Method for fine optimization of double-line highway tunnel route under dense constraints, medium and electronic device

Abstract

This invention relates to the field of highway alignment technology, and discloses a method, medium, and electronic equipment for fine-grained route optimization of dual-track highway tunnels under dense constraints. The method establishes a comprehensive geographic information model by collecting information data required for route optimization; selects a set of design variables for dual-track highways; establishes basic objective functions for tunnel portals, road-tunnel connection sections, tunnel bodies, and highway pavements based on construction costs; clarifies the characterization methods of structural constraints, regional constraints, and geometric constraints; and thus constructs a fine-grained route optimization model for dual-track highway tunnels under dense constraints. A dual-track particle swarm optimization algorithm is proposed, and an iterative evolution mechanism is constructed for the main baseline and accompanying secondary baselines to achieve accurate solution of the optimization model and obtain a comprehensive optimized solution for the dual-track tunnel route. This method has high operating efficiency and can effectively solve the problems of site selection and alignment optimization for dual-track tunnels in complex terrain, significantly improving the quality and efficiency of route design.

Description

Technical Field

[0001] This invention relates to the field of highway alignment technology, specifically to a method, medium, and electronic equipment for fine-grained route optimization of a dual-track highway tunnel under dense constraints. Background Technology

[0002] Due to their advantages of high traffic efficiency and strong risk resistance, dual-track highway tunnels have become the core structure for overcoming terrain obstacles and ensuring route continuity. The alignment design of dual-track highway tunnels should take two sets of intersections (JD) as the local control core, and simultaneously coordinate the cost optimization of earthwork excavation and roadbed and pavement construction at the tunnel portal and road connection sections. It should also meet the requirements of surrounding rock support (e.g., for Class I-III surrounding rock, the support cost is calculated based on the excavated slope area, and for Class IV and below, retaining walls are required), tunnel burial depth (the minimum burial depth Hmin is determined based on the surrounding rock grade and surface load, and the maximum burial depth Hmax is determined based on construction feasibility), and the net distance between the left and right lines (e.g., not more than 20m and preferably not more than 15m). At the same time, it is necessary to minimize the disturbance to the mountain excavation (based on engineering practice, it is generally recommended that the angle of oblique intersection between the tunnel start and end line and the slope surface be ≥30°).

[0003] However, relevant research and practice at home and abroad face significant difficulties: In terms of cost analysis, existing route selection and design methods mostly focus on the engineering cost calculation of the tunnel body section of a single-track tunnel, lacking analysis of the coupling between dual-track schemes, as well as accurate modeling of the relationship between the portal structure (such as using a straight-cut open tunnel when the slope is gentle, and using a ring-frame / end-wall / wing-wall portal when the slope is steep), the earthwork volume of the road-tunnel transition section, and the route selection and design parameters; In terms of constraint handling, hard constraints (avoidance of restricted areas, burial depth, etc.) are mostly handled using the "first determine the route, then verify" mode, which has poor dynamic adaptation and is prone to design rework; soft constraints (such as eccentric earth pressure difference, slope stability, etc.) rely on manual experience and lack quantitative mechanisms.

[0004] In summary, existing route optimization design methods have significant shortcomings in cost calculation and multi-constraint handling of double-track tunnel portals. There is an urgent need to construct a system optimization method that integrates variable parameters, objective functions, and multiple constraints to provide technical support for the fine and intelligent optimization of double-track tunnel routes under dense constraints. Summary of the Invention

[0005] The purpose of this invention is to provide a refined route optimization method for dual-track highway tunnels under dense constraints, addressing the significant shortcomings of existing route optimization design methods in areas such as cost calculation for tunnel portals and handling of multiple constraints. The specific technical solution is as follows:

[0006] A method for fine-grained route optimization of a dual-track highway tunnel under dense constraints includes the following steps:

[0007] S1. Establish a comprehensive geographic information model, specifically: collect information data required for route optimization; divide the route selection study area into a series of regular grids, and discretize the information data required for route optimization into the grids to establish a comprehensive geographic information model;

[0008] S2. Establish a mathematical optimization model, specifically: determine the design variables for the dual-line position; establish basic objective functions for the portal, road-tunnel connection section, tunnel body, and highway pavement based on construction costs; and determine dense constraint conditions based on structural constraints, regional constraints, and geometric constraints.

[0009] S3. The mathematical optimization model obtained in S2 is solved using the dual-line particle swarm optimization method, and the intelligent optimization solution of the dual-line highway tunnel route is output.

[0010] The dual-line particle swarm optimization method includes the following steps:

[0011] S3-1. Taking the left line of the road as the main baseline, define the route scheme of the left line of the road as the main particle. At the same time, taking the right line of the road as the secondary line, define the route scheme of the right line of the road as the secondary particle. Each main particle corresponds to multiple secondary particles. There is no correlation between the secondary particles of different main particles.

[0012] S3-2. Set the size of the main particle swarm, randomly initialize the positions and velocities of each particle in the main swarm, with each particle position representing a left-hand path scheme; determine the number of iterations for the main particle swarm. =1;

[0013] S3-3. Calculate the objective function value corresponding to each particle in each current main particle group, that is, the construction cost function value;

[0014] For each main particle, set the size of the accompanying particle swarm, randomly initialize the positions and velocities of each particle in the accompanying swarm, and each particle position represents a right-hand path; take the number of iterations of the accompanying particles. =1;

[0015] S3-4. Considering the constraint relationship between the main particle and the companion particle, calculate the objective function value corresponding to the position of each particle in the companion particle group corresponding to each main particle, that is, the construction cost function value.

[0016] S3-5. Optimize the individual best solution for each associated particle (pbest). B And the globally optimal solution gbest B ;

[0017] S3-6, using the pbest of the companion particles corresponding to each current main particle. B and gbest BTo determine the direction of evolution, update the particle positions in each current associated particle population and optimize the associated path scheme group;

[0018] S3-7. Make a judgment. If the iteration condition is met, output the gbest value of the companion particle corresponding to each main particle. B If the current baseline for each subject corresponds to a refined intelligent optimization solution for the associated secondary route scheme, proceed to the next step; otherwise, take... Return to steps S3-4;

[0019] S3-8: Combine the objective function calculation results of each current main particle with its corresponding companion particle gbest B Combining these factors, the best pbest of each host particle is selected. Z and gbest Z ;

[0020] S3-9, using the current main particle's pbest Z and gbest Z To determine the direction of evolution, update the particle positions in each main particle population and optimize the main route scheme group;

[0021] S3-10. Make a judgment. If the iteration condition is met, output the gbest value of the main particle. Z and its corresponding associated particles gbest B As the intelligent optimization solution for the dual-track highway tunnel route; if not, then take Return to step S3-3.

[0022] Preferably, in S1: the information data required for route optimization includes major technical standards, terrain, geological disaster areas, surface cover, land price, and engineering unit price information; the specific iterative conditions in S3-7 and S3-10 are: the globally optimal solution remains unchanged after 100 consecutive generations of evolution.

[0023] Preferably, the specific design variables for the dual-line position are determined in S2 as follows:

[0024] Highway routes are described using the parameters of horizontal intersection (HPI) and slope change point (VPI).

[0025] The dual-line design variables include the radius of the circular curve for each plane intersection point HPI. Long transition curve Intersection coordinates Vertical curve radius of VPI at each slope change point mileage of station and design elevation .

[0026] Preferably, the alignment optimization problem for dual-track highway tunnels simplifies to finding the following vector set:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] in: This represents the number of intersections on the route. The number of slope change points in the longitudinal profile of the route; , , , These are the main baseline and the associated secondary line, respectively. The coordinates of the intersection points of the routes on the plane; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the plane of the route; , These are the main baseline and the associated secondary line, respectively. The length of the horizontal transition curve on the route; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the longitudinal profile of the route; , These are the main baseline and the associated secondary line, respectively. Mileage of the longitudinal section slope change points of the route; , These are the main baseline and the associated secondary line, respectively. Elevation of the slope change points in the longitudinal profile of the route; This is the set of planar coordinates of the accompanying secondary route; This is the set of radii of the planar circular curves associated with the secondary routes; This is the set of lengths of the horizontal transition curves of the accompanying secondary routes; This is the set of radii of the circular curves in the longitudinal profile of the accompanying secondary routes; This is the set of mileages of the longitudinal section slope change points of the associated secondary route; This is a set of elevations of the slope change points in the longitudinal profile of the accompanying secondary route.

[0042] Preferably, the basic objective function expressions for the portal, road-tunnel connection section, tunnel body, and highway pavement are established based on the construction cost as follows:

[0043] ;

[0044] in: Cost of earthwork for the transition section; Cost of the supporting structure; Cost of constructing the tunnel entrance; For the cost of the tunnel body; Cost of highway route construction;

[0045] Transition section earthwork cost Calculate using the following formula:

[0046] ;

[0047] in: This refers to the volume of the excavated slope. Cost per unit volume of excavated slope; This refers to the volume of the fill slope; Cost per unit volume of embankment slope;

[0048] Cost of support structures Calculate using the following formula:

[0049] ;

[0050] in: The area of ​​the supporting structure; The width of the left side of the roadbed;

[0051] Tunnel construction cost Calculate using the following formula:

[0052] ;

[0053] in: The length of the tunnel; Cost per unit length of tunnel;

[0054] Highway route construction cost Calculate using the following formula:

[0055] ;

[0056] in: This refers to the length of the highway route. Cost per unit length of the roadbed section; Cost per unit length of road surface.

[0057] Preferably, in S2:

[0058] Structural constraints include bias constraints, mountain orthogonal constraints, burial depth constraints, and spacing constraints for separated tunnels;

[0059] Regional constraints include restricted area constraints and environmentally sensitive area constraints;

[0060] Geometric constraints include minimum curve radius constraints, maximum slope constraints, and front and rear tangent edge strength constraints.

[0061] Preferably, the bias constraint is the difference in earth pressure between the earthwork on the left and right sides of the ground line along a certain width of the calculation route. ,as follows:

[0062] ;

[0063] in: This represents the earth pressure on the left side; This represents the earth pressure on the right side; This represents the maximum allowable difference in earth pressure under certain conditions. The soil weight; The height of the soil mass; This is the earth pressure coefficient; Earth pressure;

[0064] The orthogonal constraint of the mountain is: calculate the angle between the tunnel entry / exit direction and the slope direction. According to the included angle Whether to impose a penalty constraint on the score value within a certain angle axis, as follows:

[0065] ;

[0066] in: This refers to the maximum prohibited angle under certain conditions.

[0067] The burial depth constraint is: tunnel burial depth and the minimum allowable tunnel burial depth under certain conditions and maximum burial depth The following relationship must be satisfied:

[0068] ;

[0069] The spacing constraint for separated tunnels is: spacing Greater than or equal to the minimum line spacing As shown in the following formula:

[0070] ;

[0071] in: This refers to the width of a single tunnel excavation. For calculating tunnel spacing coefficients;

[0072] The restricted area constraints form the line space of design variables. and restricted areas No intersection, as shown in the following formula:

[0073] ;

[0074] The strong constraints on the front and rear tangent edges are: the front and rear segments are fixed edges, and the middle segment is an optimized edge. The fixed edges need to satisfy the constraints of minimum clamping straight line length, maximum turning angle, minimum curve radius, and minimum circular curve length, that is, the intersection variable slides on the ray edge at the start and end points.

[0075] The present invention also discloses a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described method for fine route optimization of a dual-track highway tunnel under dense constraints.

[0076] The present invention also discloses an electronic device, comprising: a processor and a memory communicatively connected to the processor;

[0077] The memory stores computer-executed instructions;

[0078] The processor executes computer execution instructions stored in the memory to implement the fine route optimization method for dual-track highway tunnels under dense constraints as described above.

[0079] The effect of applying the technical solution of this invention is:

[0080] This invention provides a method for fine-grained route optimization of a dual-track highway tunnel under dense constraints, comprising: establishing a comprehensive geographic information model, specifically: collecting information data required for route optimization; dividing the route selection study area into a series of regular grids and discretizing the information data required for route optimization into the grids to establish a comprehensive geographic information model; establishing a mathematical optimization model, specifically: determining the dual-track design variables; establishing basic objective functions for the tunnel portal, road-tunnel connection section, tunnel body, and highway pavement using construction costs; determining dense constraint conditions based on structural constraints, regional constraints, and geometric constraints; and solving the mathematical optimization model using the dual-track particle swarm optimization method to output an intelligent optimized solution for the dual-track highway tunnel route. This invention addresses the challenge of optimizing the alignment of dual-track highway tunnels under dense constraints. It proposes a systematic technical solution integrating geographic information modeling, mathematical optimization models, and dual-track particle swarm optimization. First, a comprehensive geographic information model is established by collecting data necessary for route optimization, providing data support. Then, a mathematical optimization model is constructed, with dual-track design variables as the core, incorporating tunnel portals, road-tunnel connection sections, tunnel bodies, and highway pavement, and establishing a basic objective function based on construction costs, while also incorporating structural, regional, and geometric constraints. Finally, dual-track particle swarm optimization (DPSO) is used to achieve accurate solution calculation of the optimization model through iterative evolution of the main baseline and accompanying secondary lines, yielding a comprehensive optimized solution for the dual-track tunnel route. This method is highly automated and practical, with high operational efficiency, effectively solving the challenges of portal selection and alignment optimization for dual-track tunnels in complex terrain, significantly improving route design quality and efficiency, providing technical support for the design of dual-track highway tunnels under dense constraints, and possessing significant potential for widespread application.

[0081] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0082] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0083] Figure 1 This is a framework diagram of the route fine optimization method for a dual-track highway tunnel under dense constraints in an embodiment of the present invention;

[0084] Figure 2 yes Figure 1 A schematic diagram illustrating the process of determining the individual optimality and global optimality of the main particle and its companion particles;

[0085] Figure 3 yes Figure 1 A flowchart of a fine-grained route optimization method for dual-track highway tunnels under medium-dense constraints;

[0086] Figure 4 This is a schematic diagram illustrating the calculation of earthwork volume in the transition section in an embodiment of the present invention. Detailed Implementation

[0087] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0088] Example:

[0089] A method for fine-grained route optimization of a dual-track highway tunnel under dense constraints is described in the flowchart below. Figures 1-3 Specifically, it includes the following steps:

[0090] S1. Establish a comprehensive geographic information model.

[0091] The specific steps for establishing a comprehensive geographic information model are: collecting the information data required for route optimization; dividing the route selection study area into a series of regular grids; discretizing the information data required for route optimization into the grids; and establishing a comprehensive geographic information model.

[0092] In this embodiment, the information data required for route optimization includes major technical standards, terrain, geological disaster areas, surface cover, land price, and engineering unit price information; the grid is set within the range of 1.5m to 10m according to the actual case scale.

[0093] S2. Establish a mathematical optimization model.

[0094] Mathematical optimization models are the core logical framework for optimizing the alignment of dual-track highway tunnels. Essentially, they seek the optimal combination of alignment parameters that achieves the best economic objectives under multi-dimensional, densely constrained boundaries. The core elements include design variables, objective functions, and constraints, forming a synergistic relationship of "variables driving the objective, constraints limiting the variables." Specifically: determining the dual-track design variables (i.e., three-dimensional alignment optimization design variables); establishing basic objective functions (i.e., economic evaluation objective functions) for tunnel portals, road-tunnel connection sections, tunnel bodies, and highway pavements based on construction costs; and determining dense constraint conditions (i.e., alignment constraints) based on structural, regional, and geometric constraints.

[0095] Specifically, the design variables for the dual-track alignment are: the highway route is described using the parameters of the horizontal intersection point (HPI) and the slope change point (VPI); the dual-track design variables include the radius of the circular curve at each horizontal intersection point (HPI). Long transition curve Intersection coordinates Vertical curve radius of VPI at each slope change point mileage of station and design elevation .

[0096] Based on this, the alignment optimization problem for a two-lane highway tunnel is simplified to finding the following vector set:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] in: This represents the number of intersections on the route. The number of slope change points in the longitudinal profile of the route; , , , These are the main baseline and the associated secondary line, respectively. The coordinates of the intersection points of the routes on the plane; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the plane of the route; , These are the main baseline and the associated secondary line, respectively. The length of the horizontal transition curve on the route; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the longitudinal profile of the route; , These are the main baseline and the associated secondary line, respectively. Mileage of the longitudinal section slope change points of the route; , These are the main baseline and the associated secondary line, respectively. Elevation of the slope change points in the longitudinal profile of the route; This is the set of planar coordinates of the accompanying secondary route; This is the set of radii of the planar circular curves associated with the secondary routes; This is the set of lengths of the horizontal transition curves of the accompanying secondary routes; This is the set of radii of the circular curves in the longitudinal profile of the accompanying secondary routes; This is the set of mileages of the longitudinal section slope change points of the associated secondary route; This is a set of elevations of the slope change points in the longitudinal profile of the accompanying secondary route.

[0112] In this embodiment, the basic objective function is established as follows:

[0113] A basic objective function is established for the portal, road-tunnel connection section, tunnel body, and highway pavement based on construction costs, and the expression is as follows:

[0114] ;

[0115] in: The cost of earthwork for the transition section (unit: 10,000 yuan); Cost of support structures (unit: 10,000 yuan); Cost of the tunnel entrance (unit: 10,000 yuan); Cost of tunnel body (unit: 10,000 yuan); Cost of highway route (unit: 10,000 yuan).

[0116] Transition section earthwork cost Calculate using the following formula:

[0117] ;

[0118] in: Volume of excavated slope (unit: m) 3 ); Cost per unit volume of excavated slope (unit: 10,000 yuan / m) 3 ); Volume of fill slope (unit: m) 3 ); Cost per unit volume of fill slope (unit: 10,000 yuan / m) 3 Excavation slope volume and fill slope volume See details Figure 4 The following formula is used for calculation:

[0119] ;

[0120] in: The total length of the transition section (unit: m); , These are the widths of the left and right sides of the roadbed, respectively (unit: m). The distance along the route to the starting point of the transition section (in meters) is where ∈[0, ]); The distance (in meters) from the center of the route along its cross-section is given. ∈[- , ]; H (·) represents the terrain elevation function; To design the elevation function. In this embodiment, both the terrain elevation function and the design elevation function are existing ones. This embodiment incorporates transition earthwork into the cost calculation to achieve more refined route selection optimization.

[0121] Cost of support structures Calculate using the following formula:

[0122] ;

[0123] in: Area of ​​the support structure (unit: m²) 2 ); Width of the left side of the roadbed (unit: 10,000 yuan / m) 2 ).

[0124] Area of ​​the support structure in this embodiment Calculate using the following formula:

[0125] ;

[0126] in: The angle between the slope and the horizontal plane (unit: degrees).

[0127] The "Design Specifications for Highway Tunnels, Volume 1: Civil Engineering" (JTG 3370.1-2018) states that the cost of surrounding rock for grades I-III is determined by the area of ​​the excavated slope and the cost of support per unit area. For surrounding rock grades IV and below, retaining walls must be installed, and the calculation should be performed in wing-wall type portal tunnels.

[0128] The cost of the portal is calculated using the following method:

[0129] 1) Gentle slope:

[0130] The tunnel is considered to be a straight-cut tunnel, approximately regarded as a semi-arched ring + straight wall structure, and the slope calculation is based on the slope soil and rock calculation.

[0131] ;

[0132] in: Cost of tunnel portal under gentle slope conditions (unit: 10,000 yuan); The costs are listed separately for the three parts: the tunnel, the slope, and the backfill (unit: 10,000 yuan). The areas of the Myeongdong section are shown in m². 2 ), Length (unit: m), Cost per unit volume (unit: 10,000 yuan / m) 3 ); These are the outer diameter (m), inner diameter (m), width (m), and height (m) of the straight wall on one side of the Myeongdong section, respectively. These are the volumes of the slope section, the backfill excavation section, and the backfill structure (unit: m³). 3 ); Cost per unit volume for backfilling (unit: 10,000 yuan / m²) 3 ); Cost per unit volume of excavated slope (unit: 10,000 yuan / m) 3 ).

[0133] 2) Steep slope:

[0134] Considering the steep slope, the tunnel portal structure is divided into three types according to the surrounding rock grade: Grade I surrounding rock adopts the ring frame type, Grade II-III surrounding rock adopts the end wall type, and Grade IV surrounding rock adopts the wing wall type. In the case of worse surrounding rock properties, specific methods are considered for reinforcement or no tunnel is required. This embodiment does not discuss this situation. At the same time, the tunnel portal is set to be semi-arched, with straight walls and a semi-circle.

[0135] ;

[0136] ;

[0137] ;

[0138] in: Cost of constructing a tunnel entrance under steep slope conditions; The costs (in ten thousand yuan) for tunnels with surrounding rock grades of I, II-III, and IV, respectively, using three types of portals: ring-frame, end-wall, and wing-wall. These are the areas of the tunnel ring section (unit: m²). 2 ), Length (unit: m), Cost per unit volume (unit: 10,000 yuan / m) 3 ); Width of the end wall (unit: m); The width of the end wall is in meters; the end wall is trapezoidal in shape. The height of the end wall is (m). Area of ​​the opening (unit: m²) 2 ); These are the end wall length (unit: m) and the unit volume cost of the end wall (unit: 10,000 yuan / m). 3 ); The slope coefficient (determined by the surrounding rock grade); The radius of the opening (unit: m); These are the width (in meters) and height (in meters) of the opening, respectively. These represent the wing wall length (in meters) and wing wall volume (in cubic meters). 3 ), Unit volume cost of wing walls (unit: 10,000 yuan / m) 3 ); and These are the top and bottom widths of the wing walls, respectively (unit: m).

[0139] Tunnel construction cost Calculate using the following formula:

[0140] ;

[0141] in: Tunnel length (unit: m); Cost per unit length of tunnel (unit: 10,000 yuan / m) 3 );

[0142] Highway route construction cost Calculate using the following formula:

[0143] ;

[0144] in: The length of the highway route (unit: m); Cost per unit length of roadbed (unit: 10,000 yuan / m) 3 ); Cost per unit length of road surface (unit: 10,000 yuan / m) 3 ).

[0145] In this embodiment, the dense constraint conditions are determined based on structural constraints, region constraints, and geometric constraints as follows:

[0146] Structural constraints include bias constraints, mountain orthogonality constraints, burial depth constraints, and spacing constraints for separated tunnels; regional constraints include restricted area constraints and environmentally sensitive area constraints; geometric constraints include minimum curve radius constraints, maximum slope constraints, and front and rear tangent edge strength constraints. The specific calculations for each constraint are as follows:

[0147] 1) The bias constraint is calculated as follows: The earth pressure difference between the left and right sides of the ground line on a certain width of the calculation route is calculated using the following formula:

[0148] ;

[0149] ;

[0150] in: This represents the earth pressure on the left side; This represents the earth pressure on the right side; This represents the maximum allowable difference in earth pressure under certain conditions. The soil weight; The height of the soil mass; This is the earth pressure coefficient; For earth pressure.

[0151] In programming calculations, this can be further simplified to:

[0152] ;

[0153] Among them: the width of the soil on both sides of the route is equal. (Unit: m); The volume of soil above the design elevation is (Unit: m) 3 ); The weight of the soil within the calculation range (unit: kN); To calculate the area (unit: m) 2 ); Volume of soil on the left (unit: m) 3 ); Volume of soil on the right (unit: m) 3 ); To calculate the unit length (unit: m), the distance between piles is taken.

[0154] 2) The orthogonal constraint of the mountain is: calculate the angle between the tunnel entry / exit direction and the slope direction. (Unit: degrees), based on the included angle Whether to impose a penalty constraint on the score value within a certain angle axis, as shown in the following formula:

[0155] ;

[0156] in: This refers to the maximum prohibited angle (unit: degrees) under certain conditions. Although the Highway Tunnel Design Specification (JTG3370.1-2018) does not explicitly specify this... While specific limits are not specified, Section 7.1.2 emphasizes that "tunnel entrances should preferably be located in areas of stable terrain and favorable geological conditions, and the route should ideally be perpendicular to topographic contour lines; when oblique intersection is necessary, the angle should not be too small." Based on engineering practice, a specific oblique angle is generally recommended. ≥30°.

[0157] 3) Burial depth constraint: tunnel burial depth and the minimum allowable tunnel burial depth under certain conditions and maximum burial depth The following relationship must be satisfied:

[0158] .

[0159] The minimum burial depth is related to the surrounding rock grade and surface load, while the maximum burial depth is related to construction feasibility and is generally determined based on experience. The burial depth of a tunnel should be determined based on the "Specifications for Design of Highway Tunnels" (JTG 3370.1-2018) as the core standard, combined with local standards and numerical simulations. During the design process, special attention should be paid to the surrounding rock grade, excavation width, and construction technology. Under special geological conditions, engineering measures should be used to compensate for insufficient burial depth to ensure the safety of the tunnel structure.

[0160] 4) The spacing constraint for separated tunnels is: spacing between lines Greater than or equal to the minimum line spacing As shown in the following formula:

[0161] ;

[0162] in: The width of a single tunnel excavation (unit: m); This is the coefficient for calculating tunnel spacing, and its value is related to the surrounding rock grade. The values ​​for grades I to VI are 1.0, 1.5, 2.0, 2.5, 3.5, and 4.0, respectively, referring to the "Specifications for Design of Highway Tunnels" (JTG 3370.1-2018).

[0163] 5) Restricted area constraints form the line space of design variables. and restricted areas No intersection, as shown in the following formula:

[0164] ;

[0165] In this embodiment, geometric constraints mainly refer to the requirements of the route design specifications. According to the "Highway Route Design Specifications", the following must be met: minimum radius and minimum length of horizontal curves, minimum length of tangent between adjacent horizontal curves, maximum slope, minimum length of each slope segment, and maximum absolute difference between adjacent slopes.

[0166] The strong constraints on the front and rear tangent edges are: the front and rear segments are fixed edges, and the middle segment is an optimized edge. The fixed edges need to satisfy the constraints of minimum clamping straight line length, maximum turning angle, minimum curve radius, and minimum circular curve length, that is, the intersection variable slides on the ray edge at the start and end points.

[0167] Specifically, the tunnel's start / end point (denoted as...) The planar position can be within the mileage range. The internal adjustment, the calculation formula for this interval is as follows:

[0168] ;

[0169] In the formula: The intersection of the planes at the tunnel starting point Mileage (unit: m); for Tangent length (unit: m); The minimum length of the added line (m); Minimum allowable tangent length at the intersection of planes (unit: m); for The radius of the circular curve (unit: m); for The length of the transition curve (unit: m); for Steering angle (unit: degrees); The minimum permissible radius of a planar circular curve (unit: m); Minimum allowable length of transition curve (unit: m); Maximum permissible steering angle (unit: degrees); The intersection of the planes at the end of the tunnel Mileage (unit: m).

[0170] S3. Use the dual-line particle swarm optimization method to solve the mathematical optimization model obtained in S2, and output the intelligent optimization solution of the dual-line highway tunnel route (i.e., solve the highway tunnel route optimization model).

[0171] To solve the mathematical optimization model proposed in S2, this embodiment proposes a dual-line particle swarm optimization method, a key algorithmic innovation for solving the aforementioned mathematical optimization model. This method aims to address the problems of traditional particle swarm optimization (PSO) algorithms easily getting trapped in local optima and exhibiting poor dual-line coordination in dual-line tunnel alignment optimization. Based on the traditional PSO algorithm framework, this method adds a second PSO search stage to construct a collaborative optimization mechanism of "main baseline - accompanying secondary lines," as detailed in [link to details]. Figure 2 and Figure 3 The dual-line particle swarm optimization method includes the following steps:

[0172] S3-1. Taking the left line of the road as the main baseline, define the route scheme of the left line of the road as the main particle. At the same time, taking the right line of the road as the secondary line, define the route scheme of the right line of the road as the secondary particle. Each main particle corresponds to multiple secondary particles. There is no correlation between the secondary particles of different main particles.

[0173] S3-2. Set the size of the main particle swarm (preferably set to 100 in this embodiment), and construct the particle swarm (i.e., generate the initial particle swarm of the main particles); randomly initialize the positions and velocities of each particle in the main swarm, i.e., randomly generate the initial left-line scheme group, where each particle position represents a left-line scheme; and take the number of iterations of the main particles. =1;

[0174] S3-3. Calculate the objective function value corresponding to each particle in each current main particle group, that is, the construction cost function value;

[0175] For each main particle, a companion particle swarm size is set (60 in this embodiment), and a particle swarm is constructed (i.e., an initial companion particle swarm is generated for each main particle); the positions and velocities of each particle in the companion swarm are randomly initialized, i.e., an initial right-line scheme group is randomly generated, where each particle position represents a right-line scheme; the number of companion particle iterations is taken. =1;

[0176] S3-4. Considering the constraint relationship between the main particle and the companion particle, calculate the objective function value corresponding to the position of each particle in the companion particle group corresponding to each main particle, that is, the construction cost function value.

[0177] S3-5. Optimize the individual best solution for each associated particle (pbest). B (Personal Best) and the globally optimal solution gbest B (Global Best);

[0178] S3-6, using the pbest of the companion particles corresponding to each current main particle. B and gbestB To guide the evolutionary direction (and also serve as a guide), update the particle positions in each current associated particle population and optimize the associated path scheme group;

[0179] S3-7. Make a judgment. If the iteration condition is met, output the gbest value of the companion particle corresponding to each main particle. B If the current baseline for each subject corresponds to a refined intelligent optimization solution for the associated secondary route scheme, proceed to the next step; otherwise, take... Return to steps S3-4;

[0180] S3-8. Combine the objective function calculation results of each current main particle with its corresponding companion particle gbest. B Combining these factors, the best pbest of each host particle is selected. Z and gbest Z ;

[0181] S3-9, using the current main particle's pbest Z and gbest Z To determine the direction of evolution, update the particle positions in each main particle population and optimize the main route scheme group;

[0182] S3-10. Make a judgment. If the iteration condition is met, output the gbest value of the main particle. Z and its corresponding associated particles gbest B As the optimal solution for intelligent and precise optimization of the dual-track highway tunnel route; if not, then take Return to step S3-3.

[0183] In this embodiment, the specific iteration conditions in S3-7 and S3-10 are: the termination condition is that the global optimal solution converges, that is, the global optimal solution remains unchanged after 100 consecutive generations of evolution.

[0184] The technical solution of this embodiment is a systematic technical solution that integrates geographic information modeling, mathematical optimization model and dual-line particle swarm optimization. Specifically, it is as follows: a comprehensive geographic information model is established by collecting topographic and geological data to provide data support for optimization; then, a basic objective function is constructed with dual-line design variables (two sets of JD parameters, including the radius and coordinates of the circular curve at the intersection of the planes and the station mileage and elevation of the slope change point) as the core, which is associated with the cost of transition earthwork, support and tunnel portal sub-items, and incorporates a mathematical optimization model with dense constraints such as geometry (minimum curve radius, maximum slope), structure (biased pressure, burial depth), and region (restricted area, environmentally sensitive area); finally, dual-line particle swarm optimization (DPSO) is proposed, which achieves accurate solution of the optimization model through the iterative evolution of the main baseline and the associated secondary lines, and obtains the comprehensive optimization solution of the dual-line tunnel route. The beneficial effects of applying the above methods are as follows: The dual-line particle swarm optimization mechanism adopted in this invention can realize automated alignment search in three-dimensional space, comprehensively considering multiple factors such as engineering economy (cost optimization), structural safety (biased pressure and burial depth control), and terrain adaptation (mountain orthogonality). In particular, it can consider three types of objectives in a complex terrain environment with dense constraints: economy (precise optimization of the cost of earthwork and tunnel portals), safety (biased earth pressure difference control and tunnel burial depth compliance), and terrain adaptation (mountain orthogonality guarantee). Through dual-line particle swarm optimization, it realizes intelligent three-dimensional alignment search, outputs a group of comprehensive optimal route schemes, provides designers with rich alternative schemes, assists manual design, and improves the refinement and efficiency of dual-line highway tunnel alignment design.

[0185] This method is highly automated and practical, with high operating efficiency. It can effectively solve the problems of portal site selection and alignment optimization for dual-track tunnels in complex terrain, significantly improve the quality and efficiency of route design, provide technical support for the design of dual-track highway tunnels under dense constraints, and has good application value.

[0186] In addition, this embodiment also provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0187] The memory stores computer-executed instructions;

[0188] The processor executes computer execution instructions stored in the memory to implement the electromagnetic probability inversion method described above.

[0189] In addition, this embodiment also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the file generation method described above.

[0190] It should be noted that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. Each module can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its functions can be called and executed by a processing element of the device. Furthermore, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0191] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0192] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0193] When an integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component. Unless otherwise specified, memory can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as a USB flash drive, Random-Access Memory (RAM), Static Random-Access Memory (SRAM), Dynamic Random-Access Memory (DRAM), Enhanced Dynamic Random-Access Memory (EDRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), Resistive Random Access Memory (RRAM), High-Bandwidth Memory (HBM), and Hybrid Memory Cube (HMC). Cube, magnetic storage, flash memory, disk, optical disk, portable hard drive or magnetic disk, and other media that can store program code.

[0194] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0195] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for fine-grained route optimization of a dual-track highway tunnel under dense constraints, characterized in that, Includes the following steps: S1. Establish a comprehensive geographic information model, specifically: collect information data required for route optimization; divide the route selection study area into a series of regular grids, and discretize the information data required for route optimization into the grids to establish a comprehensive geographic information model; S2. Establish a mathematical optimization model, specifically: determine the design variables for the dual-line position; establish basic objective functions for the portal, road-tunnel connection section, tunnel body, and highway pavement based on construction costs; and determine dense constraint conditions based on structural constraints, regional constraints, and geometric constraints. S3. Use the dual-line particle swarm optimization method to solve the mathematical optimization model obtained in S2, and output the intelligent optimization solution of the dual-line highway tunnel route. The dual-line particle swarm optimization method includes the following steps: S3-1. Taking the left line of the road as the main baseline, define the route scheme of the left line of the road as the main particle. At the same time, taking the right line of the road as the secondary line, define the route scheme of the right line of the road as the secondary particle. Each main particle corresponds to multiple secondary particles. There is no correlation between the secondary particles of different main particles. S3-2. Set the size of the main particle swarm, randomly initialize the positions and velocities of each particle in the main swarm, with each particle position representing a left-hand path scheme; determine the number of iterations for the main particle swarm. =1; S3-3. Calculate the objective function value corresponding to each particle in each current main particle group, that is, the construction cost function value; For each main particle, set the size of the accompanying particle swarm, randomly initialize the positions and velocities of each particle in the accompanying swarm, and each particle position represents a right-hand path; take the number of iterations of the accompanying particles. =1; S3-4. Considering the constraint relationship between the main particle and the companion particle, calculate the objective function value corresponding to the position of each particle in the companion particle group corresponding to each main particle, that is, the construction cost function value. S3-5. Optimize the individual best solution for each associated particle (pbest). B And the globally optimal solution gbest B ; S3-6, using the pbest of the companion particles corresponding to each current main particle. B and gbest B To determine the direction of evolution, update the particle positions in each current associated particle population and optimize the associated path scheme group; S3-7. Make a judgment. If the iteration condition is met, output the gbest value of the companion particle corresponding to each main particle. B If the current baseline for each subject corresponds to a refined intelligent optimization solution for the associated secondary route scheme, proceed to the next step; otherwise, take... Return to steps S3-4; S3-8: Combine the objective function calculation results of each current main particle with its corresponding companion particle gbest B Combining these factors, the best pbest of each host particle is selected. Z and gbest Z ; S3-9, using the current main particle's pbest Z and gbest Z To determine the direction of evolution, update the particle positions in each main particle population and optimize the main route scheme group; S3-10. Make a judgment. If the iteration condition is met, output the gbest value of the main particle. Z and its corresponding associated particles gbest B As the intelligent optimization solution for the dual-track highway tunnel route; if not, then take Return to step S3-3.

2. The method for fine-grained route optimization of a dual-track highway tunnel under dense constraints according to claim 1, characterized in that, In S1: The information and data required for route optimization include major technical standards, topography, geological hazard areas, surface cover, land price, and engineering unit price information; The specific iterative conditions in S3-7 and S3-10 are: the globally optimal solution remains unchanged after 100 consecutive generations of evolution.

3. The method for fine route optimization of a dual-track highway tunnel under dense constraints according to claim 1, characterized in that, In S2, the specific design variables for the dual-line position are determined as follows: Highway routes are described using the parameters of horizontal intersection (HPI) and slope change point (VPI). The dual-line design variables include the radius of the circular curve for each plane intersection point HPI. Long transition curve Intersection coordinates Vertical curve radius of VPI at each slope change point mileage of station and design elevation .

4. The method for fine route optimization of a dual-track highway tunnel under dense constraints according to claim 3, characterized in that, The alignment optimization problem for a two-lane highway tunnel simplifies to finding the following vector set: ； ； ； ； ； ； ； ； ； ； ； ； ； ； in: This represents the number of intersections on the route. The number of slope change points in the longitudinal profile of the route; , , , These are the main baseline and the associated secondary line, respectively. The coordinates of the intersection points of the routes on the plane; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the plane of the route; , These are the main baseline and the associated secondary line, respectively. The length of the horizontal transition curve on the route; , These are the main baseline and the associated secondary line, respectively. The radius of the circular curve in the longitudinal profile of the route; , These are the main baseline and the associated secondary line, respectively. Mileage of the longitudinal section slope change points of the route; , These are the main baseline and the associated secondary line, respectively. Elevation of the slope change points in the longitudinal profile of the route; This is the set of planar coordinates of the accompanying secondary route; This is the set of radii of the planar circular curves associated with the secondary routes; This is the set of lengths of the horizontal transition curves of the accompanying secondary routes; This is the set of radii of the circular curves in the longitudinal profile of the accompanying secondary routes; This is the set of mileages of the longitudinal section slope change points of the associated secondary route; This is a set of elevations of the slope change points in the longitudinal profile of the accompanying secondary route.

5. The method for fine route optimization of a dual-track highway tunnel under dense constraints according to claim 4, characterized in that, A basic objective function is established for the portal, road-tunnel connection section, tunnel body, and highway pavement based on construction costs, and the expression is as follows: ； in: Cost of earthwork for the transition section; Cost of the supporting structure; Cost of constructing the tunnel entrance; For the cost of the tunnel body; Cost of highway route construction; Transition section earthwork cost Calculate using the following formula: ； in: This refers to the volume of the excavated slope. Cost per unit volume of excavated slope; This refers to the volume of the fill slope; Cost per unit volume of embankment slope; Cost of support structure Calculate using the following formula: ； in: The area of ​​the supporting structure; This refers to the width of the left side of the roadbed. Tunnel construction cost Calculate using the following formula: ； in: The length of the tunnel; Cost per unit length of tunnel; Highway route construction cost Calculate using the following formula: ； in: This refers to the length of the highway route. Cost per unit length of the roadbed section; Cost per unit length of road surface.

6. The method for fine route optimization of a dual-track highway tunnel under dense constraints according to any one of claims 3-5, characterized in that, In S2: Structural constraints include bias constraints, mountain orthogonal constraints, burial depth constraints, and spacing constraints for separated tunnels; Regional constraints include restricted area constraints and environmentally sensitive area constraints; Geometric constraints include minimum curve radius constraints, maximum slope constraints, and front and rear tangent edge strength constraints.

7. The method for fine route optimization of a dual-track highway tunnel under dense constraints according to claim 6, characterized in that, The bias constraint is the difference in earth pressure on the left and right sides of the ground line along a certain width of the calculation route, as follows: ； in: This represents the earth pressure on the left side; This represents the earth pressure on the right side; This represents the maximum allowable difference in earth pressure under certain conditions. The soil weight; The height of the soil mass; This is the earth pressure coefficient; Earth pressure; The orthogonal constraint of the mountain is: calculate the angle between the tunnel entry / exit direction and the slope direction. According to the included angle Whether to impose a penalty constraint on the score value within a certain angle axis, as follows: ； in: This refers to the maximum included angle that is prohibited under certain conditions. The burial depth constraint is: tunnel burial depth and the minimum allowable tunnel burial depth under certain conditions and maximum burial depth The following relationship must be satisfied: ； The spacing constraint for separated tunnels is: spacing Greater than or equal to the minimum line spacing As shown in the following formula: ； in: This refers to the width of a single tunnel excavation. For calculating tunnel spacing coefficients; The restricted area constraints form the line space of design variables. and restricted areas No intersection, as shown in the following formula: ； The strong constraints on the front and rear tangent edges are: the front and rear segments are fixed edges, and the middle segment is an optimized edge. The fixed edges need to satisfy the constraints of minimum clamping straight line length, maximum turning angle, minimum curve radius, and minimum circular curve length, that is, the intersection variable slides on the ray edge at the start and end points.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the route fine optimization method for a dual-track highway tunnel under dense constraints as described in any one of claims 1-7.

9. An electronic device, characterized in that, include: A processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the route fine optimization method for dual-track highway tunnels under dense constraints as described in any one of claims 1-7.

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