CN103745125B - A kind of determination method of road disease cause detection sequence - Google Patents

Abstract

The invention discloses a kind of determination method of road disease cause detection sequence, comprise the steps: that (1) is added up according to conventional maintenance project data, set up the causes of disease probability of happening matrix A that defect phenomenon is corresponding；(2) add up according to conventional maintenance project data, set up causes of disease and cause the probability matrix B of each defect phenomenon；(3) add up according to conventional maintenance project data, set up normalized vector h；(4) detect according to site technology, set up defect phenomenon and vector w occurs；(5) causes of disease distribution probability p is calculated；(6) according to the size of causes of disease distribution probability, causes of disease detection ordering is determined.When present invention determine that causes of disease distribution probability, consider and occurred defect phenomenon on the contribution of the road disease origin cause of formation and not occur defect phenomenon that road disease origin cause of formation volume is affected, calculating process is more scientific and reasonable, result of calculation reliability is higher, can instruct the detection work of causes of disease more scientificly.

Description

A kind of determination method of road disease cause detection sequence

Technical field

The present invention relates to a kind of determination method of road disease cause detection sequence, belong to road maintenance technology.

Background technology

By the end of the year 2012, China's highway total kilometrage has broken through 9.6 ten thousand kilometers, if 9.6 ten thousand kilometers of high speeds are ten In year, maintenance is complete, and annual maintenance mileage reaches 0.96 ten thousand kilometers, and newly-increased the most only 0.74 ten thousand kilometers of last decade China annual, Maintenance demand is huge.The appearance of pavement disease not only has influence on road-ability, and threatens travel safety.In time to road Road carries out effective maintenance can not only allow it keep good service level, and can delay even to prevent other disease Occur, thus increase the service life to a certain extent, and then good economic well-being of workers and staff and social benefit can be obtained.

Present stage, when carrying out road maintenance, is all by investigating defect phenomenon, then evaluates and predict road surface Serviceability, determines maintenance measure on this basis.The maintenance measure formulated according to serviceability, can only temporarily suppress disease existing As, can not thoroughly existing road be safeguarded.Want existing road surface carries out scientific and efficient ground maintenance, need deeply to grind Study carefully the causes of disease of road, formulate maintenance measure for causes of disease, could thoroughly punish road, increase the service life.

At present, when carrying out the detection of asphalt roads causes of disease, for detection purpose priority ordering, the most individually consider The causes of disease probability that every kind of defect phenomenon is corresponding, then by causes of disease probability corresponding for all defect phenomenon of road generation Simply it is added, is the causes of disease distribution probability on this road, according to this result, carry out detection project according to probability size Sequence.The causes of disease distribution probability determined according to this method, only individually considers every kind of defect phenomenon, not Incidence relation between having in view of defect phenomenon, and there is not the defect phenomenon impact on this road causes of disease, the most finally Distribution probability be inaccurate, and carry out detection purpose sequence according to this probability being inaccurate, it is likely that can having most The causes of disease that can occur is placed on and finally detects, and which substantially increases detection workload, reduces work efficiency.

Objectively being ranked up causes of disease detection project for more science, improve detection work efficiency, this patent is sent out Understand a kind of determination method of road disease cause detection sequence, when calculating the distribution probability of each causes of disease, consider Incidence relation between each defect phenomenon, and there is not the defect phenomenon impact on road disease origin cause of formation probability.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides a kind of road disease origin cause of formation detection The determination method of order, has considered the incidence relation between each defect phenomenon and has not occurred defect phenomenon to road disease The impact of the origin cause of formation, more science are objective, it is possible to the most scientifically instruct causes of disease detection work.

Technical scheme: for achieving the above object, the technical solution used in the present invention is:

The determination method of road disease cause detection sequence, comprises the steps:

(1) add up according to conventional maintenance project data, set up the causes of disease probability of happening square that defect phenomenon is corresponding Battle array A；

(2) add up according to conventional maintenance project data, set up causes of disease and cause the probability matrix of each defect phenomenon B；

(3) add up according to conventional maintenance project data, set up normalized vector h；

(4) detect according to site technology, set up defect phenomenon and vector w occurs；

(5) causes of disease distribution probability is calculatedThe wherein Hadamard product of ο representing matrix, i.e. Being multiplied by the element of matrix correspondence position, the matrix carrying out Hadamard product must be homotype matrix；

(6) according to the size of causes of disease distribution probability, determining causes of disease detection ordering, causes of disease distribution probability is big Causes of disease first detect.

Represent, with m, the defect phenomenon species number that all roads of statistics occur, represent what all roads of statistics occurred with n Causes of disease species number:

Causes of disease probability of happening matrix table corresponding for defect phenomenon is shown as:

$A_{m\×n}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \mathrm{...}& a_{1n}\\ a_{21}& a_{22}& \mathrm{...}& a_{2n}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ a_{m1}& a_{m2}& \mathrm{...}& a_{mn}\end{array}\right]$

Wherein, a_ijRepresenting the probability of happening of the jth kind causes of disease of i-th kind of defect phenomenon, i-th row of A represents i-th kind of disease The causes of disease probability of happening distribution that evil phenomenon is corresponding, and 0≤a_ij≤ 100%,

Causes of disease causes the probability matrix of each defect phenomenon be expressed as:

$B_{m\×n}=\left[\begin{array}{cccc}{b}_{11}& b_{12}& \mathrm{...}& b_{1n}\\ b_{21}& b_{22}& \mathrm{...}& b_{2n}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ b_{m1}& b_{m2}& \mathrm{...}& b_{mn}\end{array}\right]$

Wherein, b_ijRepresenting that jth kind causes of disease causes the probability of i-th kind of defect phenomenon, jth kind disease is shown in the jth list of B The origin cause of formation causes the probability distribution of every kind of defect phenomenon, and 0≤b_ij≤ 100%,

The causes of disease species number n that described normalized vector h is occurred by all roads added up determines,

There is vector w in described defect phenomenon_1×m=[w₁ w₂ … w_m], wherein w_iRepresent i-th kind of defect phenomenon occur with No: i-th kind of defect phenomenon occurs, then w_i=1；I-th kind of defect phenomenon does not occurs, then w_i=0.

Beneficial effect: the determination method of the road disease cause detection sequence that the present invention provides, has considered not only and has sent out The raw defect phenomenon contribution to the road disease origin cause of formation, and take into account shadow defect phenomenon not occurring to the road disease origin cause of formation Ring；The calculating of causes of disease distribution probability is carried out, it is simple to realize later data and extend and perfect by matrix, and for difference Specific aim data base can be set up in area, thus realizes accurate calculation, it is possible to the most scientifically instructs causes of disease detection work.

Accompanying drawing explanation

Fig. 1 be the present invention realize flow process；

Fig. 2 be the present invention realize principle；

Fig. 3 is the application present invention and the results contrast not applying the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings the present invention is further described.

It is illustrated in figure 1 the calculating process of a kind of road disease cause of formation distribution probability, comprises the steps:

(1) add up according to conventional maintenance project data, set up the causes of disease probability of happening square that defect phenomenon is corresponding Battle array A；

(2) add up according to conventional maintenance project data, set up causes of disease and cause the probability matrix of each defect phenomenon B；

(3) add up according to conventional maintenance project data, set up normalized vector h；

(4) detect according to site technology, set up defect phenomenon and vector w occurs；

(5) causes of disease distribution probability is calculatedThe wherein Hadamard product of ο representing matrix, i.e. Being multiplied by the element of matrix correspondence position, the matrix carrying out Hadamard product must be homotype matrix；

(6) according to the size of causes of disease distribution probability, determining causes of disease detection ordering, causes of disease distribution probability is big Causes of disease first detect.

Represent, with m, the defect phenomenon species number that all roads of statistics occur, represent what all roads of statistics occurred with n Causes of disease species number:

Causes of disease probability of happening matrix table corresponding for defect phenomenon is shown as:

$A_{m\×n}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \mathrm{...}& a_{1n}\\ a_{21}& a_{22}& \mathrm{...}& a_{2n}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ a_{m1}& a_{m2}& \mathrm{...}& a_{mn}\end{array}\right]$

Wherein, a_ijRepresenting the probability of happening of the jth kind causes of disease of i-th kind of defect phenomenon, i-th row of A represents i-th kind of disease The causes of disease probability of happening distribution that evil phenomenon is corresponding, and 0≤a_ij≤ 100%,

Causes of disease causes the probability matrix of each defect phenomenon be expressed as:

$B_{m\×n}=\left[\begin{array}{cccc}{b}_{11}& b_{12}& \mathrm{...}& b_{1n}\\ b_{21}& b_{22}& \mathrm{...}& b_{2n}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ b_{m1}& b_{m2}& \mathrm{...}& b_{mn}\end{array}\right]$

Wherein, b_ijRepresenting that jth kind causes of disease causes the probability of i-th kind of defect phenomenon, jth kind disease is shown in the jth list of B The origin cause of formation causes the probability distribution of every kind of defect phenomenon, and 0≤b_ij≤ 100%,

The causes of disease species number n that described normalized vector h is occurred by all roads added up determines,

There is vector w in described defect phenomenon_1×m=[w₁ w₂ … w_m], wherein w_iRepresent i-th kind of defect phenomenon occur with No: i-th kind of defect phenomenon occurs, then w_i=1；I-th kind of defect phenomenon does not occurs, then w_i=0.

Described causes of disease distribution probability p_1×n=[p₁ p₂ … p_n], wherein p_iRepresent that the distribution of i-th kind of causes of disease is general Rate.

Below in conjunction with example, the present invention is made further instructions.

If one road there occurs rut and bellding, determine that this road causes of disease is asphalt high-temperature stable after testing Property difference and upper layer isolate, then it is assumed that both is the causes of disease of rut and bellding.Raw statistical data is as shown in table 1；Will Raw data table is normalized with behavior base, and gained is the causes of disease probability of happening matrix that defect phenomenon is corresponding, tool Body computational methods are as shown in table 2；Being normalized with row for basis by raw data table, gained is causes of disease and causes every kind The probability matrix of defect phenomenon, circular is as shown in table 3.

Table 1 computational methods-raw statistical data

The causes of disease probability of happening matrix that table 2 computational methods-defect phenomenon is corresponding

Table 3 computational methods-causes of disease causes the probability matrix of every kind of defect phenomenon

A kind of causes of disease can cause multiple diseases phenomenon simultaneously, and therefore, once certain defect phenomenon on road surface is by certain One causes of disease determined causes, then other defect phenomenon that this causes of disease may cause also will have bigger probability Occur.For example, it is assumed that the rut of preliminary judgement bituminous paving is caused by stability at high temperature of asphalt mixture difference, then Also should be easy to bellding disease occurs, without bellding disease occurs, then poor high temperature stability is just described on this road The probability of Shi Gai road causes of disease substantially to reduce.So, when the road disease origin cause of formation being differentiated according to defect phenomenon, Not only needing the defect phenomenon considering to have occurred, it is also contemplated that nonevent defect phenomenon, the common effect of the two is only judge The important evidence of causes of disease.Principle is as shown in Figure 2.

The present invention is by causes of disease probability of happening matrix A corresponding to defect phenomenon, it is contemplated that defect phenomenon occurs Contribution to the road disease origin cause of formation, cause the probability matrix B of every kind of defect phenomenon by causes of disease, it is contemplated that disease does not occurs The evil phenomenon impact on this road causes of disease.

The method has considered not only contribution defect phenomenon occurring to the road disease origin cause of formation, and take into account and do not send out The raw defect phenomenon impact on road disease cause of formation distribution probability；The calculating of causes of disease distribution probability is carried out, just by matrix Extend and perfect in realizing later data, and specific aim data base can be set up for different regions, thus realize precisely meter Calculate.

Assuming according to raw statistical data shown in table 1, certain road only there occurs rut disease, applies the present invention and does not apply this Invention result of calculation is as shown in Figure 3.Not applying the present invention, two kinds of causes of disease distribution probabilities are identical, and these distribution probabilities are bases In statistics, not accounting for bellding and be not present on this road, it, for this road, does not has specific aim. After the application present invention, the probability of upper layer isolation only has 36.36%, this is because upper layer isolation has 57.14% may draw Play bellding (being known by table 3), but bellding does not appears on this road, so its distribution probability has declined；Poor high temperature stability Probability exceeded 60%, this is because it causes the probability of bellding to only have 25.00% (being known by table 3), much smaller than isolation 57.14%, therefore bellding does not appears on this road, and the impact on this causes of disease is less than the impact on upper layer isolation, So its distribution probability rises.In sum, when carrying out causes of disease detection, it should first detection asphalt high temperature Performance.Can see that, the causes of disease distribution probability that the present invention draws, be to have occurred and disease does not occurs having considered road Evil phenomenon is on determining in the case of the impact of the road disease origin cause of formation.

The above is only the preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (3)

1. the determination method of a road disease cause detection sequence, it is characterised in that: comprise the steps:

(1) add up according to conventional maintenance project data, set up the causes of disease probability of happening matrix A that defect phenomenon is corresponding；

Represent, with m, the defect phenomenon species number that all roads of statistics occur, represent, with n, the disease that all roads of statistics occur Origin cause of formation species number:

Causes of disease probability of happening matrix table corresponding for defect phenomenon is shown as:

$A_{m\×n}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& ...& a_{1n}\\ a_{21}& a_{22}& ...& a_{2n}\\ \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}\\ a_{m1}& a_{m2}& ...& a_{mn}\end{array}\right]$

Wherein, a_ijRepresenting the probability of happening of the jth kind causes of disease of i-th kind of defect phenomenon, i-th row of A represents that i-th kind of disease is existing As corresponding causes of disease probability of happening distribution, and 0≤a_ij≤ 100%,

(2) add up according to conventional maintenance project data, set up causes of disease and cause the probability matrix B of each defect phenomenon；

Causes of disease causes the probability matrix of each defect phenomenon be expressed as:

$B_{m\×n}=\left[\begin{array}{cccc}{b}_{11}& b_{12}& ...& b_{1n}\\ b_{21}& b_{22}& ...& b_{2n}\\ \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}& \begin{array}{c}.\\ .\\ .\end{array}\\ b_{m1}& b_{m2}& ...& b_{mn}\end{array}\right]$

Wherein, b_ijRepresenting that jth kind causes of disease causes the probability of i-th kind of defect phenomenon, jth kind causes of disease is shown in the jth list of B Cause the probability distribution of every kind of defect phenomenon, and 0≤b_ij≤ 100%,

(3) add up according to conventional maintenance project data, set up normalized vector h；

(4) detect according to site technology, set up defect phenomenon and vector w occurs；

(5) causes of disease distribution probability is calculatedThe wherein Hadamard product of o representing matrix, will matrix The element of correspondence position is multiplied, and the matrix carrying out Hadamard product must be homotype matrix；

(6) according to the size of causes of disease distribution probability, causes of disease detection ordering, the disease that causes of disease distribution probability is big are determined The evil origin cause of formation first detects.

The determination method of road disease cause detection sequence the most according to claim 1, it is characterised in that: described normalization The causes of disease species number n that vector h is occurred by all roads added up determines, h_1×n=[1 1 ... 1].

The determination method of road disease cause detection sequence the most according to claim 1, it is characterised in that: described disease is existing As there is vector w_1×m=[w₁ w₂ …w_m], wherein w_iRepresent that whether i-th kind of defect phenomenon occurs: i-th kind of defect phenomenon occurs, Then w_i=1；I-th kind of defect phenomenon does not occurs, then w_i=0.