Blasting scheme evaluation method based on multi-objective optimization under complex working conditions

Abstract

The invention provides a blasting scheme evaluation method based on multi-objective optimization under a complex working condition, and is suitable for the technical field of open bench blasting. The method comprises the steps of evaluation index determination, evaluation index quantification, multi-objective optimization model construction and intelligent optimization algorithm solving, and by constructing a multi-index mathematical model system, decision variable dynamic optimization is achieved through a genetic algorithm. The limitation that a traditional empirical method cannot quantify and balance geological conditions, explosive parameters and environmental influence is broken through; by establishing a coupling mathematical model of an adjustable decision variable and a fixed quantity, controllable parameters and uncontrollable parameters are systematically quantified, an optimal combination is found by using an improved real number coding genetic algorithm, and nonlinear optimization of mutual restriction indexes of a blasting vibration effect, a flying stone risk, dust pollution and cost control under a complex geological working condition is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of open bench blasting, and particularly relates to a blasting scheme evaluation method based on multi-objective optimization under complex working conditions. BACKGROUND

[0002] With the continuous advancement of infrastructure construction and mineral resources development in China, open bench blasting engineering is increasingly widely applied in the fields of mine exploitation and tunnel excavation. The industry puts forward higher requirements for the safety, economy and environmental protection of blasting operation, and the traditional experience-based blasting scheme design mode has been difficult to meet the multi-objective optimization demand under complex working conditions. The existing technology generally uses historical projects as a reference, takes vibration control and blasting safety as the core indicators, and other indicators meet the most basic requirements, which lacks systematic optimization consideration of safety, cost and environmental impact, resulting in the problem of uneconomical and non-environmental protection in actual application. In addition, the existing evaluation model generally has defects such as insufficient coupling of decision variables and fixed parameters and rough quantization method of indicators, for example, the mathematical relationship between uncontrollable factors such as geological conditions and adjustable variables such as charging parameters is not clearly distinguished, which reduces the actual guiding value of the model.

[0003] At the same time, the existing blasting scheme evaluation method only quantitatively evaluates the blasting scheme, lacks the optimization process of blasting parameters, and restricts the improvement of the comprehensive performance of the blasting scheme under complex working conditions, so it is urgent to build a more scientific evaluation system and optimization framework. SUMMARY

[0004] The purpose of the present application is to solve the problems of uneconomical and non-environmental protection and strong experience dependence caused by single objective optimization in traditional blasting scheme design, and a blasting scheme evaluation method based on multi-objective optimization under complex working conditions is proposed, which makes it possible to dynamically optimize vibration control, cost saving, environmental protection indicators and blasting effect, and realizes high-precision global optimization of multi-dimensional decision variables through intelligent algorithm, which can be widely applied to the technical field of open bench blasting.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] The blasting scheme evaluation method based on multi-objective optimization under complex working conditions specifically comprises the following steps:

[0007] S1, evaluation index determination, including 6 evaluation indexes of vibration velocity, fly rock distance, blasting cost, dust amount, average block size and noise impact;

[0008] S2, each evaluation index quantization, including dividing each parameter affecting the evaluation index into decision variables that can be artificially controlled and fixed quantities that cannot be controlled, and establishing a mathematical model of each evaluation index and decision variables and fixed quantities;

[0009] S3, multi-objective optimization model construction, including obtaining the evaluation index value of each of the n selected blasting schemes, the vibration speed, the fly rock distance, the blasting cost, the dust amount, the average block size and the noise influence of the i-th selected blasting scheme are dimensionless to obtain C i,1 、 i,2 、 i,3 、 i,4 、 i,5 、 i,6 , then the i-th selected blasting scheme is evaluated by a comprehensive evaluation function,

[0010]

[0011] In the formula, α j is the weight of the j-th evaluation index, F i is the i-th selected blasting scheme, C i，j is the j-th dimensionless evaluation index value of the i-th selected blasting scheme.

[0012] S4, intelligent optimization algorithm solution, including setting constraints, giving specific values of fixed quantities and decision variables, and value ranges of each evaluation index, and using an intelligent optimization algorithm to solve the selected blasting scheme with the maximum comprehensive evaluation function value.

[0013] As a preferred technical solution of the present application, in step S2, the decision variables include the maximum single-shot blasting charge Q s , the borehole diameter D, the unit price of explosives c e , the single-hole charge Q, the number of boreholes N d , the comprehensive unit price of drilling holes c d , the surface rock mass volume v b , and the explosive weight power E; the fixed quantities include the distance R from the explosion source to the protection, the geological correction coefficient K of the explosion area, the seismic wave attenuation coefficient α, the unit consumption of explosives q, the comprehensive coefficient K f , the dust accumulation amount per unit area q1, the dust accumulation area s1, the explosive energy utilization coefficient k1, the material dusting coefficient k2, the rock coefficient A and the coefficient K l .

[0014] As a preferred technical solution of the present application, in step S2, the mathematical model includes a vibration speed mathematical model, a fly rock distance mathematical model, a blasting cost mathematical model, a dust amount mathematical model, an average block size mathematical model and a noise influence mathematical model, the vibration speed mathematical model is established by the controllable maximum single-shot blasting charge Q s and the uncontrollable distance R from the explosion source to the protection, the geological correction coefficient K of the explosion area, the seismic wave attenuation coefficient α,

[0015] v=K·(Q s 1 / 3 / R)α (2)

[0016] In the formula, v is the evaluation index value of vibration speed; the fly rock distance mathematical model is established by the controllable blast hole diameter D and the uncontrollable explosive unit consumption q and comprehensive coefficient K f ,

[0017] d=K f qD (3)

[0018] In the formula, d is the evaluation index value of fly rock distance; the blasting cost mathematical model is established by the controllable explosive unit price c e , single-hole charge Q, blast hole number N d and comprehensive unit price c of drilling hole d ,

[0019] C=c e QN d +N d c d (4)

[0020] In the formula, C is the evaluation index value of blasting cost; the dust amount mathematical model is established by the controllable surface rock volume v b and the uncontrollable unit area dust accumulation amount q1, dust accumulation area s1, explosive unit consumption q, explosive energy utilization coefficient k1 and material dusting coefficient k2

[0021] p=q1s1+0.149(qk1) 2 k2v b (5)

[0022] In the formula, p is the evaluation index value of dust amount; the average block size mathematical model is established by the controllable single-hole charge Q, explosive weight power E and the uncontrollable explosive unit consumption q and rock coefficient A

[0023] X=Aq -0.8 Q 0.165 (115 / E) 0.633 (6)

[0024] In the formula, X is the evaluation index value of average block size; the noise influence mathematical model is established by the controllable maximum single-response simultaneous blasting explosive amount Q s and the uncontrollable coefficient K l ,

[0025]

[0026] In the formula, L is the evaluation index value of noise influence.

[0027] As a preferred technical scheme of the present application, in step S3, the dimensionless calculation formula is

[0028]

[0029] In the formula, z max,j - the maximum value of the jth evaluation index in the to-be-selected blasting scheme, z min,j - the maximum value of the jth evaluation index in the to-be-selected blasting scheme, z i,j - the jth evaluation index value of the ith to-be-selected blasting scheme.

[0030] As a preferred technical scheme of the present application, in step S4, the intelligent optimization algorithm selects a genetic algorithm, takes the combination of n groups of different decision variables as an initial population, sets the maximum number of iterations, the convergence threshold, the crossover probability and the mutation probability, encodes the decision variables into chromosomes, and adopts real number coding; if the value of the decision variable exceeds the value range in the iteration process, the adaptive function value corresponding to the combination of the decision variables is directly set to zero.

[0031] As a preferred technical scheme of the present application, the n to-be-selected blasting schemes are updated for a limited number of times in the solving process of the intelligent optimization algorithm by changing the value of the decision variable, that is, one to-be-selected blasting scheme corresponds to one combination of decision variables; z max,j / z min,j is the maximum / minimum jth evaluation index value in the iteration process of all to-be-selected blasting schemes.

[0032] The present application has the following beneficial effects: by constructing a mathematical model system of 6 indexes, using a genetic algorithm to realize dynamic optimization of decision variables, the limitations of traditional experience methods that cannot quantitatively balance geological conditions, explosive parameters and environmental influences are broken through; by establishing a coupling mathematical model of adjustable decision variables and fixed quantities, 8 controllable parameters such as the maximum single-response blasting explosive quantity and the blast hole diameter are systematically quantified with 11 uncontrollable parameters such as the geological correction coefficient and the seismic wave attenuation coefficient, an improved real number coding genetic algorithm is used to find the optimal combination, and the nonlinear optimization of 6 indexes such as blasting vibration effect, flyrock risk and dust pollution which are mutually restricted is realized under complex geological working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flow chart of the blasting scheme evaluation method based on multi-objective optimization under complex working conditions of the present application. DETAILED DESCRIPTION

[0034] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments given here are only for the purpose of illustrating and explaining the present application, and cannot be used to limit the present application. It should be noted that in the following description, a large number of specific details are set forth in order to fully understand the present application, however, the present application can also have other implementation manners and deformations, therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0035] The embodiment one, the blasting scheme evaluation method based on multi-objective optimization under complex working conditions specifically comprises the following steps:

[0036] S1, evaluation index determination, including 6 evaluation indexes of vibration speed, fly rock distance, blasting cost, dust amount, average block size and noise influence;

[0037] S2, each evaluation index quantization, including dividing each parameter affecting the evaluation index into decision variables controllable by human and fixed quantities uncontrollable, and establishing mathematical models of each evaluation index and decision variables and fixed quantities;

[0038] The decision variables include maximum single-shot blasting charge Q s , blast hole diameter D, explosive unit price c e , single-hole charge Q, blast hole number N d , comprehensive drilling unit price c d , surface rock mass volume v b , and explosive weight power E; the fixed quantities include the distance R from the explosion source to the protection, the geological correction coefficient K of the explosion area, the seismic wave attenuation coefficient α, the explosive unit consumption q, the comprehensive coefficient K f , the dust accumulation amount per unit area q1, the dust accumulation area s1, the explosive energy utilization coefficient k1, the material dusting coefficient k2, the rock coefficient A and the coefficient K l ;

[0039] The mathematical model includes vibration speed mathematical model, fly rock distance mathematical model, blasting cost mathematical model, dust amount mathematical model, average block size mathematical model and noise influence mathematical model, the vibration speed mathematical model is established through controllable maximum single-shot blasting charge Q s and uncontrollable distance R from the explosion source to the protection, geological correction coefficient K of the explosion area, seismic wave attenuation coefficient α,

[0040] v=K·(Q s 1 / 3 / R) α (1)

[0041] In the formula, v is the evaluation index value of vibration speed; the fly rock distance mathematical model is established through controllable blast hole diameter D and uncontrollable explosive unit consumption q, comprehensive coefficient K f ,

[0042] d=K f qD (2)

[0043] In the formula, d is the evaluation index value of fly rock distance; the blasting cost mathematical model is established through controllable explosive unit price c e , single-hole charge Q, blast hole number N d and comprehensive drilling unit price cd establishment,

[0044] C=c e QN d +N d c d (3)

[0045] wherein C is an evaluation index value of blasting cost; the mathematical model of dust amount is established by controllable surface rock mass volume v b and uncontrollable unit area dust accumulation amount q1, dust accumulation area s1, explosive unit consumption q, explosive energy utilization coefficient k1 and material dust generation coefficient k2,

[0046] p=q1s1+0.149(qk1) 2 k2v b (4)

[0047] wherein p is an evaluation index value of dust amount; the mathematical model of average block size is established by controllable single-hole charge amount Q, explosive weight power E and uncontrollable explosive unit consumption q and rock coefficient A,

[0048] X=Aq -0.8 Q 0.165 (115 / E) 0.633 (5)

[0049] wherein X is an evaluation index value of average block size; the mathematical model of noise influence is established by controllable maximum single-response simultaneous explosive amount Q s and uncontrollable coefficient K l ,

[0050]

[0051] wherein L is an evaluation index value of noise influence;

[0052] S3, a multi-objective optimization model is constructed, including obtaining evaluation index values of n selected blasting schemes, and obtaining C i,1 , C i,2 , C i,3 , C i,4 , C i,5 , C i,6 of the evaluation index values of vibration speed, fly rock distance, blasting cost, dust amount, average block size and noise influence of the i-th selected blasting scheme after dimensionless treatment, and the calculation formula of the dimensionless treatment is

[0053]

[0054] wherein z max,j is the maximum value of the j-th evaluation index in the selected blasting scheme, and z min,j- the maximum value of the jth evaluation index in the candidate blasting scheme, z i,j - the jth evaluation index value of the ith candidate blasting scheme;

[0055] The ith candidate blasting scheme is evaluated by the comprehensive evaluation function,

[0056]

[0057] In the formula, α j - the jth evaluation index weight, F i - the ith candidate blasting scheme, C i，j - the jth dimensionless evaluation index value of the ith candidate blasting scheme;

[0058] S4, intelligent optimization algorithm solution, including setting constraints, according to the actual blasting scene, determine the specific value of all fixed quantities, define the physical constraint range for each decision variable, the maximum single response explosive quantity Q s ∈[Q s,min , Q s,max ] and is an integer multiple of the single hole charge, the hole diameter D ∈ [H / 15, H / 10], H is the step height, the single hole charge Q = qabL, W1 is the minimum resistance line, the hole spacing a = (1.0-2.0)W1, the hole spacing b = (0.8-1.0)a, the filling length L2 = (0.7-1.2)W1, L is the hole depth,

[0059] The number of holes N d According to the blasting area s and the hole spacing a and the hole spacing b, the comprehensive drilling unit price c d According to the hole diameter D and the hole depth L, the surface rock volume v b According to the blasting area s and the hole depth L, the explosive unit price c e and the explosive weight power E are discretely valued according to different types of explosives,

[0060]

[0061] E ∈ {E1, E2,..., E m}(11) In the formula, c e,k - the unit price of the kth explosive, E k - the weight power value of the kth explosive, k = 1, 2,..., m; when the kth explosive is selected, the explosive unit price c e and the explosive weight power E are bound to be selected;

[0062] According to the engineering safety standards, environmental protection regulations and blasting design requirements, the value range of each evaluation index value is limited, the vibration velocity v ∈ [0, v max], flystone distance d ∈ [0, d max ], dust amount p ∈ [0, p max ], average block size X ∈ [X min , X ma x ], noise influence L ∈ [0, L max ];The maximum of the comprehensive evaluation function value is solved by using an intelligent optimization algorithm, n groups of different combinations of decision variables are used as initial populations, the maximum iteration number, the convergence threshold, the crossover probability and the mutation probability are set, the decision variables are coded into chromosomes, real number coding is adopted, each individual (chromosome) represents a combination of decision variables (Q s , D, c e , Q, N d , c d , v b , E);

[0063] For each individual, the vibration speed v, the flystone distance d, the blasting cost C, the dust amount p, the average block size X and the noise influence L are calculated according to the mathematical model formulas (1)-(6), the out-of-boundary check is performed in the calculation process, if any index exceeds its value range, it is directly determined that the scheme is not feasible, and the fitness value is zero;If all indexes are within the allowed range, the six evaluation indexes are normalized according to formula (7) to obtain C i,1 to C i,6 , wherein zmax,j and z min,j min,j are the maximum value and the minimum value of the jth index in the iteration process of all candidate schemes, that is, the maximum value and the minimum value of the jth index in all historical individuals (chromosomes), the fitness F i is calculated according to formula (8) j , the weight α c is determined in advance according to the AHP method;

[0064] The individuals with high fitness are retained to enter the next generation, the crossover probability P c is used to perform the crossover operation on the parent individuals to generate offspring individuals, the mutation probability P m is used to randomly disturb some genes (decision variable values) of the offspring individuals to increase the population diversity, if the value of the decision variable exceeds the value range, the boundary truncation method is used to reset the out-of-boundary value to the nearest boundary value;If the maximum iteration number T max is reached, or the optimal fitness of the continuous generations changes by less than ω, the algorithm is terminated, the individual with the highest fitness is selected as the optimal blasting scheme, and the combination of the decision variables and the evaluation index values are output.

[0065] To sum up, the blasting scheme evaluation method and system based on multi-objective optimization under complex working conditions have the characteristics of optimizing sensor arrangement and more intelligent and accurate early warning in the field of open bench blasting technology.

[0066] It should be understood that the above-mentioned embodiments belong to one or more embodiments of the present application, and there are many other embodiments and variations based on the present application; the ordinary skilled in the art can make modifications and variations to the present application without making pioneering innovations, and all of them belong to the protection scope of the present application.

Claims

1. A method for evaluating blasting schemes based on multi-objective optimization under complex working conditions, characterized in that, Specifically, the following steps are included: S1. Evaluation indicators are determined, including six evaluation indicators: vibration velocity, fly rock distance, blasting cost, dust amount, average block size, and noise impact. S2. Quantification of each evaluation indicator, including dividing the parameters affecting the evaluation indicators into decision variables that can be artificially controlled and fixed quantities that cannot be controlled, and establishing mathematical models of each evaluation indicator, decision variables, and fixed quantities. S3. Construction of a multi-objective optimization model, including obtaining the evaluation index values ​​of each of the n candidate blasting schemes, and dimensionlessly transforming the evaluation index values ​​of vibration velocity, flyrock distance, blasting cost, dust amount, average block size, and noise impact of the i-th candidate blasting scheme to obtain C. i,1 C i,2 C i,3 C i,4 C i,5 C i,6 Then the i-th candidate blasting scheme is evaluated using a comprehensive evaluation function. In the formula, α j - Weight of the j-th evaluation indicator, F i -The i-th candidate blasting scheme, C i，j -The j-th dimensionless post-evaluation index value of the i-th candidate blasting scheme; S4. Solving with intelligent optimization algorithm, including setting constraints, giving specific values ​​of fixed quantities and the range of values ​​of decision variables and evaluation indicators, and using intelligent optimization algorithm to solve for the candidate blasting scheme with the largest comprehensive evaluation function value.

2. The method for evaluating blasting schemes based on multi-objective optimization under complex working conditions as described in claim 1, characterized in that: In step S2, the decision variables include the maximum single-explosive charge Q. s Hole diameter D, explosive unit price c e Single-hole charge Q, number of boreholes N d Comprehensive unit price of drilling c d Surface rock mass volume v b The fixed quantities include the explosive weight and yield E; the fixed quantities include the distance R from the blast source to the protected object, the geological correction coefficient K of the blast zone, the seismic wave attenuation coefficient α, the explosive consumption q, and the comprehensive coefficient K. f Dust accumulation per unit area q1, dust accumulation area s1, explosive energy utilization coefficient k1, material dust generation coefficient k2, rock coefficient A, and coefficient K l .

3. The method for evaluating blasting schemes based on multi-objective optimization under complex working conditions as described in claim 1, characterized in that: In step S2, the mathematical model includes a vibration velocity mathematical model, a flyrock distance mathematical model, a blasting cost mathematical model, a dust quantity mathematical model, an average block size mathematical model, and a noise impact mathematical model. The vibration velocity mathematical model is determined by the controllable maximum single-shot explosive charge Q. s In addition, the distance R from the uncontrollable blast source to the protected object, the geological correction coefficient K of the blast zone, and the seismic wave attenuation coefficient α are established. v=K·(Q s 1 / 3 / R) α (2) In the formula, v is the evaluation index value of vibration velocity; the mathematical model of the flystone distance is based on the controllable borehole diameter D and the uncontrollable explosive consumption q and comprehensive coefficient K. f Establish, d = K f In equation qD (3), d represents the evaluation index value of the flyrock distance; the blasting cost mathematical model uses the controllable unit price of explosives c e Single-hole charge Q, number of boreholes N d The comprehensive unit price of drilling c d Establish, C = c e QN d +N d c d (4) In the formula, C is the evaluation index value of blasting cost; the dust amount mathematical model is based on the controllable surface rock mass volume v b And establish the uncontrollable dust accumulation per unit area q1, dust accumulation area s1, explosive consumption q, explosive energy utilization coefficient k1, and material dust generation coefficient k2. p = q1s1 + 0.149(qk1) 2 k2v b (5) In the formula, p is the evaluation index value of dust amount; the average block size mathematical model is established by the controllable single-hole charge Q, explosive weight power E, and the uncontrollable explosive consumption q and rock coefficient A. X = Aq -0.8 Q 0.165 (115 / E) 0.633 (6) In the formula, X is the evaluation index value of the average block size; the noise influence mathematical model is based on the controllable maximum single-shot explosive charge Q. s and the uncontrollable coefficient K l Establish, In the formula, L represents the evaluation index value of noise impact.

4. The method for evaluating blasting schemes based on multi-objective optimization under complex working conditions as described in claim 1, characterized in that: In step S3, the dimensionless calculation formula is: In the formula, z max,j - The maximum value of the j-th evaluation index among the candidate blasting schemes, z min,j - The maximum value of the j-th evaluation index among the candidate blasting schemes, z i,j - The j-th evaluation index value of the i-th candidate blasting scheme.

5. The method for evaluating blasting schemes based on multi-objective optimization under complex working conditions as described in claim 1, characterized in that: In step S4, the intelligent optimization algorithm uses a genetic algorithm, with n different combinations of decision variables as the initial population, and sets the maximum number of iterations, convergence threshold, crossover probability and mutation probability. The decision variables are encoded as chromosomes using real number encoding. If the value of the decision variable exceeds the range during the iteration process, the fitness function value corresponding to the combination of the decision variables is directly set to zero.

6. The method for evaluating blasting schemes based on multi-objective optimization under complex working conditions as described in claim 1, characterized in that: The n candidate blasting schemes are updated iteratively a finite number of times by changing the values ​​of decision variables during the intelligent optimization algorithm solution process; that is, each candidate blasting scheme corresponds to a combination of decision variables. max,j / z min,j Let be the maximum / minimum j-th evaluation index value during all iterations of the candidate blasting schemes.

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