Disclosure of Invention
The invention aims to provide a method and a device for optimizing steel bar blanking cost and efficiency based on an improved radial basis function, and solves the problem that the prior art only aims at the highest steel bar utilization rate and does not consider construction efficiency of a construction site, so that the matching degree with actual engineering requirements is low.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
An optimization method for steel bar blanking cost and efficiency based on an improved radial basis function comprises the following steps:
Step S1, building a three-dimensional model of the steel bar based on a design drawing and a BIM technology, and extracting relevant information of the required steel bar after the three-dimensional model of the steel bar is built, wherein the relevant information comprises the diameter, the number, the length, the volume and the quality of the steel bar;
S2, analyzing factors for optimizing the steel bar blanking, and determining weights of the factors for the steel bar blanking, wherein the factors comprise the number of excess materials for the steel bar blanking, the cutting times for the steel bar blanking and the welding times for the steel bar blanking;
S3, determining a radial basis function model of linear superposition processing according to the weight of each factor;
And S4, taking the determined radial basis function model as an objective function of the genetic algorithm optimization model, and inputting the reinforcing steel bars with different combinations and the requirement amount list of each reinforcing steel bar into the genetic algorithm optimization model for iterative computation to obtain the reinforcing steel bar blanking scheme which meets the optimization target that the total reinforcing steel bar consumption in the reinforcing steel bar three-dimensional model is minimum and the construction efficiency is optimal.
Further, the specific steps of the step S2 are as follows:
s2.1, acquiring an existing steel bar blanking cutting scheme, wherein the cutting scheme comprises the required quantity of steel bars, the quantity of raw materials of the steel bars, the quantity of residual materials, the cutting times of steel bar blanking, the welding times of steel bar blanking and the time for finishing processing the steel bars;
And S2.2, clustering the data in the steel bar blanking cutting scheme by adopting a clustering algorithm, counting clusters obtained by the clustering algorithm after the clustering algorithm is processed, and explaining the importance of each factor according to the distribution condition of each factor in each cluster and the difference degree among the clusters, namely acquiring the weight of each factor.
Further, the specific steps of explaining the importance of each factor according to the distribution condition of each factor in each cluster and the degree of difference between clusters in the step S2.2 are as follows:
Step S2.21, visualization of clustering results:
the clusters obtained by the clustering algorithm are visualized by adopting a visualization method, wherein the visualization method comprises a scatter diagram visualization method;
step S2.22, factor distribution analysis:
For each cluster, calculating statistics of each factor in the cluster to obtain distribution conditions of corresponding factors, wherein the statistics of the factors comprise mean values and variances of the factors in the cluster, and preliminarily judging importance of the factors by comparing the distribution conditions of the factors in different clusters;
step S2.23, inter-cluster difference measurement:
calculating a difference metric between different clusters;
step S2.24, factor weight determination:
distributing a weight to each factor according to the distribution condition of each factor in each cluster and the difference measurement among the clusters, wherein a larger weight indicates that the factor has a larger importance or degree of distinction in a clustering result;
and S2.25, judging whether the weights of the factors meet the requirements or not based on a scale method, if so, obtaining the correct weights, otherwise, adjusting the steel bar blanking cutting scheme in the step 2.1, and recalculating the weights.
Further, the radial basis function model in the step S3 includes 3 radial basis functions, each radial basis function includes a normalization function for normalizing the input variable and an implicit layer for processing the result obtained by the normalization function, and the output of each radial basis function is phi_i (x);
the normalization function is:
Wherein k is a normalized value, x is normalized data, including a required length of the steel bar, a required quality of the steel bar and a raw material length of the steel bar, and xmax、xmin is a maximum value and a minimum value in the normalized data respectively;
The calculation formula of the neuron number of the hidden layer is as follows:
Wherein n and l are the number of input neurons and the number of output neurons respectively, and the range of a is 1-10;
the form of linear superposition of the 3 radial basis functions is:
f(x)＝w_1*φ_1(x)+w_2*φ_2(x)+w_3*φ_3(x)
Wherein w_1, w_2 and w_3 respectively represent weights of radial basis functions obtained according to weights of various factors in linear superposition.
Further, the specific steps of the step S4 are as follows:
Step S4.1, designing a corresponding genetic algorithm optimization model based on an objective function and a flow of steel bar blanking in actual construction, namely, designing a double-layer nesting algorithm by taking the change characteristics of a steel bar stock area, a processing area and supply and demand into consideration, wherein the double-layer nesting algorithm comprises an upper layer, a middle layer, a lower layer and the objective function, the operation sequence is the upper layer, the middle layer, the lower layer and the objective function, the flow of steel bar blanking in actual construction is that steel bars in the steel bar stock area are transported to a storage area as daily or weekly consumption, the steel bars in the storage area are transported to a temporary storage area corresponding to the processing area in batches and then are processed one by one, and the change characteristics are that under the condition that the supply and demand is daily steel bar processing amount, the steel bars are allocated to the processing area according to daily processing amount, and the storage capacity and production efficiency of the processing area are required to be met by the steel bar amount entering the processing area each time;
the upper layer is a blanking scheme layer and is used for determining the sequence and the quantity of the steel bars placed in the stock pit according to the steel bars of different combinations and the requirement quantity list of each steel bar;
the middle layer is a layout pool and is used for generating the sequence and the number of the steel bars initially placed in the temporary storage area of each processing device based on the number of the steel bars determined to be placed by the blanking scheme layer;
the lower layer is a stock layout layer and is used for generating specific steel bar cutting and welding schemes, meanwhile, the length of each steel bar required quantity order required to be processed is decided by constructing heuristic rules based on the number of steel bars placed in a temporary storage area of each processing device, namely, the steel bar blanking scheme which is used for obtaining the optimal optimization target of the minimum total steel bar consumption and the optimal construction efficiency is obtained, and the specific steps are as follows:
Step S4.11, calculating the quantity ns of all the steel bars to be processed in the processing area based on the quantity of the steel bars in each steel bar demand order, namelyWherein nip represents the number of the steel bars which are preliminarily required to be configured to the corresponding p processing area by the ith steel bar demand order;
Step S4.12, calculating the average length Ls of each steel bar in each steel bar demand order based on the number ns of all the steel bars to be processed and the total length Ls of the steel bars in all the obtained steel bar demand orders, wherein the formula is as follows:
ls＝Ls/ns；
step S4.13, calculating the average length Lr of the processed steel bars required by each steel bar demand order, wherein the formula is as follows:
Lr←([nip×ls])
wherein, ζ represents a valuation;
Step S4.14, obtaining the length of the steel bar required by each steel bar required order, arranging each steel bar required order from large to small based on the length of the steel bar required by each steel bar required order, and initializing the counting variable r=1 of the steel bar required order according to the arrangement;
Step S4.15, calculating a step loss R generated after the step of adopting the minimum step principle for the steel bars of the current steel bar demand order R based on the average length Lr of the steel bars required to be processed of the current steel bar demand order R, wherein the formula is as follows:
R＝La-Lr
wherein, La is the total length of the steel bars in the steel bar demand order r:
S4.16, if R is given as average replacement loss x R, configuring the last steel bar in the current steel bar demand order in the next steel bar demand order for processing, firstly updating the number of steel bars put in a temporary storage area, putting the steel bars in the temporary storage area according to the sequence, configuring the steel bars in the processing area for processing, and if R is given as average replacement loss x R, the number of the steel bars in the temporary storage area is kept unchanged, putting the steel bars in the temporary storage area according to the sequence, and configuring the steel bars in the temporary storage area in the processing area for processing;
Step S4.17, if the r value exceeds the total number of the steel bar demand orders, ending, otherwise, jumping back to step S4.15, and taking the next steel bar demand order as the current steel bar demand order, wherein r=r+1;
And S4.2, inputting the reinforcing steel bars with different combinations into a genetic algorithm optimization model for iterative computation, and obtaining the reinforcing steel bar blanking scheme which meets the optimization target of minimum total reinforcing steel bar consumption and optimal construction efficiency in the reinforcing steel bar three-dimensional model.
Further, the blanking scheme layer solves the sequence and the quantity of the steel bars put into the stock layout pool in each demand list through a single-chain coding genetic algorithm in the genetic algorithm optimization model;
and solving and generating a specific steel bar cutting and welding scheme by the stock layout layer through a single-chain coding genetic algorithm in the genetic algorithm optimization model.
Further, the formula of the linear normalization method in the genetic algorithm optimization model is as follows:
s.t.
cip≥0
Wherein cip is the number of bars in the processing area p of the bar demand order i, B represents the number of cuts, s.t. represents the constraint condition of the genetic algorithm.
An optimizing device based on improve radial basis function's reinforcing bar unloading cost and efficiency includes:
and the steel bar information extraction module is used for: building a three-dimensional model of the steel bar based on a design drawing and a BIM technology, and extracting relevant information of the required steel bar after the three-dimensional model of the steel bar is built, wherein the relevant information comprises the diameter, the number, the length, the volume and the mass of the steel bar;
Factor weight determination model: analyzing the optimized factors of the steel bar blanking, and determining the weight of each factor of the steel bar blanking, wherein the factors comprise the number of the excess materials of the steel bar blanking, the cutting times of the steel bar blanking and the welding times of the steel bar blanking;
the radial basis function model building module: determining a radial basis function model of linear superposition processing according to the weight of each factor;
and the steel bar blanking scheme optimizing module is as follows: and taking the determined radial basis function model as an objective function of the genetic algorithm optimization model, and inputting the steel bars with different combinations and the steel bar demand amount list into the genetic algorithm optimization model for iterative computation to obtain the steel bar blanking scheme which meets the optimization target of minimum total steel bar consumption and optimal construction efficiency in the steel bar three-dimensional model.
Compared with the prior art, the invention has the advantages that:
1. The application can efficiently provide the steel bar blanking scheme with highest steel utilization rate and highest construction efficiency, and test verification results show that: the steel bar blanking optimization model provided by the application has feasibility and application value in the exploration application of actual engineering, ensures the steel bar processing efficiency, simultaneously keeps the steel bar utilization rate at about 98%, is obviously superior to the manual calculation to obtain the steel bar utilization rate, is beneficial to improving the field construction quality and reduces the steel bar waste.
2. The invention is based on a BIM model and an optimization algorithm, takes the utilization rate of the reinforcing steel bars and the construction efficiency of the blanking of the reinforcing steel bars as optimization targets, and realizes the automatic determination of the objective function and boundary conditions of the genetic algorithm by generating the objective function of the genetic algorithm by using the radial basis function, thereby ensuring the high efficiency and the accuracy of the calculation of the algorithm model, realizing the powerful butt joint with the field requirements, and effectively solving the problems of low calculation efficiency, single objective function and the like of the existing algorithm model;
3. The steel bar blanking optimization method can be integrated in various data cabins and management platforms in a secondary system frame mode, can be effectively popularized and landed in a flow application mode, and along with the continuous development of a digital technology and the gradual perfection of a digital twin system, an optimization model (steel bar blanking optimization method) can show good suitability with related systems, can be combined with more material optimization problems, forms a universal material management and control and use scheme optimization system, supports material management and control under various complex working conditions and environments, and lays a foundation for the integrated management of a full life cycle of construction.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, fig. 1 is an algorithm logic diagram of an optimization method for steel bar blanking cost and efficiency based on an improved radial basis function provided by the application. Building a three-dimensional model of the steel bar by using a BIM technology, automatically extracting relevant data information of the steel bar, and realizing batch automatic acquisition of the attributes such as the diameter, the number and the like of the steel bar; and processing the length and the number of the reinforcing steel bars, determining an objective function of a processing scheme by means of a radial basis function model according to a scheme optimization design principle of secondary blanking of the reinforcing steel bars and taking the highest reinforcing steel bar utilization rate and the best reinforcing steel bar processing efficiency as targets, and obtaining a reinforcing steel bar blanking scheme meeting the aims of minimum total reinforcing steel bar consumption and best construction efficiency optimization by iterative calculation of a genetic algorithm by taking the determined objective function as an objective function of a genetic algorithm optimization model. The technical route is shown in figure 1.
The method comprises the following specific steps:
Step S1, building a three-dimensional model of the steel bar based on a design drawing and a BIM technology, and extracting relevant information of the required steel bar after the three-dimensional model of the steel bar is built, wherein the relevant information comprises the diameter, the number, the length, the volume and the quality of the steel bar;
The national building standard design atlas (11G 101) is used as a guide, reinforcement bars required to be fed in projects are pre-simulated through Revit design software, and the simulation modeling process follows the following principles:
(1) For the situation that the length of the steel bar exceeds 9 meters (the length of the steel bar raw material in the construction site is fixed to be 9 meters), the site engineer guides the steel bar to be overlapped in the three-dimensional model of the steel bar through experience, and the overlapped position avoids the stress weak point;
(2) The visualization capability is utilized to solve the construction heavy difficulty of complex nodes in a construction site and reduce unnecessary steel bar waste;
the local reinforcement structure constructed by Revit is shown in figure 3, and the reinforcement model of the member can be better by using Revit, and the diameter and the position of the reinforcement can be accurately determined, so that the accuracy of calculation of the diameter and the length of the reinforcement required by the project is improved.
S2, analyzing factors for optimizing the steel bar blanking, and determining weights of the factors for the steel bar blanking, wherein the factors comprise the number of excess materials for the steel bar blanking, the cutting times for the steel bar blanking and the welding times for the steel bar blanking; the method comprises the following specific steps:
s2.1, acquiring an existing steel bar blanking cutting scheme, wherein the cutting scheme comprises the required quantity of steel bars, the quantity of raw materials of the steel bars, the quantity of residual materials, the cutting times of steel bar blanking, the welding times of steel bar blanking and the time for finishing processing the steel bars;
And S2.2, clustering the data in the steel bar blanking cutting scheme by adopting a clustering algorithm, counting clusters obtained by the clustering algorithm after the clustering algorithm is processed, and explaining the importance of each factor according to the distribution condition of each factor in each cluster and the difference degree among the clusters, namely acquiring the weight of each factor.
The specific steps for explaining the importance of each factor according to the distribution condition of each factor in each cluster and the difference degree among clusters are as follows:
Step S2.21, visualizing the clustering result, namely visualizing the clusters obtained by the clustering algorithm by adopting a visualization method, wherein the visualization method comprises a scatter diagram visualization method or other proper graphic representations;
S2.22, factor distribution analysis, namely, for each cluster, calculating statistics of each factor in the cluster to obtain distribution conditions of corresponding factors, wherein the statistics of the factors comprise mean values and variances of the factors in the cluster, and preliminarily judging importance of the factors by comparing the distribution conditions of the factors in different clusters; for example, the mean, variance, or other statistic of each feature in the cluster may be calculated. By comparing the distribution of the features in different clusters, the importance of the features can be primarily judged.
S2.23, calculating the difference measurement among clusters, namely calculating the difference measurement among different clusters;
Step S2.24, determining the factor weights, namely distributing a weight to each factor according to the distribution condition of each factor in each cluster and the difference measurement among the clusters, wherein the larger weight indicates that the factor has larger importance or degree of distinction in the clustering result;
and S2.25, judging whether the weights of the factors meet the requirements or not based on a scale method, if so, obtaining the correct weights, otherwise, adjusting the steel bar blanking cutting scheme in the step 2.1, and recalculating the weights.
Step S3, determining a radial basis function model of linear superposition processing according to the weight of each factor, as shown in FIG. 4; the method comprises the following specific steps:
The radial basis function model comprises 3 radial basis functions, each radial basis function comprises a normalization function for normalizing an input variable and an implicit layer for processing a result obtained by processing the normalization function, and the output of each radial basis function is phi_i (x);
the normalization function is:
Wherein k is a normalized value, x is normalized data, including a required length of the steel bar, a required quality of the steel bar and a raw material length of the steel bar, and xmax、xmin is a maximum value and a minimum value in the normalized data respectively;
The calculation formula of the neuron number of the hidden layer is as follows:
Wherein n and l are the number of input neurons and the number of output neurons respectively, and the range of a is 1-10;
the form of linear superposition of the 3 radial basis functions is:
f(x)＝w_1*φ_1(x)+w_2*φ_2(x)+w_3*φ_3(x)
wherein w_1, w_2 and w_3 respectively represent weights of radial basis functions obtained according to weights of various factors in linear superposition, and the weights are used for controlling contribution of each function to final output.
Therefore, the radial basis function model can process the length and the number of the steel bars, and the highest steel bar utilization rate and the best quantitative expression of the steel bar processing efficiency are achieved according to the scheme optimization design principle of secondary steel bar blanking.
The linear superposition mode can fully ensure the balance relation between the residual quantity of the steel bars and the processing efficiency of the steel bars in the processing process of the steel bars, and the balance relation is intuitively displayed in a numerical form, so that the manual check is convenient, and the processing efficiency of the system on the problem of optimizing the blanking of the steel bars is ensured.
And S4, taking the determined radial basis function model as an objective function of the genetic algorithm optimization model, and inputting the reinforcing steel bars with different combinations and the requirement amount list of each reinforcing steel bar into the genetic algorithm optimization model for iterative computation to obtain the reinforcing steel bar blanking scheme which meets the optimization target that the total reinforcing steel bar consumption in the reinforcing steel bar three-dimensional model is minimum and the construction efficiency is optimal.
The method comprises the following specific steps:
Step S4.1, designing a corresponding genetic algorithm optimization model based on an objective function and a flow of steel bar blanking in actual construction, namely, designing a double-layer nesting algorithm by taking the change characteristics of a steel bar stock area, a processing area and supply and demand into consideration, wherein the double-layer nesting algorithm comprises an upper layer, a middle layer, a lower layer and the objective function, the operation sequence is the upper layer, the middle layer, the lower layer and the objective function, the flow of steel bar blanking in actual construction is that steel bars in the steel bar stock area are transported to a storage area as daily or weekly consumption, the steel bars in the storage area are transported to a temporary storage area corresponding to the processing area in batches and then are processed one by one, and the change characteristics are that under the condition that the supply and demand is daily steel bar processing amount, the steel bars are allocated to the processing area according to daily processing amount, and the storage capacity and production efficiency of the processing area are required to be met by the steel bar amount entering the processing area each time;
the upper layer is a blanking scheme layer and is used for determining the sequence and the quantity of the steel bars placed in the stock pit according to the steel bars of different combinations and the requirement quantity list of each steel bar;
The middle layer is a layout pool and is used for generating the sequence and the number of the steel bars initially placed in the temporary storage area of each processing device based on the number of the steel bars determined to be placed by the blanking scheme layer; to take into account the storage constraints of the inventory area, the draw pool is designed for placement of the order and quantity of rebar demand to be placed. After a set of rebar has been processed and a certain number of rebar demand orders settled, these orders are removed from the stock pool, while new orders are added, so that the construction keeps the number of rebar demand orders in the stock pool equal to its capacity. The design mode of the stock layout pool not only converts model constraint into algorithm framework constraint, simplifies the calculation complexity for guaranteeing feasible solution, but also can be used as a channel for double-layer algorithm data interaction, effectively splits the algorithm capacity required by the blanking optimization scheme, improves the calculation performance of the algorithm, and reduces the requirement of system calculation resources.
The lower layer is a stock layout layer and is used for generating specific steel bar cutting and welding schemes, meanwhile, the length of each steel bar required quantity order required to be processed is decided by constructing heuristic rules based on the number of steel bars placed in a temporary storage area of each processing device, namely, the steel bar blanking scheme which is used for obtaining the optimal optimization target of the minimum total steel bar consumption and the optimal construction efficiency is obtained, and the specific steps are as follows:
Step S4.11, calculating the quantity ns of all the steel bars to be processed in the processing area based on the quantity of the steel bars in each steel bar demand order, namelyWherein nip represents the number of the steel bars which are preliminarily required to be configured to the corresponding p processing area by the ith steel bar demand order;
Step S4.12, calculating the average length Ls of each steel bar in each steel bar demand order based on the number ns of all the steel bars to be processed and the total length Ls of the steel bars in all the obtained steel bar demand orders, wherein the formula is as follows:
ls＝Ls/ns；
step S4.13, calculating the average length Lr of the processed steel bars required by each steel bar demand order, wherein the formula is as follows:
Lr←([nip×ls])
wherein, ζ represents a valuation;
Step S4.14, obtaining the length of the steel bar required by each steel bar required order, arranging each steel bar required order from large to small based on the length of the steel bar required by each steel bar required order, and initializing the counting variable r=1 of the steel bar required order according to the arrangement;
Step S4.15, calculating a step loss R generated after the step of adopting the minimum step principle for the steel bars of the current steel bar demand order R based on the average length Lr of the steel bars required to be processed of the current steel bar demand order R, wherein the formula is as follows:
R＝La-Lr
wherein, La is the total length of the steel bars in the steel bar demand order r:
S4.16, if R is given as average replacement loss x R, configuring the last steel bar in the current steel bar demand order in the next steel bar demand order for processing, firstly updating the number of steel bars put in a temporary storage area, putting the steel bars in the temporary storage area according to the sequence, configuring the steel bars in the processing area for processing, and if R is given as average replacement loss x R, the number of the steel bars in the temporary storage area is kept unchanged, putting the steel bars in the temporary storage area according to the sequence, and configuring the steel bars in the temporary storage area in the processing area for processing;
Step S4.17, if the r value exceeds the total number of the steel bar demand orders, ending, otherwise, jumping back to step S4.15, and taking the next steel bar demand order as the current steel bar demand order, wherein r=r+1;
And S4.2, inputting the reinforcing steel bars with different combinations into a genetic algorithm optimization model for iterative computation, and obtaining the reinforcing steel bar blanking scheme which meets the optimization target of minimum total reinforcing steel bar consumption and optimal construction efficiency in the reinforcing steel bar three-dimensional model.
The blanking scheme layer solves the sequence of the steel bars placed in the stock layout pool in each demand list through a single-chain coding genetic algorithm in the genetic algorithm optimization model;
and the stock layout layer is used for solving and generating a specific steel bar cutting and welding scheme through a single-chain coding genetic algorithm in the genetic algorithm optimization model.
The formula of the linear normalization method (namely, the constraint condition of iteration) in the genetic algorithm optimization model is as follows:
s.t.
cip≥0
Wherein cip is the number of bars in the processing area p of the bar demand order i, B represents the number of cuts, s.t. represents the constraint condition of the genetic algorithm.
For example, the total rebar demand order quantity in the machining area is 9. The length of the required processed steel bars for all orders is 12000mm,140, the holding capacity of the temporary storage area steel bars is 10.
The first order is now processed and the last rebar in the first order (i.e., the first rebar demand order) is placed in the present order for processing, at which point the step loss may be 60mm. The average displacement loss is 100mm for each order, and when the order reduction number is 1, the situation that R < average displacement loss multiplied by the order reduction number is judged that the last steel bar is processed in the order, and the number of the steel bars in the temporary storage area is supplemented to 10.
And performing second order processing, wherein the last steel bar of the first order is placed in the first order for processing, and the section difference loss is 210mm. The average replacement loss is 100mm for each order, at this time, the order reduction number is 2, and the case of R > average replacement loss multiplied by the order reduction number is determined that the steel bar is processed in the next order, and 9 steel bars are correspondingly supplemented to enter a temporary storage area.
The steel bar blanking optimization system can be divided into: model management module, check module and management module. The model management module comprises functions of model uploading, model modification and the like; the checking module is mainly used for checking the demand list; the management module comprises site management functions such as processing sheet management, distribution sheet signing, steel bar semi-finished product counting and the like. The cooperation flow of the modules is shown in fig. 6. The steel bar which is finally processed is a semi-finished product steel bar, and the finished product steel bar is obtained after the steel bar is processed and is sent to a construction site for binding.
In order to accurately generate the steel bar demand of the blanking steel bars, a worker can construct a steel bar blanking model (namely, construct a steel bar three-dimensional model based on a design drawing and a BIM technology) according to a pre-made construction progress plan and combining construction progress of different sections, and upload the related model to a model management module.
After the BIM platform is used for generating a project steel bar demand inventory, an auditor uploads the project steel bar demand inventory to a checking module in the inventory system for checking the steel bar demand inventory, so that double guarantee on the accuracy of steel bar information is realized.
If checking is correct, the related responsible person can generate bill data through the checking module, and input the bill data into a steel bar blanking optimization model (a steel bar blanking optimization method), after the steel bar blanking optimization model acquires the bill data, the optimized steel bar blanking scheme can be given through iterative optimization of a genetic algorithm optimization model, and the optimized steel bar blanking scheme is displayed in the form of images and forms. In the management module, the steel bar labor charge staff can refer to the blanking scheme, arrange processing batches and distribution batches according to the field conditions, and generate and dispatch processing sheets. In the process of dispatching the processing sheet, the work of signing the dispatching sheet, checking the steel bar semi-finished products, loading and unloading and the like which need to be completed can be processed in the management module. By means of the management module, a responsible person can synchronously issue binding bill of materials and a steel bar layout diagram so as to ensure the construction quality of the site.
And carrying out deepening design on the corresponding structure of the steel bar to be fed through BIM technology, and carrying out Revit software information statistics to obtain a steel bar demand statistical table, wherein the table is shown in Table 2. Wherein B6-10 is a round disc steel bar, and C12-20 is a straight bar steel bar. Since the C20 rebar is relatively high, the present application is schematically illustrated with the C20 rebar.
In order to facilitate the calculation of the system and better combine with the practical situation, the following schemes one, two and three add limiting conditions: the blanking scheme is used for blanking the steel bars with the same specification (namely the same length, the same volume, the same mass and the like), and does not relate to the situation of the cooperation blanking of the steel bars with various lengths. After the corresponding boundary conditions are determined, the steel bar demand information and the boundary information are brought into a multi-parent genetic algorithm (namely a genetic algorithm optimization model) based on a radial basis function model to carry out steel bar blanking target optimization calculation, and the operation result is shown in table 3.
The lower diagram is an iterative process of performing cut-off combination optimization selection operation on the genetic algorithm optimization model in the first scheme, the second scheme and the third scheme. It can be seen that in 200 iterations performed, the algorithm tends to converge around 100 times to reach the theoretical optimal approximation. The iterative process is shown in fig. 7.
From the results of the first, second and third schemes, the second scheme uses the least number of the 12m single-specification original length steel bars, the highest steel bar utilization rate, and the C12 is considered to be used as a construction steel bar in practical application, so that the optimized blanking result of the C16-C20 is compared with the manual calculation result by selecting the 12m single-specification original length steel bars, and the specific comparison result is shown in fig. 8.
The manual calculation utilization rate considered here is the utilization rate determined by calculation based on the principle of 'length before length and length collocation', and is higher than the actual utilization rate in the field. As can be seen from fig. 8, the calculation results of the reinforcement bar blanking optimization model about the type of the C16-C20 reinforcement bar are better than those of manual calculation, the reinforcement bar utilization rate can be improved by about 4%, and the waste of the reinforcement bar raw materials caused by the site reinforcement bar blanking can be reduced to the greatest extent.