CN102663252A - Combined type pavement usability performance evaluation method for underground road - Google Patents

Combined type pavement usability performance evaluation method for underground road Download PDF

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CN102663252A
CN102663252A CN201210104968XA CN201210104968A CN102663252A CN 102663252 A CN102663252 A CN 102663252A CN 201210104968X A CN201210104968X A CN 201210104968XA CN 201210104968 A CN201210104968 A CN 201210104968A CN 102663252 A CN102663252 A CN 102663252A
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usability
road surface
pavement
road
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CN102663252B (en
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王海燕
王晓华
马士宾
邢锦
张彩利
娄中波
郑利
赵巍
刘治国
康娟萍
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Tianjin Municipal Engineering Design and Research Institute
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Abstract

The invention relates to a road usability performance evaluation technology, in particular to a combined type pavement usability performance evaluation method for an underground road, which can combine randomnesss with fuzzification and can accurately evaluate road condition. In order to realize the aims, according to the technical scheme adopted by the invention, the combined type pavement usability performance evaluation method for the underground road comprises the following steps: collecting the following road condition indexes: PCI (pavement condition index), RQI (riding quality index), PSSI (pavement structure strength index) and SRI (skid resistance index); computing information distribution values at sample points; computing fuzzy probability distribution of pavement usability performance in the probability theory field; confirming fuzzy and random risks of the pavement usability performance in regions; computing comprehensive fuzzy and random risk level of the pavement usability performance; outputting a module through data; and judging level of pavement usability performance evaluation. The combined type pavement usability performance evaluation method provided by the invention is mainly applied to the road usability performance evaluation.

Description

Road tunnel composite pavement usability appraisal procedure
Technical field
The present invention relates to road usability assessment technique, relate in particular to road tunnel composite pavement usability appraisal procedure.
Background technology
The evaluation of road surface usability always is the problem that vehicle supervision department attaches great importance to.When the usability on road surface is reduced to a certain standard, just must take corresponding maintenance and reconstructional measures to recover and to improve its usability.For selecting best maintenance counterproposal, highway maintenance administrative authority will understand the behaviour in service on road surface timely and accurately, makes the judgement and the prediction of science, can know just when which kind of corresponding measure this takes.
The method of uncertain factor was a probabilistic method during present existing processing road surface was analyzed, and randomness and ambiguity coexist under still a lot of situation.Still the report that does not have both combinations.
Summary of the invention
The present invention is intended to overcome the deficiency of prior art, provides a kind of randomness is combined with ambiguity, accurately estimates the method for road conditions; For achieving the above object; The technical scheme that the present invention takes is that road tunnel composite pavement usability appraisal procedure comprises the following steps:
Gather following road conditions index: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI;
The calculating of the information distribution value of sample point:
(2-1) set road tunnel composite pavement usability and detect set of data samples:
X＝{x 1，x 2，…，x i，…，x n} (1)
X in the formula 1, x 2..., x i..., x nIt is each the road surface using property data value that comprises aforementioned road conditions index;
The discrete domain that (2-2) forms by road surface using property data histogram midrange:
U＝{u j|j＝1，2，…m} (2)
u jBe j histogram midrange, m is the maximum number of discrete domain;
(2-3) according to the information distribution value of frequency histogram method and information distribution Theoretical Calculation set of data samples:
q ij = 1 - | x i - u j | h | x i - u j | ≤ h 0 | x i - u j | > h - - - ( 3 )
H is a step-length;
Calculating the fuzzy probability of road surface usability in the probability domain distributes:
For the interval I of road surface usability j, X is divided into two part: X 1And X 2For
Figure BDA0000152364610000012
x i∈ X 1, the quantity of information of losing does
Figure BDA0000152364610000013
For x i∈ X 2, this information increment does Promptly Represent this information increment; Then according to each sample point at X 1And X 2In distribution, calculate the interval I of road surface usability jFuzzy probability in the scope distributes
Figure BDA0000152364610000021
K=1,2 ..., n+1;
Figure BDA0000152364610000022
If X={x 1, x 2..., x i..., x nBe event space, P={p kBe incident x iThe probability domain that takes place, i=1,2 ..., n, k=1,2 ..., n; π (x i)={ π kBe x iThe probability that takes place is p kThe possibility domain, then claim
Figure BDA0000152364610000023
Be incident x iThe random fuzzy risk;
Make that S is X 1Index set, X then s∈ X 1, and X 1={ x sS ∈ S}; Make that T is X 2Index set, t ∈ T, then x t∈ X 2, and X 2={ x t| t ∈ T}; Index set and outer index set in S and T are called respectively;
Wherein: ∧ representes " getting little " symbol, and promptly the drop-out amount is pressed series arrangement from small to large when calculating
Figure BDA0000152364610000031
;
∨ representes " getting big " symbol, and promptly the drop-out amount is pressed series arrangement from big to small when calculating
Figure BDA0000152364610000032
;
s i, t iWhich kind of scope is the information distribution value of representing each index be within, the X that gets with each index iOccurrence is corresponding;
Figure BDA0000152364610000033
Expression information respectively is in X 1, X 2The time the information dropout amount;
Figure BDA0000152364610000034
Expression information is in X 1The time the information increment;
Figure BDA0000152364610000035
Expression information belongs to X 2, the index size is in t respectively 1, t 2The time the information increment;
n 1Be X 1In the sample point number;
The road surface usability is confirmed each interval random fuzzy risk:
F i = Σ k = 1 l π I j ( p k ) × p k - - - ( 5 )
F in the formula iBe pavement performance index x iThe random fuzzy risk;
Road surface usability random fuzzy risk level of aggregation calculates:
Tried to achieve the random fuzzy risk of each road surface usability by formula 5 after, employing formula (6) is confirmed road surface usability random fuzzy risk level of aggregation:
F j z = Σ i = 1 m ( F i · w i ) - - - ( 6 )
In the formula, w iIt is the weight of i road surface using property data; w iSpan is between [0,1];
Data outputting module: the differentiation of road surface usability opinion rating
If be divided into 6 grades to the road surface usability
If F p z = Max { F j z } , 1≤j≤6, p ∈ [1,2 ..., 6] and (7)
Think that then this road surface usability belongs to the P class;
Figure BDA0000152364610000039
is the random fuzzy risk level of aggregation maximal value of different road surfaces usability grade.
Use digitized image inspection vehicle, vialog, autodeflectometer, road surface cornering ratio to measure car respectively and gather following road conditions index: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI.
The present invention has following technique effect:
The present invention is on the basis of probability risk; The possibility of binding events probability of happening; Fuzzy operation is introduced the road conditions evaluation, and structure calculates the random fuzzy model that the composite pavement usability is estimated on the basis of incomplete data, thereby the present invention has improved the accuracy that road conditions are estimated.
Description of drawings
Fig. 1 is based on the road tunnel composite pavement usability estimation flow figure of random fuzzy method.
Embodiment
The invention discloses a kind of road tunnel composite pavement usability random fuzzy evaluation method; This method mainly comprises: introduce fuzzy set theory, randomness is combined with ambiguity, promptly on the basis of probability risk; The possibility of binding events probability of happening, the application message distribution method; Structure calculates the random fuzzy model that road tunnel composite pavement usability is estimated on the basis of incomplete data.
Introduce fuzzy set theory; Randomness is combined with ambiguity, promptly on the basis of probability risk, the possibility of binding events probability of happening; The application message distribution method, structure calculates the random fuzzy model that the composite pavement usability is estimated on the basis of incomplete data.This method mainly comprises with lower module:
One, pre-processing module: index system is set up
Road tunnel composite pavement using property data system mainly comprises: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI.
The acquisition method of road tunnel composite pavement using property data: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI can use digitized image inspection vehicle, vialog, autodeflectometer, road surface cornering ratio to measure checkout equipment acquisitions such as car respectively.The present invention recommends said method in principle, but does not get rid of other science of employing, quick, effective specialized method.
Two, core algorithm module: the confirming of road surface usability random fuzzy risk level of aggregation
The calculating of the information distribution value of step 1, sample point
(2-1) set road tunnel composite pavement usability and detect set of data samples:
X＝{x 1，x 2，…，x i，…，x n} (1)
The discrete domain that (2-2) forms by road surface using property data histogram midrange:
U＝{u j|j＝1，2，…m} (2)
u jBe j histogram midrange, m is the maximum number of discrete domain;
(2-3) according to the information distribution value of frequency histogram method and information distribution Theoretical Calculation set of data samples:
q ij = 1 - | x i - u j | h | x i - u j | ≤ h 0 | x i - u j | > h - - - ( 3 )
X in the above formula iBe each performance index value, h is a step-length.
Step 2, the fuzzy probability of calculating road surface usability in the probability domain distribute
For the interval I of road surface usability j, X is divided into two part: X 1And X 2For x iX 1, the quantity of information of losing does
Figure BDA0000152364610000043
For x i∈ X 2, this information increment does
Figure BDA0000152364610000044
Promptly
Figure BDA0000152364610000045
Represent this information increment.Then according to each sample point at X 1And X 2In distribution, calculate the interval I of road surface usability jFuzzy probability in the scope distributes
Figure BDA0000152364610000046
K=1,2 ..., n+1.
Figure BDA0000152364610000051
So-called Probability p (x i) be meant incident x iThe ultimate value of frequency when the independent random frequency in sampling is tending towards infinite that occurs.And possibility π (x i) be meant x iMake a certain true degree of setting up, it is irrelevant that it can follow the machine sampling experiment.If X={x 1, x 2..., x i..., x nBe event space, P={p kBe incident x iThe probability domain that takes place, i=1,2 ..., n, k=1,2 ..., n; π (x i)={ π kBe x iThe probability that takes place is p kThe possibility domain, then claim
Figure BDA0000152364610000052
Be incident x iThe random fuzzy risk.
Make that S is X 1Index set,
Figure BDA0000152364610000053
X then s∈ X 1, and X 1={ x s| s ∈ S}.Make that T is X 2Index set, t ∈ T, then x t∈ X 2, and X 2={ x t| t ∈ T}.Index set and outer index set in S and T are called respectively.
Wherein: ∧ representes " getting little " symbol, and promptly the drop-out amount is pressed series arrangement from small to large when calculating ; ∨ representes " getting big " symbol, and the series arrangement that promptly the drop-out amount is pressed from big to small when calculating
Figure BDA0000152364610000062
gets final product.
s i, t iWhich kind of scope is the information distribution value of representing each index be within, the X that gets with each index iOccurrence is corresponding;
Expression information respectively is in X 1, X 2The time the information dropout amount;
Figure BDA0000152364610000064
Expression information is in X 1The time the information increment;
Figure BDA0000152364610000065
Expression information belongs to X 2, the index size is in t respectively 1, t 2The time the information increment.
n 1Be X 1In the sample point number.
Step 3, road surface usability are confirmed each interval random fuzzy risk
F i = Σ k = 1 l π I j ( p k ) × p k - - - ( 5 )
F in the formula iBe pavement performance index x iThe random fuzzy risk.
Step 4, road surface usability random fuzzy risk level of aggregation calculate
Tried to achieve the random fuzzy risk of each road surface usability by formula 5 after, employing formula 6 is confirmed road surface usability random fuzzy risk level of aggregation:
F j z = Σ i = 1 m ( F i · w i ) - - - ( 6 )
In the formula, w iIt is the weight of i road surface using property data.w iSpan is between [0,1].
Three, data outputting module: the differentiation of road surface usability opinion rating
If be divided into 6 grades to the road surface usability
If F p z = Max { F j z } , 1≤j≤6, p ∈ [1,2 ..., 6] and (7)
Think that then this road surface usability belongs to the P class.
Figure BDA0000152364610000069
is the random fuzzy risk level of aggregation maximal value of different road surfaces usability grade.
Calculated examples
Certain underground road composite pavement usability each item achievement data in 2008,20 every.Be respectively: pavement condition index x I1={ x i| i=1,2 ..., 20}=(82.4,76.7,71.3,72.3,73.7,40.8,39.0,67.0,58.9,71.2,73.3,62.1,72.8,78.0,64.4,60.7,84.3,74.3,72.4,61.1); Ride quality index x I2={ x i| i=1,2 ..., 20}=(94.8,96.1,94.894.594.3,93.5,93.6,93.8,94.7,92.6,90.0,92.9,94.8,96.3,94.9,95.2,95.3,94.3,95.7,96.3); Pavement structural strength index x I3={ x i| i=1,2 ..., 20}=(45.4,75.1,82.5,76.6,89.7,47.3,58.7,66.6,79.4,70.8,57.4,49.3,45.3,57.3,51.8,59.1,78.8,44.8,64.6,35.7); Cling property index x I4={ x i| i=1,2 ..., 20}=(90.20,66.00,66.73,73.78,74.73,69.50,75.01,66.72,69.05,63.25,66.54,75.52,73.02,71.83,65.63,68.74,69.11,68.49,69.13,66.83)
By the requirement packet count m=6 of probability statistics medium frequency histogram way, promptly between dividing regions I 11=[37.5,47.5], I 21=[47.5,57.5], I 31=[57.5,67.5], I 41=[67.5,77.5], I 51=[77.5,87.5], I 61=[87.5,97.5]; I 12=[86.5,88.5], I 22=[88.5,90.5], I 32=[90.5,92.5], I 42=[92.5,94.5], I 52=[94.5,96.5], I 62=[96.5,98.5]; I 13=[35,45], I 23=[45,55], I 33=[55,65], I 43=[65,75], I 53=[75,85], I 63=[85,95]; I 14=[62.5,67.5], I 24=[67.5,72.5], I 34=[72.5,77.5], I 24=[77.5,82.5], I 24=[82.5,87.5], I 24=[87.5,92.5].Correspondingly, discrete domain is: U 1={ u J1| j=1,2,3,4,5,6}=(42.5,52.5,62.5,72.5,82.5,92.5); U 2={ u J2| j=1,2,3,4,5,6}=(87.5,89.5,91.5,93.5,95.5,97.5); U 3={ u J3| j=1,2,3,4,5,6}=(40,50,60,70,80,90); U 4={ u J4| j=1,2,3,4,5,6}=(65,70,75,80,85,90).Step-length h wherein 1=10, h 2=2, h 3=10, h 4=5.
Make the probability domain
Figure BDA0000152364610000071
For I 11, can get X 11={ x 6, x 7, X 21={ x 1, x 2, x 3, x 4, x 5, x 8, x 9, x 10, x 11, x 12, x 13, x 14, x 15, x 16, x 17, x 18,, x 19, x 20, S={6,7}, T={1,2,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20}.
Utilize formula 3, can controlled some u 11From information source x 61The information of=40.8 acquisitions
q 61 = 1 - | x 61 - u 1 | h 1 = 1 - | 40.8 - 42.5 10 = 0.83
In like manner distribute all x iTo u j, i=1,2 ... 20, j=1,2 ..., 6 can show X message structure situation on U.Because of length is limit, the present invention just lists out pavement condition index X 11Information distribution on U is as shown in table 1
Table 1 pavement condition index X 11Information distribution on U
q ij u 1 u 2 u 3 u 4 u 5 u 6
x 1 0 0 0 0.01 0.99 0
x 2 0 0 0 0.58 0.42 0
x 3 0 0 0.12 0.88 0 0
x 4 0 0 0.02 0.98 0 0
x 5 0 0 0 0.88 0.12 0
x 6 0.83 0 0 0 0 0
x 7 0.65 0 0 0 0 0
x 8 0 0 0.55 0.45 0 0
x 9 0 0.36 0.64 0 0 0
x 10 0 0 0.13 0.87 0 0
x 11 0 0 0 0.92 0.08 0
x 12 0 0.04 0.96 0 0 0
x 13 0 0 0 0.97 0.03 0
x 14 0 0 0 0.45 0.55 0
x 15 0 0 0.81 0.19 0 0
x 16 0 0.18 0.82 0 0 0
x 17 0 0 0 0 0.82 0.18
x 18 0 0 0 0.82 0.18 0
x 19 0 0 0.01 0.99 0 0
x 20 0 0.14 0.86 0 0 0
For I 1tBy
Figure BDA0000152364610000081
Calculate
Figure BDA0000152364610000082
Utilize formula then, calculate
Figure BDA0000152364610000083
The result that in like manner can get on interval lists in table 2, does not enumerate one by one as space is limited.
The fuzzy probability of table 2 pavement condition index distributes
Figure BDA0000152364610000084
Figure BDA0000152364610000091
Can get each interval random fuzzy probability of road surface usability is table 3.
Each interval random fuzzy probability of this road tunnel road surface usability of table 3
Project F 1 F 2 F 3 F 4 F 5 F 6
Pavement condition index 0.1275 0.065 0.6455 1.383 0.409 0.009
The ride quality index 0 0.05 0.0125 1.803 2.407 0.125
The structural strength index 0.356 0.812 0.580 0.4245 0.4645 0.075
The cling property index 1.2225 1.861 0.3315 0.005 0 0.05
Data from table 3 can be made following analysis:
(1) this road tunnel composite pavement situation index, ride quality index, structural strength index, cling property index are in [67.5 respectively; 77.5], [94.5; 96.5], the possibility of [45,55] and [67.5,72.5] is maximum; Can find out that from the expressway with the Performance Detection data its pavement performance data many places are in this interval.
(2) pavement condition index is in F 1Random fuzzy probability 0.1275 greater than being in F 2Random fuzzy probability 0.065, occur unusual.This is because highway section K85+000～K86+000, K 86+000～K 87+000 pavement condition index are 40.8 and 39.0, is between the interval [37.5,47.5], and is not in the detection data in interval [47.5,57.5].This interval probability is that " the data drift " by the detection data 58.9 of K88+000～K89+000, K95+000～K96+000 and 60.4 causes.
(3) the ride quality exponent data is in F 4, F 5Fuzzy probability be respectively 1.803 and 2.407, and F 1, F 2, F 3, F 6The fuzzy probability sum be 0.1875, be far smaller than it and be in F 4, F 5Fuzzy probability, explain that its most of data are between 92.5 and 96.5.
(4) structural strength index F 2, F 3Fuzzy probability be 0.812 and 0.580, and be in other interval risks and compare and occupy certain advantage.The intensity index that has 6 highway sections in 20 highway sections is in [45,55] and 5 data are between [55,65] F 1, F 4, F 5Fuzzy probability be 0.356,0.4245 and 0.4645, the three is about the same, is in F 2, F 3And F 1, F 4, F 5The pavement performance data basic identical.From the structural strength index, the usability globality in several highway sections is stronger.
(5) the ride quality exponent data is in F 1, F 2Fuzzy probability 1.2225,1.861, explain that most data are in [62.5,72.5], can find out F in addition 6Fuzzy probability be 0.05, this is because the ride quality index in K80+000～K81+000 highway section is 90.20, forms isolated island.From detecting data, this highway section pavement performance obviously is better than other highway sections.
By the weight coefficient of each interval random fuzzy probability of road surface usability and each performance index, can further draw the whole random fuzzy probability of the road surface usability level of aggregation of multichannel section.
Carry out the normalization processing so tackle its codomain, the conversion method of employing nonlinear fitting converts variant codomain to 0-1 interval numerical value and sees table 4.
Each interval random fuzzy probability conversion values of table 4 road surface usability
Figure BDA0000152364610000101
Can this road tunnel road surface usability random fuzzy probability level of aggregation be:
F p z = { F 1 z , F 2 z , F 3 z , F 4 z , F 5 z , F 6 z } = { 0.07,0.15,0.15,0.33,0.26,0.02 } . If be divided into 6 grades to the road surface usability, according to the degree of confidence principle, it is 0.6 that reliability is set, and then this road tunnel road surface usability is in Probability maximum.
Use digitized image inspection vehicle method, vialog acquisition method, autodeflectometer detection method, road surface cornering ratio to measure the car method and measure following road conditions index: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI.

Claims (2)

1. a road tunnel composite pavement usability appraisal procedure is characterized in that, comprises the following steps:
Gather following road conditions index: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI;
The calculating of the information distribution value of sample point:
(2-1) set road tunnel composite pavement usability and detect set of data samples:
X＝{x 1，x 2，…，x i，…，x n} (1)
X in the formula 1, x 2..., x i..., x nIt is each the road surface using property data value that comprises aforementioned road conditions index;
The discrete domain that (2-2) forms by road surface using property data histogram midrange:
U＝{u j|j＝1，2，…m} (2)
u jBe j histogram midrange, m is the maximum number of discrete domain;
(2-3) according to the information distribution value of frequency histogram method and information distribution Theoretical Calculation set of data samples:
q ij = 1 - | x i - u j | h | x i - u j | ≤ h 0 | x i - u j | > h - - - ( 3 )
H is a step-length;
Calculating the fuzzy probability of road surface usability in the probability domain distributes:
For the interval I of road surface usability j, X is divided into two part: X 1And X 2For
Figure FDA0000152364600000012
x i∈ X 1, the quantity of information of losing does
Figure FDA0000152364600000013
For x i∈ X 2, this information increment does
Figure FDA0000152364600000014
Promptly
Figure FDA0000152364600000015
Represent this information increment; Then according to each sample point at X 1And X 2In distribution, calculate the interval I of road surface usability jFuzzy probability in the scope distributes
Figure FDA0000152364600000016
K=1,2 ..., n+1;
Figure FDA0000152364600000021
If X={x 1, x 2..., x i..., x nBe event space, P={p kBe incident x iThe probability domain that takes place, i=1,2 ..., n, k=1,2 ..., n; π (x i)={ π kBe x iThe probability that takes place is p kThe possibility domain, then claim
Figure FDA0000152364600000022
Be incident x iThe random fuzzy risk;
Make that S is X 1Index set, X then s∈ X 1, and X 1={ x sS ∈ S}; Make that T is X 2Index set, t ∈ T, then x t∈ X 2, and X 2={ x t| t ∈ T}; Index set and outer index set in S and T are called respectively;
Wherein: ∧ representes " getting little " symbol, and promptly the drop-out amount is pressed series arrangement from small to large when calculating ; ∨ representes " getting big " symbol, and promptly the drop-out amount is pressed series arrangement from big to small when calculating
Figure FDA0000152364600000032
;
s i, t iWhich kind of scope is the information distribution value of representing each index be within, the X that gets with each index iOccurrence is corresponding;
Figure FDA0000152364600000033
Expression information respectively is in X 1, X 2The time the information dropout amount;
Figure FDA0000152364600000034
Expression information is in X 1The time the information increment;
Figure FDA0000152364600000035
Expression information belongs to X 2, the index size is in t respectively 1, t 2The time the information increment;
n 1Be X 1In the sample point number;
The road surface usability is confirmed each interval random fuzzy risk:
F i = Σ k = 1 l π I j ( p k ) × p k - - - ( 5 )
F in the formula iBe pavement performance index x iThe random fuzzy risk;
Road surface usability random fuzzy risk level of aggregation calculates:
Tried to achieve the random fuzzy risk of each road surface usability by formula 5 after, employing formula 6 is confirmed road surface usability random fuzzy risk level of aggregation:
F j z = Σ i = 1 m ( F i · w i ) - - - ( 6 )
In the formula, w iIt is the weight of i road surface using property data; wThe i span is between [0,1];
Data outputting module: the differentiation of road surface usability opinion rating
If be divided into 6 grades to the road surface usability
If F p z = Max { F j z } , 1≤j≤6, p ∈ [1,2 ..., 6] and (7)
Think that then this road surface usability belongs to the P class;
Figure FDA0000152364600000039
is the random fuzzy risk level of aggregation maximal value of different road surfaces usability grade.
2. road tunnel composite pavement usability appraisal procedure as claimed in claim 1; It is characterized in that, use digitized image inspection vehicle, vialog, autodeflectometer, road surface cornering ratio to measure car respectively and gather following road conditions index: pavement condition index PCI, ride quality index RQI, pavement structural strength indices P SSI and cling property index SRI.
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CN103745127A (en) * 2014-01-26 2014-04-23 东南大学 Conservation measure auxiliary decision method specific for road disease causes
CN106526149A (en) * 2016-11-22 2017-03-22 山西省交通科学研究院 Method for predicting pavement performance on basis of transport durations and traffic volumes
CN107273703A (en) * 2017-07-11 2017-10-20 河南省交通规划设计研究院股份有限公司 A kind of Pavement Condition distribution situation Forecasting Methodology
CN109697515A (en) * 2018-12-28 2019-04-30 深圳高速工程顾问有限公司 Pavement of road management method, device, storage medium and computer equipment
CN111445097A (en) * 2019-01-16 2020-07-24 淮阴工学院 Comprehensive performance evaluation index system and evaluation method for colored antiskid pavement
CN111639837A (en) * 2020-04-30 2020-09-08 同济大学 Road network service performance evaluation method and device, storage medium and terminal
CN117557255A (en) * 2024-01-12 2024-02-13 吉林大学 Dangerous scene driving risk assessment system and method for automatic driving automobile

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003186917A (en) * 2001-12-18 2003-07-04 Mitsubishi Heavy Ind Ltd Vehicle virtual performance evaluating device
CN101629407A (en) * 2009-08-21 2010-01-20 赵怀志 Pavement structural strength forecasting method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003186917A (en) * 2001-12-18 2003-07-04 Mitsubishi Heavy Ind Ltd Vehicle virtual performance evaluating device
CN101629407A (en) * 2009-08-21 2010-01-20 赵怀志 Pavement structural strength forecasting method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王晓华等: "地下道路复合式路面结构力学分析", 《河北工业大学学报》, vol. 40, no. 4, 15 August 2011 (2011-08-15), pages 80 - 84 *
王海燕等: "地下道路阻燃沥青混合料路用性能研究", 《城市道桥与防洪》, no. 12, 15 December 2010 (2010-12-15), pages 137 - 140 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103091480A (en) * 2013-01-07 2013-05-08 河北工业大学 Entropy weight-based underground road bituminous pavement service performance evaluation method
CN103150472A (en) * 2013-02-27 2013-06-12 南京信息工程大学 Fog disaster risk assessment method based on information diffusion theory
CN103745125A (en) * 2014-01-26 2014-04-23 东南大学 Determining method for road disease cause detection sequence
CN103745127A (en) * 2014-01-26 2014-04-23 东南大学 Conservation measure auxiliary decision method specific for road disease causes
CN103745127B (en) * 2014-01-26 2016-08-17 东南大学 Maintenance measure aid decision-making method for the road disease origin cause of formation
CN103745125B (en) * 2014-01-26 2016-08-31 东南大学 A kind of determination method of road disease cause detection sequence
CN106526149A (en) * 2016-11-22 2017-03-22 山西省交通科学研究院 Method for predicting pavement performance on basis of transport durations and traffic volumes
CN107273703A (en) * 2017-07-11 2017-10-20 河南省交通规划设计研究院股份有限公司 A kind of Pavement Condition distribution situation Forecasting Methodology
CN107273703B (en) * 2017-07-11 2020-02-07 河南省交通规划设计研究院股份有限公司 Pavement service performance distribution condition prediction method
CN109697515A (en) * 2018-12-28 2019-04-30 深圳高速工程顾问有限公司 Pavement of road management method, device, storage medium and computer equipment
CN111445097A (en) * 2019-01-16 2020-07-24 淮阴工学院 Comprehensive performance evaluation index system and evaluation method for colored antiskid pavement
CN111639837A (en) * 2020-04-30 2020-09-08 同济大学 Road network service performance evaluation method and device, storage medium and terminal
CN111639837B (en) * 2020-04-30 2023-02-10 同济大学 Road network service performance evaluation method and device, storage medium and terminal
CN117557255A (en) * 2024-01-12 2024-02-13 吉林大学 Dangerous scene driving risk assessment system and method for automatic driving automobile

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