CN116150928B - Intelligent generation and optimization method for road vertical section based on Monte Carlo simulation - Google Patents
Intelligent generation and optimization method for road vertical section based on Monte Carlo simulation Download PDFInfo
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Abstract
The invention discloses a method for intelligently generating and optimizing a longitudinal section of a road based on Monte Carlo simulation, which relates to the technical field of road design, and comprises the steps of determining all possible local extremum points according to slope changes of all points in a topographic graph of the road, dividing the road into a plurality of road sections according to positions of every two local extremum points, generating a design line mode with minimum filling total earth area in each road section by using Monte Carlo simulation technology, splicing each two road sections into a splicing area by using a mode of connecting vertical curves with minimum slope changes, and splicing the splicing areas in pairs to obtain a new splicing area until the splicing area covers a complete road; the generated design line and vertical curve are road longitudinal section patterns; the flexibility and the generation efficiency of the vertical section generation process are enhanced.
Description
Technical Field
The invention relates to the technical field of road design, in particular to a method for intelligently generating and optimizing a road vertical section based on Monte Carlo simulation.
Background
Earthwork costs are one of the major cost items of highway construction projects. The amount of earthwork and the cost of earthwork are primarily dependent on the geometry of the road vertical line; the traditional road vertical section design method has the defects that the scheme is greatly influenced by human subjective factors, the working strength is high, the design is complicated, and the formed scheme is not an optimal scheme in economy and technology;
the existing design of the road vertical section by using the computer technology comprises explicit enumeration, numerical search, dynamic programming, linear programming, genetic algorithm and the like; the method has the advantages that the whole road is used as a problem space, so that when a local road has special limit, the method cannot be flexibly dealt with, the combination of the variable slope points in the road is required to be subjected to exhaustive traversal, and the generated variable data and the number of the variable slope points are exponentially increased, so that the method is not suitable for the problem of generating the longitudinal section of a long-distance road;
therefore, a method for intelligently generating and optimizing the road vertical section based on Monte Carlo simulation is provided.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the method for intelligently generating and optimizing the road vertical section based on the Monte Carlo simulation, which enhances the flexibility and the generation efficiency of the vertical section generating process.
To achieve the above object, an embodiment according to a first aspect of the present invention proposes a method for intelligent generation optimization of a road profile based on monte carlo simulation, comprising the steps of:
step one: acquiring road related parameters of the road according to the properties of the road and the road design speed;
the road related parameters comprise maximum longitudinal waves, shortest slope lengths and maximum composite slope data;
step two: drawing an actual topography curve graph of the road vertical section according to the road coordinate data acquired by field measurement; the initial position of the road is taken as an origin, and a plane XY rectangular coordinate system is established; wherein the x-axis is the advancing direction of the road, and the y-axis represents the height of each point on the road;
step three: traversing all local extremum points in the topographic map, and screening out the local extremum points according to the distance between each pair of adjacent local extremum points;
step four: dividing the topographic map into a plurality of discrete areas uniformly along the x-axis direction by a preset discrete interval h, and taking the midpoint of each discrete area as a candidate slope change point;
step five: dividing a topographic map into a plurality of road segments according to the positions of local extreme points, wherein each road segment comprises two local extreme points;
step six: generating two convex optimization models according to the actual topography graph of each road section with the aim of minimizing the filling engineering quantity, solving each convex optimization model by using a Monte Carlo simulation method, and selecting two or three of candidate slope changing points of the road section as slope changing points according to the solved solution set;
step seven: selecting two adjacent road segments as splicing areas in a non-overlapping mode along the positive direction sequence of the x axis of the coordinate system; it will be appreciated that co-generationA plurality of splice areas; wherein N is the number of road segments;
step eight: for each splicing area, taking the minimum gradient change value as a target, and respectively selecting a candidate slope change point from the last design line of the left road section and the first design line of the right road section as a starting point and an ending point of a vertical curve; designing a vertical curve for the selected starting point and the selected ending point according to the road design standard; it will be appreciated that the vertical curve can be used as a connection section between every two road sections;
step nine: continuously selecting two adjacent splicing areas as new splicing areas in a non-overlapping mode along the positive direction of the x-axis in sequence, and repeating the step eight until the whole road is used as one splicing area; it will be appreciated that the number of repetitions of step eight is at mostSecondary times;
step ten: the design line in each road section and the vertical curve between every two road sections are taken as the final road longitudinal section graph.
The selection mode of traversing all local extreme points in the topographic map is as follows:
step value s, slope change threshold C and shortest slope change distance D are set in advance according to actual experience; marking the total length of the road as L; it should be noted that s < < discrete interval h, and shortest slope change distance > > discrete interval h;
calculating the slope ck of points with the coordinates of s, 2*s and 3*s … K x s on the topographic map respectively; marking the y-axis coordinate of each point as yk; wherein k is a positive integer, andthe method comprises the steps of carrying out a first treatment on the surface of the Wherein K satisfies->The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the calculation formula of the slope is: />；
Screening out |c (k+1) -ck| from all points<C, marking the selected point set as P, wherein P is the set of all local extreme points; the number of points contained in the point set P is marked as M, the x-axis coordinate of each point in the point set P is marked as xpm, and the y-axis coordinate is marked as ypm; wherein m is a positive integer, andthe method comprises the steps of carrying out a first treatment on the surface of the It can be understood that the slope change exceeds the slope change threshold C, indicating that an inflection point, i.e., a local extremum point, appears in the road; obviously, the time complexity required for this search mode is a constant time associated with the K value;
for the screened point set, judging whether each point m needs to be removed or not in sequence according to the sequence from small to large of the x-axis coordinates of the points; the way to determine if the point m needs to be removed is: if the x-axis distance between the point m+1 and the point m is smaller than the shortest slope change distance D, removing the point m from the point set P, and updating the number of each point in the point set P again to ensure the continuity of the number;
further, the terrain graph is divided into a plurality of road segments by:
the first discrete area of the first road section is the first discrete area, and the final discrete area is the first discrete areaThe discrete areas of the end point are positioned at the midpoint between the third local extreme point and the second local extreme point;
the initial discrete area of each road section is the firstA plurality of discrete areas; the end discrete region is->A plurality of discrete areas; wherein i=1, 2, … I; wherein I is the number of road segments; each discrete area comprises two local extreme points, so that each road section comprises three curve roads with different gradients;
the method for generating two convex optimization models and solving by using the Monte Carlo simulation method comprises the following steps:
the two generated convex optimization models comprise a first convex optimization model and a second convex optimization model, wherein the first convex optimization model aims at selecting two candidate slope changing points as slope changing points, and a design line is arranged between the two selected slope changing points and is a design line corresponding to the road section; the second convex optimization model selects three candidate slope changing points as slope changing points, sets two design lines according to the sequence from small to large of x-axis coordinates, marks the land bulge above the design lines as excavated earthwork, marks the concave area below the design lines as filled earthwork, and marks the excavated earthwork area and the filled earthwork area as total earthwork to be filled; the total soil area to be filled corresponds to the filling engineering quantity, and the larger the total soil area to be filled is, the larger the corresponding filling engineering quantity is;
the first convex optimization model sets a binary variable for the candidate slope change point in each discrete area in the road section; when the variable value is 0, the candidate slope change point is not a slope change point; when the variable value is 1, the candidate slope changing point is indicated to be a slope changing point; the first convex optimization model takes the total earth area to be excavated as an optimization target, takes the first slope changing point at the left side of the first local extremum point, the second slope changing point at the right side of the second local extremum point, the number of the slope changing points is 2, the gradient of the design line meets the maximum longitudinal slope limit, the gradient length meets the shortest gradient length limit and the synthesized gradient meets the maximum synthesized gradient limit as limiting conditions;
the second convex optimization model sets a binary variable for the candidate slope change point in each discrete area in the road section; the second convex optimization model takes the total earth area to be filled as an optimization target; the first variable slope point is positioned at the left side of the first local extreme point, the second variable slope point is positioned between the first local extreme point and the second local extreme point, and the third variable slope point is positioned at the right side of the second local extreme point; the number of the variable slope points is 3, and the gradient of each design line meets the maximum longitudinal gradient limit, the gradient length meets the shortest gradient length limit and the synthesized gradient meets the maximum synthesized gradient limit as limiting conditions;
randomly simulating the first convex optimization model and the second convex optimization model by using a Monte Carlo method to obtain optimal solutions of the first convex optimization model and the second convex optimization model, comparing the optimal solutions, and selecting a solution with smaller total earth area as a decision result of a slope changing point of the road section; the decision result of the variable slope point of the road section refers to selecting a candidate variable slope point with a binary variable value of 1 from the optimal solution as the variable slope point;
the mode of respectively selecting a candidate slope changing point from the last design line of the left road section and the first design line of the right road section as the starting point and the ending point of the vertical curve is as follows:
traversing all combinations of candidate variable slope points of a discrete area covered by the last design line of the left road section and candidate variable slope points of a discrete area covered by the first design line of the right road section in each splicing area; each combination comprises two candidate slope changing points, and a left starting point and a right ending point can form a design line; selecting a combination which meets the maximum longitudinal slope, the shortest slope length and the maximum composite slope limit from all combinations and has the minimum slope change value; the selection mode with the minimum gradient change value is to select a design line with the minimum gradient change value r; the calculation formula of the gradient change value r is r= |n-n1|+|n-n2|; the method comprises the steps of carrying out a first treatment on the surface of the Wherein n1 and n2 are the slopes of the last design line of the left road section and the first design line of the right road section respectively; n is the slope of the design line connecting the two road segments.
Compared with the prior art, the invention has the beneficial effects that:
according to the road map construction method, all possible local extremum points are determined according to slope changes of various points in the road map, the road is divided into a plurality of road sections according to positions of every two local extremum points, a design line mode with minimum total earth area is generated in each road section by utilizing Monte Carlo simulation technology, each two road sections are spliced into a splicing area in a mode of connecting vertical curves with minimum slope changes, and the splicing areas are spliced pairwise to obtain a new splicing area until the splicing area covers a complete road;
1. for partial sections with special restrictions, the method can be added in a convex optimization model in the road section, and can also be restricted in the traversal of the starting point and the end point of the connecting vertical curve of the splicing area, so that the generation scheme of the whole vertical section has flexibility and dynamics, and can meet the special requirements of various roads;
2. because the invention involves two or three points in the road section, the number of the variables of the required convex optimization is polynomial level, and the operation speed is far higher than that of an exponential algorithm; furthermore, the road sections and the splicing areas are spliced pairwise, so that the required splicing times are in logarithmic level; in summary, the algorithm of the invention is polynomial level in operation complexity and time complexity, and the operation efficiency and the required calculation power are far lower than those of other exponential level algorithms.
Drawings
FIG. 1 is a flow chart of intelligent generation and optimization steps for a road profile in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Earthwork costs are one of the major cost items of highway construction projects. The amount of earthwork and the cost of earthwork are primarily dependent on the geometry of the road vertical line; the traditional road vertical section design method has the defects that the scheme is greatly influenced by human subjective factors, the working strength is high, the design is complicated, and the formed scheme is not an optimal scheme in economy and technology;
as shown in fig. 1, the method for intelligently generating and optimizing the road profile based on the monte carlo simulation comprises the following steps:
step one: acquiring road related parameters of the road according to the properties of the road and the road design speed;
it is understood that in the road design specification, roads are divided into highways and urban roads; the design speed is set in both the highway and the urban road; in roads or urban roads of different design speeds, the maximum longitudinal waves of the roads are different; in order to ensure the safety of the automobile in the driving process and the comfort of passengers, the shortest slope length of the road is further limited;
preferably, the road related parameters include maximum longitudinal wave, shortest slope length and maximum synthetic slope data; wherein, the longitudinal slope refers to the ratio of the height between two points of the same slope section on the longitudinal section of the road line to the horizontal distance thereof; the slope length refers to the length between two adjacent slope changing points on the longitudinal section of the road; the composite gradient is the gradient formed by the longitudinal slope and the super-high transverse slope of the route on the road section with super-high speed;
step two: drawing an actual topography curve graph of the longitudinal section of the road according to the road coordinate data acquired by field measurement; the initial position of the road is taken as an origin, and a plane XY rectangular coordinate system is established; wherein the x-axis is the advancing direction of the road, and the y-axis represents the height of each point on the road;
step three: traversing all local extremum points in the topographic map, and screening out the local extremum points according to the distance between each pair of adjacent local extremum points;
step four: dividing the topographic map into a plurality of discrete areas uniformly along the x-axis direction by a preset discrete interval h, and taking the midpoint of each discrete area as a candidate slope change point; preferably, the discrete interval h may be set to 0.1 meter;
step five: dividing a topographic map into a plurality of road segments according to the positions of local extreme points, wherein each road segment comprises two local extreme points;
step six: for each road section, generating two convex optimization models with the aim of minimizing the filling engineering quantity, solving by using a Monte Carlo simulation method, and selecting two or three of candidate slope changing points of the road section as slope changing points; it can be understood that a line segment can be formed between every two slope changing points, and the line segment is the design line of the road;
step seven: selecting two adjacent road segments as splicing areas in a non-overlapping mode along the positive direction sequence of the x axis of the coordinate system; it will be appreciated that co-generationA plurality of splice areas; wherein N is the number of road segments;
step eight: for each splicing area, taking the minimum gradient change value as a target, and respectively selecting a candidate slope change point from the last design line of the left road section and the first design line of the right road section as a starting point and an ending point of a vertical curve; designing a vertical curve for the selected starting point and the selected ending point according to the road design standard; it will be appreciated that the vertical curve can be used as a connection section between every two road sections;
step nine: continuously selecting two adjacent splicing areas as new splicing areas in a non-overlapping mode along the positive direction of the x-axis in sequence, and repeating the step eight until the whole road is used as one splicing area; it will be appreciated that the number of repetitions of step eight is at mostSecondary times;
step ten: the design line in each road section and the vertical curve between every two road sections are taken as the final road longitudinal section graph.
Preferably, the selection mode of all local extreme points in the traversing topography graph is as follows:
step value s, slope change threshold C and shortest slope change distance D are set in advance according to actual experience; marking the total length of the road as L; it should be noted that s < < discrete interval h, and shortest slope change distance > > discrete interval h;
calculating the slope ck of points with the coordinates of s, 2*s and 3*s … K x s on the topographic map respectively; marking the y-axis coordinate of each point as yk; wherein k is a positive integer, andthe method comprises the steps of carrying out a first treatment on the surface of the Wherein K satisfies->The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the calculation formula of the slope is: />；
Screening out |c (k+1) -ck| from all points<C, marking the selected point set as P, wherein P is the set of all local extreme points; the number of points contained in the point set P is marked as M, the x-axis coordinate of each point in the point set P is marked as xpm, and the y-axis coordinate is marked as ypm; wherein m is a positive integer, andthe method comprises the steps of carrying out a first treatment on the surface of the It can be understood that the slope change exceeds the slope change threshold C, indicating that an inflection point, i.e., a local extremum point, appears in the road; obviously, the searching mode of the local extreme point only needs to traverse all K points once, so the required time complexity is constant time related to the K value;
further, in order to avoid the influence of small protrusions on the result in the road, for the screened point set, judging whether each point m needs to be removed or not in sequence from small to large according to the x-axis coordinates of the points; preferably, the manner of determining whether the point m needs to be removed is as follows: if the x-axis distance between the point m+1 and the point m is smaller than the shortest slope change distance D, removing the point m from the point set P, and updating the number of each point in the point set P again to ensure the continuity of the number;
further, the method for dividing the topographic map into a plurality of road segments according to the positions of the local extreme points is as follows:
the first discrete area of the first road section being the first discrete area of which the end point isDiscrete area is the firstThe discrete areas of the end point are positioned at the midpoint between the third local extreme point and the second local extreme point;
the initial discrete area of each road section is the firstA plurality of discrete areas; the end discrete region is->A plurality of discrete areas; wherein i=1, 2, … I; wherein I is the number of road segments; it can be understood that each discrete area contains two local extreme points, and thus, each road section contains three curved roads with different gradients;
still further, two convex optimization models are generated and solved by using the Monte Carlo simulation method in the following manner:
the two generated convex optimization models comprise a first convex optimization model and a second convex optimization model, wherein the first convex optimization model aims at selecting two candidate slope changing points as slope changing points, and a design line is arranged between the two selected slope changing points and is a design line corresponding to the road section; the second convex optimization model selects three candidate slope changing points as slope changing points, and sets two design lines according to the sequence from small to large of x-axis coordinates;
marking the land bulge above the design line as an excavated earthwork, marking the concave area below the design line as a filled earthwork, and marking the excavated earthwork area and the filled earthwork area as the total earthwork to be filled; the total soil area to be filled corresponds to the filling engineering quantity, and the larger the total soil area to be filled is, the larger the corresponding filling engineering quantity is;
the first convex optimization model sets a binary variable for the candidate slope change point in each discrete area in the road section; when the variable value is 0, the candidate slope change point is not a slope change point; when the variable value is 1, the candidate slope changing point is indicated to be a slope changing point; the first convex optimization model takes the total earth area to be excavated as an optimization target, takes the first slope changing point at the left side of the first local extremum point, the second slope changing point at the right side of the second local extremum point, the number of the slope changing points is 2, the gradient of the design line meets the maximum longitudinal slope limit, the gradient length meets the shortest gradient length limit and the synthesized gradient meets the maximum synthesized gradient limit as limiting conditions;
the second convex optimization model sets a binary variable for the candidate slope change point in each discrete area in the road section; the second convex optimization model takes the total earth area to be filled as an optimization target; the first variable slope point is positioned at the left side of the first local extreme point, the second variable slope point is positioned between the first local extreme point and the second local extreme point, and the third variable slope point is positioned at the right side of the second local extreme point; the number of the variable slope points is 3, and the gradient of each design line meets the maximum longitudinal gradient limit, the gradient length meets the shortest gradient length limit and the synthesized gradient meets the maximum synthesized gradient limit as limiting conditions; preferably, the total earth area calculates the area above the design line to be excavated and the area below the design line to be filled by using a calculus technology in an interweaving mode of the topographic map and the design line;
in a further embodiment of the present invention, if the engineering party has other special restrictions on the road in a certain road section, the restrictions may be made by adding restriction conditions in the first convex optimization model and the second convex optimization model;
randomly simulating the first convex optimization model and the second convex optimization model by using a Monte Carlo method to obtain optimal solutions of the first convex optimization model and the second convex optimization model, comparing the optimal solutions, and selecting a solution with smaller total earth area as a decision result of a slope changing point of the road section; the decision result of the variable slope point of the road section refers to selecting a candidate variable slope point with a binary variable value of 1 from the optimal solution as the variable slope point; it can be understood that the generated design line may have a slope length greater than the maximum slope length, and in the actual road construction process, gentle slope sections may be added to the road section to avoid the situation;
preferably, the mode of selecting a candidate slope change point from the last design line of the left road section and the first design line of the right road section as a starting point and an ending point of the vertical curve is as follows:
traversing all combinations of candidate variable slope points of a discrete area covered by a last design line of a left road section and candidate variable slope points of a discrete area covered by a first design line of a right road section in each splicing area, wherein it can be understood that each combination comprises two candidate variable slope points, and a left starting point and a right ending point can form one design line; selecting a combination which meets the maximum longitudinal slope, the shortest slope length, the maximum synthetic slope limit and the special limit from all combinations and has the minimum slope change value; the selection mode with the minimum gradient change value is to select a design line with the minimum gradient change value r; the calculation formula of the gradient change value r is r= |n-n1|+|n-n2|; the method comprises the steps of carrying out a first treatment on the surface of the Wherein n1 and n2 are the slopes of the last design line of the left road section and the first design line of the right road section respectively; n is the slope of the design line connecting the two road segments; it will be appreciated that the minimal change in grade means that the passenger is required to bear the least amount of jolt; wherein the special limit is other limit conditions set by the engineering party on the road section.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The intelligent generation and optimization method for the road vertical section based on Monte Carlo simulation is characterized by comprising the following steps of:
step one: acquiring road related parameters of the road according to the properties of the road and the road design speed;
step two: drawing an actual topography curve graph of the road vertical section according to the road coordinate data acquired by field measurement; the initial position of the road is taken as an origin, and a plane XY rectangular coordinate system is established; wherein the x-axis is the advancing direction of the road, and the y-axis represents the height of each point on the road;
step three: traversing all local extreme points in the topographic map;
step four: dividing the topographic map into a plurality of discrete areas uniformly along the x-axis direction by a preset discrete interval h, and taking the midpoint of each discrete area as a candidate slope change point;
step five: dividing a topographic map into a plurality of road segments according to the positions of local extreme points, wherein each road segment comprises two local extreme points;
step six: generating two convex optimization models according to the actual topography graph of each road section with the aim of minimizing the filling engineering quantity, solving each convex optimization model by using a Monte Carlo simulation method, and selecting two or three of candidate slope changing points of the road section as slope changing points according to the solved solution set;
step seven: selecting two adjacent road segments as splicing areas in a non-overlapping mode along the positive direction sequence of the x axis of the coordinate system;
step eight: for each splicing area, selecting a combination with a minimum gradient change value, and respectively selecting a candidate variable slope point from the last design line of the left road section and the first design line of the right road section as a starting point and an ending point of the vertical curve according to the combination of the minimum gradient change value;
step nine: continuously selecting two adjacent splicing areas as new splicing areas in a non-overlapping mode along the positive direction of the x-axis in sequence, and repeating the step eight until the whole road is used as one splicing area;
step ten: the design line in each road section and the vertical curve between every two road sections are taken as the final road longitudinal section graph.
2. The method for intelligently generating an optimization of a road profile based on monte carlo simulation according to claim 1, wherein the road related parameters include maximum longitudinal wave, shortest slope length and maximum synthetic slope data.
3. The method for intelligently generating and optimizing road vertical section based on Monte Carlo simulation according to claim 2, wherein the selection mode of traversing all local extreme points in the topographic map is as follows:
step value s, slope change threshold C and shortest slope change distance D are set in advance according to actual experience; marking the total length of the road as L;
calculating the slope ck of points with the coordinates of s, 2*s and 3*s … K x s on the topographic map respectively; marking the y-axis coordinate of each point as yk; wherein k is a positive integer, andthe method comprises the steps of carrying out a first treatment on the surface of the Wherein K satisfies->The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the calculation formula of the slope is: />；
Points of |c (k+1) -ck| < C are selected from all points, and the selected point set is marked as P, wherein P is the set of all local extreme points.
4. A method of intelligent road profile generation optimization based on monte carlo simulation according to claim 3, wherein the terrain graph is divided into road segments by:
marking the number of points contained in the point set P as M; marking the x-axis coordinate of each point in the point set P as xpm and the y-axis coordinate as ypm; wherein m is a positive integer, and；
the first discrete area of the first road section is the first discrete area, and the final discrete area is the first discrete areaDiscrete areas, saidThe discrete region of the end point is at the midpoint position of the third local extreme point and the second local extreme point;
the initial discrete area of each road section is the firstA plurality of discrete areas; the end discrete region is->A plurality of discrete areas; wherein i=1, 2, … I; where I is the number of road segments.
5. The method for intelligently generating and optimizing road vertical section based on Monte Carlo simulation according to claim 4, wherein two convex optimization models are generated and solved by using Monte Carlo simulation method, the method is as follows:
the two generated convex optimization models comprise a first convex optimization model and a second convex optimization model, wherein the first convex optimization model aims at selecting two candidate variable slope points as variable slope points, and a design line is arranged between the two selected variable slope points; the second convex optimization model selects three candidate slope changing points as slope changing points, marks the land bulge above the design line as an excavated earthwork, marks the concave area below the design line as a filled earthwork, marks the excavated earthwork area and the filled earthwork area as the total earthwork area to be filled, and the total earthwork area to be filled corresponds to the filling engineering quantity;
randomly simulating the first convex optimization model and the second convex optimization model by using a Monte Carlo method to obtain optimal solutions of the first convex optimization model and the second convex optimization model, comparing the total earth area to be filled, and selecting the optimal solution with smaller total earth area to be filled as a decision result of a slope changing point of the road section; the decision result of the variable slope point of the road section refers to selecting a candidate variable slope point with a binary variable value of 1 from the optimal solution as the variable slope point.
6. The method for intelligently generating an optimization for a road profile based on monte carlo simulation according to claim 5, wherein the first convex optimization model sets a binary variable for candidate slope change points in each discrete region in the road segment; when the variable value is 0, the candidate slope change point is not a slope change point; when the variable value is 1, the candidate slope changing point is indicated to be a slope changing point; the first convex optimization model takes the total earth area to be filled as an optimization target, the first slope changing point is positioned at the left side of the first local extremum point, the second slope changing point is positioned at the right side of the second local extremum point, the number of the slope changing points is 2, the gradient of the design line meets the maximum longitudinal gradient limit, the gradient length meets the shortest gradient length limit and the synthetic gradient meets the maximum synthetic gradient limit as limiting conditions.
7. The method for intelligently generating and optimizing a road profile based on monte carlo simulation according to claim 6, wherein the second convex optimization model sets a binary variable for candidate slope change points in each discrete area in the road section; the second convex optimization model takes the total earth area to be filled as an optimization target; the first variable slope point is positioned at the left side of the first local extreme point, the second variable slope point is positioned between the first local extreme point and the second local extreme point, and the third variable slope point is positioned at the right side of the second local extreme point; the number of the variable slope points is 3, and the gradient of each design line meets the maximum longitudinal gradient limit, the gradient length meets the shortest gradient length limit and the composite gradient meets the maximum composite gradient limit as limiting conditions.
8. The method for intelligently generating and optimizing a road profile based on monte carlo simulation according to claim 7, wherein the mode of selecting one candidate slope change point from the last design line of the left road segment and the first design line of the right road segment as the starting point and the ending point of the vertical curve is as follows:
traversing all combinations of candidate variable slope points of a discrete area covered by the last design line of the left road section and candidate variable slope points of a discrete area covered by the first design line of the right road section in each splicing area;
each combination comprises two candidate slope changing points, and a left starting point and a right ending point form a design line for connecting two road sections;
selecting a combination which meets the maximum longitudinal slope, the shortest slope length and the maximum composite slope limit from all combinations and has the minimum slope change value; the selection mode with the minimum gradient change value is to select a design line with the minimum gradient change value r; the calculation formula of the gradient change value r is r= |n-n1|+|n-n2|; wherein n1 and n2 are the slopes of the last design line of the left road section and the first design line of the right road section respectively; n is the slope of the design line connecting the two road segments.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205240A (en) * | 2015-09-14 | 2015-12-30 | 北方工业大学 | Method for automatic fitting, automatic optimization and interactive design of subway longitudinal section |
| CN106250615A (en) * | 2016-07-26 | 2016-12-21 | 铁道第三勘察设计院集团有限公司 | Circuit vertical section design method before laying a railway track |
| CN113722917A (en) * | 2021-09-01 | 2021-11-30 | 中山大学 | Thermal history simulation method and system based on fission track length distribution on vertical section |
| CN115718985A (en) * | 2022-11-17 | 2023-02-28 | 华设设计集团股份有限公司 | Reconstruction and extension highway model optimization method based on GIS |
-
2023
- 2023-04-14 CN CN202310398372.3A patent/CN116150928B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205240A (en) * | 2015-09-14 | 2015-12-30 | 北方工业大学 | Method for automatic fitting, automatic optimization and interactive design of subway longitudinal section |
| CN106250615A (en) * | 2016-07-26 | 2016-12-21 | 铁道第三勘察设计院集团有限公司 | Circuit vertical section design method before laying a railway track |
| CN113722917A (en) * | 2021-09-01 | 2021-11-30 | 中山大学 | Thermal history simulation method and system based on fission track length distribution on vertical section |
| CN115718985A (en) * | 2022-11-17 | 2023-02-28 | 华设设计集团股份有限公司 | Reconstruction and extension highway model optimization method based on GIS |
Non-Patent Citations (2)
| Title |
|---|
| Analysis of langevin monte carlo via convex optimization;Alain Durmus 等;《The Journal of Machine Learning Research》;第20卷(第1期);2666–2711 * |
| 工矿企业厂外道路纵断面优化设计研究;王秋平 等;《西安建筑科技大学学报(自然科学版)》;第44卷(第1期);14-19 * |
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