CN112231793B - Automatic obstacle avoidance arrangement method for reinforcing steel bars in reinforced concrete member intersection region based on binary particle swarm optimization algorithm - Google Patents

Automatic obstacle avoidance arrangement method for reinforcing steel bars in reinforced concrete member intersection region based on binary particle swarm optimization algorithm Download PDF

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CN112231793B
CN112231793B CN202010961737.5A CN202010961737A CN112231793B CN 112231793 B CN112231793 B CN 112231793B CN 202010961737 A CN202010961737 A CN 202010961737A CN 112231793 B CN112231793 B CN 112231793B
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伍洲
刘界鹏
冯亮
李盛
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Chongqing University Industrial Technology Research Institute
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Abstract

The invention provides a method for automatically avoiding and arranging steel bars in a reinforced concrete member intersection region based on a binary particle swarm optimization algorithm. The method comprises the steps of representing reinforcing steel bars and members at nodes in a discretization mode, intelligently arranging the main beam reinforcing steel bars in one direction by using a binary particle swarm optimization algorithm, intelligently arranging the main beam reinforcing steel bars in the other direction by using the binary particle swarm optimization algorithm, storing the main beam reinforcing steel bars as reinforcing steel bar center line coordinates, adjusting the reinforcing steel bar center line coordinates, outputting the adjusted reinforcing steel bar center line coordinates and the like. The method solves the problems that the existing steel bar collision detection technology can only detect collision but can not automatically adjust the position of the steel bar, the automation level is low, the accuracy is insufficient, and the like.

Description

Automatic obstacle avoidance arrangement method for reinforcing steel bars in crossed area of reinforced concrete member based on binary particle swarm optimization algorithm
Technical Field
The invention relates to the technical field of information, in particular to a method for automatically avoiding obstacles and arranging reinforcing steel bars in a reinforced concrete member intersection area based on a binary particle swarm optimization algorithm.
Background
In construction engineering, reinforced concrete is an indispensable member, and plays a very critical role in building design and construction. The reinforced concrete structure is a structure made of concrete reinforced by steel bars, the steel bars bear tension, the concrete bears pressure, and the reinforced concrete structure has the advantages of firmness, durability, good fireproof performance, steel saving compared with a steel structure, low cost and the like. The design of the reinforcement of a reinforced concrete element is often dependent only on the stress conditions, boundary conditions and dimensions of the element. However, in the whole design and construction stage, because the construction drawings are complicated and various, the communication of designers is insufficient, the actual environment of a construction site and the like, the reinforcing steel bar drawings designed by construction designers sometimes can meet the actual requirements. At present, in the construction stage of reinforced concrete, steel bars are arranged according to a design drawing of a building structure, if the problem of steel bar collision occurs, measures of' reworking, drawing modification and construction are generally not taken, and at the moment, workers can only simply carry out collision-free arrangement of the steel bars. The process is tedious, time-consuming and labor-consuming, and increases construction cost, which affects construction quality.
Therefore, how to avoid the collision of the reinforcing bars in the design stage is very critical to the construction. Currently, the most representative solution is to use software for collision detection in structural design based on a specific algorithm. Specifically, the algorithm in the solution is expressed as follows: based on the member center, the algorithm uses a geometric bounding box of a particular shape and size to determine if there is an intersection between different members. In the aspect of software implementation in the solution, for example, navisworks software can perform collision detection in structural design, and further generate a collision list. However, the above solution still has major limitations. According to the scheme, only structural collision detection can be performed, and the generated collision list still needs to be designed again by a structural designer by combining drawings which do not generate collision before. If the result of the redesign still has a problem through collision detection, the redesign is also needed. Once the building group is various, the number of collision detection points in the building is too many, the rib arrangement design process may be repeated continuously, and an optimal rib arrangement design scheme is difficult to obtain.
Reinforced concrete beam column joints are the basic and more complex part of structural design. On one hand, beam-column joints are the most common structural members in building structures and are important components of main structures, so that the construction quality of the beam-column joints influences the quality of the whole building. On the other hand, the stress of beam column node is very complicated, and the reinforcing bar is criss-cross in the node district to the quantity variety is many, if can properly solve the collision problem of node reinforcing bar, then the intelligent reinforcing bar of whole structure arranges that just can be expected.
Disclosure of Invention
The invention aims to provide a method for automatically avoiding and arranging steel bars in a crossed area of a reinforced concrete member based on a binary particle swarm optimization algorithm, so as to solve the problems in the prior art.
The technical scheme adopted for achieving the purpose of the invention is that the automatic obstacle avoidance and arrangement method for the steel bars in the crossed area of the reinforced concrete members based on the binary particle swarm optimization algorithm comprises the following steps:
1) And establishing a three-dimensional spatial axis network according to a structural design drawing, and representing the reinforcing steel bars and the members of the beam column node area of the building in a discretization manner. The steel bars comprise beam steel bars in the x-axis direction, beam steel bars in the y-axis direction and column inner longitudinal bars in the z-axis direction. The member comprises an embedded part, profile steel and a connecting plate. The x-axis direction represents the transverse axis direction in the structural design drawing, the y-axis direction represents the longitudinal axis direction in the structural design drawing, and the z-axis direction represents the vertical direction. The intersection of the a axis of the lateral axes and the 1 axis of the longitudinal axes serves as the origin of the global coordinates.
2) And according to the discretization representation result, taking the longitudinal bars and the members in the column as barriers, and performing intelligent arrangement on the reinforcing bars in the x-axis direction by using a binary particle swarm optimization algorithm.
2.1 Initialize a matrix of pop rows and N columns. Wherein pop is the number of individuals in the population. And N is the step number of one-time optimization. The matrix elements are 0,1, -1,2, -2,3. Respectively placing all reinforcing steel bar intelligent bodies in the beam in the x direction at respective starting points (x) st 、y st 、z st ). The executable action of the rebar agent is (right, front, back, up, down, still). The executable actions right, front, back, up, down, and stationary correspond to x value increase by 5, y value decrease by 5, z value increase by 5, z value decrease by 5, and coordinate invariant class 6 events, respectively, and the corresponding elements are 3,1, -1,2, -2,0, respectively.
2.2 According to the location of the intelligent agentThree dimensional coordinate (x) st 、y st 、z st ) Moving N steps, calculating the end point (x) of each individual in the population pop end 、y end 、z end ). Solution of end point (x) end 、y end 、z end ) Distance d from the end point of the intelligent steel bar body end And judging whether the collision occurs with the obstacle in the path. Wherein, if collision occurs, the adaptive function value is infinite. And if no collision occurs, the self-adaptive function value is 0. Calculating d end And the sum of the adaptive function values, and selecting the individual with the minimum sum from the population pop as the optimal path.
2.3 Binary encoding of matrix elements 0,1, -1,2, -2,3 results in a matrix of pop rows 3*N columns, depending on the current matrix of pop rows and N columns. The coding mode is as follows: 0 corresponds to 0,0,0.1 corresponds to 0,0,1. -1 corresponds to 0,1,0.2 corresponds to 0,1,1. -2 corresponds to 1,0,0.3 corresponds to 1,0,1. And performing 0/1 variation on elements of the matrix of the pop rows 3*N columns to obtain a new matrix of the pop rows 3*N columns, and decoding the new matrix of the pop rows 3*N columns into a new matrix of the pop rows N columns according to an encoding mode.
2.4 According to step 2.2) the individual with the smallest neutralization value of the new population pop is selected, and then the individual with the smallest neutralization value is selected from the optimal individual of the population pop and the optimal individual of the new population pop to serve as the optimal individual.
2.5 Obtaining the execution action of N steps according to the obtained optimal individual, executing the selected action, and moving to the next path planning point (x) n 、y n 、z n )。
2.6 ) the following 2.2) -2.5) are executed in a loop until each rebar agent reaches the plane of the respective end abscissa.
2.7 A path satisfying the termination condition is reserved as a reinforcing bar arrangement output result in the x-direction beam.
3) And (3) taking the arrangement result of the reinforcing steel bars in the x-axis direction, the longitudinal bars in the column and the original components as barriers, and performing intelligent arrangement on the reinforcing steel bars in the y-axis direction by using a binary particle swarm optimization algorithm.
4) And (3) storing the arrangement results of the two direction beam reinforcements obtained in the step 2) and the step 3) as the coordinates of the center line of the reinforcements.
5) And adjusting the coordinates of the center line of the steel bar based on the design specifications and the actual engineering.
6) And outputting the adjusted center line coordinates of the steel bars.
Further, the central point coordinate of the bottom of the column of a single node is used as the origin of the local coordinate, and the global coordinate of the point is (x) origin ,y origin ,z origin ). And establishing a three-dimensional model according to the structural design drawing, and discretizing the three-dimensional model into coordinates expressed by basic units.
Further, in the step 2) and the step 3), the arrangement of the reinforcing steel bars is represented as the path planning of the reinforcing steel bar intelligent agent by using a binary particle swarm optimization algorithm. And recording the path coordinate information of the intelligent steel bar body as the center line coordinate of the steel bar. Wherein, the position coordinate of the reinforcing steel bar intelligent body is expressed as:
Figure BDA0002680789000000041
Figure BDA0002680789000000042
Figure BDA0002680789000000043
in the formula, x before And global coordinates in the x-axis direction of the three-dimensional space obtained by modeling are represented. y is before And global coordinates representing the modeled y-axis direction. z is a radical of before Representing the modeled global coordinates in the z-direction. x is the number of after The discretized local coordinates in the x-axis direction are represented. y is after And local coordinates in the y-axis direction obtained by discretization are shown. z is a radical of after The discretized local coordinates in the z-direction are represented. d represents the basic unit used in modeling.
Further, the step 5) specifically comprises the following steps:
5.1 For the center line coordinates of each bar in the x-axis direction, the cycle is as follows: if the y coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the coordinates of the two points are unchanged, and if the y coordinate values are not equal, a value with a larger difference with the y coordinate value of the starting point is selected as the y coordinate value of all the central line coordinates of the steel bar. After the cycle is completed, the position of the steel bar is adjusted and no bending occurs.
5.2 The center line coordinates of the reinforcing steel bars in the y-axis direction are subjected to the following operations: for the track coordinates of the top rebar of each beam, the cycle is as follows: if the x coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the x coordinate values of the two points are not equal, a value with a larger difference from the x coordinate value of the starting point is selected as the x coordinate value of all the central line coordinates of the steel bar. And comparing the z coordinate values of the two points, if the two points are equal, keeping the z coordinates of the two points unchanged, and if the two points are not equal, selecting a value with a larger difference with the z coordinate value of the starting point as the z coordinate value of all the centerline coordinates of the steel bar. After the cycle is completed, the position of the steel bar is adjusted and no bending occurs. For the track coordinates of the bottom steel bars of each beam, the cycle is as follows: if the x coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the x coordinate values of the two points are not equal, a value which is different from the x coordinate value of the starting point by a larger value is selected as the x coordinate values of all the central line coordinates of the steel bar. For a new steel bar center line coordinate, if three adjacent points a, b and c are not on the same straight line and the point b is a bending point of the steel bar, finding out a first bending point of the steel bar, finding out a point e with the largest difference with a z coordinate value of a starting point, connecting the points b and e, adjusting the coordinate in the straight line according to an angle given by a specification, and symmetrically adjusting the bottom steel bar with bending about a node center line. After circulation is completed, the position of the steel bar is unchanged, and the steel bar is symmetrically bent in accordance with the specification only in the node area.
5.3 Drawing a steel bar track image, reserving coordinates of a steel bar central line, and waiting for output.
The technical effects of the invention are undoubted:
A. the method can intelligently detect and adjust the steel bar collision error at the node based on the designed engineering drawing;
B. the method can be used for designing the equipment pipeline, and can intelligently detect and adjust the errors of pipeline collision on the basis of a designed equipment pipeline drawing;
C. the operation is simple, the convergence rate is high, and the realization is easy.
Drawings
FIG. 1 is a flow chart of an arrangement method;
FIG. 2 is a schematic diagram of a defective steel bar of an original design;
FIG. 3 is a sectional view taken along line 1-1;
FIG. 4 is a cross-sectional view taken along line 2-2;
FIG. 5 is a cross-sectional view of FIGS. 3-3;
FIG. 6 is a cross-sectional view of FIGS. 4-4;
FIG. 7 is a flow chart of an improved binary particle swarm optimization algorithm;
FIG. 8 is a perspective view of the center line axis of the rebar without adjustment;
FIG. 9 is a top view of the center line of the reinforcement bar without adjustment;
FIG. 10 is a perspective view of an adjusted rebar centerline axis;
FIG. 11 is a top view of the center line of the rebar after adjustment;
fig. 12 is a schematic view of a reinforced concrete beam-column joint model.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
The embodiment aims at the problems that only collision can be detected but the position of the reinforcing steel bar cannot be automatically adjusted, the calculation time is long, the accuracy is insufficient and the like in the existing reinforcing steel bar collision detection technology, and the problem of obstacle avoidance planning of a movable intelligent body is transformed into the arrangement of the reinforcing steel bar. Each rebar is considered a movable agent. The movable intelligent body starts from the starting point of each reinforcing steel bar, and the target point is the terminal point of the reinforcing steel bar. The operating environment of the mobile agent is shown in fig. 12. Wherein, 12a is reinforced concrete beam column node model sketch map, and 12b is reinforced concrete beam column node reinforcing bar sketch map.
Referring to fig. 1, the embodiment discloses an automatic obstacle avoidance arrangement method for reinforcing steel bars in a reinforced concrete member intersection region based on a binary particle swarm optimization algorithm, which includes the following steps:
1) And establishing a three-dimensional spatial axis network according to a structural design drawing, and representing the reinforcing steel bars and the members of the beam column node area of the building in a discretization manner. The steel bars comprise beam steel bars in the x-axis direction, beam steel bars in the y-axis direction and column inner longitudinal bars in the z-axis direction. The member comprises an embedded part, profile steel and a connecting plate. The x-axis direction represents the transverse axis direction in the structural design drawing, the y-axis direction represents the longitudinal axis direction in the structural design drawing, and the z-axis direction represents the vertical direction. The transverse axis in the positioning axis is written by capitalized English letters in sequence, and the longitudinal axis is written by Arabic numerals in sequence. The intersection of the a axis of the lateral axes and the 1 axis of the longitudinal axes serves as the origin of the global coordinates.
In the embodiment, the design and construction drawings of the original beam-column joint are shown in fig. 3 to 6. The minimum diameter of the steel bar is 6mm. According to the drawing, the problem of steel bar collision in a node area exists in the original design steel bar drawing. And establishing a three-dimensional model according to the structural design drawing. And discretizing each coordinate point of the central line of the reinforcing steel bar into specific local three-dimensional coordinates by using millimeters as a basic unit length. The discrete process is as follows: a certain point of the defined node area is a local coordinate system origin coordinate, and the global coordinate of the point is (x) origin ,y origin ,z origin ) (ii) a The arrangement of the reinforcing steel bars is expressed as path planning of a reinforcing steel bar intelligent body, and path coordinate information of the reinforcing steel bar intelligent body is the center line coordinate of the reinforcing steel bars; the other reinforcing bars are regarded as obstacles to be avoided, and the position coordinates of the reinforcing bar intelligent body are expressed as follows:
Figure BDA0002680789000000061
Figure BDA0002680789000000062
Figure BDA0002680789000000063
in the formula, x before And global coordinates in the x-axis direction of the three-dimensional space obtained by modeling are represented. y is before And global coordinates representing the modeled y-axis direction. z is a radical of before Representing the modeled global coordinates in the z-direction. x is the number of after The discretized local coordinates in the x-axis direction are represented. y is after And local coordinates in the y-axis direction obtained by discretization are shown. z is a radical of after The discretized local coordinates in the z-direction are represented. d represents the basic unit used in modeling.
All the bars are represented by bar center line coordinates.
2) And according to the discretization representation result, taking the longitudinal bars and the members in the column as barriers, and performing intelligent arrangement on the reinforcing bars in the x-axis direction by using a binary particle swarm optimization algorithm. Referring to fig. 7, a start point and an end point of a center line of a reinforcement of the beam in the x-axis direction are preset, and coordinates of the start point and the end point of the center line of the reinforcement are discretized. And the reinforcing steel bar intelligent bodies are sent from the starting points and intelligently arranged according to a binary particle swarm optimization algorithm.
The starting point and the end point of the center line of the steel bar of the beam in the x-axis direction are preset, and the point coordinates are expressed in a discretization mode. A set of points is sought through which the rebar agent passes as it moves in the workspace. The rebar agent path composed of these points is represented as a binary encoding of the particle locations. And (3) taking the path length as an adaptive value and a non-collision obstacle as a constraint condition, and finding a shortest non-collision path from the starting point to the end point of the central line of the steel bar by the steel bar intelligent body through multiple iterations.
2.1 Initialize a matrix of pop rows and N columns. Wherein pop is the number of individuals in the population. And N is the step number of one-time optimization. The matrix elements are 0,1, -1,2, -2,3. Respectively placing all steel bar intelligent bodies in the beam in the x direction at respective starting points x st 、y st 、z st . The executable actions of the intelligent reinforcing steel bar body are right, front, back, up, down and still. Book (I)In the embodiment, since the minimum diameter of the reinforcing bar is 6mm, the step size of each step is set to 5. The executable actions right, front, back, up, down, and stationary correspond to x value increase by 5, y value decrease by 5, z value increase by 5, z value decrease by 5, and coordinate invariant class 6 events, respectively, and the corresponding elements are 3,1, -1,2, -2,0, respectively.
2.2 According to the three-dimensional coordinate x of the steel bar intelligent body st 、y st 、z st After moving N steps, calculating the end point x of each individual in the population pop end 、y end 、z end . Solving for endpoint x end 、y end 、z end Distance d from the end point of the intelligent steel bar body end And judging whether the collision with the obstacle occurs in the path. Wherein, if collision occurs, the adaptive function value is infinite. If no collision occurs, the adaptive function value is 0. Calculating d end And the sum of the adaptive function values, and selecting the individual with the minimum sum from the population pop as the optimal path.
2.3 Binary encoding of matrix elements 0,1, -1,2, -2,3, in accordance with the current pop row N column matrix, yields a pop row 3*N column matrix. The coding mode is as follows: 0 corresponds to 0,0,0.1 corresponds to 0,0,1. -1 corresponds to 0,1,0.2 corresponds to 0,1,1. -2 corresponds to 1,0,0.3 corresponds to 1,0,1. And performing 0/1 variation on elements of the matrix of the pop rows 3*N columns to obtain a new matrix of the pop rows 3*N columns, and decoding the new matrix of the pop rows 3*N columns into a new matrix of the pop rows N columns according to an encoding mode.
2.4 According to step 2.2) the individual with the smallest neutralization value of the new population pop is selected, and then the individual with the smallest neutralization value is selected from the optimal individual of the population pop and the optimal individual of the new population pop to serve as the optimal individual.
2.5 Obtaining the execution action of N steps according to the obtained optimal individual, executing the selected action, and moving to the next path planning point x n 、y n 、z n . I.e. the next path planning point (x) n 、y n 、z n ) Starting point (x) for next optimization st 、y st 、z st )。
2.6 ) the following 2.2) -2.5) are executed in a loop until each rebar agent reaches the plane of the respective end abscissa.
2.7 The path satisfying the termination condition is retained as the arrangement output result of the reinforcing bars in the x-direction beam. During specific implementation, the steel bars of the beam in the x direction are not bent and meet the condition of no collision with the steel bars of the column. The program thus outputs the above-mentioned rebar centerline coordinates.
3) And (3) taking the arrangement result of the reinforcing steel bars in the x-axis direction, the longitudinal bars in the column and the original components as barriers, and performing intelligent arrangement on the reinforcing steel bars in the y-axis direction by using a binary particle swarm optimization algorithm.
According to the structural design drawing, under the guidance of concrete structural design specifications (GB 50010-2010), setting a start point and an end point of a center line of a reinforcing bar of a beam in the y-axis direction, and discretizing the start point of the reinforcing bar by an end point coordinate according to the discretization formula in step 1). And the reinforcing steel bar intelligent bodies are sent from the starting points and intelligently arranged according to a binary particle swarm optimization algorithm. The binary particle swarm optimization algorithm mainly comprises the following steps:
3.1 Initialize a matrix of pop rows and N columns, where pop refers to the number of individuals in the population, N refers to the number of steps in an optimization, and the matrix elements are 0,1, -1,2, -2,3. Arranging the reinforcing steel bars in the beam in the x direction, and respectively arranging all reinforcing steel bar intelligent bodies in the beam in the y direction at respective starting points (x) st 、y st 、z st ). The executable actions of the steel bar agent of the beam are (front, left, right, upper, lower and still), corresponding to 6 types of events of y value increase 5, x value decrease 5, z value increase 5, z value decrease 5 and coordinate invariance, and the corresponding elements are 3,1, -1,2 and-2,0 respectively. Note that since the minimum diameter of the reinforcing bar is 6mm, the step size of each step is set to 5.
3.2 The following 3.3) -3.6) cycles are executed until the following end conditions are met: each reinforcing steel bar intelligent body reaches the plane of the vertical coordinate of the respective end point.
3.3 According to the three-dimensional coordinates (x) of the steel bar intelligent body st 、y st 、z st ) After moving N steps, calculating the end point of each individual in the population pop(x end 、y end 、z end ) And finding the end point (x) end 、y end 、z end ) Distance d from reinforcing steel bar intelligent body terminal point end And judging whether the collision occurs to the obstacle in the path (the self-adaptive function value is infinite when the collision occurs, and otherwise, the self-adaptive function value is 0). Calculating d end And the sum of the adaptive function values, and selecting the individual with the minimum sum from the population pop as the optimal path.
3.4 According to the current pop row and N column matrix, binary coding is carried out on the matrix elements 0,1, -1,2, -2,3 to obtain a pop row 3*N column matrix, and the specific coding mode is as follows: 0 corresponds to 0,0,0;1 corresponds to 0,0,1; -1 corresponds to 0,1,0;2 corresponds to 0,1,1; -2 corresponds to 1,0,0;3 corresponds to 1,0,1. And performing 0/1 variation on elements of the matrix of the pop rows 3*N columns to obtain a new matrix of the pop rows 3*N columns, and decoding the new matrix of the pop rows 3*N columns into a new matrix of the pop rows N columns according to an encoding mode.
3.5 According to step 23) the individual with the smallest neutralization value of the new population pop is selected, and then the individual with the smallest neutralization value is selected from the optimal individual of the population pop and the optimal individual of the new population pop as the optimal individual.
3.6 Obtaining the execution action of N steps according to the obtained optimal individual, executing the selected action, and moving to the next path planning point (x) n 、y n 、z n ) I.e. the next path planning point is the starting point (x) for the next optimization st 、y st 、z st )。
3.7 After the circulation is finished, the path meeting the termination condition is reserved as the arrangement output result of the reinforcing steel bars in the beam in the y direction.
During specific implementation, if the reinforcing steel bars of the y-direction beam are constructed according to the original design drawing scheme, the reinforcing steel bars collide with other arranged reinforcing steel bars. The algorithm provided by the embodiment can revise the arrangement position of the reinforcing steel bars, and the path is revised according to the following mode: along the y direction, when the reinforcing bar intelligent object will collide with other members or arranged reinforcing bars, the reinforcing bar intelligent object is bent to the direction that can let it not collide.
4) And (3) storing the arrangement results of the two direction beam reinforcements obtained in the step 2) and the step 3) as the coordinates of the center line of the reinforcements. Referring to fig. 8 and 9, through calculation of the binary particle swarm optimization algorithm, collision between the reinforcing steel bars is guaranteed, and as the basic unit is millimeter, the calculation result is guaranteed to be accurate. But the bending of the reinforcing bars does not meet the requirements of the specification and the actual construction.
5) And adjusting the coordinates of the central line of the steel bar based on the design specifications and the actual engineering.
5.1 The path of all the steel bar intelligent bodies of the beam in the x-axis direction, namely the center line coordinates of all the steel bars in the x-axis direction. The following operations are carried out to meet the requirements of concrete structure design specifications (GB 50010-2010) on steel bar arrangement: for the centerline coordinates of each bar, the loop is as follows: if the y coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the coordinates of the two points are unchanged, and if the y coordinate values are not equal, a value with a larger difference with the y coordinate value of the starting point is selected as the y coordinate value of all the central line coordinates of the steel bar. After the cycle is completed, the position of the reinforcing steel bars is adjusted and bending does not occur.
5.2 The path of all the steel bar intelligent bodies of the beam in the y-axis direction, namely the center line coordinates of all the steel bars in the y-axis direction. The following operations are carried out to meet the requirements of concrete structure design specifications (GB 50010-2010) on steel bar arrangement: for the track coordinates of the top rebar of each beam, the cycle is as follows: if the y coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the y coordinate values are not equal, a value with a larger difference with the x coordinate value of the starting point is selected as the y coordinate value of all the central line coordinates of the steel bar; and comparing the z coordinate values of the two points, if the two points are equal, keeping the z coordinates of the two points unchanged, and if the two points are not equal, selecting a value which is larger in difference with the z coordinate value of the starting point as the z coordinate value of all the center line coordinates of the steel bar. After the cycle is completed, the position of the reinforcing steel bars is adjusted and bending does not occur. For the track coordinates of the bottom steel bars of each beam, the cycle is as follows: if the x coordinate values of two adjacent points a and b of the center line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the x coordinate values of the two points are not equal, a value which is different from the x coordinate value of the starting point by a larger value is selected as the x coordinate values of all the center line coordinates of the steel bar; for a new steel bar center line coordinate, if three adjacent points a, b and c are not on the same straight line and the point b is a bending point of the steel bar, finding out a first bending point of the steel bar, finding out a point e with the largest difference with a z coordinate value of a starting point, connecting the points b and e, adjusting the coordinate in the straight line according to an angle given by a specification, and symmetrically adjusting the bottom steel bar with bending about a node center line. After circulation is completed, the position of the steel bar is unchanged, and the steel bar is symmetrically bent in accordance with the specification only in the node area.
5.3 Drawing a track image according to the obtained central line coordinates of the steel bars, reserving the central line coordinates of the steel bars, and waiting for output.
When the method is concretely implemented, the beam steel bars in different directions are adjusted by adopting a corresponding method. The method comprises the steps of firstly judging whether the direction of a beam steel bar is the x-axis direction or the y-axis direction, and then judging whether the position of the beam steel bar is a top steel bar or a bottom steel bar. And finally, adjusting positions or bending different reinforcing steel bars to ensure that the reinforcing steel bars are not collided.
6) And outputting the adjusted center line coordinates of the steel bars. Referring to fig. 10 and 11, through calculation of the binary particle swarm optimization algorithm and adjustment of coordinates of center lines of reinforcing steel bars, reinforcing steel bar arrangement which does not cause collision of reinforcing steel bars and meets concrete structure design specifications (GB 50010-2010) and actual engineering requirements is obtained. The effect proves that the steel bars are reasonably arranged without additional human interference factors, and the steel bars in the design and construction drawing of the reinforced concrete beam-column joint area are accurately adjusted to be arranged without collision and meeting concrete structure design specifications (GB 50010-2010) and engineering actual requirements, so that the method is practical and effective.

Claims (2)

1. A reinforced concrete member intersection area reinforcing steel bar automatic obstacle avoidance arrangement method based on a binary particle swarm optimization algorithm is characterized by comprising the following steps:
1) Establishing a three-dimensional spatial axis network according to a structural design drawing, and representing reinforcing steel bars and members in a beam column node area of a building in a discretization manner; wherein, theThe reinforcing steel bars comprise beam reinforcing steel bars in the x-axis direction, beam reinforcing steel bars in the y-axis direction and column inner longitudinal steel bars in the z-axis direction; the member comprises an embedded part, profile steel and a connecting plate; the x-axis direction represents the transverse axis direction in the structural design drawing, the y-axis direction represents the longitudinal axis direction in the structural design drawing, and the z-axis direction represents the vertical direction; the intersection point of the axis A in the transverse axes and the axis 1 in the longitudinal axes is used as the origin point of the global coordinate; one point of the node area is defined as a local coordinate system origin; the global coordinate of the origin of the local coordinate system is (x) origin ,y origin ,z origin );
2) According to the discretization representation result, taking the longitudinal bars and the members in the column as barriers, and performing intelligent arrangement on the reinforcing bars in the x-axis direction by using a binary particle swarm optimization algorithm; representing the arrangement of the reinforcing steel bars as the path planning of a reinforcing steel bar intelligent body by using a binary particle swarm optimization algorithm; recording the path coordinate information of the intelligent steel bar body as the center line coordinate of the steel bar; wherein, the position coordinate of the reinforcing steel bar intelligent body is expressed as:
Figure FDA0004097918710000011
Figure FDA0004097918710000012
Figure FDA0004097918710000013
in the formula, x before Representing the global coordinate of the x-axis direction of the three-dimensional space obtained by modeling; y is before Representing the global coordinate of the y-axis direction obtained by modeling; z is a radical of before Representing the global coordinate of the z direction obtained by modeling; x is the number of after Local coordinates in the x-axis direction obtained by discretization are represented; y is after Local coordinates in the y-axis direction obtained by discretization are represented; z is a radical of after The discretization-obtained local coordinate in the z direction is represented; d represents the basic unit used in modeling;
The step 2) specifically comprises the following substeps:
2.1 Initialize a matrix of pop rows and N columns; wherein pop is the number of individuals in the population; n is the step number of one-time optimization; the matrix elements are 0,1, -1,2, -2,3; respectively placing all reinforcing steel bar intelligent bodies in the beam in the x direction at respective starting points (x) st 、y st 、z st ) (ii) a The executable actions of the intelligent reinforcing steel bar body are right, front, back, up, down and static; the executable actions right, front, back, up, down and still correspond to class 6 events of x value increase 5, y value decrease 5, z value increase 5, z value decrease 5 and coordinate invariance, respectively, and the corresponding elements are 3,1, -1,2, -2,0;
2.2 According to the three-dimensional coordinate (x) of the steel bar intelligent body st 、y st 、z st ) And after moving N steps, calculating the end point (x) of each individual in the population pop end 、y end 、z end ) (ii) a Solution of end point (x) end 、y end 、z end ) Distance d from reinforcing steel bar intelligent body terminal point end Judging whether the collision occurs to the obstacle in the path; if collision occurs, the adaptive function value is infinite; if no collision occurs, the self-adaptive function value is 0; calculating d end And the sum of the adaptive function values, and selecting the individual with the minimum sum from the population pop as the optimal path;
2.3 Binary encoding of matrix elements 0,1, -1,2, -2,3 in accordance with the current pop row N column matrix to yield a pop row 3*N column matrix; the coding mode is as follows: 0 corresponds to 0,0,0;1 corresponds to 0,0,1; -1 corresponds to 0,1,0;2 corresponds to 0,1,1; -2 corresponds to 1,0,0;3 corresponds to 1,0,1; performing 0/1 variation on elements of the matrix of 3*N columns of pop rows to obtain a new matrix of 3*N columns of pop rows, and decoding the new matrix of 3*N columns of pop rows into a new matrix of N columns of pop rows according to an encoding mode;
2.4 According to step 2.2) selecting the individual with the smallest neutralization value of the new population pop, and selecting the individual with the smallest neutralization value from the optimal individual of the population pop and the optimal individual of the new population pop as the optimal individual;
2.5 Obtaining the execution action of N steps according to the obtained optimal individual, executing the selected action, and moving to the next path planning point (x) n 、y n 、z n );
2.6 Circularly executing the following 2.2) -2.5) until each reinforcing steel bar intelligent body reaches the plane of the horizontal coordinate of the respective end point;
2.7 Reserving a path meeting the termination condition as a reinforcing steel bar arrangement output result in the beam in the x direction;
3) Taking the arrangement result of the reinforcing steel bars in the x-axis direction, the longitudinal bars in the column and the original members as barriers, and performing intelligent arrangement of the reinforcing steel bars in the y-axis direction by using a binary particle swarm optimization algorithm;
4) Storing the arrangement results of the two directional beam steel bars obtained in the step 2) and the step 3) as the coordinates of the central line of the steel bar;
5) Adjusting the center line coordinate of the steel bar based on the design specification and the actual engineering; the step 5) specifically comprises the following substeps:
5.1 For the centerline coordinates of each bar in the x-axis direction, the cycle is as follows: if the y coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the coordinates of the two points are unchanged, and if the y coordinate values of the two points are not equal, a value with a larger difference with the y coordinate value of the starting point is selected as the y coordinate value of all the central line coordinates of the steel bar; after circulation is completed, the position of the steel bar is adjusted and bending does not occur;
5.2 The center line coordinates of the reinforcing steel bars in the y-axis direction are subjected to the following operations: for the track coordinates of the top rebar of each beam, the cycle is as follows: if the x coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the x coordinate values of the two points are not equal, a value with a larger difference from the x coordinate value of the starting point is selected as the x coordinate values of all the central line coordinates of the steel bar; comparing the z coordinate values of the two points, if the two points are equal, keeping the z coordinates of the two points unchanged, and if the two points are not equal, selecting a value with a larger difference with the z coordinate value of the starting point as the z coordinate value of all the centerline coordinates of the steel bar; after circulation is completed, the position of the steel bar is adjusted and bending does not occur; for the track coordinates of the bottom steel bars of each beam, the cycle is as follows: if the x coordinate values of two adjacent points a and b of the central line coordinate of the steel bar are equal, the x coordinate values of the two points are unchanged, and if the x coordinate values of the two points are not equal, a value with a larger difference from the x coordinate value of the starting point is selected as the x coordinate values of all the central line coordinates of the steel bar; for a new central line coordinate of the reinforcing steel bar, if three adjacent points a, b and c are not on the same straight line and the point b is a bending point of the reinforcing steel bar, finding out a first bending point of the reinforcing steel bar, finding out a point e with the largest difference with a z coordinate value of a starting point, connecting the point b with the point e, adjusting the coordinate in the straight line according to an angle given by a specification, and symmetrically adjusting the bottom reinforcing steel bar which is bent about a node central line; after circulation is completed, the position of the steel bar is unchanged, and the steel bar is symmetrically bent in accordance with the specification only in the node area;
5.3 Drawing a steel bar track image, reserving coordinates of a steel bar central line, and waiting for output;
6) And outputting the adjusted center line coordinates of the steel bars.
2. The automatic obstacle avoidance and arrangement method for the rebars in the crossed area of the reinforced concrete members based on the binary particle swarm optimization algorithm according to claim 1, characterized in that: the coordinate of the central point of the bottom of the column of a single node is taken as the origin of local coordinates, and the global coordinate of the point is (x) origin ,y origin ,z origin ) (ii) a And establishing a three-dimensional model according to the structural design drawing, and discretizing the three-dimensional model into coordinates expressed by basic units.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102622482A (en) * 2012-03-06 2012-08-01 中国科学院工程热物理研究所 Fan optimization arrangement method based on binary particle swarm optimization (BPSO)
CN102967857A (en) * 2012-11-28 2013-03-13 西安电子科技大学 Particle swarm optimization-based cooperative tracking method of sensor network to maneuvering target
CN109446566A (en) * 2018-09-25 2019-03-08 重庆大学产业技术研究院 Reinforcing bar intelligent barrier avoiding arrangement method at a kind of node based on intensified learning
CN109492744A (en) * 2018-10-30 2019-03-19 华北电力大学 A kind of mixed running optimal control method that discrete binary particle swarm algorithm is coupled with fuzzy control

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105527964B (en) * 2015-12-28 2018-04-17 桂林电子科技大学 A kind of robot path planning method
CN109162407B (en) * 2018-09-25 2020-08-04 重庆大学产业技术研究院 Automatic obstacle avoidance method for reinforcing steel bars in crossed area of reinforced concrete member based on artificial potential field method
CN109582027B (en) * 2019-01-14 2022-02-22 哈尔滨工程大学 Improved particle swarm optimization algorithm-based USV cluster collision avoidance planning method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102622482A (en) * 2012-03-06 2012-08-01 中国科学院工程热物理研究所 Fan optimization arrangement method based on binary particle swarm optimization (BPSO)
CN102967857A (en) * 2012-11-28 2013-03-13 西安电子科技大学 Particle swarm optimization-based cooperative tracking method of sensor network to maneuvering target
CN109446566A (en) * 2018-09-25 2019-03-08 重庆大学产业技术研究院 Reinforcing bar intelligent barrier avoiding arrangement method at a kind of node based on intensified learning
CN109492744A (en) * 2018-10-30 2019-03-19 华北电力大学 A kind of mixed running optimal control method that discrete binary particle swarm algorithm is coupled with fuzzy control

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
林佳瑞 ; 张建平 ; .基于BIM的施工资源配置仿真模型自动生成及应用.施工技术.2016,(第18期),1-6. *
魏声云 ; 张静 ; 郭虹 ; 李鸥 ; .改进二进制粒子群优化的节点选择算法.西安电子科技大学学报.2015,(第02期),150-156. *

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