficients, and Fs is the inertial pressure withstand value, and WS is the rainwater impact value;

[0033] The obtained road surface state value HZ is compared with the state threshold X3. If HZ < X3, a normal operation signal is generated; otherwise, if HZ ≥ X3, a road surface abnormality signal is generated.

[0034] As a further aspect of the present invention, rainwater information is collected by a rainwater collection module and transmitted to a rainwater analysis module, and the rainwater information includes rainwater composition, rainfall amount, and duration of continuous rainfall.

[0035] As a further aspect of the present invention, a road surface information acquisition module is also included, which is used to collect road surface information of asphalt pavement. The road surface information includes the construction structure and construction parameters of asphalt pavement and the monitored road sections of asphalt pavement. The road surface information acquisition module then transmits the collected road surface information to the road surface monitoring module and the pressure analysis module respectively.

[0036] Compared with existing technologies, the advantages of this invention are:

[0037] This invention analyzes traffic flow and vehicle information of monitored road sections to determine the inertial pressure bearing capacity of those sections. Simultaneously, based on real-time collected rainwater composition and rainfall, it calculates the real-time rainwater impact value. By comprehensively considering both vehicle inertial pressure bearing capacity and rainwater impact value, it can fully and accurately determine the road surface condition of the monitored road sections. This helps managers gain a deeper understanding of the road surface damage under vehicle loads and rainwater effects. Furthermore, based on the road surface condition value, it generates real-time road surface anomaly signals, enabling staff to promptly develop targeted maintenance plans, rationally allocate maintenance resources, and further ensure safe and smooth road traffic. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0039] 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.

[0040] Reference Figure 1 An asphalt pavement evaluation system based on rainwater analysis includes a pavement information acquisition module, a pavement monitoring module, a pressure analysis module, a rainwater acquisition module, a rainwater analysis module, an integrated analysis module, and an evaluation output module.

[0041] The road surface information acquisition module is used to collect road surface information of asphalt pavement, including the construction structure and construction parameters of asphalt pavement and the monitored road sections of asphalt pavement. Then, the road surface information acquisition module transmits the collected road surface information to the road surface monitoring module and the pressure analysis module respectively.

[0042] The road surface monitoring module is used to set up monitoring equipment in the monitored road section according to the monitored road section, and collect the operation information of the monitored road section based on the monitoring equipment and transmit it to the pressure analysis module. In this embodiment, the monitoring equipment is a camera. The operation information of the monitored road section refers to the usage status of the asphalt pavement and the traffic operation status in the monitored road section, specifically including traffic flow information and vehicle load information.

[0043] The pressure analysis module is used to acquire operational information and, based on this information, determine the inertial pressure tolerance value of the monitored road segment. Specific methods for determining the inertial pressure tolerance value include:

[0044] S1: Using the current time as a node, obtain historical operational information within the valid time period. The specific value of the valid time period is set by those skilled in the art based on big data experience. Furthermore, in this embodiment, the valid time period is set to 3 months.

[0045] Set a unit time and divide the operational information within the effective time according to the unit time to obtain information intervals. In this embodiment, the unit time is set to 1 day.

[0046] S2: Based on image recognition technology, the vehicles in each information interval are identified and counted to obtain the interval flow value Li, where i represents different information intervals and i∈[1,I], and I represents the total number of information intervals.

[0047] Then, based on the normal distribution algorithm, the mean value of the interval flow rate Li is calculated respectively. and standard deviation According to the mean and standard deviation Set the normal range Q Where k is a threshold, and in this embodiment, k is set to 2;

[0048] The interval flow value Li is compared with the normal interval Q. If Li∈Q, the corresponding interval flow value Li is marked as normal data. Otherwise, if Li∉Q, the corresponding interval flow value Li is marked as abnormal data. After all interval flow values ​​Li have been compared, the abnormal data in the interval flow values ​​Li is deleted, so that all the data in the interval flow values ​​Li are normal data.

[0049] S3: Based on image recognition technology, the outline of the vehicle in the information interval is identified. Furthermore, when identifying the outline of the vehicle in the information interval, only the vehicles in the information interval corresponding to the normal data are identified.

[0050] Then, based on the vehicle's outline, the vehicles in the information range are classified into vehicle types, including small vehicles, medium-sized vehicles, and large vehicles.

[0051] Furthermore, when classifying vehicles based on their outlines, it is necessary to estimate the vehicle's volume based on the outline. Then, the vehicle's volume is compared with volume thresholds X1 and X2. If the vehicle's volume is less than or equal to the volume threshold X1, the corresponding vehicle is marked as a small vehicle. If the vehicle's volume is greater than X1 and less than or equal to X2, the corresponding vehicle is marked as a medium-sized vehicle. If the vehicle's volume is greater than X2, the corresponding vehicle is marked as a large vehicle. The specific values ​​of the volume thresholds X1 and X2 are set by those skilled in the art based on big data experience.

[0052] S4: Count the vehicle types in each information interval to get the number of each vehicle type. Then divide the number of vehicle types by the interval flow value in the corresponding information interval to get the vehicle type ratio.

[0053] Arbitrarily select a vehicle model and mark it as the target vehicle model. Extract the vehicle model ratio of the target vehicle model from all information intervals under normal data and calculate the mean. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle model. Then, set the remaining vehicle models as the target vehicle models in turn and process them in the same way to obtain the inertial flow ratio Cj of all vehicle models, where j represents different vehicle models.

[0054] Obtain the interval traffic flow value Li under normal data, and recalculate the mean to obtain the inertial traffic flow Ga of the monitored road segment;

[0055] S5: Using the formula The inertial pressure bearing value Fs of the monitored road segment per unit time is obtained, where Zj represents the reference mass corresponding to different vehicle types, J represents the total number of vehicle types, and further, the reference mass is a threshold. The specific value of the reference mass of different vehicle types is set by those skilled in the art based on big data experience.

[0056] The pressure analysis module then transmits the inertial pressure withstand value Fs of the monitored road segment per unit time to the integrated analysis module;

[0057] The rainwater collection module is used to collect rainwater information of the monitored road section in real time and transmit it to the rainwater analysis module. The rainwater information includes rainwater composition, rainfall amount and duration of continuous rainfall. Furthermore, the rainwater composition includes dissolved oxygen and acidic substances, etc.

[0058] The rainwater analysis module is used to acquire rainwater information, perform environmental analysis on the rainwater information, and determine the rainwater impact value. Specific methods for determining the rainwater impact value include:

[0059] SS1: Identify the influencing components in rainwater composition, and obtain the proportion of the influencing components in the rainwater composition. Mark this proportion as the influence ratio value Ym, where m represents different influencing components, and m∈[1,M], indicating that there are M influencing components. Influencing components refer to components that have a negative impact on asphalt pavement, such as dissolved oxygen and acidic substances. Dissolved oxygen will accelerate the oxidation process of asphalt with the participation of water, and acidic substances will neutralize the alkaline substances in the asphalt pavement, thereby destroying the chemical structure of asphalt and accelerating the damage to the pavement.

[0060] SS2: Formula-based Calculate the component impact value CF, where R represents the rainfall in the monitored road section. This represents the influence coefficient of component m on asphalt pavement under unit rainfall. The specific values ​​were obtained by those skilled in the art through big data calculations.

[0061] Then use the formula The rainfall impact value WS was calculated, where, Let be the porosity of the asphalt pavement, and k be the water stability of the asphalt, where 0 < k < 1. Furthermore, The specific values ​​of and k are determined by the actual construction parameters of the monitored road section. At the same time, the larger the rainwater impact value WS is, the greater the negative impact of rainwater on the monitored road section. Conversely, the smaller the rainwater impact value WS is, the smaller the negative impact of rainwater on the monitored road section.

[0062] It should be further explained that the above calculation is a dimensionless calculation process, which can eliminate the complexity caused by different units between different physical quantities, and make it easier to summarize, analyze and generalize, thus making it easier to discover the patterns and trends in the data.

[0063] The rainwater analysis module then transmits the rainwater impact values ​​to the integrated analysis module;

[0064] The integrated analysis module is used to obtain the inertial pressure bearing value Fs and the rainwater impact value WS of the monitored road segment, and to evaluate the pavement condition value HZ of the monitored road segment based on the inertial pressure bearing value Fs and the rainwater impact value WS. The specific methods for evaluating the pavement condition value HZ include:

[0065] Using formula The road surface condition value HZ was calculated, where b1 is the base coefficient, and 0 < b1 < 1, and t is the duration of rainfall. and These are the proportionality coefficients, and Furthermore, b1, and The specific value was obtained by those skilled in the art through big data calculations;

[0066] The obtained road surface state value HZ is compared with the state threshold X3. If HZ < X3, a normal operation signal is generated; otherwise, if HZ ≥ X3, a road surface abnormality signal is generated. Then, the integrated analysis module and the evaluation output module are set to a one-way communication connection.

[0067] The evaluation output module is used to acquire the road surface condition value HZ and display it in real time on the terminal device. At the same time, when the output evaluation module detects abnormal road surface signals, it generates an audio-visual reminder message in real time and alerts the management personnel.

[0068] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An asphalt pavement evaluation system based on rainwater analysis, characterized in that, include: The road surface monitoring module is used to collect operational information of the monitored road section and transmit it to the pressure analysis module; The pressure analysis module is used to acquire historical operational information. Based on the historical operational information, it identifies and processes the interval traffic flow value and vehicle type in each information interval to obtain the inertial traffic flow of the information interval and the inertial traffic flow ratio of the vehicle type. At the same time, it calculates the inertial traffic flow and the inertial traffic flow ratio together to obtain the inertial pressure withstand value. The methods for determining the inertial pressure bearing capacity include: S1: Using the current time as the node, obtain historical operation information within the valid time period. The valid time period is set to 3 months. The operation information refers to the usage status of the asphalt pavement and the traffic operation status in the monitored road section. Set the unit time and divide the operational information within the effective time into information intervals according to the unit time, where the unit time is set to 1 day; S2: Based on image recognition technology, the vehicles in each information interval are identified and counted to obtain the interval flow value Li, where i represents different information intervals and i∈[1,I], and I represents the total number of information intervals. Based on the normal distribution algorithm, the mean value of the flow rate Li in each interval is calculated. and standard deviation According to the mean and standard deviation Set the normal range Q k is set to 2; The interval flow value Li is compared with the normal interval Q. If Li∈Q, the corresponding interval flow value Li is marked as normal data. Otherwise, if Li∉Q, the corresponding interval flow value Li is marked as abnormal data. After all interval flow values ​​Li have been compared, the abnormal data in the interval flow values ​​Li is deleted, so that all the data in the interval flow values ​​Li are normal data. Obtain the interval traffic flow value Li under normal data, and recalculate the mean to obtain the inertial traffic flow Ga of the monitored road segment; The vehicle types in each information interval are counted to obtain the number of each vehicle type. Then, the number of vehicle types is divided by the interval flow 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 from all information intervals under normal data and calculate the mean. Mark the obtained mean calculation result as the inertial flow ratio of the target vehicle model. Then, set the remaining vehicle models as the target vehicle models in turn and process them in the same way to obtain the inertial flow ratio Cj of all vehicle models, where j represents different vehicle models. Using formula The inertial pressure bearing value Fs of the monitored road section per unit time is obtained, where Zj represents the reference mass corresponding to different vehicle types, and J represents the total number of vehicle types. The rainwater analysis module is used to process the composition of rainwater information and calculate the rainwater impact value based on the influencing components in the rainwater composition and the real-time rainfall. The integrated analysis module is used to comprehensively process the inertial pressure bearing value and the rainwater impact value to determine the road surface condition value of the monitored road section, and then generate road surface anomaly signals based on the road surface condition value. The evaluation output module is used to display the road surface status value on the terminal device, and at the same time, it generates sound and light reminder information based on the road surface abnormality signal, and provides real-time reminders to the management personnel.

2. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, Vehicle types include small vehicles, medium-sized vehicles, and large vehicles. Methods for identifying vehicle types include: Based on image recognition technology, the outlines of vehicles in the information range are identified. Based on the vehicle outlines, the vehicles in the information range are classified into different vehicle types. The vehicle volume is estimated based on the vehicle outlines. Then, the vehicle volume is compared with volume thresholds X1 and X2. If the vehicle volume is less than or equal to the volume threshold X1, the corresponding vehicle is marked as a small vehicle. If the vehicle volume is greater than X1 and less than or equal to X2, the corresponding vehicle is marked as a medium-sized vehicle. If the vehicle volume is greater than X2, the corresponding vehicle is marked as a large vehicle.

3. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, The methods for calculating the impact of rainfall include: SS1: Identify the influencing components in rainwater composition, and obtain the proportion of the influencing components in the rainwater composition. Mark this proportion as the influence ratio value Ym, where m represents different influencing components, and m∈[1,M], indicating that there are M kinds of influencing components. Influencing components refer to components that have a negative impact on asphalt pavement. SS2: Formula-based Calculate the component impact value CF, where R represents the rainfall in the monitored road section. This represents the influence coefficient of the influencing component m on the asphalt pavement under unit rainfall. Reuse formula The rainwater impact value WS was calculated, where, Let be the porosity of the asphalt pavement, and k be the water stability of the asphalt.

4. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, Methods for obtaining road surface condition values ​​and road surface anomaly signals include: Using formula The road surface condition value HZ is calculated, where b1 is the base coefficient, and 0 < b1 < 1, and t is the duration of rainfall. and These are the proportionality coefficients, and Fs is the inertial pressure withstand value, and WS is the rainwater impact value; The obtained road surface state value HZ is compared with the state threshold X3. If HZ < X3, a normal operation signal is generated; otherwise, if HZ ≥ X3, a road surface abnormality signal is generated.

5. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, Rainfall information is collected by the rainwater acquisition module and transmitted to the rainwater analysis module. The rainwater information includes rainwater composition, rainfall amount, and duration of rainfall.

6. The asphalt pavement evaluation system based on rainwater analysis according to claim 1, characterized in that, It also includes a road surface information acquisition module, which is used to collect road surface information of asphalt pavement. The road surface information includes the construction structure and construction parameters of asphalt pavement, as well as the monitored road sections of asphalt pavement. The road surface information acquisition module then transmits the collected road surface information to the road surface monitoring module and the pressure analysis module respectively.

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