Intelligent design method and system for fiber composite anchor rod

Abstract

The invention relates to the technical field of civil engineering and composite materials, in particular to a fiber composite anchor rod intelligent design method and system.The method comprises the following steps that carbon fibers and glass fibers are used for manufacturing a carbon-glass hybrid anchor rod core part, and meanwhile, LCP fiber bundles are used for weaving the surface of the carbon-glass hybrid anchor rod core part to form a woven layer; winding LCP fiber bundles on the surface of the braid layer to form LCP fiber winding ribs, so as to obtain the fiber composite anchor rod; adjusting the adjustable parameters of the fiber composite material anchor rods to produce a plurality of fiber composite material anchor rods so as to construct a production parameter prediction model, and using the production parameter prediction model to obtain production parameter prediction values of the fiber composite material anchor rods when anchor rod performance parameters are given; and producing the fiber composite anchor rod according to the production parameter predicted value. By means of the method, the fiber composite anchor rod high in mechanical property and economical efficiency can be produced, the production efficiency can be improved, and the production cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering and composite materials, and in particular to an intelligent design method and system for fiber composite anchor rods. Background Art

[0002] Anchor support is a commonly used form of rock and soil anchoring, which can be used in underground engineering, slope stabilization, tunnel construction, mine tunnels, deep foundation pit support and various rock and soil reinforcement projects. It provides stable support by burying anchors in the rock and soil, and using the bonding and friction between the anchors and the surrounding rock and soil to enhance the overall stability of the rock and soil and ensure the safety of the engineering structure. Currently, the commonly used steel anchors have the disadvantages of insufficient strength, excessive deadweight, easy corrosion, large creep and relaxation, etc., which makes it increasingly difficult to meet the construction needs of high performance and long life of rock and soil anchoring in infrastructure construction in the new era.

[0003] The use of fiber composites to make anchor rods can fundamentally solve the problems existing in steel anchor rods. The existing fiber composite anchor rods mainly include glass fiber composite anchor rods, basalt fiber composite anchor rods and carbon fiber composite anchor rods. However, there are still some problems with the existing fiber composite anchor rods, which prevents them from being used on a large scale. Among them, glass fiber composite and basalt fiber composite anchor rods have large creep, low creep rupture stress, and low shear and bending strength; carbon fiber composite anchor rods have a small elongation at break, a high price, and low shear and bending strength.

[0004] In addition, in the traditional production process of fiber composite anchors, the production of fiber composite anchors that meet construction requirements is often based on experience and trial and error, which is not only inefficient but also costly. If fiber composite anchors that exceed construction requirements are directly selected, it will also bring about high costs and waste resources.

[0005] Therefore, it is necessary to explore a new fiber composite anchor production solution to solve the above technical problems. Summary of the invention

[0006] In view of the defects in the prior art, the present invention provides a fiber composite anchor intelligent design method and system.

[0007] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides an intelligent design method for fiber composite anchor rods, the method comprising the following steps: using carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core, and using LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a woven layer; after weaving the surface of the carbon-glass hybrid anchor rod core, using LCP fiber bundles to wrap the surface of the woven layer to form LCP fiber winding ribs, thereby obtaining a fiber composite anchor rod; adjusting the adjustable parameters of the fiber composite anchor rod to produce a plurality of the fiber composite anchor rods, thereby constructing a fiber composite anchor rod production prediction data set; constructing a production parameter prediction model based on the fiber composite anchor rod production prediction data set, and using the production parameter prediction model to obtain the production parameter prediction value of the fiber composite anchor rod when the anchor rod performance parameters are given; producing the fiber composite anchor rod according to the production parameter prediction value. The present invention can produce fiber composite anchor rods with high mechanical properties and economic efficiency, and can improve production efficiency and reduce production costs.

[0008] Optionally, the method of using carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core and using LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a braided layer comprises the following steps: At the same time, at least one carbon fiber and at least one glass fiber are extracted and straightened, and then the carbon fiber and the glass fiber are impregnated with a thermosetting resin so that the fibers are bonded to each other; After the carbon fiber and the glass fiber are impregnated with thermosetting resin, the carbon-glass hybrid anchor core is formed by hybrid pultrusion technology of the carbon fiber and the glass fiber, and the surface of the carbon-glass hybrid anchor core is woven with LCP fiber bundles to form the woven layer.

[0009] Optionally, after braiding the surface of the carbon-glass hybrid anchor core, winding LCP fiber bundles on the surface of the braided layer to form LCP fiber winding ribs, thereby obtaining the fiber composite anchor comprises the following steps: The braided carbon-glass hybrid anchor rod core is impregnated with a thermosetting resin, and then an LCP fiber bundle is wound around the surface of the braided layer in a threaded shape to form an LCP fiber-wound rib, thereby obtaining a semi-finished anchor rod; The anchor rod semi-finished product is cured at a set temperature and pressure, so that the carbon-glass hybrid anchor rod core, the braided layer and the LCP fiber winding rib are tightly combined to obtain the fiber composite anchor rod.

[0010] Optionally, adjusting the adjustable parameters of the fiber composite anchor to produce a plurality of the fiber composite anchors, and then constructing a fiber composite anchor production prediction data set comprises the following steps: Adjusting the values ​​of different adjustable parameters to generate a plurality of adjustable parameter combinations, wherein the adjustable parameters include fiber mixing ratio, weaving angle, weaving density, anchor rod diameter and anchor rod length; Produce the corresponding fiber composite anchor rod according to the adjustable parameter combination and test its bending strength, tensile strength and shear strength; A fiber-reinforced anchor production prediction dataset is constructed using all adjustable parameter combinations and the corresponding flexural strength, tensile strength, and shear strength of the fiber-reinforced anchor.

[0011] Optionally, the anchor performance parameters include anchor diameter, anchor length, bending strength, tensile strength and shear strength, and the production parameters of the fiber composite anchor include fiber mixing ratio, weaving angle and weaving density; The step of constructing a production parameter prediction model based on the fiber composite anchor production prediction data set and using the production parameter prediction model to obtain a production parameter prediction value of the fiber composite anchor when the anchor performance parameters are given comprises the following steps: Combine the multi-strategy improved genetic algorithm and optimized BP neural network into a combined prediction model; Using the fiber composite anchor production prediction data set to train and verify the combined prediction model to obtain the production parameter prediction model; The given anchor performance parameters are input into the production parameter prediction model to obtain the production parameter prediction values ​​of the fiber composite anchor.

[0012] Optionally, the multi-strategy improved genetic algorithm performs the following steps when running: Setting algorithm parameters, and generating chromosomes according to the initial weights and initial thresholds of the optimized BP neural network to initialize the population; The genetic algorithm iteration starts, the fitness value of each chromosome is calculated, and then the chromosome difference is calculated, and the calculated chromosome difference is compared with the difference threshold; When the chromosome difference is less than the difference threshold, the chromosome is changed using a chromosome change strategy until the chromosome difference is not less than the difference threshold; When the chromosome difference is not less than the difference threshold, performing chromosome crossover and mutation according to an adaptive crossover probability model and an adaptive mutation probability model to generate a new chromosome; It is determined whether the maximum number of iterations has been reached, and when the maximum number of iterations has been reached, the best chromosome is output; otherwise, the new chromosome obtained in the current iteration step is entered into the next iteration step.

[0013] Optionally, the chromosome difference, the adaptive crossover probability model and the adaptive mutation probability model respectively satisfy the following relationships:

[0014]

[0015]

[0016] Wherein, R is the chromosome difference, n is the number of chromosomes, is the fitness value of the jth chromosome, P is the crossover probability of the chromosome, is the starting value of the crossover probability, is the stable value of crossover probability, is the larger fitness value of the two chromosomes involved in the crossover, is the maximum fitness value of the chromosome, is the probability of chromosome mutation, is the starting value of mutation probability, is the stable value of mutation probability, is the fitness value of the chromosome that needs to be mutated.

[0017] Optionally, the chromosome alteration strategy comprises the following steps: Sort the chromosomes in descending order of the fitness values ​​to obtain a chromosome sequence, and divide the chromosome sequence into a first sequence and a second sequence, wherein the first sequence is the first half of the chromosome sequence; Extracting a gene combination from each chromosome in the second sequence according to the set number of chromosome gene changes, and then constructing an original gene combination sequence according to the chromosome order; Continuously adjusting the order of gene combinations in the original gene combination sequence to obtain a new gene combination sequence, and taking the gene combination sequence with the greatest difference from the original gene combination sequence as the final gene combination sequence; According to the location of the genes in the original gene combination sequence on the chromosome, the final gene combination sequence is used to change the genes on the chromosome in the second sequence.

[0018] Optionally, the step of combining the multi-strategy improved genetic algorithm and the optimized BP neural network into a combined prediction model comprises the following steps: An adaptive weight update model is introduced into the BP neural network to obtain the optimized BP neural network, and the adaptive weight update model satisfies the following relationship:

[0019] in, is the weight of the optimized BP neural network at the i+1th iteration step, is the weight of the optimized BP neural network at the i-th iteration step, , and are all algebraic expressions, , , , MSE is the mean square error of the optimized BP neural network, is the weight of the optimized BP neural network at the i-1th iteration step, is the learning rate used by the optimized BP neural network in the i-1th iteration step, is a random number greater than 0 and less than 1; The optimized BP neural network is used as the main part of the combined prediction model to predict production parameters, and the multi-strategy improved genetic algorithm is used as the secondary part of the combined prediction model to optimize the initial weight and initial threshold of the optimized BP neural network.

[0020] In a second aspect, the present invention provides a fiber composite anchor intelligent design system, the fiber composite anchor intelligent design system uses a fiber composite anchor intelligent design method provided by the present invention, the system comprises: a fiber composite anchor production subsystem, the fiber composite anchor production subsystem is used to use carbon fiber and glass fiber to make a carbon-glass hybrid anchor core, and use LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor core to form a woven layer; after weaving the surface of the carbon-glass hybrid anchor core, the LCP fiber bundles are then wound on the surface of the woven layer to form an LCP fiber winding rib, thereby obtaining a fiber Composite anchor rod; an anchor rod testing and data acquisition subsystem, the anchor rod testing and data acquisition subsystem is used to adjust the adjustable parameters of the fiber composite anchor rod to produce a plurality of the fiber composite anchor rods, thereby constructing a fiber composite anchor rod production prediction data set; a production parameter judgment subsystem, the production parameter judgment subsystem is used to construct a production parameter prediction model based on the fiber composite anchor rod production prediction data set, and use the production parameter prediction model to obtain the production parameter prediction value of the fiber composite anchor rod when the anchor rod performance parameters are given, thereby enabling the fiber composite anchor rod production subsystem to produce the fiber composite anchor rod according to the production parameter prediction value.

[0021] The present invention has at least the following beneficial effects: 1. The core of the carbon-glass hybrid anchor rod of the present invention is formed by hybrid pultrusion of carbon fiber and glass fiber, and has high strength and corrosion resistance, which effectively improves the mechanical properties of the anchor rod, reduces the cost as much as possible, and improves the economy of the anchor rod.

[0022] 2. The present invention uses LCP fibers to weave and wrap on the surface of the core of the carbon-glass hybrid anchor rod, which further enhances the shear resistance and bending resistance of the anchor rod and can improve the anchoring effect of the anchor rod.

[0023] 3. The fiber composite anchor produced by the present invention uses carbon fiber, glass fiber and LCP fiber as main materials, and has excellent corrosion resistance and creep resistance, overcoming the problems of easy rust and performance degradation of traditional steel anchors, and enabling the anchors to maintain long-term mechanical properties and structural stability in various harsh environments.

[0024] 4. The present invention constructs an accurate and reliable production parameter prediction model for optimizing the design of production parameters, which can quickly obtain the optimal combination that meets specific performance requirements. It not only improves the design and production efficiency of anchor rods, but also eliminates the trial and error process in production, further reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of a process of an intelligent design method for a fiber composite anchor rod according to an embodiment of the present invention; Figure 2 A schematic diagram of the production process of a fiber composite anchor rod according to an embodiment of the present invention; Figure 3 A schematic diagram of a fiber composite anchor rod according to an embodiment of the present invention; Figure 4 A schematic diagram of the framework of a fiber composite anchor intelligent design system according to an embodiment of the present invention.

[0027] Among them: 1-fiber yarn rack, 2-calibration plate, 3-main dipping tank, 4-pre-mold, 5-braiding machine, 6-guide hole, 7-secondary dipping tank, 8-winding machine, 9-heating and curing mold, 10-traction device, 11-cutting saw, 12-carbon-glass hybrid anchor core, 13-braiding layer, 14-LCP fiber winding rib. DETAILED DESCRIPTION

[0028] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0029] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0030] It should be noted in advance that, in an optional embodiment, except for independent explanations, the same symbols or letters appearing in all formulas have the same meanings and values.

[0031] In an alternative embodiment, see Figures 1 to 3 The present invention provides a fiber composite anchor intelligent design method, the method comprising the following steps: S1. Using carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core, and using LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a braided layer.

[0032] Wherein, step S1 specifically includes the following steps: S11, extracting at least one carbon fiber and at least one glass fiber at the same time and straightening them, and then impregnating the carbon fiber and the glass fiber with thermosetting resin to make the fibers bond to each other.

[0033] Specifically, in this embodiment, at least one carbon fiber and at least one glass fiber are simultaneously extracted from the fiber creel 1 by the traction device 10, and the carbon fiber and the glass fiber are straightened by the calibration plate 2 to ensure the uniform arrangement and neatness of the fiber bundles. After being straightened, the carbon fiber and the glass fiber enter the main impregnation tank 3 filled with thermosetting resin, so that the fiber bundles are impregnated with the thermosetting resin, and then the fibers are bonded to each other, and at the same time, the necessary corrosion resistance and high strength performance are provided for the produced fiber composite anchor.

[0034] More specifically, carbon fibers and glass fibers need to be extracted from the fiber creel 1 according to a preset fiber mixing ratio, and the thermosetting resin in the main dipping tank 3 is selected to be epoxy resin or vinyl ester resin. In other optional embodiments, the thermosetting resin can also be selected according to different engineering requirements.

[0035] S12. After the carbon fiber and the glass fiber are impregnated with thermosetting resin, the carbon-glass hybrid anchor core is formed by a hybrid pultrusion technology of the carbon fiber and the glass fiber, and at the same time, the surface of the carbon-glass hybrid anchor core is woven using an LCP fiber bundle to form the woven layer.

[0036] Specifically, in this embodiment, the produced fiber composite anchor is pulled by the pulling device 10, and then the fiber bundle impregnated with thermosetting resin is pulled through the tubular preform 4, and the fiber bundle impregnated with thermosetting resin is squeezed in the preform 4 to form a carbon-glass hybrid anchor core 12, and at the same time, the braiding machine 5 will use 16 LCP fiber bundles to braid its surface to form a uniform braided layer 13. LCP fiber is liquid crystal polyarylate fiber, which has excellent strength and wear resistance, and can effectively improve the shear and bending resistance of the anchor.

[0037] Furthermore, the diameter of the preform 4 will affect the diameter of the carbon-glass hybrid anchor core 12, so the diameter of the carbon-glass hybrid anchor core 12 can be changed by changing the diameter of the preform 4, thereby changing the diameter of the produced fiber composite anchor. In addition, when the carbon-glass hybrid anchor core 12 is braided using LCP fiber bundles, the braiding angle and braiding density of the braiding machine 5 need to be set.

[0038] S2. After weaving the surface of the carbon-glass hybrid anchor rod core, LCP fiber bundles are wound on the surface of the braided layer to form LCP fiber winding ribs, thereby obtaining a fiber composite anchor rod.

[0039] Wherein, step S2 specifically includes the following steps: S21, impregnating the braided carbon-glass hybrid anchor rod core with a thermosetting resin, and then using LCP fiber bundles to spirally wind around the surface of the braided layer to form LCP fiber-wound ribs, thereby obtaining a semi-finished anchor rod.

[0040] Specifically, in this embodiment, under the traction of the traction device 10, the braided carbon-glass hybrid anchor rod core 12 enters the secondary dipping tank 7 filled with thermosetting resin through the guide hole 6 for secondary dipping treatment, providing an attachment basis for the winding fiber. The braided carbon-glass hybrid anchor rod core 12 enters the winding machine 8 after the secondary dipping treatment. The winding machine 8 uses two LCP fiber bundles to wind on the braided layer 13 in a threaded shape, and the distance between the two LCP fiber bundles is fixed during the winding process to form an LCP fiber winding rib 14, thereby obtaining a semi-finished anchor rod.

[0041] Furthermore, the LCP fiber winding rib can enhance the anchoring effect of the fiber composite anchor in grouting, and the winding angle of the LCP fiber bundle and the distance between two LCP fiber bundles can be adjusted according to actual needs to change the mechanical bite force and embedding effect of the fiber composite anchor.

[0042] S22, curing the anchor rod semi-finished product at a set temperature and pressure, so that the carbon-glass hybrid anchor rod core, the braided layer and the LCP fiber winding rib are tightly combined to obtain the fiber composite anchor rod.

[0043] Specifically, in this embodiment, the anchor rod semi-finished product is fed into the heating and curing mold 9, and the temperature and pressure in the heating and curing mold 9 are adjusted to cure the thermosetting resin, so that the carbon-glass hybrid anchor rod core 12, the braided layer 13 and the LCP fiber winding rib 14 are tightly combined to form a whole under the action of the thermosetting resin, and finally a fiber composite anchor rod is obtained.

[0044] Furthermore, after the fiber composite anchor rod passes through the traction device 10, it is cut by the cutting saw 11 to obtain fiber composite anchor rods of different lengths. Figure 3 shown.

[0045] S3. Adjusting the adjustable parameters of the fiber composite anchor rod to produce a plurality of the fiber composite anchor rods, thereby constructing a fiber composite anchor rod production prediction data set.

[0046] Wherein, step S3 specifically includes the following steps: S31. Adjust the values ​​of different adjustable parameters to generate multiple adjustable parameter combinations, wherein the adjustable parameters include fiber mixing ratio, weaving angle, weaving density, anchor rod diameter and anchor rod length.

[0047] Specifically, in this embodiment, the adjustable parameters refer to parameters that can be adjusted or designed before producing the fiber composite anchor.

[0048] Furthermore, in other optional embodiments, the adjustable parameters may also include fiber material properties, resin material properties, fiber volume ratio, etc.

[0049] S32, producing the corresponding fiber composite anchor rod according to the adjustable parameter combination and testing its bending strength, tensile strength and shear strength.

[0050] Specifically, in this embodiment, the tests of the bending strength, tensile strength and shear strength of the fiber composite anchor can be achieved by existing technical means.

[0051] S33. Construct a fiber composite anchor production prediction data set using all adjustable parameter combinations and the corresponding bending strength, tensile strength and shear strength of the fiber composite anchor.

[0052] S4. Constructing a production parameter prediction model based on the fiber composite anchor production prediction data set, and using the production parameter prediction model to obtain production parameter prediction values ​​of the fiber composite anchor when anchor performance parameters are given.

[0053] Among them, the anchor performance parameters include the anchor diameter, anchor length, tensile strength and shear strength, and the anchor production parameters include fiber mixing ratio, weaving angle and weaving density. The reason why the length and diameter of the fiber composite anchor are taken as the data to be predicted in this embodiment is that the length and diameter of the anchor are limited by its installation position during engineering construction, and in order to produce an anchor suitable for the installation position when the installation position of the anchor is determined, the length and diameter of the anchor are also required to be taken as the performance parameters of the fiber composite anchor.

[0054] In other optional embodiments, the anchor performance parameters may further include the cost of the fiber composite anchor per unit length, and the anchor production parameters may further include fiber material properties, resin material properties, and fiber volume ratio.

[0055] Step S4 specifically includes the following steps: S41. Combine the multi-strategy improved genetic algorithm and optimized BP neural network into a combined prediction model.

[0056] Wherein, step S41 specifically includes the following steps: S411, introducing an adaptive weight updating model into the BP neural network to obtain the optimized BP neural network.

[0057] Specifically, in this embodiment, the adaptive weight update model satisfies the following relationship:

[0058] in, To optimize the weight of the BP neural network at the i+1th iteration step, To optimize the weight of the BP neural network in the i-th iteration step, , and are all algebraic expressions, , , , MSE is the mean square error of the optimized BP neural network, To optimize the weight of the BP neural network in the i-1th iteration step, To optimize the learning rate used by the BP neural network in the i-1th iteration step, A random number greater than 0 and less than 1.

[0059] More specifically, the traditional BP neural network updates weights based on the gradient descent method, but the weight update based on the gradient descent method will bring about the problems of slow convergence speed and easy to fall into the local minimum. The adaptive weight update model provided by the present invention can limit the learning rate according to the change of the partial derivative of the weight of the mean square error of the optimized BP neural network to dynamically update the weight, improve the weight update speed, and avoid the problem that the traditional BP neural network is easy to fall into the local minimum when the gradient is close to 0, thereby improving the training speed and prediction accuracy of the algorithm, thereby improving the accuracy of the prediction of production parameters.

[0060] S412, using the optimized BP neural network as the main part of the combined forecasting model to predict production parameters, and using the multi-strategy improved genetic algorithm as the secondary part of the combined forecasting model to optimize the initial weight and initial threshold of the optimized BP neural network.

[0061] Specifically, in this embodiment, the multi-strategy improved genetic algorithm performs the following steps when running: H1. Set algorithm parameters and generate chromosomes according to the initial weights and initial thresholds of the optimized BP neural network to initialize the population.

[0062] Specifically, in this embodiment, the algorithm parameters refer to the parameters that need to be set in advance in the multi-strategy improved genetic algorithm, including population size, selection operator, crossover operator, mutation operator, crossover probability starting value, crossover probability stable value, mutation probability starting value and mutation probability stable value. Among them, in this embodiment, the crossover probability starting value, crossover probability stable value, mutation probability starting value and mutation probability stable value are set to 50, 0.7, 0.9, 0.01 and 0.1 respectively, and the settings of the selection operator, crossover operator and mutation operator can refer to the prior art.

[0063] Furthermore, the initial weights and initial thresholds of the optimized BP neural network are randomly generated. According to the initial weights and initial thresholds of the optimized BP neural network, chromosomes are generated in a real number coding manner to initialize the population. Each chromosome is an individual in the population, and each chromosome represents a possible combination of initial weights and initial thresholds. In addition, since the chromosomes are generated according to the initial weights and initial thresholds of the optimized BP neural network, the genes on the chromosomes can be divided into two types: weight genes and threshold genes. Each weight gene corresponds to a possible initial weight, and each threshold gene corresponds to a possible initial threshold.

[0064] H2. Genetic algorithm iteration begins, calculating the fitness value of each chromosome, and then calculating the chromosome difference, and comparing the calculated chromosome difference with the difference threshold.

[0065] Specifically, in this embodiment, the optimization of the initial weight and initial threshold of the optimized BP neural network using the multi-strategy improved genetic algorithm is to improve the prediction accuracy of the production parameters, and the mean square error between the predicted data and the actual data of the optimized BP neural network can reflect the performance of the optimized BP neural network, so the inverse of the mean square error between the predicted data and the actual data of the optimized BP neural network is considered as the fitness function of the multi-strategy improved genetic algorithm. In addition, if the mean square error between the predicted data and the actual data of the optimized BP neural network is 0, the fitness value is directly taken as 100.

[0066] Furthermore, in the later stages of the genetic algorithm, the individual's adaptability to the environment increases significantly, and the chromosome differences decrease significantly, which reduces the algorithm's ability to explore in the later stages and makes the algorithm prone to falling into local optimality. Therefore, this embodiment calculates the chromosome differences and compares them with the difference threshold to determine whether the chromosomes in the population are consistent, providing data support for the subsequent implementation of the chromosome change strategy to improve the algorithm's ability to explore in the later stages. The chromosome differences satisfy the following relationship:

[0067] Where R is the chromosome difference, n is the number of chromosomes, is the fitness value of the jth chromosome.

[0068] H3. When the chromosome difference is less than the difference threshold, the chromosome is changed using a chromosome change strategy until the chromosome difference is not less than the difference threshold.

[0069] Specifically, in this embodiment, the chromosome change strategy is used to improve the chromosome difference and avoid the algorithm from falling into the local optimum. Each time the chromosome is changed using the chromosome change strategy, a new population will be obtained. Each time a new population is obtained, it is necessary to return to step H2 until the chromosome difference of the new population is not less than the difference threshold. The chromosome change strategy includes the following steps: L1. Sort the chromosomes in descending order of the fitness values ​​to obtain a chromosome sequence, and divide the chromosome sequence into a first sequence and a second sequence, wherein the first sequence is the first half of the chromosome sequence.

[0070] Specifically, in this embodiment, the first sequence and the second sequence are respectively expressed as and , is the jth chromosome in the chromosome sequence, If the number of chromosomes is even, then If the number of chromosomes is odd, then or .

[0071] L2. Extract a gene combination from each chromosome in the second sequence according to the set number of chromosome gene changes, and then construct the original gene combination sequence according to the chromosome order.

[0072] Specifically, in this embodiment, the number of chromosome gene changes is set, and the number of chromosome gene changes is usually set to an even number, wherein the weight gene and the threshold gene each account for half. According to the set number of chromosome gene changes K, for any chromosome in the second sequence, K genes are randomly extracted from the gene combination of the chromosome. The first half of the genes in each gene combination are weight genes, and the second half of the genes are threshold genes. According to the above description, the original gene combination sequence can be constructed according to the order of the chromosomes in the second sequence, and it is easy to know that the gene combination in the original gene combination sequence corresponds one to one with the chromosomes in the second sequence.

[0073] Furthermore, the extracted gene combination can be expressed as , is the kth gene extracted from the jth chromosome, and Located at the kth position on the jth chromosome, , It should be noted that when extracting genes to form a gene combination, the number of extracted genes for different chromosomes in the second sequence is K, but the gene positions may be different, and the data corresponding to the genes at the same position in the gene combination are of the same type. For example, assuming that the genes on each chromosome are numbered starting from 1 using positive integers, then and Although they all represent the same type of gene extracted from the first position on the chromosome, the first position on the m+1th chromosome may be the position of the third gene on the chromosome, while the first position on the m+2th chromosome may be the position of the seventh gene on the chromosome.

[0074] L3. Continuously adjust the order of the gene combinations in the original gene combination sequence to obtain a new gene combination sequence, and use the gene combination sequence with the greatest difference from the original gene combination sequence as the final gene combination sequence.

[0075] Specifically, in this embodiment, the order of the gene combinations in the original gene combination sequence is continuously and randomly adjusted to obtain a new gene combination sequence. Each adjustment requires judging the difference between the new gene combination sequence and the original gene combination sequence. Finally, the gene combination sequence with the greatest difference from the original gene combination sequence is taken as the final gene combination sequence.

[0076] More specifically, the difference between the new gene combination sequence and the original gene combination sequence is determined by the following relationship:

[0077] Among them, y is the difference between the new gene combination sequence and the original gene combination sequence, is the initial weight value or initial threshold corresponding to the kth gene in the tth gene combination in the new gene combination sequence, is the initial weight value or initial threshold corresponding to the kth gene in the tth gene combination in the original gene combination sequence.

[0078] L4. According to the position of the genes in the original gene combination sequence on the chromosome, use the final gene combination sequence to change the genes on the chromosome in the second sequence.

[0079] Specifically, in this embodiment, the genes in the gene combination in the original gene combination sequence are recorded as disruptive genes, and the gene combination in the final gene combination sequence is sequentially matched with the chromosomes in the second sequence. Next, for any chromosome in the second sequence, the disruptive gene is sequentially replaced with the gene of the chromosome in the final gene combination sequence, so that the gene change of the chromosome in the second sequence can be achieved.

[0080] H4. When the chromosome difference is not less than the difference threshold, chromosome crossover and mutation are performed according to an adaptive crossover probability model and an adaptive mutation probability model to generate a new chromosome.

[0081] Specifically, in this embodiment, the adaptive crossover probability model and the adaptive mutation probability model respectively satisfy the following relationships:

[0082]

[0083] Among them, P is the crossover probability of chromosome, is the starting value of the crossover probability, is the stable value of crossover probability, is the larger fitness value of the two chromosomes involved in the crossover, is the maximum fitness value of the chromosome, is the probability of chromosome mutation, is the starting value of mutation probability, is the stable value of mutation probability, is the fitness value of the chromosome that needs to be mutated.

[0084] Furthermore, the adaptive crossover probability model and adaptive mutation probability model provided in this embodiment can automatically adjust the crossover probability and mutation probability according to the individual fitness value and the fitness value of the population as a whole. This can solve the problems of premature convergence and local optimal solutions existing in the existing BP neural network while ensuring the global search capability of the algorithm, thereby improving the prediction efficiency and accuracy of production parameters.

[0085] H5. Determine whether the maximum number of iterations has been reached, and output the best chromosome when the maximum number of iterations has been reached, otherwise allow the new chromosome obtained in the current iteration step to enter the next iteration step.

[0086] Specifically, in this embodiment, the best chromosome output when the maximum number of iterations is reached is a combination of the best initial weight and the best initial threshold. If the maximum number of iterations is not reached, then return to step H2.

[0087] S42: Use the fiber composite anchor production prediction data set to train and verify the combined prediction model to obtain the production parameter prediction model.

[0088] S43, inputting the given anchor performance parameters into the production parameter prediction model, thereby obtaining the production parameter prediction values ​​of the fiber composite anchor.

[0089] Specifically, in this embodiment, the production parameter prediction values ​​include a fiber mixing ratio prediction value, a weaving angle prediction value, and a weaving density prediction value.

[0090] S5. Produce the fiber composite anchor according to the predicted value of the production parameter.

[0091] Specifically, in this embodiment, after obtaining the predicted values ​​of the production parameters, carbon fibers and glass fibers can be extracted from the fiber creel 1 according to the predicted value of the fiber mixing ratio, and can be weaved on the core of the carbon-glass hybrid anchor rod using a weaving machine 5 according to the predicted value of the weaving angle and the weaving density.

[0092] It should be noted that, in some cases, the actions described in the specification can be performed in a different order and still achieve the desired results. In this embodiment, the order of steps given is only to make the embodiment appear clearer and easier to explain, rather than to limit it.

[0093] In an alternative embodiment, see Figure 2 and Figure 4The present invention also provides a fiber composite anchor intelligent design system, which uses a fiber composite anchor intelligent design method provided in this embodiment to improve the practicality of the method and the production efficiency of the fiber composite anchor. The system includes a fiber composite anchor production subsystem A1, an anchor test and data acquisition subsystem A2, and a production parameter judgment subsystem A3.

[0094] The fiber composite anchor rod production subsystem A1 is used to use carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core, and use LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a woven layer; after weaving the surface of the carbon-glass hybrid anchor rod core, the LCP fiber bundles are wound around the surface of the woven layer to form LCP fiber winding ribs, thereby obtaining a fiber composite anchor rod.

[0095] Specifically, in this embodiment, the fiber composite anchor rod production subsystem A1 includes a fiber creel 1, a calibration plate 2, a primary dipping tank 3, a pre-mold 4, a braiding machine 5, a guide hole 6, a secondary dipping tank 7, a winding machine 8, a heating and curing mold 9, a traction device 10 and a cutting saw 11. The specific operation process of the fiber composite anchor rod production subsystem A1 is shown in step S1 and step S2.

[0096] The anchor rod testing and data acquisition subsystem A2 is used to adjust the production parameters of the fiber composite anchor rod to produce a plurality of the fiber composite anchor rods, and then construct a fiber composite anchor rod production prediction data set.

[0097] Specifically, in this embodiment, the anchor rod test and data acquisition subsystem A2 executes the content described in step S3. The anchor rod test and data acquisition subsystem A2 includes a parameter setting module, a data acquisition module, a data transmission module, and various instruments for testing the tensile strength and shear strength of fiber composite anchor rods. First, the parameter setting module randomly generates multiple production parameter combinations, and then the relevant personnel adjust the fiber composite anchor rod production subsystem A1 according to the generated production parameter combinations, and then produce multiple fiber composite anchor rods. After that, the relevant personnel perform performance tests on all fiber composite anchor rods and input the obtained data into the data acquisition module to generate a fiber composite anchor rod production prediction data set combined with the production parameter combination. Finally, the data transmission module transmits the fiber composite anchor rod production prediction data set to the production parameter judgment subsystem A3 through the Internet of Things.

[0098] The production parameter judgment subsystem A3 is used to build a production parameter prediction model based on the fiber composite anchor production prediction data set, and use the production parameter prediction model to obtain the production parameter prediction value of the fiber composite anchor when the anchor performance parameters are given, so as to enable the fiber composite anchor production subsystem A1 to produce the fiber composite anchor according to the production parameter prediction value.

[0099] Specifically, in this embodiment, the production parameter judgment subsystem A3 specifically performs the content described in step S4. The production parameter judgment subsystem A3 includes a data input module, a data processing module and a data output module. The data input module receives the fiber composite anchor production prediction data set obtained by the anchor test and data acquisition subsystem A2 through the Internet of Things. The data processing module uses the production parameter prediction model to obtain the production parameter prediction value of the fiber composite anchor when the anchor performance parameters are given. The data output module outputs the production parameter prediction value.

[0100] After obtaining the predicted values ​​of the production parameters, the relevant personnel adjusted the fiber composite anchor production subsystem A1 so that it could produce the fiber composite anchor according to the predicted values ​​of the production parameters.

[0101] In summary, firstly, the present invention forms a carbon-glass hybrid anchor rod core with high strength and corrosion resistance through mixed pultrusion of carbon fiber and glass fiber, which effectively improves the mechanical properties of the anchor rod, reduces the cost as much as possible, and improves the economy of the anchor rod. Secondly, the present invention uses LCP fiber to weave and wind on the surface of the carbon-glass hybrid anchor rod core to form a woven layer and LCP fiber winding ribs, further enhancing the shear resistance and bending resistance of the anchor rod, and can improve the anchoring effect of the anchor rod. Thirdly, the fiber composite anchor rod produced by the present invention uses carbon fiber, glass fiber and LCP fiber as the main materials, has excellent corrosion resistance and creep resistance, overcomes the problem of easy rust and performance attenuation of traditional steel anchor rods, and enables the anchor rod to maintain long-term mechanical properties and structural stability in various harsh environments. Finally, the present invention constructs an accurate and reliable production parameter prediction model for optimizing the design of production parameters, which can quickly obtain the optimal combination that meets specific performance requirements, not only improving the design and production efficiency of the anchor rod, but also eliminating the trial and error process in production, and further reducing production costs.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A fiber composite anchor intelligent design method, characterized in that: The steps include: Using carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core, and using LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a braided layer; After the surface of the carbon-glass hybrid anchor core is braided, an LCP fiber bundle is wound on the surface of the braided layer to form an LCP fiber winding rib, thereby obtaining a fiber composite anchor; adjusting the adjustable parameters of the fiber composite anchor to produce a plurality of the fiber composite anchors, thereby constructing a fiber composite anchor production prediction data set; Building a production parameter prediction model based on the fiber composite anchor production prediction data set, and using the production parameter prediction model to obtain production parameter prediction values ​​of the fiber composite anchor when anchor performance parameters are given; The fiber composite anchor is produced according to the predicted production parameter values.

2. The fiber composite anchor intelligent design method according to claim 1, characterized in that: The method of using carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core and using LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a braided layer comprises the following steps: At the same time, at least one carbon fiber and at least one glass fiber are extracted and straightened, and then the carbon fiber and the glass fiber are impregnated with a thermosetting resin so that the fibers are bonded to each other; After the carbon fiber and the glass fiber are impregnated with thermosetting resin, the carbon-glass hybrid anchor core is formed by hybrid pultrusion technology of the carbon fiber and the glass fiber, and the surface of the carbon-glass hybrid anchor core is woven with LCP fiber bundles to form the woven layer.

3. The intelligent design method for fiber composite anchor according to claim 1, characterized in that: After the surface of the carbon-glass hybrid anchor core is braided, LCP fiber bundles are wound on the surface of the braided layer to form LCP fiber winding ribs, thereby obtaining a fiber composite anchor, which includes the following steps: The braided carbon-glass hybrid anchor rod core is impregnated with a thermosetting resin, and then an LCP fiber bundle is wound around the surface of the braided layer in a threaded shape to form an LCP fiber-wound rib, thereby obtaining a semi-finished anchor rod; The anchor rod semi-finished product is cured at a set temperature and pressure, so that the carbon-glass hybrid anchor rod core, the braided layer and the LCP fiber winding rib are tightly combined to obtain the fiber composite anchor rod.

4. The fiber composite anchor intelligent design method according to claim 1, characterized in that: The step of adjusting the adjustable parameters of the fiber composite anchor to produce a plurality of the fiber composite anchors and then constructing a fiber composite anchor production prediction data set comprises the following steps: Adjusting the values ​​of different adjustable parameters to generate multiple adjustable parameter combinations, wherein the adjustable parameters include fiber mixing ratio, weaving angle, weaving density, anchor rod diameter and anchor rod length; Produce the corresponding fiber composite anchor rod according to the adjustable parameter combination and test its bending strength, tensile strength and shear strength; A fiber-reinforced anchor production prediction dataset is constructed using all adjustable parameter combinations and the corresponding flexural strength, tensile strength, and shear strength of the fiber-reinforced anchor.

5. The fiber composite anchor intelligent design method according to claim 1, characterized in that: The anchor performance parameters include anchor diameter, anchor length, bending strength, tensile strength and shear strength, and the production parameters of the fiber composite anchor include fiber mixing ratio, weaving angle and weaving density; The step of constructing a production parameter prediction model based on the fiber composite anchor production prediction data set and using the production parameter prediction model to obtain a production parameter prediction value of the fiber composite anchor when the anchor performance parameters are given comprises the following steps: Combine the multi-strategy improved genetic algorithm and optimized BP neural network into a combined prediction model; Using the fiber composite anchor production prediction data set to train and verify the combined prediction model to obtain the production parameter prediction model; The given anchor performance parameters are input into the production parameter prediction model to obtain the production parameter prediction values ​​of the fiber composite anchor.

6. The fiber composite anchor intelligent design method according to claim 5, characterized in that: The multi-strategy improved genetic algorithm performs the following steps when running: Setting algorithm parameters, and generating chromosomes according to the initial weights and initial thresholds of the optimized BP neural network to initialize the population; The genetic algorithm iteration starts, the fitness value of each chromosome is calculated, and then the chromosome difference is calculated, and the calculated chromosome difference is compared with the difference threshold; When the chromosome difference is less than the difference threshold, the chromosome is changed using a chromosome change strategy until the chromosome difference is not less than the difference threshold; When the chromosome difference is not less than the difference threshold, performing chromosome crossover and mutation according to an adaptive crossover probability model and an adaptive mutation probability model to generate a new chromosome; It is determined whether the maximum number of iterations has been reached, and when the maximum number of iterations has been reached, the best chromosome is output; otherwise, the new chromosome obtained in the current iteration step is entered into the next iteration step.

7. The fiber composite anchor intelligent design method according to claim 6, characterized in that: The chromosome difference, the adaptive crossover probability model and the adaptive mutation probability model respectively satisfy the following relationships: ， ， ， Wherein, R is the chromosome difference, n is the number of chromosomes, is the fitness value of the jth chromosome, P is the crossover probability of the chromosome, is the starting value of the crossover probability, is the stable value of crossover probability, is the larger fitness value of the two chromosomes involved in the crossover, is the maximum fitness value of the chromosome, is the probability of chromosome mutation, is the starting value of mutation probability, is the stable value of mutation probability, is the fitness value of the chromosome that needs to be mutated.

8. The fiber composite anchor intelligent design method according to claim 6, characterized in that: The chromosome alteration strategy comprises the following steps: Sort the chromosomes in descending order of the fitness values ​​to obtain a chromosome sequence, and divide the chromosome sequence into a first sequence and a second sequence, wherein the first sequence is the first half of the chromosome sequence; Extracting a gene combination from each chromosome in the second sequence according to the set number of chromosome gene changes, and then constructing an original gene combination sequence according to the chromosome order; Continuously adjusting the order of gene combinations in the original gene combination sequence to obtain a new gene combination sequence, and taking the gene combination sequence with the greatest difference from the original gene combination sequence as the final gene combination sequence; According to the location of the genes in the original gene combination sequence on the chromosome, the final gene combination sequence is used to change the genes on the chromosome in the second sequence.

9. The fiber composite anchor intelligent design method according to claim 5, characterized in that: The method of combining the multi-strategy improved genetic algorithm and the optimized BP neural network into a combined prediction model comprises the following steps: An adaptive weight update model is introduced into the BP neural network to obtain the optimized BP neural network, and the adaptive weight update model satisfies the following relationship: ， in, is the weight of the optimized BP neural network at the i+1th iteration step, is the weight of the optimized BP neural network at the i-th iteration step, , and are all algebraic expressions, , , , MSE is the mean square error of the optimized BP neural network, is the weight of the optimized BP neural network at the i-1th iteration step, is the learning rate used by the optimized BP neural network in the i-1th iteration step, is a random number greater than 0 and less than 1; The optimized BP neural network is used as the main part of the combined prediction model to predict production parameters, and the multi-strategy improved genetic algorithm is used as the secondary part of the combined prediction model to optimize the initial weight and initial threshold of the optimized BP neural network.

10. A fiber composite anchor intelligent design system, the fiber composite anchor intelligent design system using the fiber composite anchor intelligent design method according to any one of claims 1 to 9, characterized in that: include: A fiber composite anchor rod production subsystem, the fiber composite anchor rod production subsystem is used to use carbon fiber and glass fiber to make a carbon-glass hybrid anchor rod core, and use LCP fiber bundles to weave the surface of the carbon-glass hybrid anchor rod core to form a braided layer; after weaving the surface of the carbon-glass hybrid anchor rod core, the LCP fiber bundles are wound on the surface of the braided layer to form LCP fiber winding ribs, thereby obtaining a fiber composite anchor rod; An anchor rod testing and data acquisition subsystem, the anchor rod testing and data acquisition subsystem is used to adjust the adjustable parameters of the fiber composite anchor rod to produce a plurality of the fiber composite anchor rods, and then construct a fiber composite anchor rod production prediction data set; A production parameter judgment subsystem, the production parameter judgment subsystem is used to build a production parameter prediction model based on the fiber composite anchor production prediction data set, and use the production parameter prediction model to obtain the production parameter prediction value of the fiber composite anchor when the anchor performance parameters are given, so as to enable the fiber composite anchor production subsystem to produce the fiber composite anchor according to the production parameter prediction value.

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