Cold recycled pavement quality management and control system and method based on data analysis technology

Abstract

The invention discloses a cold-recycling pavement quality management and control system and method based on a data analysis technology, and belongs to the technical field of cold-recycling pavement quality management and control. The method comprises the steps that S10, the real-time rut depth of each position of a maintenance road section is predicted; s20, predicting the pavement damage degree of the maintained road section; s30, predicting the real-time quality control index of the maintenance road section; s40, the maintenance time of the maintenance road section is determined; and S50, predicting the real-time quality index of the cold recycled pavement. According to the method, the maintenance time of the maintenance road section is determined according to the pavement damage degree of the maintenance road section and the distribution condition of the driving vehicles with different axle loads in the real-time traffic flow of the maintenance road section, so that the to-be-maintained road section can be still applicable before the quality of the to-be-maintained road section is managed and controlled, and the maintenance efficiency of the to-be-maintained road section is improved. And the real-time construction quality of the cold recycling pavement can be predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of cold regenerated pavement quality control, and in particular to a cold regenerated pavement quality control system and method based on data analysis technology. Background Art

[0002] Cold recycling technology is a technology that uses special machinery at room temperature to continuously complete the milling and crushing of old pavement structure layers, adding recycled materials, mixing, paving, rolling and other operations to re-form a structural layer with a certain bearing capacity.

[0003] At present, before maintenance construction is carried out on old asphalt pavement, the technical condition of the pavement in the maintenance section needs to be evaluated. This process requires a large amount of manpower and time costs, and it is difficult to effectively analyze the pavement conditions at each location when evaluating and analyzing pavement damage, rutting, flatness, etc. through existing detection technology. At the same time, under the current high-volume traffic, the demand for rapid traffic opening is more urgent, and the maintenance construction time needs to be reduced. At the same time, practical projects have found that the amount of cold recycled materials used and the deviation of construction parameters will reduce the pavement flatness of cold recycled pavement, so the quality control of cold recycled pavement is particularly important. Summary of the invention

[0004] The purpose of the present invention is to provide a cold regenerated pavement quality control system and method based on data analysis technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a cold regenerated pavement quality control method based on data analysis technology, the method comprising:

[0006] S10: The movement trajectory of each vehicle on the maintenance section is determined by using a camera installed on the maintenance section, and the historical traffic load of the maintenance section is analyzed in combination with the axle load of each vehicle. Based on the analysis results, the real-time rutting depth at each position of the maintenance section is predicted;

[0007] S20: determining the real-time distribution of uneven areas in the maintenance section according to the real-time rutting depth at each position of the maintenance section, analyzing the correlation between the uneven areas in combination with the distance between the uneven areas, and predicting the road surface damage degree of the maintenance section in combination with the cracks existing in the maintenance section;

[0008] S30: Based on the traffic flow distribution coefficient of the maintenance section, predict the real-time quality control index of the maintenance section;

[0009] S40: Determine the maintenance time of the maintenance section;

[0010] S50: After determining the maintenance time, the cold regeneration technology is used to treat the road surface of the maintenance section, and the parameters during the construction process are collected in real time. Based on the collected parameters, the real-time quality index of the cold regenerated road surface is predicted. According to the predicted real-time quality index, the construction operation parameters are controlled in real time.

[0011] Further, the S10 includes:

[0012] S101: randomly selecting a point on the maintenance section as the coordinate origin, constructing a three-dimensional space coordinate system, measuring the axle load of each historical vehicle entering the maintenance section according to the dynamic weighing system, acquiring continuous single-frame images of each historical vehicle traveling on the maintenance section through a camera installed in the maintenance section, determining the motion trajectory of each historical vehicle on the maintenance section based on the acquired image information, and acquiring the cumulative number of axle load actions at each position in the maintenance section based on the determined motion trajectory;

[0013] S102: A point is randomly selected on the road surface of the maintenance section, and each axle load is numbered in order from small to large. The numbering result is: i = 1, 2, ..., m; m represents the total number of numbers. At time t, the total number of times k of vehicles with the i-th axle load passing through the selected position point is calculated. it Conduct collection;

[0014] S103: According to Predict the rutting depth at the selected location at time t, where h i represents the axle load value corresponding to the i-th axle load, H represents the standard axle load of the maintenance section, a represents the weight coefficient, b represents the adjustment coefficient, v represents the historical average speed of vehicles passing the selected location point, V represents the maximum speed allowed by the maintenance section, T represents the historical average temperature of the selected location point, R represents the temperature value, and the rutting depth of the vehicle at the current temperature value begins to be proportional to the temperature. When v / V<0, When v / V ≥ 0, When T / R<1, When T / R ≥ 1, e represents a constant and e>1.

[0015] Further, the S20 includes:

[0016] S201: constructing a real-time three-dimensional model of the maintenance section according to the real-time rutting depth at each position of the maintenance section, displaying the constructed real-time three-dimensional model in a three-dimensional space coordinate system, determining the real-time distribution of uneven areas in the maintenance section according to the constructed real-time three-dimensional model, and numbering each uneven area, the numbering result is: j=1,2,…,n; n represents the total number of uneven areas in the maintenance section;

[0017] S202: In the three-dimensional space coordinate system, the position coordinates of the lowest point at the bottom of each uneven area are obtained, and the distance value between the two uneven areas is calculated according to the three-dimensional distance formula. At time t, according to The correlation coefficient between the uneven area numbered j and the uneven area numbered g is predicted, where d jgt represents the distance between the lowest point at the bottom of the uneven area numbered j and the lowest point at the bottom of the uneven area numbered g at time t, g = 1, 2, ..., n and g ≠ j;

[0018] S203: Let y ct =maxy jt ,y jt Indicates the rut depth corresponding to the lowest point at the bottom of the uneven area numbered j at time t, and obtains the rut depth limit G of the maintenance section. When there is no historical vehicle passing through the monitoring area of ​​each camera installed in the maintenance section, the camera collects the image of the maintenance section corresponding to each monitoring area. Based on the acquired image information, the number of cracks existing in the maintenance section corresponding to each monitoring area and the actual length of each crack are obtained. Based on the acquired information, the pavement damage degree of the maintenance section is predicted at time t. The specific prediction formula is:

[0019]

[0020] Among them, f cjt represents the correlation coefficient between the lowest point at the bottom of the damaged area numbered j and the lowest point at the bottom of the damaged area numbered c, N represents the total number of cracks in the maintenance section, η=1 or η=0, when N=0, η=0, when N≠0, η=1, γ q represents the actual length of the crack numbered q in the maintenance section, q = 1, 2, ..., X, represents the number of each crack in the maintenance section, X represents the total number of cracks in the maintenance section, w 1 、w 2 、w 3 Both represent weight coefficients, and w 1 +w 2 +w 3 =1,P tIt represents the pavement damage degree of the maintenance section at time t. Since the influence of temperature and vehicle speed on the rutting depth is taken into account in the calculation of the rutting depth, and the pavement damage degree is obtained based on the rutting depth, there is no need to add the above-mentioned influencing factors into the prediction process of the pavement damage degree, which reduces the prediction complexity of the pavement damage degree and is conducive to improving the prediction accuracy and prediction rate.

[0021] Furthermore, the specific method of predicting the real-time quality control index of the maintenance section in S30 is:

[0022] At time t, the total number of historical times of vehicles with different axle loads passing through the maintenance section and the total number of historical times of vehicles passing through the maintenance section L t To obtain, at time t, according to F it =E i / L calculates the distribution coefficient of historical vehicles with the i-th axle load passing through the maintenance section, E it It represents the total number of times that historical vehicles with the i-th axle load have passed through the maintenance section at time t;

[0023] The traffic flow Q of the maintenance section at time t+1*r t+1*r To obtain, the traffic flow refers to the total number of vehicles passing through the maintenance section in the time period [t, t+1*r];

[0024] according to The quality control index of the maintenance section at time t+1*r is predicted, where r represents the interval time value and X represents the empirical coefficient.

[0025] Furthermore, the specific method for determining the maintenance time of the maintenance section in S40 is:

[0026] If 0.6≤D t+1*r ≤1, it means that the maintenance time of the maintenance section is t+1*r;

[0027] If 0≤D t+1*r <0.6, it means that there is no need to maintain the maintenance section at time t+1*r.

[0028] Further, the S50 includes:

[0029] S501: At the determined maintenance time, the cold recycling technology is used to treat the road surface of the maintenance section. During the road surface treatment process, five operation steps are required: road surface milling → adding recycled materials → mixing → paving → rolling;

[0030] S502: When performing each operation, real-time parameters in the construction process are collected, and the real-time quality index of the cold recycled pavement is predicted by combining the coefficient of variation corresponding to each construction operation parameter calculated based on the total amount of old asphalt pavement material milled by the pavement milling machine in the maintenance section. The construction operation parameters include the average milling speed of the pavement milling machine on the maintenance section, the amount of emulsified asphalt, the amount of water, the average paving speed of the slipform paver on the mixed recycled material, and the average rolling speed of the roller on the mixed recycled material;

[0031] S503: Compare the quality index of the cold regenerated pavement at each moment with the set threshold. If the quality index of the cold regenerated pavement at the corresponding moment is less than the set threshold, the collected construction operation parameters are controlled at the corresponding moment. If the quality index of the cold regenerated pavement at the corresponding moment is greater than or equal to the set threshold, there is no need to control the collected construction operation parameters at the corresponding moment.

[0032] Furthermore, the specific method of calculating the coefficient of variation corresponding to each construction operation parameter in S502 is:

[0033] Coefficient of variation for emulsified asphalt dosage:

[0034] according to The coefficient of variation of the amount of emulsified asphalt was calculated, where Y 1 It represents the actual amount of emulsified asphalt used in the process of adding recycled materials, and Y represents the theoretical amount of emulsified asphalt used in the process of adding recycled materials;

[0035] For the coefficient of variation of water usage:

[0036] according to The coefficient of variation of water content was calculated, where S 1 It represents the actual amount of water used in the mixing process, and S represents the theoretical water content used in the mixing process.

[0037] Furthermore, the specific method of predicting the real-time quality index of the cold regenerated pavement in S502 is:

[0038] When the road milling operation is being carried out, the time period for the road milling operation is [μ 0 ,μ 1 ], the road milling machine is used for the maintenance section in [μ 0 ,μ′ 1 ]Average milling speed during the time period To obtain;

[0039] according to For cold recycled pavement in μ′ 1 The quality index at the moment is predicted, where μ 0<μ′ 1 ≤μ 1 , U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the road quality;

[0040] When adding recycled materials, the time period for adding recycled materials is [μ 1 +1,μ 2 ],pass

[0041] For cold recycled pavement in μ′ 2 The quality index at the moment is predicted, μ 1 +1<μ′ 2 ≤μ 2 , μ 2 Indicates the time when the recycled material addition operation is completed;

[0042] When performing mixing operations, the time period for the mixing operation is [μ 2 +1,μ 3 ],pass For cold recycled pavement in μ′ 3 The quality index at the moment is predicted, μ 2 +1<μ′ 3 ≤μ 3 , μ 3 Indicates the time when the mixing operation is completed;

[0043] During the paving operation, the time period for the paving operation is recorded as [μ 3 +1,μ 4 ], for mixed recycled materials in [μ 3 +1,μ′ 4 ]Average paving speed during the time period To obtain;

[0044] according to For cold recycled pavement in μ′ 4 The quality index at the moment is predicted, μ 3 +1<μ′ 4 ≤μ 4 ;

[0045] When rolling operation is performed, the time period for rolling operation is recorded as [μ 4 +1,μ 5 ], for mixed recycled materials in [μ 4 +1,μ′ 5 ]Average rolling speed during the time period To obtain;

[0046] according to For cold recycled pavement in μ′ 5The quality index at the moment is predicted, μ 4 +1<μ′ 5 ≤μ 5 ;

[0047] Among them, U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the road quality, A′ represents the average rolling speed of the maintenance section while ensuring the road quality, W′ represents the maximum paving speed of the maintenance section while ensuring the road quality, δ 1 ,δ 2 ,δ 3 ,δ 4 ,δ 5 ,δ 6 Both represent proportionality coefficients and δ 1 +δ 2 +δ 3 +δ 4 +δ 5 +δ 6 =1.

[0048] A cold regenerated pavement quality control system based on data analysis technology, the system comprising a rutting depth prediction module, a pavement damage degree prediction module to be maintained, a quality control index prediction module, a maintenance time determination module and a cold regenerated pavement quality control module;

[0049] The rutting depth prediction module is used to predict the real-time rutting depth at each location of the maintenance section;

[0050] The pavement damage degree prediction module to be maintained is used to predict the pavement damage degree of the maintenance section;

[0051] The quality control index prediction module is used to predict the real-time quality control index of the maintenance section;

[0052] The maintenance time determination module is used to determine the maintenance time of the maintenance section;

[0053] The cold regenerated pavement quality control module is used to predict the real-time quality index of the cold regenerated pavement and control the construction operation parameters in real time according to the prediction results.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0055] 1. The present invention determines the cumulative number of axle load actions at various positions on the road surface to be maintained through the movement trajectory of each historical vehicle on the maintenance section, thereby determining the cumulative number of standard load actions at various positions on the maintenance section. Combined with the average driving speed of each historical vehicle on the maintenance section and the historical average temperature of the maintenance section, the real-time rutting depth at various positions on the maintenance section is predicted, which is conducive to accurately analyzing the deformation of the maintenance section. Compared with manual measurement, the analysis accuracy and efficiency are improved.

[0056] 2. The present invention determines the real-time distribution of uneven areas generated in the maintenance section based on the constructed real-time three-dimensional model, analyzes the correlation between the uneven areas, and predicts the pavement damage degree of the maintenance section in combination with the maximum rutting depth of each uneven area and the cracking conditions existing in the maintenance section. Since the pavement damage degree is obtained based on the rutting depth, there is no need to add environmental factors and vehicle speed factors into the damage degree prediction process again, which reduces the complexity of damage degree prediction and is conducive to improving prediction accuracy and prediction rate.

[0057] 3. The present invention predicts the real-time quality control index of the road surface to be maintained through the road surface damage degree of the maintenance section and the distribution of vehicles with different axle loads on the maintenance section in the real-time traffic flow, and then determines the maintenance time of the maintenance section, ensuring that the road surface to be maintained can continue to be used before the quality control of the road surface is carried out, and at the same time ensuring that the damage degree of the maintenance section is low when the maintenance section is maintained, that is, ensuring that the maintenance section can be restored to the optimal traffic capacity in a short time, reducing the maintenance construction time of the maintenance section, and further improving the quality control effect of the system on the road surface to be maintained.

[0058] 4. The present invention uses the cold regeneration technology to collect real-time construction parameters when treating the road surface of the maintenance section, and the total amount of old asphalt pavement material milled out by the road milling machine in the maintenance section. The coefficient of variation corresponding to each construction parameter is calculated to predict the real-time quality index of the cold regenerated pavement, thereby realizing the control of the construction quality of the cold regenerated pavement during the construction process, achieving real-time and effective monitoring, and improving the control strength of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 It is a schematic diagram of the workflow of a cold regenerated pavement quality control system and method based on data analysis technology of the present invention;

[0061] Figure 2 It is a schematic structural diagram of the working principle of a cold regenerated pavement quality control system and method based on data analysis technology of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1 and Figure 2 The present invention provides a technical solution: a cold regenerated pavement quality control method based on data analysis technology, the method comprising:

[0064] S10: The movement trajectory of each vehicle on the maintenance section is determined by using a camera installed on the maintenance section, and the historical traffic load of the maintenance section is analyzed in combination with the axle load of each vehicle. Based on the analysis result, the real-time rutting depth at each position of the maintenance section is predicted. The rutting is the indentation left by the wheels of the vehicle after driving on the road surface.

[0065] S10 includes:

[0066] S101: randomly selecting a point on the maintenance section as the origin of coordinates, constructing a three-dimensional space coordinate system, measuring the axle load of each historical vehicle entering the maintenance section according to a dynamic weighing system (a dynamic weighing system is a technology for measuring the weight of a vehicle in real time during operation, and measuring the axle load by a dynamic weighing system belongs to the prior art), acquiring continuous single-frame images of each historical vehicle traveling on the maintenance section through a camera installed in the maintenance section, determining the motion trajectory of each historical vehicle on the maintenance section based on the acquired image information, and acquiring the cumulative axle load action times at each position in the maintenance section based on the determined motion trajectory, wherein the cumulative axle load action times refers to the number of times historical vehicles with different axle loads pass the same position, and the axle load refers to the sum of the weights borne by each wheel of the vehicle;

[0067] S102: A point is randomly selected on the road surface of the maintenance section, and each axle load is numbered in order from small to large. The numbering result is: i = 1, 2, ..., m; m represents the total number of numbers. At time t, the total number of times k of vehicles with the i-th axle load passing through the selected position point is calculated. it Conduct collection;

[0068] S103: According to Predict the rutting depth at the selected location at time t, where h i represents the axle load value corresponding to the i-th axle load, H represents the standard axle load of the maintenance section (the concept of cumulative equivalent axle times is used in pavement design. There are many types of vehicles traveling on the road, and different types of vehicles and different times of action have different effects on the pavement. To facilitate pavement design, the mixed traffic volume composed of different types of vehicles needs to be converted into an equivalent axle time of a certain unified axle load. This unified axle load is called the standard axle load), a represents the weight coefficient, b represents the adjustment coefficient, v represents the historical average speed of historical vehicles passing the selected location point, V represents the maximum speed allowed for the maintenance section, T represents the historical average temperature of the selected location point, R represents the temperature value, and the rutting depth of the vehicle at the current temperature value begins to be proportional to the temperature. When v / V<0, When v / V ≥ 0, When T / R<1, When T / R ≥ 1, e represents a constant and e>1;

[0069] Until all the position points on the maintenance section are selected, the real-time rutting depth prediction of each position on the maintenance section can be realized;

[0070] S20: determining the real-time distribution of the uneven areas of the maintenance section according to the real-time rutting depths at each position of the maintenance section, analyzing the correlation between the uneven areas in combination with the distances between the uneven areas, and predicting the road surface damage degree of the maintenance section in combination with the cracking conditions existing in the maintenance section;

[0071] The S20 includes:

[0072] S201: constructing a real-time three-dimensional model of the maintenance section according to the real-time rutting depth at each position of the maintenance section, displaying the constructed real-time three-dimensional model in a three-dimensional space coordinate system, determining the real-time distribution of uneven areas in the maintenance section according to the constructed real-time three-dimensional model, and numbering each uneven area, the numbering result is: j=1,2,…,n; n represents the total number of uneven areas in the maintenance section;

[0073] S202: In the three-dimensional space coordinate system, the position coordinates of the lowest point at the bottom of each uneven area are obtained, and the distance value between the two uneven areas is calculated according to the three-dimensional distance formula (the three-dimensional distance formula is a mathematical formula for calculating the distance between two points in three-dimensional space). At time t, according to The correlation coefficient between the uneven area numbered j and the uneven area numbered g is predicted, where d jgtrepresents the distance between the lowest point at the bottom of the uneven area numbered j and the lowest point at the bottom of the uneven area numbered g at time t, g = 1, 2, ..., n and g ≠ j;

[0074] S203: Let y ct =maxy jt ,y jt Indicates the rutting depth corresponding to the lowest point at the bottom of the uneven area numbered j at time t, and obtains the rutting depth limit G of the maintenance section. The rutting depth limit refers to the maximum rutting depth allowed in the maintenance section without affecting the driving safety of the driving vehicle. The rutting depth limit = 0.5 * pavement thickness of the maintenance section. When there is no historical vehicle passing through the monitoring area of ​​each camera installed in the maintenance section, the camera is used to collect images of the maintenance section corresponding to each monitoring area. Based on the acquired image information, the number of cracks existing in the maintenance section corresponding to each monitoring area and the actual length of each crack are obtained. The actual length of the crack refers to the length of the crack in the maintenance section. According to the actual width of the maintenance section / the width of the maintenance section in the image = the actual length of the crack / the length of the crack in the image, the actual length of the crack is calculated. Based on the acquired information, at time t, the pavement damage degree of the maintenance section is predicted. The specific prediction formula is:

[0075]

[0076] Among them, f cjt represents the correlation coefficient between the lowest point at the bottom of the damaged area numbered j and the lowest point at the bottom of the damaged area numbered c. N represents the total number of cracks in the maintenance section. Since N represents the total number of cracks, it can be seen that N is an integer ≥ 0, η = 1 or η = 0. When N = 0, η = 0. When N ≠ 0, η = 1. q represents the actual length of the crack numbered q in the maintenance section, q = 1, 2, ..., X, represents the number of each crack in the maintenance section, X represents the total number of cracks in the maintenance section, w 1 、w 2 、w 3 Both represent weight coefficients, and w 1 +w 2 +w 3 =1,P t It represents the pavement damage degree of the maintenance section at time t;

[0077] S30: Based on the traffic flow distribution coefficient of the maintenance section, the real-time quality control index of the maintenance section is predicted. The specific method is as follows:

[0078] At time t, the total number of historical times of vehicles with different axle loads passing through the maintenance section and the total number of historical times of vehicles passing through the maintenance section L t To obtain, at time t, according to F it =E i / L calculates the distribution coefficient of historical vehicles with the i-th axle load passing through the maintenance section, E it It represents the total number of times that historical vehicles with the i-th axle load have passed through the maintenance section at time t;

[0079] The traffic flow Q of the maintenance section at time t+1*r t+1*r To obtain, the traffic flow refers to the total number of vehicles passing through the maintenance section in the time period [t, t+1*r];

[0080] according to The quality control index of the maintenance section at time t+1*r is predicted, where r represents the interval time value and X represents the empirical coefficient. The empirical coefficient refers to the linear training model obtained by training the linear model using the historical pavement damage degree and historical traffic flow of the maintenance section as training data. The weight of the linear training model is called the empirical coefficient.

[0081] S40: Determine the maintenance time of the maintenance section, the specific method is:

[0082] If 0.6≤D t+1*r ≤1, it means that the maintenance time of the maintenance section is t+1*r;

[0083] If 0≤D t+1*r <0.6, it means that there is no need to maintain the maintenance section at time t+1*r;

[0084] S50: After the maintenance time is determined, the cold regeneration technology is used to treat the road surface of the maintenance section, and the parameters in the construction process are collected in real time. Based on the collected parameters, the real-time quality index of the cold regenerated road surface is predicted, and the construction operation parameters are controlled in real time according to the predicted real-time quality index;

[0085] S50 includes:

[0086] S501: At the determined maintenance time, the cold recycling technology is used to treat the road surface of the maintenance section. During the road surface treatment process, five operation steps are required: road surface milling → adding recycled materials → mixing → paving → rolling;

[0087] S502: When performing each operation, collect the real-time parameters of the construction operation process, and calculate the coefficient of variation corresponding to each construction operation parameter based on the total amount of old asphalt pavement material milled out by the road milling machine in the maintenance section. For the coefficient of variation of the amount of emulsified asphalt: according to The coefficient of variation of the amount of emulsified asphalt was calculated, where Y 1 It represents the actual amount of emulsified asphalt used in the process of adding recycled materials, and Y represents the theoretical emulsified asphalt content used in the process of adding recycled materials. Y is determined by the test results obtained by testing old asphalt road materials with different proportions of recycled materials. It belongs to the existing technology. The coefficient of variation of water usage is: according to The coefficient of variation of water content was calculated, where S 1 It represents the actual amount of water used in the mixing process, and S represents the theoretical water content used in the mixing process. S is determined by the test results obtained by compaction tests of old asphalt pavement materials with different water contents after adding recycled materials. It belongs to the existing technology and predicts the real-time quality index of cold recycled pavement. The specific method is as follows:

[0088] When the road milling operation is being carried out, the time period for the road milling operation is [μ 0 ,μ 1 ], the road milling machine (the road milling machine is mainly used for excavation and renovation of asphalt concrete surface layers in highways, urban roads, airports, freight yards, etc.) is used to maintain the road section in [μ 0 ,μ′ 1 ]Average milling speed during the time period To obtain;

[0089] according to For cold recycled pavement in μ′ 1 The quality index at the moment is predicted, where μ 0 <μ′ 1 ≤μ 1 , U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the road quality. hour, when hour,

[0090] When adding recycled materials, the recycled materials include mixed aggregates and stabilizers, etc. The time period for adding recycled materials is [μ 1 +1,μ 2 ],pass For cold recycled pavement in μ′ 2 The quality index at the moment is predicted, μ 1 +1<μ′ 2 ≤μ2 , μ 2 Indicates the time when the recycled material addition operation is completed;

[0091] When performing mixing operations, the time period for the mixing operation is [μ 2 +1,μ 3 ],pass For cold recycled pavement in μ′ 3 The quality index at the moment is predicted, μ 2 +1<μ′ 3 ≤μ 3 , μ 3 Indicates the time when the mixing operation is completed;

[0092] During the paving operation, the time period for the paving operation is recorded as [μ 3 +1,μ 4 ], for mixed recycled materials in [μ 3 +1,μ′ 4 ]Average paving speed during the time period To obtain;

[0093] according to For cold recycled pavement in μ′ 4 The quality index at the moment is predicted, μ 3 +1<μ′ 4 ≤μ 4 ,when hour, when hour,

[0094] When rolling operation is performed, the time period for rolling operation is recorded as [μ 4 +1,μ 5 ], for mixed recycled materials in [μ 4 +1,μ′ 5 ]Average rolling speed during the time period To obtain;

[0095] according to For cold recycled pavement in μ′ 5 The quality index at the moment is predicted, μ 4 +1<μ′ 5 ≤μ 5 , mixed recycled materials refer to materials obtained by mixing old asphalt pavement materials, added recycled materials and water;

[0096] Among them, U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the road quality, A′ represents the average rolling speed of the maintenance section while ensuring the road quality, W′ represents the maximum paving speed of the maintenance section while ensuring the road quality, δ 1 ,δ 2 ,δ 3 ,δ 4 ,δ 5 ,δ 6 Both represent proportionality coefficients and δ 1 +δ 2 +δ 3 +δ 4 +δ 5 +δ 6 =1;

[0097] The construction operation parameters include the average milling speed of the road milling machine on the maintenance section, the amount of emulsified asphalt, the amount of water, the average paving speed of the slipform paver on the mixed recycled materials, and the average rolling speed of the roller on the mixed recycled materials;

[0098] S503: Compare the quality index of the cold regenerated pavement at each moment with the set threshold. If the quality index of the cold regenerated pavement at the corresponding moment is less than the set threshold, the collected construction operation parameters are controlled at the corresponding moment. If the quality index of the cold regenerated pavement at the corresponding moment is greater than or equal to the set threshold, there is no need to control the collected construction operation parameters at the corresponding moment, 0.8≤set threshold≤1.

[0099] A cold regenerated pavement quality control system based on data analysis technology, the system includes a rutting depth prediction module, a pavement damage degree prediction module to be maintained, a quality control index prediction module, a maintenance time determination module and a cold regenerated pavement quality control module;

[0100] The rutting depth prediction module is used to predict the real-time rutting depth at each location of the maintenance section;

[0101] The pavement damage degree prediction module to be maintained is used to predict the pavement damage degree of the maintenance section;

[0102] The quality control index prediction module is used to predict the real-time quality control index of the maintenance section;

[0103] The maintenance time determination module is used to determine the maintenance time of the maintenance section;

[0104] The cold regenerated pavement quality control module is used to predict the real-time quality index of the cold regenerated pavement and control the construction operation parameters in real time based on the prediction results.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0106] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cold regenerated pavement quality control method based on data analysis technology, characterized in that: The method comprises: S10: The movement trajectory of each vehicle on the maintenance section is determined by using a camera installed on the maintenance section, and the historical traffic load of the maintenance section is analyzed in combination with the axle load of each vehicle. Based on the analysis results, the real-time rutting depth at each position of the maintenance section is predicted; S20: determining the real-time distribution of uneven areas in the maintenance section according to the real-time rutting depth at each position of the maintenance section, analyzing the correlation between the uneven areas in combination with the distance between the uneven areas, and predicting the road surface damage degree of the maintenance section in combination with the cracks existing in the maintenance section; S30: Based on the traffic flow distribution coefficient of the maintenance section, predict the real-time quality control index of the maintenance section; S40: Determine the maintenance time of the maintenance section; S50: After determining the maintenance time, the cold regeneration technology is used to treat the road surface of the maintenance section, and the parameters during the construction process are collected in real time. Based on the collected parameters, the real-time quality index of the cold regenerated road surface is predicted. According to the predicted real-time quality index, the construction operation parameters are controlled in real time.

2. According to claim 1, a cold regenerated pavement quality control method based on data analysis technology is characterized in that: The S10 includes: S101: randomly selecting a point on the maintenance section as the coordinate origin, constructing a three-dimensional space coordinate system, measuring the axle load of each historical vehicle entering the maintenance section according to the dynamic weighing system, acquiring continuous single-frame images of each historical vehicle traveling on the maintenance section through a camera installed in the maintenance section, determining the motion trajectory of each historical vehicle on the maintenance section based on the acquired image information, and acquiring the cumulative number of axle load actions at each position in the maintenance section based on the determined motion trajectory; S102: A point is randomly selected on the road surface of the maintenance section, and each axle load is numbered in order from small to large. The numbering result is: i = 1, 2, ..., m; m represents the total number of numbers. At time t, the total number of times k of vehicles with the i-th axle load passing through the selected position point is calculated. it Conduct collection; S103: According to Predict the rutting depth at the selected location at time t, where h i represents the axle load value corresponding to the i-th axle load, H represents the standard axle load of the maintenance section, a represents the weight coefficient, b represents the adjustment coefficient, v represents the historical average speed of vehicles passing the selected location point, V represents the maximum speed allowed by the maintenance section, T represents the historical average temperature of the selected location point, R represents the temperature value, and the rutting depth of the vehicle at the current temperature value begins to be proportional to the temperature. When v / V<0, When v / V ≥ 0, When T / R<1, When T / R ≥ 1, e represents a constant and e>

1.

3. According to claim 2, a cold regenerated pavement quality control method based on data analysis technology is characterized in that: The S20 includes: S201: constructing a real-time three-dimensional model of the maintenance section according to the real-time rutting depth at each position of the maintenance section, displaying the constructed real-time three-dimensional model in a three-dimensional space coordinate system, determining the real-time distribution of uneven areas in the maintenance section according to the constructed real-time three-dimensional model, and numbering each uneven area, the numbering result is: j=1,2,…,n; n represents the total number of uneven areas in the maintenance section; S202: In the three-dimensional space coordinate system, the position coordinates of the lowest point at the bottom of each uneven area are obtained, and the distance value between the two uneven areas is calculated according to the three-dimensional distance formula. At time t, according to The correlation coefficient between the uneven area numbered j and the uneven area numbered g is predicted, where d jgt represents the distance between the lowest point at the bottom of the uneven area numbered j and the lowest point at the bottom of the uneven area numbered g at time t, g = 1, 2, ..., n and g ≠ j; S203: Let y ct =maxy jt ,y jt Indicates the rut depth corresponding to the lowest point at the bottom of the uneven area numbered j at time t, and obtains the rut depth limit G of the maintenance section. When there is no historical vehicle passing through the monitoring area of ​​each camera installed in the maintenance section, the camera collects the image of the maintenance section corresponding to each monitoring area. Based on the acquired image information, the number of cracks existing in the maintenance section corresponding to each monitoring area and the actual length of each crack are obtained. Based on the acquired information, the pavement damage degree of the maintenance section is predicted at time t. The specific prediction formula is: Among them, f cjt represents the correlation coefficient between the lowest point at the bottom of the damaged area numbered j and the lowest point at the bottom of the damaged area numbered c, N represents the total number of cracks in the maintenance section, η=1 or η=0, when N=0, η=0, when N≠0, η=1, γ q represents the actual length of the crack numbered q in the maintenance section, q = 1, 2, ..., X, represents the number of each crack in the maintenance section, X represents the total number of cracks in the maintenance section, w1, w2, w3 all represent weight coefficients, and w1 + w2 + w3 = 1, P t It indicates the pavement damage degree of the maintenance section at time t.

4. The cold regenerated pavement quality control method based on data analysis technology according to claim 3 is characterized in that: The specific method of predicting the real-time quality control index of the maintenance section in S30 is: At time t, the total number of historical times of vehicles with different axle loads passing through the maintenance section and the total number of historical times of vehicles passing through the maintenance section L t To obtain, at time t, according to Fi t =E i / L calculates the distribution coefficient of historical vehicles with the i-th axle load passing through the maintenance section, Ei t It represents the total number of times that historical vehicles with the i-th axle load have passed through the maintenance section at time t; The traffic flow Q of the maintenance section at time t+1*r t+1 *r to obtain; according to The quality control index of the maintenance section at time t+1*r is predicted, where r represents the interval time value and X represents the empirical coefficient.

5. The cold regenerated pavement quality control method based on data analysis technology according to claim 4 is characterized in that: The specific method of determining the maintenance time of the maintenance section in S40 is: If 0.6≤Dt+1*r≤1, it means that the maintenance time of the maintenance section is t+1*r; If 0≤Dt+1*r0.6, it means that there is no need to maintain the maintenance section at time t+1*r.

6. A cold regenerated pavement quality control method based on data analysis technology according to claim 5, characterized in that: The S50 includes: S501: At the determined maintenance time, the cold recycling technology is used to treat the road surface of the maintenance section. During the road surface treatment process, five operation steps are required: road surface milling → adding recycled materials → mixing → paving → rolling; S502: When performing each operation, real-time parameters in the construction process are collected, and the real-time quality index of the cold recycled pavement is predicted by combining the coefficient of variation corresponding to each construction operation parameter calculated based on the total amount of old asphalt pavement material milled by the pavement milling machine in the maintenance section. The construction operation parameters include the average milling speed of the pavement milling machine on the maintenance section, the amount of emulsified asphalt, the amount of water, the average paving speed of the slipform paver on the mixed recycled material, and the average rolling speed of the roller on the mixed recycled material; S503: Compare the quality index of the cold regenerated pavement at each moment with the set threshold. If the quality index of the cold regenerated pavement at the corresponding moment is less than the set threshold, the collected construction operation parameters are controlled at the corresponding moment. If the quality index of the cold regenerated pavement at the corresponding moment is greater than or equal to the set threshold, there is no need to control the collected construction operation parameters at the corresponding moment.

7. A cold regenerated pavement quality control method based on data analysis technology according to claim 6, characterized in that: The specific method of calculating the coefficient of variation corresponding to each construction operation parameter in S502 is: Coefficient of variation for emulsified asphalt dosage: according to The coefficient of variation of the amount of emulsified asphalt was calculated, where Y1 represents the actual amount of emulsified asphalt used in the process of adding recycled materials, and Y represents the theoretical amount of emulsified asphalt used in the process of adding recycled materials; For the coefficient of variation of water usage: according to The coefficient of variation of water content was calculated, where S1 represents the actual amount of water used in the mixing process and S represents the theoretically used water content in the mixing process.

8. A cold regenerated pavement quality control method based on data analysis technology according to claim 7, characterized in that: The specific method of predicting the real-time quality index of the cold regenerated pavement in S502 is: When performing road milling operations, the time period for road milling operations is recorded as [μ0, μ1], and the average milling speed of the road milling machine on the maintenance section in the time period [μ0, μ′1] is calculated. To obtain; according to The quality index of the cold regenerated pavement at the moment μ′1 is predicted, where μ0<μ′1≤μ1, and U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the pavement quality; When adding recycled materials, the time period for adding recycled materials is recorded as [μ1+1,μ2]. The quality index of cold recycled pavement at the moment μ′2 is predicted, μ1+1<μ′2≤μ2, μ2 represents the time when the recycled material addition operation is completed; When performing mixing operations, the time period for the mixing operation is recorded as [μ2+1,μ3]. The quality index of cold regenerated pavement at the moment μ′3 is predicted, μ2+1<μ′3≤μ3, μ3 represents the time when the mixing operation is completed; During the paving operation, the time period for the paving operation is recorded as [μ3+1,μ4], and the average paving speed of the mixed recycled material in the time period [μ3+1,μ′4] is To obtain; according to Predict the quality index of cold regenerated pavement at the moment μ′4, μ3+1<μ′4≤μ4; During the rolling operation, the time period for the rolling operation is recorded as [μ4+1,μ5], and the average rolling speed of the mixed recycled materials in the time period [μ4+1,μ′5] is To obtain; according to Predict the quality index of cold regenerated pavement at the moment μ′5, μ4+1<μ′5≤μ5; Among them, U′ represents the maximum milling speed of the road milling machine on the maintenance section while ensuring the road quality, A′ represents the average rolling speed of the maintenance section while ensuring the road quality, W′ represents the maximum paving speed of the maintenance section while ensuring the road quality, δ1, δ2, δ3, δ4, δ5, δ6 all represent proportional coefficients and δ1+δ2+δ3+δ4+δ5+δ6=1.

9. A cold regenerated pavement quality control system based on data analysis technology applied to the cold regenerated pavement quality control method based on data analysis technology according to any one of claims 1 to 8, characterized in that: The system includes a rutting depth prediction module, a pavement damage degree prediction module for maintenance, a quality control index prediction module, a maintenance time determination module and a cold regenerated pavement quality control module; The rutting depth prediction module is used to predict the real-time rutting depth at each location of the maintenance section; The pavement damage degree prediction module to be maintained is used to predict the pavement damage degree of the maintenance section; The quality control index prediction module is used to predict the real-time quality control index of the maintenance section; The maintenance time determination module is used to determine the maintenance time of the maintenance section; The cold regenerated pavement quality control module is used to predict the real-time quality index of the cold regenerated pavement and control the construction operation parameters in real time according to the prediction results.

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