Staged road use performance prediction method and system considering fund constraint

Abstract

The invention provides a staged road use performance prediction method and system considering fund constraints, and belongs to the technical field of road engineering. According to the method, the current nonlinear road use performance degradation problem is linearized, the nonlinearity of the degradation process is accurately quantified in a staged manner, and each stage is represented by a linear model, so that the parameters of the model and the automatic acquisition process thereof have obvious physical significance. According to the method, the decay rate is corrected through the total maintenance expenditure, the deterioration trend of the road use performance under different capital constraint conditions can be effectively obtained from the fluctuating road use performance data, and quantitative optimization of the maintenance strategy is achieved. The method is of a data driving type and supports dynamic updating of historical data, so that prediction precision and reliability of maintenance strategy decision are improved, and when newly accumulated dynamic use performance index data, maintenance history and traffic load data exist or static attributes are changed, an updated staged model and a prediction result can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of road engineering, and particularly relates to a method and system for predicting the road performance in stages considering financial constraints. Background Art

[0002] The road performance such as road damage, smoothness, rutting, skid resistance, etc. tends to deteriorate as the cumulative traffic load increases, and it gradually becomes difficult to meet the requirements of road users for travel safety and travel speed. Predicting this trend is very important because it can guide the selection of engineering measures, maintenance timing, project quantities, and financial planning, so that the road performance can be maintained above a reasonable threshold in an economical way.

[0003] Currently, there is no prediction model that can effectively extract maintenance information from the fluctuating road performance, associate it with the maintenance funds, and thus establish a prediction model for the deterioration trend of road performance under financial constraints. This kind of prediction model considers some decision parameters and can better support decision-making and in-depth data value mining compared with the traditional way where the prediction and decision-making functions are relatively independent.

[0004] The deterioration of road performance is non-linear. There are problems such as the physical meaninglessness of parameters and difficulty in regression when using curve forms for prediction, and there is a problem of a process black box when using intelligent algorithms for prediction. There is an urgent need for a data-driven staged broken-line prediction model to solve the above problems. Summary of the Invention

[0005] The present invention is made to solve the above problems, and its purpose is to provide a method and system for predicting the road performance in stages considering financial constraints.

[0006] The present invention provides a method for predicting the road performance in stages considering financial constraints, which has the following characteristics and includes the following steps: S10, inputting data into a standard table to obtain an initial data set. The fields of the standard table include road attributes, performance indicators and their detection times, engineering measures, the engineering intensity of engineering measures, the completion time of engineering measures, the engineering nature of engineering measures, traffic load, and data years. Among them, the engineering nature includes routine maintenance and / or non-routine maintenance; S20, aggregating the data in the initial data set according to the unique identifier in the fields, corresponding the starting point of the data year with the starting point of the life cycle of non-routine maintenance in the engineering nature, and ensuring that the ending point of the data year does not exceed the ending point of the life cycle, and finally arranging the data in ascending order of the data year to obtain a summary data set; S30, establishing a staged prediction model for each performance indicator in the summary data set: PPI i =PPI i-1 -k i ·ΔESAL i where PPI is the performance indicator, PPI iis the maximum value of PPI in the i-th stage, k i is the comprehensive decay rate in the i-th stage, ESAL i is the traffic load up to the i-th stage, ΔESAL i = ESAL i - ESAL i-1 , ESAL0 = 0; S40. According to the summary data set, draw a scatter plot of the performance index and the data year, extract the continuously decaying segments, and then calculate the natural decay rate k of each stage 0,i , remove the data in the scatter plot that is lower than the performance index of the largest data year, and construct a unique monotonically decreasing broken line to calculate the comprehensive decay rate k of each stage i ; S50. Calculate the corresponding contribution ratio of the engineering measures according to the engineering measures, engineering properties, completion time, and detection time of the performance index in each stage; S60. Allocate the total maintenance funds to the daily maintenance funds and non-daily maintenance funds corresponding to the engineering properties. When the total maintenance funds change, update the comprehensive decay rate: where, k new,i is the updated comprehensive decay rate corresponding to the i-th stage after the change of the total maintenance funds, R is the daily maintenance funds, S j is the non-daily maintenance funds corresponding to the j-th non-daily maintenance, R new is the corresponding R after the change of the total maintenance funds, α R is the contribution ratio of the daily maintenance, S new,j is the S corresponding to the j-th non-daily maintenance after the change of the total maintenance funds j , is the contribution ratio of the non-daily maintenance; S70. Use k new,i to update the k i in the stage-by-stage prediction model, and then realize the stage-by-stage prediction of the road use performance

[0007] In the stage-by-stage road use performance prediction method considering capital constraints provided by the present invention, it can also have the following characteristics: where, in step S10, the road attributes include route code and / or route name, direction, lane number, start and end point related data, road structure material related data, and lane width, the performance index includes any one or more of PCI, RQI, RDI, PBI, PWI, SRI, or PSSI, the traffic load is the cumulative equivalent axle load application times, in step S20, the unique identifier includes route code and / or route name, direction, lane number, start and end points, and road structure material related data, and the start and end points are determined by the start and end point related data

[0008] In the phased road performance prediction method considering capital constraints provided by the present invention, it may further have the following characteristics: Among them, in step S10, the road attributes further include any one or more of structures, total number of one-way lanes, technical grade, or pavement type.

[0009] In the phased road performance prediction method considering capital constraints provided by the present invention, it may further have the following characteristics: Among them, in step S10, the data related to road structure materials includes surface course material, surface course thickness, middle course material, middle course thickness, bottom course material, bottom course thickness, upper base course material, upper base course thickness, middle base course material, middle base course thickness, subbase course material, subbase course thickness, cushion course material, cushion course thickness, and soil base type. The data related to the starting and ending points includes starting point stake number, ending point stake number, special stake number head, special stake starting point stake number, special stake ending point stake number, section starting point, and section ending point. In step S20, the starting and ending points are determined by the starting point stake number and the ending point stake number, or by the special stake number head, the special stake starting point stake number, and the special stake ending point stake number, or by the section starting point and the section ending point.

[0010] In the phased road performance prediction method considering capital constraints provided by the present invention, it may further have the following characteristics: Among them, in step S40, if there is 1 sample under a certain road structure material, then: calculate the natural decay rate k of each stage 0,i in the following way: take all continuous decay segments and directly obtain the mean value of the slopes to get it. Calculate the comprehensive decay rate k i in the following way: after constructing a unique monotonically decreasing broken line for the scatter plot after removing the corresponding data, calculate the mean value of the slopes to get it.

[0011] In the phased road performance prediction method considering capital constraints provided by the present invention, it may further have the following characteristics: Among them, in step S40, if there are multiple samples under a certain road structure material, then: for the same data related to road structure materials, take the reciprocal of the weighted average of the reciprocals of the natural decay rates of all data samples according to the road section area, that is, obtain the natural decay rate corresponding to the combination of data related to road structure materials containing the same data related to road structure materials. For the same data related to road structure materials, take the reciprocal of the weighted average of the reciprocals of the comprehensive decay rates of all data samples according to the road section area, that is, obtain the comprehensive decay rate corresponding to the combination of data related to road structure materials containing the same data related to road structure materials.

[0012] In the method for predicting the staged road use performance considering financial constraints provided by the present invention, it may further have the following characteristics: Among them, in step S50, the calculation method of the contribution ratio includes the following steps: S50-1, take the segment with monotonically increasing performance indicators in the scatter plot, and obtain the detection times of the earliest and latest performance indicators corresponding to this segment; S50-2, for the completion time within the interval of the detection times of the earliest and latest performance indicators obtained in step S50-1, calculate the cumulative engineering strength year by year respectively; S50-3, within two adjacent detection times, if the engineering nature of the engineering measures includes routine maintenance and non-routine maintenance, the contribution ratio of the non-routine maintenance in the engineering measures is calculated according to its proportion of the cumulative engineering strength, and the contribution ratio of the routine maintenance in the engineering measures is 0. If the engineering nature of the engineering measures is only routine maintenance, the contribution ratio of the routine maintenance is 1.

[0013] In the method for predicting the staged road use performance considering financial constraints provided by the present invention, it may further have the following characteristics: Among them, in step S60, the non-routine maintenance includes any one or more of preventive maintenance, repair maintenance, new construction, reconstruction and expansion, and reconstruction.

[0014] In the method for predicting the staged road use performance considering financial constraints provided by the present invention, it may further have the following characteristics: Among them, in step S60, when the total maintenance funds increase, first increase the routine maintenance funds R, and then increase the non-routine maintenance funds S j , when the total maintenance funds decrease, first reduce the non-routine maintenance funds S j , and then reduce the routine maintenance funds R. The routine maintenance funds R have a limit, and the non-routine maintenance funds S j have no limit.

[0015] The present invention also provides a system for predicting the staged road use performance considering financial constraints, which has the following characteristics: It uses the method for predicting the staged road use performance considering financial constraints in any one of the foregoing, and realizes the staged use performance prediction of the road through a staged prediction model after updating the comprehensive decay rate according to the total maintenance funds, including: a data analysis unit, which is used for the user to input road attributes, performance indicators and their detection times, engineering measures, engineering strength of engineering measures, completion time of engineering measures, engineering nature of engineering measures, traffic load, and data year, and then processes to obtain a summary data set; a prediction model construction unit, which is connected to the data analysis unit and is used to establish a staged prediction model according to the summary data set; a decay rate calculation unit, which is connected to the data analysis unit and the prediction model construction unit, and is used to obtain the natural decay rate k after analyzing and calculating the summary data set and the staged prediction model 0,i and the comprehensive decay rate k i; and a comprehensive decay rate update unit, connected to the data analysis unit, the prediction model construction unit, and the decay rate calculation unit, for calculating the corresponding contribution ratio of the engineering measures and then combining it with the natural decay rate k 0,i and the comprehensive decay rate k i the daily maintenance funds, and the non-daily maintenance funds to obtain the updated comprehensive decay rate k new,i , and using the updated comprehensive decay rate k new,i to update the k i in the phased prediction model. Among them, after the comprehensive decay rate of the phased prediction model constructed by the prediction model construction unit is updated, the phased service performance of the road is predicted.

[0016] Functions and Effects of the Invention

[0017] A method and system for phased prediction of road service performance considering financial constraints according to the present invention linearize the current non-linear problem of road service performance deterioration, and accurately quantify the non-linearity of the deterioration process in a phased manner. Each stage is characterized by a linear model, making the parameters of the model and their automatic acquisition process have obvious physical meanings.

[0018] A method and system for phased prediction of road service performance considering financial constraints according to the present invention correct the decay rate through the total maintenance funds, and can effectively obtain the deterioration trend of road service performance under different financial constraint conditions from the fluctuating road service performance data, realizing the quantitative optimization of maintenance strategies.

[0019] A method and system for phased prediction of road service performance considering financial constraints according to the present invention is data-driven, supports dynamic update of historical data, thereby improving the prediction accuracy and the reliability of maintenance strategy decisions. When there are newly accumulated dynamic service performance index data, maintenance history, traffic load data, or when the static attributes change, the method / system of the present invention can obtain updated phased models and prediction results. Description of the Drawings

[0020] Figure 1 is a flowchart of a method for phased prediction of road service performance considering financial constraints according to an embodiment of the present invention;

[0021] Figure 2 is a scatter plot of road service performance indicators according to an embodiment of the present invention;

[0022] Figure 3 is the influence of the total maintenance funds parameter in a method for phased prediction of road service performance considering financial constraints according to an embodiment of the present invention. Detailed Embodiments

[0023] In order to make the technical means, creative features, achieved objectives and effects realized by the present invention easy to understand, the following embodiments will specifically elaborate on a phased road performance prediction method and system considering capital constraints of the present invention in conjunction with the accompanying drawings.

[0024] Figure 1 It is a flowchart of a phased road performance prediction method considering capital constraints according to an embodiment of the present invention.

[0025] As Figure 1 shown, this embodiment provides a phased road performance prediction method considering capital constraints, including the following steps:

[0026] S10. Input data into a standard table to obtain an initial data set.

[0027] The fields of the standard table include:

[0028] 1. Road attributes (route code and / or route name, direction, lane number, start and end point related data, road structure material related data, lane width, including structures, total number of one-way lanes, technical grade, and pavement type).

[0029] Among them, the start and end point related data includes start point stake number, end point stake number, special stake number head, special stake start point stake number, special stake end point stake number, section start point, and section end point.

[0030] Among them, the road structure material related data includes surface course material, surface course thickness, middle course material, middle course thickness, bottom course material, bottom course thickness, upper base course material, upper base course thickness, middle base course material, middle base course thickness, subbase course material, subbase course thickness, cushion material, cushion thickness, and soil base type.

[0031] 2. Performance indicators and their detection times (performance indicator PPI includes any one or more of PCI, RQI, RDI, PBI, PWI, SRI, or PSSI).

[0032] 3. Engineering measures.

[0033] 4. Engineering strength of engineering measures (ratio of engineering area to total section area).

[0034] 5. Completion time of engineering measures.

[0035] 6. Engineering nature of engineering measures (routine maintenance and / or non-routine maintenance).

[0036] Among them, non-routine maintenance includes any one or more of preventive maintenance, rehabilitation maintenance, new construction, reconstruction and expansion, and reconstruction.

[0037] 7. Traffic load (cumulative equivalent axle load action times).

[0038] 8. Data year.

[0039] S20, Aggregate the initial data set according to the unique identifier in the field, and make the starting point of the data year correspond to the starting point of the life cycle of non-daily maintenance in the project nature (i.e., corresponding to preventive maintenance, repair maintenance, new construction, renovation and expansion, and reconstruction), and make the end point of the data year not exceed the end point of the life cycle. Finally, sort the data in ascending order of the data year to obtain the summary data set.

[0040] Among them, the unique identifier includes route code and / or route name, direction, lane number, starting and ending points, and data related to road structure materials.

[0041] The starting and ending points are determined by the starting stake number and ending stake number, or by the special stake number head, special stake starting stake number, and special stake ending stake number, or by the section starting point and section ending point.

[0042] S30, Establish a phased prediction model for each performance indicator in the summary data set:

[0043] PPI i = PPI i-1 -k i ·ΔESAL i

[0044] Among them, PPI is the performance indicator, and PPI i is the maximum value of PPI in the i-th stage, k i is the comprehensive decay rate in the i-th stage, ESAL i is the increment of the cumulative equivalent axle load application times up to the i-th stage, ΔESAL i = ESAL i -ESAL i-1 , ESAL0 = 0.

[0045] Specifically in this embodiment, the value of the stage number i is taken as 5. The PPI intervals corresponding to stages 1 to 5 are [100, 90), [90, 80), [80, 70), [70, 60), [60, 0] set in advance, respectively.

[0046] S40, Draw a scatter plot and calculate the natural decay rate k 0,i and the comprehensive decay rate k i , including the following sub-steps:

[0047] S41, Draw a scatter plot of the performance indicator (the performance indicator PPI in this embodiment specifically selects RQI) and the data year according to the summary data set, as Figure 2 shown.

[0048] AsFigure 2 As shown, according to the completion time of engineering measures, attach the engineering measures and the engineering intensity information of the engineering measures to the Figure 2 scatter points above, corresponding to the data year. Attach the traffic load (cumulative equivalent axle load application times) to the Figure 2 scatter points below, corresponding to the data year. Mark the unique identifier (route code and / or route name, direction, lane number, start and end points, and data related to road structure materials) above Figure 2 of

[0049] S42, after extracting the continuously decaying segments in the scatter plot, calculate the natural decay rate k of each stage in step S30 0,i , after removing the data in the scatter plot that is lower than the performance index of the largest data year, construct a uniquely monotonically decreasing broken line to calculate the comprehensive decay rate k of each stage i .

[0050] In this step:

[0051] If there is 1 sample under a road structure material, then:

[0052] (1) The method for calculating the natural decay rate k of each stage is: take all continuously decaying segments and directly obtain the mean value of the slopes. 0,i

[0053] (2) The method for calculating the comprehensive decay rate k of each stage is: after constructing a uniquely monotonically decreasing broken line for the scatter plot after removing the corresponding data, obtain the mean value of the slopes. i

[0054] If there are multiple samples under a road structure material, then:

[0055] (1) For the data related to the same road structure material, take the reciprocal of the weighted average of the reciprocals of the natural decay rates of all data samples by the road section area, that is, obtain the natural decay rate corresponding to the road structure material data combination containing the data related to the same road structure material.

[0056] (2) For the data related to the same road structure material, take the reciprocal of the weighted average of the reciprocals of the comprehensive decay rates of all data samples by the road section area, that is, obtain the comprehensive decay rate corresponding to the road structure material data combination containing the data related to the same road structure material.

[0057] S50, calculate the corresponding contribution ratio of the engineering measures, including the following sub-steps S50-1 to S50-3:

[0058] S50-1, take the segments with monotonically increasing performance indicators in the scatter plot, and obtain the detection times of the earliest and latest performance indicators corresponding to the segments.

[0059] S50-2, calculating the accumulated engineering intensity year by year within the interval between the earliest and latest performance indicator detection times obtained in step S50-1.

[0060] S50-3: Within the time between two adjacent inspections, if the engineering nature of the engineering measures includes daily maintenance and non-routine maintenance, the contribution ratio of non-routine maintenance in the engineering measures shall be calculated according to the proportion of its cumulative engineering intensity, and the contribution ratio of daily maintenance in the engineering measures shall be 0; if the engineering nature of the engineering measures is only daily maintenance, the contribution ratio of daily maintenance shall be 1.

[0061] Specifically in this embodiment, the process of step S42 is recorded in the data table (Table 2) shown below. Figure 2 The calculation results of the samples shown in Table 2 are shown below. The total maintenance volume is the cumulative value of the difference when the next PPI is greater than the previous one.

[0062] For this road section, non-routine maintenance includes milling and covering. The cumulative engineering intensity of daily maintenance and non-routine maintenance and their contribution ratio to the total maintenance volume are calculated separately.

[0063] Among them, the cumulative engineering intensity of a certain engineering measure is the cumulative value of the engineering intensity year by year; the contribution ratio of a certain engineering measure is the weighted average of the contribution ratios of a certain engineering measure year by year, and the weight is the proportion of the annual maintenance volume in the total maintenance volume.

[0064] Table 2 (data base table)

[0065]

[0066] In this step, each specific unique identifier (route code and / or route name, direction, lane number, start and end points, and road structure material related data) corresponds to one of the above Tables 2. These tables are summarized according to different road structures and materials, as described in step S42, and the natural decay rate k 0,i , comprehensive decay rate k i is the k of all corresponding data samples 0,i , k i The reciprocal of is the reciprocal of the weighted average of the road section area. The total maintenance volume, cumulative maintenance intensity, and contribution ratio are taken as the weighted average of the road section area.

[0067] In Table 2 above, for Figure 2For the single sample shown, in the first stage, the cumulative engineering strength of milling and overlaying is 0.171 + 0.024 = 0.195, but the contribution ratio of milling and overlaying is 0. (As described in step S50, take the segments of PPI growth to calculate the cumulative engineering strength and the corresponding contribution ratio of each maintenance measure. After the implementation of the milling and overlaying measures in 2020 and 2021, the PPI did not increase, so its contribution ratio is directly 0.)

[0068] In Table 2 above, for Figure 2 the single sample shown, in the second stage, there was only one repair, that is, the milling and overlaying with an engineering strength of 0.014 implemented in 2022 (when RQI was in the second stage). After implementation, by the next inspection time, that is, in 2023, the RQI increased by 4.71. Since no other non-routine maintenance was implemented in the second stage and there were no other RQI growth segments, the contribution ratio of the milling and overlaying implemented in 2022 is 1.

[0069] Figure 2 The summary results under the road structure and materials shown in the scatter plot of road performance indicators of

[0070] are shown in Table 3 below. Figure 2 (Summary results under the road structure and materials shown in the scatter plot of road performance indicators of

[0071]

[0072] For Figure 2 the single sample shown, there is no RQI decline segment in the second stage (there is only one growth segment from 2022 to 2023), so the corresponding natural decay rate and comprehensive decay rate cannot be calculated in Table 2. There are such segments in other samples, so the natural decay rate and comprehensive decay rate in the second stage in Table 3 have values.

[0073] S60. Calculate the comprehensive decay rate under the given maintenance funds, including the following sub-steps S61 to S62:

[0074] S61. Calculate the total maintenance funds:

[0075]

[0076] where M is the total maintenance funds, R is the routine maintenance funds, S j is the non-routine maintenance funds corresponding to the jth non-routine maintenance, M ≥ 0, R ≥ 0, S j ≥ 0.

[0077] In this step, when the total maintenance funds M increase, first increase the routine maintenance funds R, and then increase the non-routine maintenance funds S j ; when the total maintenance funds decrease, first reduce the non-routine maintenance funds Sj , and then reduce the daily maintenance cost R; there is a limit for the daily maintenance cost R, and there is no limit for the non-daily maintenance cost S j There is no limit.

[0078] The non-daily maintenance includes any one or more of preventive maintenance, repair maintenance, new construction, renovation and expansion, and reconstruction. (The milling and overlay in step S50 belongs to the repair maintenance in non-daily maintenance)

[0079] S62, when the total maintenance cost changes, update the comprehensive decay rate:

[0080]

[0081] where k new,i is the updated comprehensive decay rate corresponding to the i-th stage after the change of the total maintenance cost, R new is the R corresponding to after the change of the total maintenance cost, α R is the contribution ratio of daily maintenance, S new,j is the S corresponding to the j-th non-daily maintenance after the change of the total maintenance cost j , is the contribution ratio of non-daily maintenance.

[0082] S70, after using k new,i to update the k i in the stage-by-stage prediction model, the stage-by-stage prediction of the road usage performance is realized.

[0083] The methods of steps S10 to S70 are data-driven. When there are newly accumulated dynamic usage performance index data, maintenance history, traffic load data, or when the static attributes change, through the above steps, an updated model and operation results can be obtained.

[0084] This embodiment also provides a stage-by-stage road usage performance prediction system considering capital constraints, which uses a stage-by-stage road usage performance prediction method provided in this embodiment, and realizes the stage-by-stage prediction of the road usage performance through a stage-by-stage prediction model after updating the comprehensive decay rate according to the total maintenance cost.

[0085] A stage-by-stage road usage performance prediction system considering capital constraints in this embodiment includes a data analysis department, a prediction model construction department, a decay rate calculation department, and a comprehensive decay rate update department.

[0086] The data analysis department is used to process the input of road attributes, performance indicators and their detection times, engineering measures, engineering intensities of engineering measures, completion times of engineering measures, engineering natures of engineering measures, traffic loads, and data years by the method of steps S10 to S20 to obtain a summary data set.

[0087] The prediction model construction unit is connected to the data analysis unit and is used to establish a phased prediction model according to the method in step S30 based on the aggregated data set.

[0088] The decay rate calculation unit is connected to the data analysis unit and the prediction model construction unit and is used to obtain the natural decay rate k through analysis and calculation of the aggregated data set and the phased prediction model according to the method in step S40 0,i and the comprehensive decay rate k i .

[0089] The comprehensive decay rate update unit is connected to the data analysis unit, the prediction model construction unit, and the decay rate calculation unit and is used to calculate the corresponding contribution ratio of the engineering measures according to the methods in steps S50 to S70, and then combine it with the natural decay rate k 0,i , the comprehensive decay rate k i , the daily maintenance funds, and the non-daily maintenance funds to obtain the updated comprehensive decay rate k new,i , and use the updated comprehensive decay rate k new,i to update the k i in the phased prediction model.

[0090] Among them, after the comprehensive decay rate of the phased prediction model constructed by the prediction model construction unit is updated by the comprehensive decay rate update unit, it predicts the phased service performance of the road.

[0091] <Test case>

[0092] This test case uses a phased road service performance prediction system considering capital constraints in the embodiment and conducts actual tests according to a phased road service performance prediction method considering capital constraints in the embodiment.

[0093] This test case uses the data of the northern section of the Shanghai Outer Ring Expressway, and the proportion of the daily maintenance funds R in the total maintenance funds M is 0.87.

[0094] When the total maintenance funds M remain unchanged, k new,i is the value in Table 3 of the embodiment, and the model drawing result is as shown in Figure 3 part (a), and part (a) shows that the RQI requires 32234138 and 92886552 cumulative equivalent axle load applications to reach 90 and 80 respectively.

[0095] When the total maintenance funds M are reduced to 0.5 times the original, the daily maintenance funds R new become 0.43 times the original R, and the non-daily maintenance funds S new,j become 0 times the original S j .

[0096] k is calculated according to the updated formula of the comprehensive decay rate new,1 = 3.63×10 -7 , k new,2 = 3.26×10 -7 , k new,3 , k new,4 , k new,5 are missing values because there is no decay data for the corresponding stage

[0097] The result of the model drawing is shown in part (b) of Figure 3 . Part (b) shows that it takes 27556489 and 58168364 cumulative equivalent axle load applications respectively for the RQI to reach 90 and 80 from 100, and the decay is faster compared to part (a).

[0098] Functions and effects of the embodiment

[0099] A method and system for phased road performance prediction considering capital constraints in this embodiment linearize the current non - linear road performance deterioration problem, and accurately quantify the non - linearity of the deterioration process in a phased manner. Each stage is characterized by a linear model, making the parameters of the model and their automatic acquisition process have obvious physical meanings

[0100] A method and system for phased road performance prediction considering capital constraints in this embodiment can effectively obtain the deterioration trend of road performance under different capital constraint conditions from fluctuating road performance data by allocating the decay rate with the total maintenance funds, and realize the quantitative optimization of maintenance strategies

[0101] A method and system for phased road performance prediction considering capital constraints in this embodiment are data - driven, support the dynamic update of historical data, thereby improving the prediction accuracy and the reliability of maintenance strategy decisions. When there are newly accumulated dynamic performance index data, maintenance history, traffic load data, or when the static attributes change, the method / system of this embodiment can obtain updated phased models and prediction results

[0102] Those skilled in the art of this industry should understand that the present invention is not limited by the above - mentioned embodiments. What is described in the above - mentioned embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents

Claims

1. A phased road use performance prediction method considering capital constraints, characterized in that It includes the following steps: S10. Input data into a standard table to obtain an initial data set. The fields of the standard table include road attributes, performance indicators and their detection times, engineering measures, the engineering intensity of the engineering measures, the completion time of the engineering measures, the engineering nature of the engineering measures, traffic load, and data year. Among them, the engineering nature includes routine maintenance and / or non-routine maintenance; S20. Aggregate the data in the initial data set according to the unique identifier in the fields, correspond the starting point of the data year with the starting point of the life cycle of non-routine maintenance in the engineering nature, and ensure that the end point of the data year does not exceed the end point of the life cycle. Finally, sort the data in ascending order of the data year to obtain a summary data set; S30. Establish a phased prediction model for each performance indicator in the summary data set: PPI i = PPI i-1 - k i ·ΔESAL i , where PPI is the performance index, and PPI i is the maximum value of PPI in the i-th stage, and k i is the comprehensive decay rate in the i-th stage, and ESAL i is the traffic load up to the i-th stage, and ΔESAL i = ESAL i - ESAL i-1 , and ESAL0 = 0; S40. Draw a scatter plot of the performance metric against the data year based on the summarized data set, extract the continuously decaying segments therefrom, and calculate the natural decay rate k for each stage. 0,i Remove the data in the scatter plot that is lower than the performance metric of the largest data year, and construct a uniquely monotonically decreasing broken line to calculate the comprehensive decay rate k for each stage. i ; S50. Calculate the corresponding contribution ratio of the engineering measures according to the engineering measures, the engineering nature, the completion time, and the detection time of the performance indicators in each stage; S60. Allocate the total maintenance funds into routine maintenance funds and non-routine maintenance funds corresponding to the engineering nature. When the total maintenance funds change, update the comprehensive decay rate: where k new,i is the updated comprehensive decay rate corresponding to the i-th stage after the change in the total maintenance cost, R is the daily maintenance cost, and S j is the non-daily maintenance cost corresponding to the j-th type of non-daily maintenance, and R new is the R corresponding to after the change in the total maintenance cost, and α R is the contribution ratio of the daily maintenance, and S new,j is the S corresponding to the j-th type of non-daily maintenance after the change in the total maintenance cost j , is the contribution ratio of the non-daily maintenance; S70, using k new,i After updating k in the phased prediction model, i the phased usage performance prediction of the road is realized.

2. The phased road performance prediction method considering capital constraints according to claim 1, characterized in that: Among them, In step S10, the road attributes include route code and / or route name, direction, lane number, start and end point related data, road structure material related data, and lane width, The performance indicators include any one or more of PCI, RQI, RDI, PBI, PWI, SRI, or PSSI, The traffic load is the cumulative equivalent axle load application times, In step S20, the unique identifier includes the route code and / or the route name, the direction, the lane number, the start and end points, and the road structure material related data. The start and end points are determined by the start and end point related data.

3. The phased road performance prediction method considering capital constraints according to claim 2, characterized in that: Among them, In step S10, the road attributes further include any one or more of structures, total number of one-way lanes, technical grade, or pavement type.

4. The phased road performance prediction method considering capital constraints according to claim 2, characterized in that: Among them, In step S10, the road structure material related data includes surface course material, surface course thickness, middle course material, middle course thickness, bottom course material, bottom course thickness, upper base course material, upper base course thickness, middle base course material, middle base course thickness, subbase course material, subbase course thickness, cushion material, cushion thickness, and subgrade type, The start and end point related data includes start point stake number, end point stake number, special stake number head, special stake start point stake number, special stake end point stake number, section start point, and section end point, In step S20, the start and end points are determined by the start point stake number and the end point stake number, or by the special stake number head, the special stake start point stake number, and the special stake end point stake number, or by the section start point and the section end point.

5. The phased road use performance prediction method considering capital constraints according to claim 4, characterized in that: Among them, In step S40, if there is 1 sample under a certain road structure material, then: Calculate the natural decay rate k at each stage 0,i The method is as follows: Take all continuous decay segments and directly calculate the average value of the slopes to obtain Calculate the comprehensive decay rate k at each stage i The method is as follows: After constructing a unique monotonically decreasing broken line for the scatter plot after removing the corresponding data, calculate the average value of the slopes.

6. The phased road use performance prediction method considering capital constraints according to claim 5, characterized in that: Among them, In step S40, if there are multiple samples under a certain road structure material, then: For the relevant data of the same road structure material, take the reciprocal of the weighted average of the reciprocals of the natural decay rates of all data samples by the road section area, that is, obtain the natural decay rate corresponding to the road structure material data combination containing the relevant data of the same road structure material. For the relevant data of the same road structure material, take the reciprocal of the weighted average of the reciprocals of the comprehensive decay rates of all data samples by the road section area, that is, obtain the comprehensive decay rate corresponding to the road structure material data combination containing the relevant data of the same road structure material.

7. The phased road use performance prediction method considering capital constraints according to claim 1, characterized in that: Among them, In step S50, the calculation method of the contribution ratio includes the following steps: S50-1, take the segment where the performance index in the scatter plot monotonically increases, and obtain the earliest and latest detection times of the performance index corresponding to this segment; S50-2, calculate the cumulative engineering strength year by year respectively within the interval of the earliest and latest detection times of the performance index obtained in step S50-1 for the completion time; S50-3, within two adjacent detection times, if the engineering nature of the engineering measure includes the daily maintenance and the non-daily maintenance, then the contribution ratio of the non-daily maintenance in the engineering measure is calculated according to its proportion of the cumulative engineering strength, and the contribution ratio of the daily maintenance in the engineering measure is 0. If the engineering nature of the engineering measure is only the daily maintenance, then the contribution ratio of the daily maintenance is 1.

8. The phased road use performance prediction method considering capital constraints according to claim 1, characterized in that: Among them, In step S60, the non-daily maintenance includes any one or more of preventive maintenance, repair maintenance, new construction, renovation and expansion, and reconstruction.

9. The phased road use performance prediction method considering capital constraints according to claim 1, characterized in that: Among them, In step S60, when the total maintenance cost increases, first increase the daily maintenance cost R, and then increase the non-daily maintenance cost S j , When the total maintenance funds decrease, first reduce the non-daily maintenance funds S j , and then reduce the daily maintenance funds R The daily maintenance funds R have a limit, and the non-daily maintenance funds S j have no limit.

10. A phased road use performance prediction system considering capital constraints, characterized in that, Using the phased road use performance prediction method considering capital constraints described in any one of claims 1 to 9, and realizing the phased use performance prediction of the road through the phased prediction model after updating the comprehensive decay rate in accordance with the total maintenance funds, including: The data analysis department is used for the user to input road attributes, performance indicators and their detection times, engineering measures, the engineering strength of the engineering measures, the completion time of the engineering measures, the engineering nature of the engineering measures, traffic load, and the data year, and then process to obtain a summary data set; The prediction model construction department is connected to the data analysis department and is used for establishing a phased prediction model according to the summary data set. The decay rate calculation unit, connected to the data analysis unit and the prediction model construction unit, is configured to analyze and calculate the aggregated data set and the phased prediction model to obtain the natural decay rate k 0,i and the comprehensive decay rate k i ; and The comprehensive decay rate update unit, connected to the data analysis unit, the prediction model construction unit, and the decay rate calculation unit, is used to calculate the corresponding contribution ratio of the engineering measures and then combine it with the natural decay rate k 0,i , the comprehensive decay rate k i , the daily maintenance funds and the non-daily maintenance funds to obtain the updated comprehensive decay rate k new,i , and use the updated comprehensive decay rate k new,i to update the k i in the phased prediction model. Among them, the staged prediction model constructed by the prediction model construction unit predicts the staged usage performance of the road after its comprehensive decay rate is updated.

Images