CN116150854A - Tunnel blasting parameter optimizing method based on rock mass structural plane information and related components - Google Patents
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Abstract
The invention discloses a tunnel blasting parameter optimizing method, a device and related components based on rock mass structural plane information, and relates to the field of tunnel construction, wherein the method comprises the following steps: acquiring blasting parameters and surrounding rock geological information of each region; normalizing all quantized surrounding rock geological information, respectively inputting the normalized surrounding rock geological information and blasting parameters into a pre-built model, and outputting the following parameters: the super-underexcavation area parameter, the maximum linear super-underexcavation parameter, the vault subsidence parameter and the block stone size parameter; acquiring a smooth blasting parameter range table; inputting surrounding rock geological information in a target area to the optimized model, and outputting target parameters; and acquiring and outputting optimal smooth blasting parameters except surrounding rock geological information by utilizing an optimizing algorithm. The method can accurately obtain the optimal smooth blasting parameters so as to solve the problem of poor blasting effect caused by different surrounding rocks at different parts of the tunnel and reduce the phenomenon of over-and-under excavation.
Description
Technical Field
The invention relates to the field of tunnel construction, in particular to a tunnel blasting parameter optimizing method and device based on rock mass structural plane information and related components.
Background
At present, the drilling and blasting method is the most general construction method in tunnel engineering, especially mountain tunnel engineering, however, the situation that the blasting effect is not ideal often exists in the actual construction process, wherein the main reason is that proper blasting parameters are not selected according to surrounding rock geological conditions, and the situation that rock mass structural planes are distributed on different positions such as a tunnel vault, a vault shoulder, a vault waist and the like is different is considered, so that the surrounding rock blasting forming effect of different positions of a tunnel is often different under the traditional blasting scheme, and then the phenomenon of over-undermining is generated. The damage caused by the tunnel overexcavation is great, for example, excessive overexcavation can increase the rock discharge amount of the tunnel tunneling, the corresponding engineering amount of the primary support can also be increased, and the construction progress can be seriously influenced while the construction cost is increased; the phenomenon of stress concentration can be locally generated due to the over-and-under excavation, so that potential safety hazards are caused, and the safety and stability of long-term use of the tunnel are affected.
The phenomenon of overexcavation is common in the process of excavating a rock tunnel, and is particularly common for the rock mass developed by joint cracks and the like. Aiming at rock mass developed by joints and the like, how to determine smooth blasting parameters such as the number of blastholes, the distance between blastholes, the distribution of blastholes, the loading capacity, the loading structure and the like, no systematic theory can be directly applied at the present stage, and a learner can dynamically adjust the smooth blasting parameters through a site smooth blasting test, so that two problems exist in optimization of the smooth blasting parameters: firstly, tunnel blasting is influenced by rock structural surfaces such as joint cracks, the damage and destruction mechanisms of vaults, shoulders and waists are different, and the super-underexcavation difference is large, so that parameter optimization is required for blasting forming mechanisms at different parts of tunnel sections; secondly, based on the photo-explosion parameter optimization of the field smooth blasting test, the process is complex, the construction progress is influenced, a mathematical model in a strict sense cannot be established by the method, and the achievement popularization is poor.
Along with the development of the intelligent of the modern tunnel technology, the information of the surrounding rock structural surface of the tunnel can be mastered more accurately through electronic equipment. And acquiring data of smooth blasting parameters and post-blasting effects and geological information on site, and establishing sample data with proper magnitude to train and test the deep learning model. For different parts of the tunnel excavation section, a mapping model between surrounding rock real-time geological information and blasting parameters and excavation section displacement, overexcitation and block stone size is established, and blasting parameters capable of minimizing the overexcitation amount and the block stone size are optimized.
Disclosure of Invention
The invention aims to provide a tunnel blasting parameter optimizing method, device and related components based on rock mass structural plane information, and aims to solve the problems of potential safety hazards in construction caused by overexcavation and underexcavation in the existing drilling and blasting construction process.
In order to solve the technical problems, the aim of the invention is realized by the following technical scheme: the tunnel blasting parameter optimizing method based on rock mass structural plane information comprises the following steps:
acquiring blasting parameters, and acquiring surrounding rock geological information of each area based on a preset face digging block rule;
Based on a preset quantization rule, performing quantization processing on the surrounding rock geological information of each region;
normalizing all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
acquiring the hardness of surrounding rock of a current section, and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters;
inputting surrounding rock geological information in a target area to the optimized model, and outputting target parameters;
and acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm based on the smooth blasting parameter range table and the target parameters.
Further, the blasting parameters include the following parameters: peripheral hole charge structure, peripheral hole spacing, minimum resistance line, blast hole density coefficient, peripheral hole charge concentration and blast hole quantity.
Further, the acquiring surrounding rock geological information of each region based on the preset face digging and blocking rule comprises:
symmetrically dividing the digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
and after each small area is obtained, acquiring surrounding rock geological information of all the small areas, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
Further, the quantization processing for the surrounding rock geological information of each region based on the preset quantization rule includes:
acquiring the state of the combination degree of the structural surface;
based on the state of the bonding degree of the structural surface, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0.3, the bonding degree of the structural surface with poor state of the bonding degree of the structural surface is assigned to 0.7, and the bonding degree of the structural surface with very poor state of the bonding degree of the structural surface is assigned to 1.
Further, the inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model respectively includes:
inputting the normalized current surrounding rock geological information and blasting parameters into an input layer, a 3-layer hidden layer and an output layer, and optimizing the model through a mean square error loss function, wherein an activation function among the input layer, the hidden layer and the output layer adopts the following formula: a=max (0, b) i ) Wherein a is the output of the basic processing unit of the model, b i The node number of the hidden layer is calculated as follows:
n a ＝2n b +1
wherein n is a ，n b And n c The number of nodes of the input layer, the hidden layer and the output layer is respectively;
is [1, 10]An arbitrary constant therebetween; the fitness function of the model is calculated according to the following formula:
wherein n is the number of test samples; y is ij ，y’ ij The measured value and the predicted value of the jth component of the ith test sample are respectively represented by vault sag parameters, maximum linear overbreak parameters and overbreak parametersA digging area parameter and a maximum stone diameter parameter.
Further, the obtaining and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by using an optimizing algorithm based on the smooth blasting parameter range table and the target parameters includes:
Taking each blasting parameter as blasting parameter particles, and calculating the adaptability of each blasting parameter particle according to the following formula:
wherein g o For the minimum value in known sample data, N is the initial population value number of the smooth blasting parameters, v i1 ,v i2 ,v i3 ,v i4 The method comprises the steps of super-underexcavation area parameter, maximum linear super-underexcavation parameter, vault sinking parameter and stone block size parameter;
judging whether the super-underexcavated area is minimum and whether the preset iteration times are met, outputting optimal smooth blasting parameters except the target parameters if one of the conditions is met, and returning to the optimizing step if the two conditions are not met.
In addition, the technical problem to be solved by the invention is to provide a tunnel blasting parameter optimizing device based on rock mass structural plane information, which comprises:
the acquisition unit is used for acquiring blasting parameters and acquiring surrounding rock geological information of each area based on a preset face digging block rule;
the quantization unit is used for carrying out quantization processing on the surrounding rock geological information of each region based on a preset quantization rule;
the training unit is used for carrying out normalization processing on all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
The preparation unit is used for obtaining the hardness of surrounding rock of the current section and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters;
the output unit is used for inputting surrounding rock geological information in the target area to the optimized model and outputting target parameters;
and the optimizing unit is used for acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm based on the smooth blasting parameter range table and the target parameters.
Further, the quantization unit includes:
the symmetrical unit is used for symmetrically dividing the digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
the dividing unit is used for acquiring surrounding rock geological information of all the small areas after the small areas are obtained, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
In addition, the embodiment of the invention also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the tunnel blasting parameter optimizing method based on the rock mass structural plane information in the first aspect is realized when the processor executes the computer program.
In addition, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor causes the processor to execute the tunnel blasting parameter optimizing method based on the rock mass structural plane information according to the first aspect.
The embodiment of the invention discloses a tunnel blasting parameter optimizing method, a device and related components based on rock mass structural plane information, wherein the method comprises the following steps: acquiring blasting parameters, and acquiring surrounding rock geological information of each area based on a preset face digging block rule; based on a preset quantization rule, performing quantization processing on the surrounding rock geological information of each region; normalizing all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached; acquiring the hardness of surrounding rock of a current section, and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters; inputting surrounding rock geological information in a target area to the optimized model, and outputting target parameters; and acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm based on the smooth blasting parameter range table and the target parameters.
According to the method, the optimal smooth blasting parameters can be accurately obtained, so that the problem of poor blasting effect caused by different surrounding rocks at different positions of a tunnel is solved, the phenomenon of over-and-under-excavation is reduced, the effect of non-uniform distribution control is achieved, and finally the optimal blasting effect after blasting construction is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a tunnel blasting parameter optimizing method based on rock mass structural plane information according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a block diagram of a tunnel blasting parameter optimizing method based on rock structural plane information according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a model of a tunnel blasting parameter optimizing method based on rock mass structural plane information according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an error map of output of a model of a tunnel blasting parameter optimizing method based on rock mass structural plane information according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a tunnel blasting parameter optimizing device based on rock mass structural plane information provided by an embodiment of the invention;
fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a flow chart of a tunnel blasting parameter optimizing method based on rock mass structural plane information according to an embodiment of the present invention;
as shown in fig. 1, the method includes steps S101 to S106.
S101, acquiring blasting parameters, and acquiring surrounding rock geological information of each area based on a preset face digging block rule;
s102, carrying out quantization processing on the surrounding rock geological information of each region based on a preset quantization rule;
s103, normalizing all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
S104, acquiring the hardness of surrounding rock of the current section, and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters;
s105, inputting surrounding rock geological information in a target area into the optimized model, and outputting target parameters;
s106, based on the smooth blasting parameter range table and the target parameters, acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm.
The tunnel blasting is influenced by the rock structural surfaces such as joint cracks, the damage and destruction mechanisms of the vault, the shoulder supply and the arch surrounding rock are different, and the super-underexcavation difference is large, so that parameter optimization is required for the blasting forming mechanism of different parts of the tunnel section; that is, the influence of the differences of surrounding rocks at different parts of the tunnel on the blasting effect is provided, and a tunnel blasting parameter optimizing method based on rock mass structural plane information is provided, which is specific:
in this embodiment, the blasting parameters in step S101 include the following parameters: peripheral hole charge structure, peripheral hole spacing, minimum resistance line, blast hole density coefficient, peripheral hole charge concentration, and number of blast holes; because the tunnel construction will ask the investigation department to perform advanced geological forecast on the tunnel, the surrounding rock and the bottom layer condition in front of the tunnel can be recorded, so that the classification condition of the surrounding rock and the hardness degree of the surrounding rock can be obtained, and a smooth blasting parameter range table (such as the smooth blasting parameter range table described in step S104) corresponding to the current excavation surface can be obtained, for example, as shown in the following table one:
Table one: smooth blasting parameter range table
Because rock mass structural plane is different in distribution condition of different positions such as tunnel vault, arch shoulder, arch waist, often leads to the tunnel different positions surrounding rock blasting shaping effect to have the difference under traditional blasting scheme, and then produces the phenomenon of super-undermining, so this application obtains the surrounding rock geological information of each region based on the face partitioning rule of digging of predetermineeing, promptly, carry out the separation scanning to the face of digging of different regions, obtain corresponding surrounding rock geological information, then based on the quantization rule of predetermineeing, carry out quantization to the surrounding rock geological information of current region, then input surrounding rock geological information after the quantization and blasting parameter to the model and train, the model outputs the numerical value of 4 parameters, namely super-undermining area parameter, biggest linear super-undermining parameter, vault subsidence parameter and stone size parameter.
In the training process, an optimization algorithm is adopted to optimize the weight and the threshold of the model, the optimization algorithm is adopted to perform parameter optimization on the initial weight and the threshold of the model, and after the maximum optimization iteration number or the preset training target is reached, the optimized model is output.
After model training and optimization are finished, surrounding rock geological information of a target area is input into an optimized model to obtain corresponding 4 parameters (super-undermining area parameter, maximum linear super-undermining parameter, vault sinking parameter and block stone size parameter), then based on the obtained smooth blasting parameter range table, according to the group of optimization with the best output parameters (namely, the best blasting effect), other input parameters (namely, the surrounding hole loading structure, the surrounding hole spacing, the minimum resistance line, the blast hole density coefficient, the surrounding hole loading concentration degree and the blast hole number) except for the surrounding rock geological information are searched by utilizing an optimization algorithm, in other words, the group of optimal smooth blasting parameters with the best output parameters are searched and output.
According to the tunnel blasting parameter optimizing method based on rock mass structural plane information, the problem of poor blasting effect caused by different surrounding rocks at different positions of a tunnel is solved, the phenomenon of super-undermining is reduced, the effect of non-uniform distribution is achieved, the optimal blasting effect after blasting construction is finally achieved, and it is understood that blasting parameters corresponding to minimum vault sinking required by specific engineering or blasting parameters corresponding to optimal block stone size can be selected according to actual engineering.
It should be noted that the training target in step S103 may be that the error between the predicted value and the actual value is small enough, and may be set to be smaller than 10 -2 The time output model, this number can be set specifically according to the requirement, and is not limited specifically.
In a specific embodiment, the step S101 includes the following steps:
s10, symmetrically dividing a digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
s11, after each small area is obtained, surrounding rock geological information of all the small areas is obtained, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
For ease of understanding, as shown in fig. 2 in conjunction with the drawings of the present specification, the present application divides the face into a symmetrical left and right 2 large areas based on the arch, shoulder and waist positions, and for each large area, the face is divided into a plurality of small areas from top to bottom, where the example includes 4 small areas (i.e. (1) (2) (3) (4) in the drawing), but it should be understood that if the rock mass structural face of the actual face is relatively complex, the division area may be further increased, for example, the face may be divided into 6 or 8 small areas, and the small areas corresponding to the left and right 2 large areas are not necessarily symmetrical, or the left large area may have 4 small areas, and the right large area has 3 small areas, which is not specifically limited in the present application.
In summary, the situation of the structural surface (digging surface) must be fully considered, so that the suitable blasting parameters can be optimized for different geological conditions.
In a specific embodiment, the step S102 includes the following steps:
s20, acquiring the state of the combination degree of the structural surface;
s21, based on the state of the bonding degree of the structural surface, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0.3, the bonding degree of the structural surface with poor state of the bonding degree of the structural surface is assigned to 0.7, and the bonding degree of the structural surface with poor state of the bonding degree of the structural surface is assigned to 1.
In this embodiment, different values are given to the degree of bonding of the structural faces according to the state of the degree of bonding of the structural faces, wherein the state of the degree of bonding of the structural faces is determined by:
the state of the bonding degree of the structural surface is good: the opening degree of the digging surface is less than 1mm, and no filler exists; or the opening degree of the digging surface is in the range of 1-3 mm, and the digging surface is siliceous or iron cementing; or the opening degree of the digging surface is more than 3mm, and the structural surface is rough and is siliceous cementation.
The state of the degree of bonding of the structural faces is generally: the opening of the digging surface is 1-3 mm, and the digging surface is calcium or clay cementing; or the opening degree of the digging surface is more than 3mm, and the structural surface is rough and is calcium or iron cementing.
The state of the degree of bonding of the structural faces is poor: the opening degree of the digging surface is 1-3 mm, and the structural surface is flat and straight, and is argillaceous or argillaceous and calcareous cementation; or the opening degree of the digging surface is more than 3mm, and most of the digging surface is filled with muddy or rock debris.
The state of the bonding degree of the structural surface is extremely poor: the thickness of the filler is larger than the waviness.
In the process of acquiring the geological information of the surrounding rock, the opening degree of the digging surface can be acquired through manual survey, the geological information of the surrounding rock can also be acquired according to three-dimensional reconstruction of a computer, the opening degree of the digging surface is obtained, and the opening degree of the digging surface is a basic parameter belonging to a rock mass structural surface.
In a specific embodiment, the step S103 includes the following steps:
s30, inputting the normalized current surrounding rock geological information and blasting parameters into an input layer, a hidden layer of 3 layers and an output layer, and optimizing the model through a mean square error loss function, wherein an activation function among the input layer, the hidden layer and the output layer adopts the following formula: a=max (0, b) i ) Wherein a is the output of the basic processing unit of the model, b i The node number of the hidden layer is calculated as follows:
n a ＝2n b +1
wherein n is a ，n b And n c The number of nodes of the input layer, the hidden layer and the output layer is respectively;
is [1, 10]An arbitrary constant therebetween; the fitness function of the model is calculated according to the following formula: />
Wherein n is the number of test samples; y is ij ，y’ ij The measured value and the predicted value of the j component of the i test sample are respectively, wherein the j component represents the j vault sinking parameter, the j maximum linear overexcavation parameter, the j undermined area parameter or the j maximum block stone diameter parameter.
In this embodiment, the specific steps of optimization using the optimization algorithm are as follows:
1) The following mathematical model pairs were used to find the target parameters:
D＝|C·Leader_Pos(t)-Pos(t)|
Pos(t+1)＝Leader_Pos(t)-A·D_Leader
Wherein t represents the iteration number at the time; leader_pos (t) represents the current target parameter position vector; pos (t) and Pos (t+1) respectively represent the positions of other parameters except the target parameter at the current moment and the next moment; d represents the wrapping step length; C. a is a coefficient.
2) The number of target parameters is reduced using the following mathematical model:
D＝|Leader_Pos(t)-Pos(t)|
Pos(t+1)＝Leader_Pos(t)+D·e bl ·cos(2πl)
wherein b represents a spiral line shape; l is the random quantity of [ -1,1 ].
3) The search range is enlarged by using a random selection mode, and the mathematical model is as follows:
D＝|X_rand(t)-Pos(t)|
Pos(t+1)＝X_rand(t)-A·D
wherein x_rand (t) represents the randomly selected parameter position.
4) After the iteration is completed, the optimal solution is leader_pos,
and obtaining the weight value and the threshold value range of the neural network after the optimal solution is obtained, wherein the weight value and the threshold value range of the neural network are leader_pos. Namely there is
w1＝Leader_Pos(1：inputnum*hiddennum)；
B1＝Leader_Pos(inputnum*hiddennum+1：inputnum*hiddennum+hiddennum)；
w2＝Leader_Pos(inputnum*hiddennum+hiddennum+1：inputnum*hiddennum+hiddennum+hiddennum*outputnum)；
B2＝Leader_Pos(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1：inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum)；
Wherein w1 represents the weight from the input layer to the hidden layer, w2 represents the weight from the hidden layer to the output layer, B1 represents the neuron threshold of the hidden layer, B2 represents the threshold of the output layer, and inputnum, hiddennum, outputnum represents the number of input layers, the number of hidden layers and the number of output layers respectively.
After the model is initially built, a training set needs to be acquired first, blasting parameters are acquired on site, and the blasting parameters are output through the model: after the super-undermining area parameter, the maximum linear super-undermining parameter, the vault sinking parameter and the block stone size parameter are established, sample data with proper magnitude are established to train and test a deep learning model, wherein input parameters with the greatest influence on the blasting effect are selected, and calculation can be performed according to the following formula:
Wherein x is ai Representing an input variable; x is x bi Representing an output variable; n represents the number of data; i denotes a data sequence number.
In combination with the above formula, the input parameters of the present application finally include: surrounding rock geological information (included angle of connecting line of structural face and blast hole, trace length of structural face, interval of structural face and combination degree of structural face), blasting parameters (peripheral hole charging structure, peripheral hole interval, minimum resistance line, blast hole density coefficient (E/W), peripheral hole charging concentration degree and blast hole quantity), wherein collecting data is carried out in a partitioning and partitioning way, and data collection and data training are respectively carried out on different parts of a tunnel. The regional data acquisition is generally performed as shown in figure 2 of the drawings, wherein the peripheral hole charge configuration is quantified in terms of the uncoupled coefficients over the axial distribution of the cartridge.
And then training input and output parameters, outputting a trained model, specifically, taking an included angle of a connecting line of a structural surface and a blast hole, the trace length of the structural surface, the distance of the structural surface, the combination degree of the structural surface, the charging structure of the peripheral hole, the distance of the peripheral hole, the minimum resistance line, the blast hole density coefficient (E/W), the charging concentration (total charging amount) and the number of blast holes as input parameters, wherein the output parameters are required to show the post-blasting effect, and taking the super-undermining area parameter, the maximum linear super-undermining parameter, the vault sinking parameter and the block stone size parameter as output parameters.
For the collected data (input parameters), 90% of the data was used for training and 10% of the data was used for prediction. R of model prediction 2 MSE and RMSE are used as evaluation indexes, when the error conditions required by the model are met, the model is considered to be trained, images of the true value and the predicted value and error diagrams of the true value and the predicted value are drawn (the error diagrams are output after the training is finished, and the error diagrams are trained through an analysis neural network such as the error diagrams, and the error is small enough), as shown in fig. 4.
In a specific embodiment, the step S106 includes the following steps:
s40, respectively taking all the blasting parameters as blasting parameter particles, and calculating the adaptability of each blasting parameter particle according to the following formula:
wherein g o For the minimum value in known sample data, N is the initial population value number of the smooth blasting parameters, v i1 ,v i2 ,v i3 ,v i4 Is the super-undermining area parameter, the maximum linear super-undermining parameter and the archTop sinking parameters, stone size parameters;
s41, judging whether the super-underexcavated area is minimum and whether the preset iteration times are met, if one of the conditions is met, executing the step S42, and if the two conditions are not met, returning to the optimizing step S40.
S42, outputting the optimal smooth blasting parameters except the target parameters.
In this embodiment, after all the acquired blasting parameters are input into the model, the maximum super-undermining area parameter, the maximum linear super-undermining parameter, the vault subsidence parameter and the block stone size parameter are output, after the fitness value of each blasting particle is calculated, whether the super-undermining area parameter is minimum or the iteration number is satisfied is judged, if one of the conditions is satisfied, the optimal smooth blasting parameter is output, if the 2 conditions are not satisfied, the position update and the particle update are performed, the iteration number is increased by 1, and then the parameters are substituted into the trained model again, so that one of the conditions is satisfied, in other words, the optimal blasting parameter which minimizes the super-undermining (or the vault subsidence or the block stone size meeting the engineering requirements) is found by using the optimizing algorithm, and in the table (smooth blasting parameter range table), each blasting parameter represents one particle, namely the peripheral hole charging structure, the peripheral hole spacing, the minimum resistance line, the gun hole density coefficient, the peripheral hole charging concentration and the gun hole number represent one particle.
The mathematical principle of the optimizing algorithm is as follows:
1) Initializing: the position of using the initialized all objects is shown by the following equation:
W i ＝xj i +rand×(sj i -xj i )；i＝1,2,…,N
Wherein sj and xj are the upper and lower limits of the search space, N is the sample size, W i For the initial position of individual i, rand is [0,1]A random number is included.
The volume and density it occupies is determined by the following formula:
m i ＝rand
v i ＝rand
also, assuming that the target object is in a fluid, it must have an acceleration a as follows:
a i ＝xj i +rand×(sj i -xj i )
2) Updating the volume and the density of the object:
wherein m is best 、v best Is the density and volume of the optimal individuals in the population.
3) Transferring operators and density factors:
in the early stages of algorithm iteration, collision occurs between objects, and as time goes by, objects try to reach equilibrium, this process is implemented by ZY:
wherein t is the current iteration number, and tmax is the maximum iteration number. Likewise, the density factor ρ also contributes to the algorithm transition from the global search to the local search:
4) Search stage
When ZY is less than or equal to 0.5, collision occurs among individuals, so that random individual sj is selected to update acceleration of individual i
Note that ZY +.0.5 ensures that the search is performed in one third of the iterations. Development activities will occur when ZY > 0.5.
5) In the development stage, no collision exists between objects, at this time ZY is more than 0.5, and the individuals do not collide with each other any more, and then the acceleration updating mode is as follows:
6) Normalized acceleration the percent change was calculated using the formula
Wherein,,
the normalized acceleration is represented, s represents the upper limit of the normalization range, and x represents the lower limit of the normalization range.
7) Object location update
If ZY is less than or equal to 0.5, updating and using the object position in the ith t+1 iteration:
if T F >0.5, the object location update at the ith t+1 iteration is used:
wherein: where w is the position of the individual and f is randomly valued in { -1,1}, tmax is 1 and if T >1 is taken its value is 1.
The embodiment of the invention also provides a tunnel blasting parameter optimizing device based on the rock mass structural plane information, which is used for executing any embodiment of the tunnel blasting parameter optimizing method based on the rock mass structural plane information. Specifically, referring to fig. 5, fig. 5 is a schematic block diagram of a tunnel blasting parameter optimizing device based on rock structural plane information according to an embodiment of the present invention.
As shown in fig. 5, the tunnel blasting parameter optimizing apparatus 500 based on rock mass structural plane information includes:
an obtaining unit 501, configured to obtain blasting parameters, and obtain surrounding rock geological information of each area based on a preset face-digging block rule;
A quantization unit 502, configured to perform quantization processing on the surrounding rock geological information of each region based on a preset quantization rule;
the training unit 503 is configured to normalize all quantized surrounding rock geological information, input the blasting parameters and normalized surrounding rock geological information to a pre-built model, and output the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
a preparation unit 504, configured to obtain a hardness of a surrounding rock of a current section, and generate a smooth blasting parameter range table based on the hardness of the surrounding rock, where the smooth blasting parameter range table includes the blasting parameters;
the output unit 505 is configured to input surrounding rock geological information in the target area to the optimized model, and output target parameters;
and the optimizing unit 506 is configured to obtain and output an optimal smooth blasting parameter except for the surrounding rock geological information by using an optimizing algorithm based on the smooth blasting parameter range table and the target parameter.
The device has solved the tunnel different positions country rock and has led to the problem that the blasting effect is poor, reduces the phenomenon of super-under excavation, and reaches the effect of inhomogeneous cloth accuse, finally realizes the optimal blasting effect after the blasting construction.
In a specific embodiment, the quantization unit includes:
the symmetrical unit is used for symmetrically dividing the digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
the dividing unit is used for acquiring surrounding rock geological information of all the small areas after the small areas are obtained, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and units described above may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
The tunnel blasting parameter optimizing device based on rock mass structural plane information can be implemented in the form of a computer program which can be run on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 1100 is a server, and the server may be a stand-alone server or a server cluster formed by a plurality of servers.
With reference to FIG. 6, the computer device 1100 includes a processor 1102, memory, and a network interface 1105 connected through a system bus 1101, wherein the memory may include a non-volatile storage medium 1103 and an internal memory 1104.
The non-volatile storage medium 1103 may store an operating system 11031 and computer programs 11032. The computer program 11032, when executed, may cause the processor 1102 to perform a tunnel blasting parameter optimization method based on rock mass structural plane information.
The processor 1102 is operable to provide computing and control capabilities to support the operation of the overall computer device 1100.
The internal memory 1104 provides an environment for the execution of a computer program 11032 in the non-volatile storage medium 1103, which computer program 11032, when executed by the processor 1102, causes the processor 1102 to perform a tunnel blasting parameter optimization method based on rock mass structural plane information.
The network interface 1105 is used for network communication such as providing transmission of data information, etc. It will be appreciated by those skilled in the art that the architecture shown in fig. 6 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 1100 to which the present inventive arrangements may be implemented, and that a particular computer device 1100 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 6 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 6, and will not be described again.
It should be appreciated that in embodiments of the invention, the processor 1102 may be a central processing unit (Central Processing Unit, CPU), the processor 1102 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program realizes the tunnel blasting parameter optimizing method based on rock mass structural plane information when being executed by a processor.
The storage medium is a physical, non-transitory storage medium, and may be, for example, a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The tunnel blasting parameter optimizing method based on rock mass structural plane information is characterized by comprising the following steps of:
acquiring blasting parameters, and acquiring surrounding rock geological information of each area based on a preset face digging block rule;
based on a preset quantization rule, performing quantization processing on the surrounding rock geological information of each region;
normalizing all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
acquiring the hardness of surrounding rock of a current section, and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters;
inputting surrounding rock geological information in a target area to the optimized model, and outputting target parameters;
And acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm based on the smooth blasting parameter range table and the target parameters.
2. The method for optimizing tunnel blasting parameters based on rock mass structural plane information according to claim 1, wherein the blasting parameters include the following parameters: peripheral hole charge structure, peripheral hole spacing, minimum resistance line, blast hole density coefficient, peripheral hole charge concentration and blast hole quantity.
3. The method for optimizing tunnel blasting parameters based on rock mass structural plane information according to claim 2, wherein the acquiring surrounding rock geological information of each region based on a preset face-digging block rule comprises:
symmetrically dividing the digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
and after each small area is obtained, acquiring surrounding rock geological information of all the small areas, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
4. The method for optimizing tunnel blasting parameters based on rock mass structural plane information according to claim 3, wherein the quantization of the surrounding rock geological information of each region based on a preset quantization rule comprises:
Acquiring the state of the combination degree of the structural surface;
based on the state of the bonding degree of the structural surface, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0, the bonding degree of the structural surface with good state of the bonding degree of the structural surface is assigned to 0.3, the bonding degree of the structural surface with poor state of the bonding degree of the structural surface is assigned to 0.7, and the bonding degree of the structural surface with very poor state of the bonding degree of the structural surface is assigned to 1.
5. The method for optimizing tunnel blasting parameters based on rock mass structural plane information according to claim 4, wherein the inputting the blasting parameters and normalized surrounding rock geological information into the pre-built model respectively comprises:
inputting the normalized current surrounding rock geological information and blasting parameters into an input layer, a 3-layer hidden layer and an output layer, and optimizing the model through a mean square error loss function, wherein an activation function among the input layer, the hidden layer and the output layer adopts the following formula: a=max (0, b) i ) Wherein a is the output of the basic processing unit of the model, b i The node number of the hidden layer is calculated as follows:
n a ＝2n b +1
wherein n is a ，n b And n c The number of nodes of the input layer, the hidden layer and the output layer is respectively;
is [1, 10]An arbitrary constant therebetween; the fitness function of the model is calculated according to the following formula:
wherein n is the number of test samples; y is ij ，y′ ij The measured value and the predicted value of the jth component of the ith test sample are respectively, and the components represent a vault sag parameter, a maximum linear overexcavation parameter, an underexcavation area parameter and a maximum block stone diameter parameter.
6. The method for optimizing tunnel blasting parameters based on rock structural plane information according to claim 5, wherein the obtaining and outputting optimal smooth blasting parameters except for the surrounding rock geological information by using an optimizing algorithm based on the smooth blasting parameter range table and the target parameters comprises:
taking each blasting parameter as blasting parameter particles, and calculating the adaptability of each blasting parameter particle according to the following formula:
wherein g o For the minimum value in known sample data, N is the initial population value number of the smooth blasting parameters, v i1 ,v i2 ,v i3 ,v i4 The method comprises the steps of super-underexcavation area parameter, maximum linear super-underexcavation parameter, vault sinking parameter and stone block size parameter;
Judging whether the super-underexcavated area is minimum and whether the preset iteration times are met, outputting optimal smooth blasting parameters except the target parameters if one of the conditions is met, and returning to the optimizing step if the two conditions are not met.
7. Tunnel blasting parameter optimizing device based on rock mass structural plane information, characterized by comprising:
the acquisition unit is used for acquiring blasting parameters and acquiring surrounding rock geological information of each area based on a preset face digging block rule;
the quantization unit is used for carrying out quantization processing on the surrounding rock geological information of each region based on a preset quantization rule;
the training unit is used for carrying out normalization processing on all quantized surrounding rock geological information, respectively inputting the blasting parameters and the normalized surrounding rock geological information into a pre-built model, and outputting the following parameters: the method comprises the steps of performing parameter optimization on an initial weight and a threshold value of a model by using an optimization algorithm, and outputting the optimized model after the maximum optimization iteration number or a preset training target is reached;
The preparation unit is used for obtaining the hardness of surrounding rock of the current section and generating a smooth blasting parameter range table based on the hardness of the surrounding rock, wherein the smooth blasting parameter range table contains the blasting parameters;
the output unit is used for inputting surrounding rock geological information in the target area to the optimized model and outputting target parameters;
and the optimizing unit is used for acquiring and outputting the optimal smooth blasting parameters except for the surrounding rock geological information by utilizing an optimizing algorithm based on the smooth blasting parameter range table and the target parameters.
8. The tunnel blasting parameter optimizing apparatus based on rock mass structural plane information according to claim 7, wherein the quantization unit comprises:
the symmetrical unit is used for symmetrically dividing the digging surface into left and right 2 large areas based on a preset digging surface blocking rule, and dividing the 2 large areas into a plurality of small areas from top to bottom;
the dividing unit is used for acquiring surrounding rock geological information of all the small areas after the small areas are obtained, wherein the surrounding rock geological information comprises an included angle between a structural surface and a blast hole connecting line, the trace length of the structural surface, the distance between the structural surfaces and the combination degree of the structural surfaces.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the tunnel blasting parameter optimizing method based on rock mass structural plane information according to any one of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the method of optimizing tunnel blasting parameters based on rock mass structural plane information according to any one of claims 1 to 6.
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