Method for predicting concentration of accumulated sand on road

Abstract

A road sand accumulation concentration prediction method comprises the following steps: selecting influence factors of road sand accumulation conditions in different section forms, setting an influence factor orthogonal test, and inputting the set influence factor test into computational fluid mechanics simulation software for simulation to obtain a road sand accumulation rule; and establishing a regression prediction model of the influence factors and the road accumulated sand concentration. According to the method, a plurality of sand accumulation influence factors are collected through a fluid mechanics simulation means to fit the sand wind distribution characteristics under different road surface forms, the real-time prediction of the road sand accumulation concentration is realized by establishing the regression prediction model of the sand accumulation influence factors, the driving risk potential safety hazard can be sent out in time, and the driving safety is improved. And workers can be timely reminded to clean accumulated sand on the road, so that potential safety hazards are reduced.

Description

Technical field:

[0001] The invention belongs to the technical field of traffic safety prediction, and in particular relates to a method for predicting road sand accumulation concentration. Background technology:

[0002] Most of China's sandy areas are distributed in huge inland mountain basins and vast high plains, forming an arc-shaped strip from the northwest, North China to the northeast. In recent years, in order to promote the development of the western region, the construction of highways in the western region has developed rapidly, and more and more highways choose to cross the desert area to reduce the traffic distance, improve transportation efficiency and save transportation costs. With the construction and opening of more and more highways in desert areas, the safety issues in the operation of highways in desert areas have become increasingly prominent. The sand accumulation on highways in windy and sandy environments is very serious. The sand accumulation will reduce the friction coefficient of the road surface and have a huge impact on driving safety. However, the sand accumulation on the road often needs to be discovered through manual inspections, which cannot detect the road sand accumulation the fastest, and requires a lot of manpower and material resources. Therefore, it is urgent to design a method for predicting the concentration of road sand accumulation, issue early warnings in time, and reduce the safety hazards of sandy roads. Summary of the invention:

[0003] In view of the current lack of road sand concentration prediction problem, the present invention designs a road sand concentration prediction method to achieve the purpose of early warning of road sand concentration.

[0004] A method for predicting road sand concentration, the method comprising the following steps:

[0005] The influencing factors of road sand accumulation under different cross-sectional forms are selected, and the influencing factors of highway road sand accumulation under windy and sandy environment are comprehensively considered. The method of controlling variables is adopted to set up an orthogonal test of influencing factors. The set orthogonal test of influencing factors is input into the computational fluid dynamics simulation software for simulation to obtain the law of road sand accumulation; a regression prediction model of the influencing factors and road sand concentration is established.

[0006] Preferably, the method further comprises determining a road sand accumulation risk level.

[0007] Preferably, a fuzzy comprehensive evaluation method is used to determine the road sand accumulation risk level.

[0008] Preferably, the computational fluid dynamics simulation software is Ansys software.

[0009] Preferably, the cross-section forms include embankment, cutting, half embankment and half cutting, and the factors affecting the sand accumulation on the road include wind speed, wind direction, roadbed slope ratio, roadbed height, and road width.

[0010] Preferably, the said establishing a regression prediction model of influencing factors and road sand concentration is specifically as follows: respectively establishing the optimal univariate nonlinear regression model of each influencing factor and road sand concentration, using data statistical analysis software to perform multivariate linear regression on the optimal univariate regression nonlinear model of road sand concentration for each influencing factor, and establishing a regression prediction model of each influencing factor and road sand concentration, specifically as follows: Multi-factor linear regression model of embankment road surface sand concentration:

[0011]

[0012] Multi-factor linear regression model of road cutting and road surface sediment concentration:

[0013] Y2=-1.313x1+0.029x2+5.559x3-0.891x4+0.355x5+36.825(R 2 =0.732)#

[0014] Multi-factor linear regression model of sand concentration in the half-embankment, half-cutting and windward road area:

[0015] Y3=-0.661x1+0.008x2+0.979x3+0.43x4-0.074x5+15.468(R 2 =0.754)##

[0016] Multi-factor linear regression model of sediment concentration in the semi-embankment, semi-cutting and leeward road area:

[0017] Y4=-2.565x1+0.354x2+2.703x3+0.469x4+0.067x5+23.33(R 2 =0.851)

[0018] Where Y i is the concentration of sand on the road surface, x1 is the wind speed, x2 is the wind direction, x3 is the height, x4 is the width, and x5 is the slope ratio.

[0019] Preferably, the road model is constructed by using Ansys software in the form of point-line-surface, and the road model is determined by the cross-section form; the parameters of the road model include wind and sand flow parameters and model processing parameters.

[0020] Preferably, the data statistical analysis software is SPSS software.

[0021] The present invention collects multiple sand accumulation influencing factors through fluid mechanics simulation and fits the wind and sand distribution characteristics under different road surface forms. By establishing a regression prediction model of the sand accumulation influencing factors, the real-time prediction of road sand concentration can be achieved. It can timely issue driving risk safety hazards and can also promptly remind staff to clean up road sand to reduce safety hazards. Description of the drawings:

[0022] Attached Figure 1 is the mesh division result of this embodiment. Specific implementation method:

[0023] In order to make the technical solution of the present invention easier to understand, the technical solution of the present invention is now clearly and completely described in the form of specific embodiments.

[0024] The Uma Expressway model was constructed using Ansys software in the form of point-line-surface. After the model was built, the model needed to be meshed and calculated. Too much meshing would cause the model to run for a long time and waste unnecessary resources. Therefore, in this embodiment, the length of the model's air inlet buffer section was set to 20m, and the length of the air outlet buffer section was set to 40m. After the road model was built, meshing was performed to determine the calculation unit. The meshing results are shown in Figure 2. Figure 1 The detailed simulation parameters in the model are as follows: the wind and sand are all fluid media, and the sand density is 2650kg / m 3 , the road model section x = 0m is the wind speed inlet (IN), the model inlet wind speed is 15m / s, the section x = 100m is the wind speed outlet (OUT), the ground is set as the wall (WALL), the sand roughness height is 0.0025m, the sand roughness constant is 0.5, the sandbox volume fraction is 0.04, and the remaining sections are set as symmetric boundary conditions (symmetric). In the residual monitoring, the residual absolute standards of continuity, u-air, u-sand, v-air, v-sand, w-air, w-sand, k, epsilon, and vf-sand are all set to 0.000001.

[0025] Three road section forms, namely embankment, cutting, and half embankment and half cutting, were selected. Wind speed, wind direction, roadbed slope ratio, roadbed height, and road width were selected as influencing factors. The control variable method was used to analyze the variation law of road sand accumulation degree of highways with different roadbed types under different meteorological conditions. The simulation strategy is shown in Table 1:

[0026] Table 1 Simulation strategy of factors affecting road sediment accumulation under different parameter conditions

[0027]

[0028] Through Ansys simulation, the simulation results of influencing factors under various road section forms are shown in Table 2 and Table 3:

[0029] Table 2 Simulation results of sediment concentration of various influencing factors

[0030]

[0031]

[0032] Table 3 Orthogonal test simulation results for each section

[0033]

[0034] The optimal univariate nonlinear regression model of each influencing factor and road sand concentration was established respectively. SPSS data statistical analysis software was used to perform multiple linear regression on the optimal univariate regression nonlinear model of road sand concentration for each influencing factor, and the regression prediction model of each influencing factor and road sand concentration was established, as follows:

[0035] Multi-factor linear regression model of embankment road surface sediment concentration:

[0036]

[0037] Multi-factor linear regression model of road cutting surface sediment concentration:

[0038] Y2=-1.313x1+0.029x2+5.559x3-0.891x4+0.355x5+36.825(R 2 =0.732)#

[0039] Multi-factor linear regression model of sand concentration in the half-embankment, half-cutting and windward road area:

[0040] Y3=-0.661x1+0.008x2+0.979x3+0.43x4-0.074x5+15.468(R 2 =0.754)##

[0041] Multi-factor linear regression model of sediment concentration in the semi-embankment, semi-cutting and leeward road area:

[0042] Y4=-2.565x1+0.354x2+2.703x3+0.469x4+0.067x5+23.33(R 2 =0.851)

[0043] Where Y i is the concentration of sand on the road surface, x1 is the wind speed, x2 is the wind direction, x3 is the height, x4 is the width, and x5 is the slope ratio.

[0044] The fuzzy comprehensive evaluation method is used to determine the road sand accumulation risk level. The specific formula is as follows:

[0045] It is known that there is a finite set L: L = {l 1,l2 .....l n}; Where: l i(i=1,2,…,n) represents the research object.

[0046] Let a finite set V: V = {v1,v2.....v m}; Where: V j (j=1,2,…,m) represents the judgment set.

[0047] Assume there is another finite set U: U = {u1,u2,…,u p}; Where: u k (k=1,2......p) respectively correspond to different influencing factors u k The weights of A1+A2+,…,A p =1

[0048] Considering the values ​​of different influencing factors in the evaluation set, we get the i-th single factor evaluation matrix R i , which is a matrix with p rows and m columns:

[0049] In the formula, any value of r kj The method of obtaining the single factor evaluation matrix of other research objects (i=1,2,…,i-1,i+1,…,n) by expressing the proportion of the kth influencing factor in the jth item of the evaluation set is similar to R i similar.

[0050] In summary, the comprehensive evaluation of the i-th research object is Get the comprehensive evaluation method and J of other research objects (i=1,2,……i-1,i+1,…,n) i similar.

[0051] Comprehensive evaluation of J i Class normalization, to obtain the normalized comprehensive evaluation result G of the i-th research object i , that is, Make G i =(g 1j )1×m; where: g 1j =(j=1,2,...,m);

[0052] The method for obtaining the normalized comprehensive evaluation results of other research objects (i=1,2,…,i-1,i+1,…,n) is similar to G i Similarly, let the coefficient matrix parameter Y of the normalized comprehensive evaluation result be:

[0053] Y=(y d1 )m×1; where: y d1 (d=1,2,...,m).

[0054] Finally, the comprehensive evaluation coefficient H of the i-th research object is obtainedi =H i =G i Y; The method of obtaining the comprehensive evaluation coefficient of other research objects (i=1,2,…,i-1,i+1,…,n) is the same as H i Similar, and H1+H2+...+H n =1.

[0055] It can be seen that the final result of the above calculation method is a set of comprehensive evaluation coefficients. According to the size of the coefficient, the dangerousness of the research object can be ranked to determine the sand accumulation risk level.

[0056] It should be noted that the embodiments described herein are only partial embodiments of the present invention, rather than all implementations of the present invention, and the embodiments are only exemplary, and their role is only to provide a more intuitive and clear way to understand the content of the present invention, rather than to limit the technical solutions described in the present invention. Without departing from the concept of the present invention, all other implementation methods that can be thought of by ordinary technicians in this field without creative work, and other simple replacements and various changes to the technical solutions of the present invention, all belong to the protection scope of the present invention.

Claims

1. A method for predicting road sand concentration, characterized in that: The method comprises the following steps: selecting influencing factors of road sand accumulation under different cross-section forms, setting an orthogonal test of the influencing factors, inputting the set influencing factor test into computational fluid dynamics simulation software for simulation, and obtaining the law of road sand accumulation; and establishing a regression prediction model of the influencing factors and road sand concentration.

2. A method for predicting road sediment concentration as claimed in claim 1, characterized in that: The method also includes determining a road sand accumulation risk level.

3. A method for predicting road sand concentration as claimed in claim 2, characterized in that: The fuzzy comprehensive evaluation method is used to determine the road sand accumulation risk level.

4. A method for predicting road sediment concentration as claimed in claim 1, characterized in that: The computational fluid dynamics simulation software is Ansys software.

5. A method for predicting road sediment concentration as claimed in claim 1, characterized in that: The cross-section forms include embankment, cutting, and half embankment and half cutting. The factors affecting the sand accumulation on the road include wind speed, wind direction, roadbed slope ratio, roadbed height, and road width.

6. A method for predicting road sediment concentration as claimed in claim 1, characterized in that: The specific steps of establishing the regression prediction model of influencing factors and road sand concentration are as follows: respectively establishing the optimal univariate nonlinear regression model of each influencing factor and road sand concentration, using data statistical analysis software to perform multivariate linear regression on the optimal univariate regression nonlinear model of road sand concentration for each influencing factor, and establishing the regression prediction model of each influencing factor and road sand concentration.

7. A method for predicting road sediment concentration as claimed in claim 4, characterized in that: The road model is constructed by using Ansys software in the form of point-line-surface, and the road model is determined by the cross-section form; the parameters of the road model include wind-sand flow parameters and model processing parameters.

8. A method for predicting road sediment concentration as claimed in claim 6, characterized in that: The data statistical analysis software is SPSS software.

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