CN115391938B - Full-length adhesive anchor rod damage mode identification method under shearing action - Google Patents
Full-length adhesive anchor rod damage mode identification method under shearing action Download PDFInfo
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Abstract
The method is used for solving the problem that under the influence of different factors, the definition of the damage mode of the anchor rod is unclear when the anchor rod is sheared, based on a material mechanics combination deformation theory and an intensity theory, the judging process of pure shearing damage, tensile shearing damage and stretch bending damage of the anchor rod is embedded into a UDEC (universal data center) calculation main program to obtain a training data set, and the mapping relation between the anchor rod damage mode and each characteristic parameter under the shearing action is established through a random forest classification algorithm and a particle swarm optimization algorithm, so that the damage mode generated when the anchor rod is sheared is effectively judged. The method provided by the application can accurately judge the damage mode of the full-length adhesive anchor rod according to the provided rock mass, structural surface and anchor rod parameters, provides a reference for evaluating the shearing resistance of the anchor rod, and has important significance for optimizing the rock slope and deep rock mass anchoring support parameters.
Description
Technical Field
The application belongs to the technical field of side slopes and roadway supports, and particularly relates to a method for judging a damage mode of a full-length adhesive anchor rod under a shearing action.
Background
The full-length adhesive anchor rod has the advantages of simple structure, convenient construction and economy, and is widely applied to slopes and underground chambers. Because of the existence of a large number of structural surfaces such as joints, cracks and the like in the natural rock mass, under the action of artificial disturbance or ground stress, local rock mass can be sheared and greatly deformed along the structural surfaces, so that the damage caused by the full-length bonded anchor rod is not usually pure tensile damage or mortar viscosity breaking in the traditional sense, but is combined damage under the combined action of shearing force, bending moment and axial force.
What failure mode of the bolt occurs is directly related to the rock, structural plane and anchoring parameters, but the definition of what failure mode of the bolt occurs at different values of the characteristic parameters is currently unclear. Because different failure modes have obvious influence on the yielding or failure resistance of the anchor rod, the mapping relation between the failure mode of the anchor rod and each characteristic parameter is established, the failure mode of the anchor rod is effectively predicted, and the method has important practical significance for evaluating the shearing resistance of the anchor rod and guiding the practice of rock mass anchoring engineering.
Disclosure of Invention
The technical problem to be solved by the method is to provide a method for judging the damage mode of the full-length adhesive anchor rod under the shearing action, aiming at the problem that the definition of the damage mode of the full-length adhesive anchor rod under the shearing action is unclear in the existing research.
The technical scheme adopted for solving the technical problems is as follows: the method for judging the damage mode of the full-length adhesive anchor rod under the shearing action comprises the following steps:
q1, establishing a rock structural surface shearing numerical model based on UDEC software, and implanting a full-length adhesive anchor rod in the center of the numerical model;
q2, embedding a judging process of pure shearing damage, tensile shearing damage and stretch bending damage of the anchor rod into a numerical calculation main program through a self-contained FISH programming language of UDEC software;
q3, selecting characteristic parameters influencing the anchor rod damage mode under the shearing action, wherein the characteristic parameters comprise: diameter D of anchor rod and axial yield strength f of anchor rod y Ultimate tensile strength f u The anchor rod inclination angle alpha, the rock uniaxial compressive strength and the smaller value sigma of the mortar uniaxial compressive strength c And a structural face shear angle θ;
q4, selecting three groups of typical anchor-added structural surface shear tests, substituting relevant characteristic parameters into a numerical model, calibrating normal coupling springs and tangential coupling spring parameters in the anchor rod unit according to a shear force-shear displacement curve, and ensuring that the failure mode of the anchor rod in the numerical test is consistent with the failure mode of the anchor rod in the test;
q5, selecting proper characteristic parameters according to common geological environment and common anchor rod specification in engineeringRange of (2) and/or (l) i (l i Is a positive integer and l of each parameter i May be different) levels; the characteristic parameters are specifically as follows:
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and
q6, designing n groups of numerical tests through orthogonal tests according to the range and the level of each characteristic parameter divided in the step Q5, wherein n is generally greater than 20;
q7, on the basis of the steps Q3 and Q4, solving n groups of numerical tests designed in the step Q6 one by one, recording anchor rod damage modes under each group of numerical tests, and establishing a data set E containing n samples, wherein the form of each sample in the data set is E i ＝(x i ,FM i ) Wherein x is i ＝(D i ,f yi ,f ui ,α i ,σ ci ,θ i )；i＝1,2,3...,n，FM i Representing the failure mode of the anchor rod, wherein the pure shear failure is denoted as "PS" (pure-shear), the tensile shear failure is denoted as "TS" (tensile-shear), and the stretch-bending failure is denoted as "TB" (tensile-bond);
q8, learning the data set by adopting a random forest classification algorithm, and x of each sample in the data set E in the step Q7 i FM as an input variable i As output variable, establishing a random forest classification model;
q9, intelligently optimizing the super parameters in the random forest classification model established in the step Q8 by adopting a particle swarm algorithm, wherein the super parameters comprise the number G of decision trees, the maximum depth d of the random forest and the maximum feature number k tried in a single tree when a random attribute principle is adopted, and establishing an optimized anchor rod damage mode judgment model;
q10, for a full-length adhesive anchor rod in a specific geological environment, when rock mass on two sides of a structural surface occursIn the case of relative sliding or a tendency to relative sliding, the anchor rod is subjected to a strong shearing action, in which case the characteristic variables [ D, f ] are determined y ,f u ,α,σ c ,θ]And (3) introducing the failure mode into the anchor rod failure mode judging model established in the step (Q9), wherein the obtained failure mode is the failure mode of the anchor rod under the shearing action judged by the algorithm.
Further, in step Q1, the Rockbolt unit is used to simulate a full-length adhesive anchor rod, and compared with anchor rods simulated by other structural units, the Rockbolt unit has the capabilities of tensile strength, shearing resistance and bending resistance, and is suitable for the condition that the anchor rod is sheared.
Further, in step Q2, three failure modes of pure shear failure, stretch shear failure and stretch bending failure are embedded into the main calculation program, and the main flow is as follows:
a) When main program operation is carried out and the operation is carried out to step i, the address of the first node of the Rockbolt unit is found through a FISH built-in function, and the axial force, the shearing force and the bending moment stored in the node are extracted;
b) Judging whether the anchor rod enters a yield state or not by using the following formula (1):
wherein: sigma (sigma) e For the yield strength of the anchor rod, M 0 And N 0 Is the bending moment and the axial force of the anchor rod, W is the bending section coefficient,
a is the sectional area of the anchor rod;
c) If the yield state judging formula (1) is not satisfied, the anchor rod is considered to be unyielding, and the Tresca breaking criterion (2) is continuously used for judging whether the anchor rod is broken by pure shearing or not:
wherein: q (Q) 0 Is the shearing force of one point of the anchor rod, N u Is the axial ultimate strength of the anchor rodCorresponding axial limit force.
If the above formula (2) is satisfied, the anchor is considered to be purely shear damaged, a first character string "PS" is outputted, and the yield value (yield-tension) and the yield strain (tension-failure-strain) of the anchor are set to a default value (1×10) -10 ) The program judges that the anchor rod is damaged, and the calculation program is terminated; if the formula (2) is not satisfied, considering that the anchor rod is still in an elastic state, repeating iteration on the next node address until node traversal of the whole anchor rod is completed, and performing next main program operation by making i=i+1;
the pure shear damage is not strictly pure shear damage with only shear stress, but is approximately regarded as pure shear damage because the axial force is small and the shearing force is dominant at this time;
d) If the yield state judging formula (1) is satisfied, the anchor rod is considered to yield at the point, and the anchor rod enters a plastic state; bending moment M at this time in the main routine 0 Assigning a plastic moment (plastic-moment) to the anchor rod, forming a plastic hinge, wherein the bending moment is not increased after reaching the plastic moment, and the axial force is further increased along with the increase of the shearing displacement of the structural surface, so that the failure mode of the anchor rod after entering the plastic state needs to be judged;
e) After the anchor rod enters a plastic state, if the axial force and the shearing force of one point meet the Mises damage criterion (3), the tensile shear damage is considered to occur at the point:
if the above equation (3) is satisfied, a second character string "TS" is outputted, and the yield value and the yield strain of the anchor rod are set to default values (1×10 -10 ) The calculation program is terminated;
f) If the above formula (3) is not satisfied, continuing the determination: if the axial force and bending moment at one point satisfy the following relation (4), it is considered that stretch bending failure occurs, a third character string "TB" is outputted, and the yield value and yield strain of the anchor rod are set to default values (1×10 -10 ) The calculation procedure terminates:
g) If the judgment formulas (3) and (4) of the pull-up shear damage and the stretch bending damage are not satisfied, the anchor rod is in a plastic state, but still the damage limit is not reached, node traversal is needed to be continued at the moment, if the node traversal is still not satisfied after the node traversal, i=i+1 is needed to carry out next main program operation until any damage mode judgment is successful, and the main program terminates operation.
Further, the random forest classification algorithm adopted in the step Q8 uses a C4.5 decision tree as a base learner, and when the k-th sample occupation ratio in the current sample set R is pk (k=1, 2..+ -, |y|) according to the basic theory of the C4.5 decision tree, the purity of the sample set is measured by adopting an information entropy Ent (R), where the information entropy has a definition formula as follows:
assuming that the discrete attribute a has V possible values, the maximum information gain rate is used for selecting the partition attribute of the decision tree, and the definition formula of the information gain rate is as follows:
wherein:
wherein: r is R v All of the R's included for the v-th branch node have a value of a on attribute a v Is a sample of (a).
Since the input attributes of the invention are allThe continuous value adopts a dichotomy method, and for a certain continuous attribute a, the adjacent attribute is valued [ a ] i ,a i+1 ) Is the median point of (2)
As candidate dividing points, the dividing points are then examined like discrete values, and the optimal dividing point is selected for dividing the sample set, wherein i is a positive integer.
Further, learning the data set E in step Q8 using a random forest classification algorithm includes: according to a random forest classification theory, a data set E containing n training samples is randomly sampled based on a bagging method, G sampling sets (G, n and m are all positive integers) containing m training samples are obtained and are respectively used for training each decision tree.
Further, in step Q8, the random forest classification algorithm votes on the classification result of each decision tree by adopting a voting method, and the predicted result is the most voted destruction mode, expressed as:
wherein: x is a sample of the sample, and,
the output at class mark j (destruction mode) at the i-th base learner is represented, G is the number of decision trees, and H (x) is the final output.
Further, in step Q9, the number G of decision trees, the maximum depth d of random forest and the maximum feature number k tried in a single tree when adopting a random attribute principle are selected, and the invention adopts a particle swarm optimization algorithm to perform global intelligent optimization on the three parameters to obtain an optimal combination (G, d, k), thereby maximally improving the model performance, and specifically comprises the following steps:
a) Initializing particle and population speed, and setting maximum iteration number I max =300, particle population number 25, defining minimum error rate W min ；
b) Determining corresponding parameter combinations (G, d, k) according to the particle numbers and the population speed, and substituting the parameter combinations into a model to obtain a prediction result;
c) Adopting a 10-fold cross verification method, constructing an fitness function by adopting an error rate W in each fold cross verification process, and calculating the fitness value of an individual:
wherein: k is the sample set, f represents the learner, f (x i ) For the prediction result of the random forest classification model, y i Is the actual result;
d) Updating the particle speed and the particle position, performing the next iteration, and repeating the step b and the step c;
e) Ending the iteration when the set maximum iteration number is reached, and outputting the minimum error rate W min Corresponding parameter combinations (G i ,d i ,k i ) Substituting the model to generate an optimized judgment model.
According to the method for judging the damage mode of the full-length adhesive anchor rod under the shearing action, based on the theory of material mechanical combination deformation and strength, three damage modes of the full-length adhesive anchor rod under the shearing action are embedded into a UDEC numerical calculation main program, a data set of different damage modes of the anchor rod under different parameters is established through numerical calculation, a random forest classification-particle swarm optimization combined algorithm is adopted to construct a mapping relation between the anchor rod damage mode and rock, a structural surface and anchor rod parameters, and a full-length adhesive anchor rod damage mode judgment model under the shearing action is established.
The method for judging the damage mode of the full-length adhesive anchor rod under the shearing action has the following beneficial effects: the mapping relation between the anchor rod failure mode and the rock, structural surface and anchor rod parameters is constructed, the failure mode of the anchor rod under the shearing action in a specific geological environment and under different anchoring parameters can be accurately judged, and the method has important practical significance for accurately evaluating the shearing resistance of the anchor rod and guiding the practice of rock mass anchoring engineering.
Drawings
The application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a general flow chart of anchor rod failure mode determination;
FIG. 2 is a flow chart of the anchor failure mode determination in UDEC numerical software;
FIG. 3 is a flow chart of an anchor rod failure mode judgment model established by adopting a random forest classification algorithm and a particle swarm optimization algorithm;
fig. 4 is a schematic illustration of the bolt being sheared.
In fig. 4, 1, an anchor rod, 2, an upper rock block, 3, a lower rock block, 4, a structural surface, 5, a fixing surface, A, an anchor rod shearing area, B and a movement direction.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
Referring to fig. 1 to 3, a method according to an embodiment of the present application is specifically described herein; the embodiment of the application provides a method for judging the damage mode of a full-length adhesive anchor rod under the shearing action, which specifically comprises the following steps:
p1, establishing an anchored structural surface shearing numerical model in UDEC software, and simulating an anchor rod under the shearing action by adopting a Rockbolt unit;
p2, expressing the anchor rod failure mode judging flow in the figure 2 by using a FISH language, and embedding the anchor rod failure mode judging flow into a numerical simulation calculation main program;
p3, selecting three groups of typical shear test data of the anchor-added structural surface, and respectively obtaining parameters [ D, f ] y ,f u ,α,σ c ,θ]Substituting the parameters into a numerical model established by P2, calibrating the parameters of a normal coupling spring and a tangential coupling spring in a Rockbolt unit according to a shear force-shear displacement curve, and ensuring that the failure modes are consistent;
p4, selecting a proper range and a proper level for each parameter according to a common geological environment in engineering and common anchor rod specification experience, wherein the proper range and the proper level are shown in a table 1:
table 1 parameter values ranges and levels
P5, designing 25 groups of tests through an L25 orthogonal table, substituting 25 groups of test parameters into a numerical model established by P2 for calculation as shown in a table 2, and obtaining a corresponding anchor rod failure mode;
TABLE 2 orthogonal test chart
P6, through the 25 groups of numerical tests in P5, a data set E containing 25 groups of training data is obtained, and each sample in the data set E is in the form of E i ＝(x i ,FM i ) Wherein x is i ＝(D i ,f yi ,f ui ,α i ,σ ci ,θ i )；i＝1,2,3...,25；
P7, adopting a random forest classification algorithm to classify x in the step P6 i ＝(D i ,f yi ,f ui ,α i ,σ ci ,θ i ) FM as an input variable i As output variable, building an anchor rod failure mode judging model;
and P8, performing global optimization on the number G of decision trees in the random forest classification model established in the step P7, the maximum depth d of the random forest and the maximum feature number k tried in a single tree when a random attribute principle is adopted by using a particle swarm optimization algorithm in the figure 3, and establishing an optimized anchor rod damage mode judgment model. Finally, when the number of the trees is 90, the maximum depth is 4, and the maximum feature number is 3, the fitness is highest (the error rate is lowest), and the model is optimal;
p9, for a specific anchor rod shearing case, determining input parameters [ D, f y ,f u ,α,σ c ,θ]And substituting the model into the judgment model established in the P8 to obtain a corresponding anchor rod failure mode.
Example 2
For better illustration, an anchoring structural face shear test is taken as an example for the detailed description. Referring to fig. 4, in fig. 4, a rock bolt 1 is inserted into a structural system formed by an upper rock mass 2 and a lower rock mass 3, a structural surface 4 is formed between the upper rock mass 2 and the lower rock mass 3, an inclination angle alpha=90° between the rock bolt 1 and the structural surface 4, a diameter d=4 mm of the rock bolt 1 adopted in the test is set to be equal to or smaller than a diameter d=4 mm, and a yield strength f of the rock bolt 1 is set to be equal to or smaller than a diameter f of the rock bolt 1 y =475 MPa, ultimate strength f u ＝580MPa，σ c The structural plane shear angle θ=13.67°, the circular area a in fig. 4 represents the sheared area of the rock bolt 1, and the arrow indicates the direction B representing the direction of movement of the lower rock mass, = 51.44 MPa. After the test is finished, the anchor rod 1 is taken out for observation, and the anchor rod 1 is found to have obvious pull-shear damage characteristics.
All parameters of the embodiment 2 are within the value range of the characteristic parameters of the data set in the embodiment 1, so that the model established in the embodiment 1 can be used for judging, the shearing test in the embodiment 2 of the application is judged by using the model of the embodiment 1, and the finally output anchor rod failure mode is TS and is consistent with the experimental observation result. Example 2 further verifies that the method proposed in the embodiment of the present application has good accuracy of judgment.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (7)
1. The method for judging the damage mode of the full-length adhesive anchor rod under the shearing action is characterized by comprising the following steps:
q1, establishing a rock structural surface shearing numerical model based on UDEC software, and implanting a full-length adhesive anchor rod in the center of the numerical model;
q2, embedding a judging process of pure shearing damage, tensile shearing damage and stretch bending damage of the anchor rod into a numerical calculation main program through a self-contained FISH programming language of UDEC software;
q3, selecting characteristic parameters influencing the anchor rod damage mode under the shearing action, wherein the characteristic parameters comprise: diameter D of anchor rod and axial yield strength f of anchor rod y Ultimate tensile strength f u The anchor rod inclination angle alpha, the rock uniaxial compressive strength and the smaller value sigma of the mortar uniaxial compressive strength c And a structural face shear angle θ;
q4, selecting three groups of typical anchor-added structural surface shear tests, substituting the relevant characteristic parameters into a numerical model, calibrating normal coupling springs and tangential coupling spring parameters in the anchor rod unit according to a shear force-shear displacement curve, and ensuring that the failure mode of the anchor rod in the numerical test is consistent with the failure mode of the anchor rod in the test;
q5, selecting a proper range and l according to the common geological environment in engineering and the common anchor rod specification i A level; the characteristic parameters are specifically as follows:
and->
Q6, designing n groups of numerical tests through orthogonal tests according to the range and the level of each characteristic parameter divided in the step Q5;
q7, on the basis of the steps Q3 and Q4, solving n groups of numerical tests designed in the step Q6 one by one, recording anchor rod damage modes under each group of numerical tests, and establishing a data set E containing n samples, wherein each sample in the data set E is in the form of E i ＝(x i ,FM i ) Wherein x is i ＝(D i ,f yi ,f ui ,α i ,σ ci ,θ i ) The method comprises the steps of carrying out a first treatment on the surface of the i=1, 2,3., n, FM represents the failure mode of the bolt;
q8, learning the data set E by adopting a random forest classification algorithm, and obtaining x of each sample in the data set E in the step Q7 i FM as an input variable i As output variable, establishing a random forest classification model;
q9, intelligently optimizing the super parameters in the random forest classification model established in the step Q8 by adopting a particle swarm algorithm, wherein the super parameters comprise the number G of decision trees, the maximum depth d of the random forest and the maximum feature number k tried in a single tree by adopting a random attribute principle, and establishing an optimized anchor rod damage mode judgment model;
q10, for a full-length adhesive anchor rod in a specific geological environment, when rock masses at two sides of a structural surface slide relatively or have a tendency of sliding relatively, the anchor rod is subjected to strong shearing action, and at the moment, characteristic parameters [ D, f are determined y ,f u ,α,σ c ,θ]And (3) introducing the failure mode of the anchor rod into the failure mode judging model of the anchor rod established in the step (Q9), wherein the obtained failure mode is the failure mode of the anchor rod under the shearing action judged by an algorithm.
2. The method of claim 1, wherein the step Q1 uses a Rockbolt unit to simulate a full length cohesive bolt.
3. The method for judging the failure mode of a full-length adhesive anchor rod under a shearing action according to claim 2, wherein the process of embedding the judging process of pure shearing failure, stretch shearing failure and stretch bending failure into the numerical calculation main program in the step Q2 is as follows:
a) When the main program operation is carried out and the operation is carried out to the stepi, the address of the first node of the Rockbolt unit is found through a FISH built-in function, and the axial force, the shearing force and the bending moment stored in the node are extracted;
b) Judging whether the anchor rod enters a yield state or not by using the following formula (1):
wherein: sigma (sigma) e For the yield strength of the anchor rod, M 0 And N 0 Is the bending moment and the axial force of the anchor rod, W is the bending section coefficient,
a is the sectional area of the anchor rod;
c) If the yield state judging formula (1) is not satisfied, the anchor rod is considered to be unyielding, and the Tresca breaking criterion (2) is continuously used for judging whether the anchor rod is broken by pure shearing or not:
wherein: q (Q) 0 Is the shearing force of one point of the anchor rod, N u The axial limit force is corresponding to the axial limit strength of the anchor rod;
if the formula (2) is met, the anchor rod is considered to be damaged by pure shearing, a first character string is output, the yield value and the yield strain of the anchor rod are set as default values, the program can judge that the anchor rod is damaged, and the calculation program is terminated; if the formula (2) is not satisfied, considering that the anchor rod is still in an elastic state, repeating iteration on the next node address until node traversal of the whole anchor rod is completed, and performing next main program operation by making i=i+1;
d) If the yield state judging formula (1) is satisfied, the anchor rod is considered to yield at the point, and the anchor rod enters a plastic state; bending moment M at this time in the main routine 0 The plastic moment assigned to the anchor rod is formed by plastic hinge, the bending moment is not increased after reaching the plastic moment, the axial force is further increased along with the increase of the shearing displacement of the structural surface, and the failure mode of the anchor rod after entering the plastic state is judged at the moment;
e) If the axial force and shear force of a point meet Mises failure criterion (3), it is considered that a pull shear failure occurs at that point:
if the formula (3) is satisfied, outputting a second character string, setting the yield value and the yield strain of the anchor rod as default values, and terminating the calculation program;
f) If the above formula (3) is not satisfied, continuing the determination: if the axial force and the bending moment at one point meet the following relational expression (4), the stretch bending damage is considered to occur, a third character string is output, the yield value and the yield strain of the anchor rod are set as default values, and the calculation program is terminated:
g) If the judgment formulas (3) and (4) of the pull-up shear damage and the stretch bending damage are not satisfied, the anchor rod is in a plastic state, but the damage limit is not reached, node traversal is needed to be continued at the moment, if the judgment formulas are still not satisfied after the node traversal, i=i+1 is needed to carry out next main program operation until any damage mode judgment is successful, and the main program terminates operation.
4. The method for judging failure mode of full-length adhesive anchor rod under shearing action as claimed in claim 1, wherein the random forest classification algorithm adopted in the step Q8 uses C4.5 decision tree as a base learner, and the proportion of the kth sample in the current sample set R is p according to the basic theory of C4.5 decision tree k (k=1, 2, |y|) the information entropy is used to measure the purity of the sample set:
assuming that the discrete attribute a has V possible values, the maximum information gain rate is used for selecting the partition attribute of the decision tree:
wherein:
wherein: r is R v All of the R's included for the v-th branch node have a value of a on attribute a v Is a sample of (a).
5. The method for judging the failure mode of the full-length adhesive anchor rod under the shearing action according to claim 1, wherein the step Q8 is characterized in that a random forest classification algorithm is adopted to learn the data set E, and the method comprises the steps of randomly sampling the data set E containing n training samples based on a bagging method according to a random forest classification theory to obtain G sampling sets containing m training samples, wherein the G sampling sets are respectively used for training each decision tree.
6. The method for judging the failure mode of a full-length adhesive anchor rod under the shearing action according to claim 1, wherein the random forest classification algorithm in the step Q8 adopts a voting method to vote on the classification result of each decision tree, and the predicted result is the failure mode with the most votes, and is expressed as follows by a formula:
wherein:
is shown in the firstOutput on class label j on i base learners.
7. The method for judging the failure mode of a full-length adhesive anchor rod under the shearing action according to claim 1, wherein the intelligent optimization of the super parameters in the random forest classification model established in the step Q8 by adopting a particle swarm algorithm in the step Q9 comprises the following steps:
a) Initializing particle and population speed, and setting maximum iteration number I max =300, particle population number 25, defining minimum error rate W min ；
b) Determining corresponding parameter combinations (G, d, k) according to the particle numbers and the population speeds, and substituting the parameter combinations into the random forest classification model to obtain a prediction result;
c) Adopting a 10-fold cross verification method, constructing an fitness function by adopting an error rate W in each fold cross verification process, and calculating the fitness value of an individual:
wherein: k is the sample set, f represents the learner, f (x i ) Predicting the result, y, for the random forest classification model i Is the actual result;
d) Updating the particle speed and the particle position, performing the next iteration, and repeating the step b and the step c;
e) Ending the iteration when the set maximum iteration number is reached, and outputting the minimum error rate W min Corresponding parameter combinations (G i ,d i ,k i ) Substituting the model into the random forest classification model to generate an optimized judgment model.
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