A tunnel blasting impact prediction and analysis method and system based on machine vision

Abstract

The present invention relates to the field of tunnel blasting impact prediction and analysis, and specifically discloses a tunnel blasting impact prediction and analysis method and system based on machine vision. The present invention collects relevant data of tunnel blasting projects, screens historically similar blasting projects of the tunnel blasting project based on the relevant data of the tunnel blasting project, and further obtains blasting information of the historically similar blasting projects; constructs a numerical simulation three-dimensional model of the tunnel blasting project based on the relevant data of the tunnel blasting project using numerical simulation software, simulates the tunnel blasting, and obtains blasting information of the blasting simulated by the tunnel blasting simulation model; and predicts the impact of tunnel blasting on structural safety, ground deformation, and the surrounding environment based on the blasting information of the historically similar blasting projects and the blasting simulated by the tunnel blasting simulation model, thereby helping engineers optimize blasting design to reduce adverse effects and ensure the safety and efficiency of operations.

Description

Technical Field

[0001] This invention relates to the field of tunnel blasting impact prediction and analysis, and specifically to a machine vision-based method and system for predicting and analyzing tunnel blasting impact. Background Technology

[0002] With the acceleration of urbanization, the demand for infrastructure such as transportation networks, water supply systems, and power transmission is increasing daily. Tunnels, as crucial channels connecting different geographical areas, are of paramount importance, especially in mountainous, hilly, or densely populated urban areas where surface construction may be limited by terrain, environment, or space. Tunnels can effectively overcome these obstacles and provide direct connections. Tunnel blasting technology can effectively solve problems in infrastructure construction and resource development, while simultaneously considering economic benefits and environmental protection, leading to its continuously expanding application scope.

[0003] Predictive analysis of the impact of tunnel blasting is crucial for ensuring project safety, protecting the environment, and controlling costs, and is an indispensable part of modern tunnel engineering.

[0004] However, existing methods for predicting and analyzing the impact of tunnel blasting have some limitations and shortcomings in practical applications.

[0005] For example, Chinese Patent CN118153461 B discloses a method and system for predicting and analyzing vibration effects in blasting of interlayered tunnels. The method includes the following steps: collecting blasting point data of the interlayered tunnel, including the type, thickness, and physical characteristics of the geological layers; simulating the response of the strata to the blast using the blasting point data; and establishing a correlation model between the geology and blasting parameters. By collecting tunnel blasting point data and simulating the response of the strata to the blast, a correlation model between the geology and blasting parameters is constructed, enabling personalized prediction data analysis and model building of vibration effects. This allows for the assessment of the impact of different blasting schemes on the surrounding geological structure, optimization of blasting parameters, and effective reduction of environmental damage. Based on the prediction results, the vibration threshold is adjusted, and the blasting design is modified according to the actual blasting conditions to ensure that vibration is controlled within safe limits. This improves the accuracy and safety of blasting operations, reduces the impact on surrounding buildings, and optimizes resource utilization and engineering costs.

[0006] For example, Chinese patent CN117831655A discloses a method for predicting the range of fractures caused by blasting damage in surrounding rock. This method includes obtaining working condition parameters, constructing a model for predicting the range of fractures caused by blasting damage in surrounding rock, obtaining the relationship between the fracture range of different fracture zones in the surrounding rock, and obtaining the range of fractures caused by blasting damage in surrounding rock based on the working condition parameters and the relationship between the fracture range of different fracture zones. By comprehensively considering the principal stress, initial ground stress, initial damage, borehole cavity expansion, rock mass plastic damage, and shear dilatation characteristics in the surrounding rock, this method can reveal the influence mechanism of initial ground stress and intermediate principal stress on the range of fractures caused by blasting damage in surrounding rock. This improves the efficiency of blasting surrounding rock and effectively predicts the fracture range in actual deep-buried tunnel drilling and blasting projects.

[0007] The above-mentioned existing technologies have the following shortcomings: (1) When predicting the impact of tunnel blasting, most of them are predicted by constructing mathematical models. The prediction method is relatively simple and does not combine other prediction methods for comprehensive analysis, such as data on similar tunnel blasting events in history. As a result, the reliability and accuracy of the prediction results of the impact of tunnel blasting are insufficient.

[0008] (2) When predicting the impact of tunnel blasting, most studies analyze the single aspect of the impact of tunnel blasting, such as the vibration effect of tunnel blasting or the range of damage and cracks caused by tunnel blasting. However, they do not analyze the multifaceted impact of tunnel blasting, such as the impact of tunnel blasting on structural safety, the impact on ground deformation, and the impact on the surrounding environment. Consequently, they cannot fully grasp the potential impact of tunnel blasting, which is not conducive to optimizing blasting design to reduce adverse effects and ensure the safety and efficiency of operations. Summary of the Invention

[0009] In view of this, in order to solve the problems mentioned in the background technology, a method and system for predicting and analyzing the impact of tunnel blasting based on machine vision is proposed.

[0010] The technical solution adopted by this invention to solve its technical problem is as follows: Firstly, this invention provides a method for predicting and analyzing the impact of tunnel blasting based on machine vision, comprising the following steps:

[0011] Step 1: Data Collection for Tunnel Blasting: Collect relevant data for the tunnel blasting project, including geological and topographical information, boundary condition information, and blasting design parameters.

[0012] Step 2: Obtaining Blasting Information for Similar Blasting Projects: Obtain relevant data for each historical tunnel blasting project, further filter historical similar blasting projects, and obtain blasting information for historical similar blasting projects. The blasting information includes seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information.

[0013] Step 3: Obtaining blasting information from the tunnel blasting model: Based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, a numerical simulation three-dimensional model of the tunnel blasting project is constructed using numerical simulation software. The tunnel blasting is simulated to obtain the blasting information of the simulated blasting in the tunnel blasting simulation model.

[0014] Step 4: Prediction of the impact of blasting on structural safety: Based on historical similar blasting projects and tunnel blasting simulation models, the seismic effect information, vibration information, stress and strain information, tunnel structure damage information, and flyrock fragment information of the blasting are simulated. The impact coefficient of the tunnel blasting project on structural safety is analyzed and feedback is provided.

[0015] Step 5: Prediction of the impact of blasting on ground deformation: Based on the ground damage information simulated by historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation and provide feedback.

[0016] Step Six: Prediction of the Impact of Blasting on the Surrounding Environment: Based on the air shock wave information, sound wave information, and air quality information of similar historical blasting projects and tunnel blasting simulation models, analyze the impact coefficient of the tunnel blasting project on the surrounding environment and provide feedback.

[0017] Secondly, the present invention also provides a machine vision-based tunnel blasting impact prediction and analysis system, including: a tunnel blasting related data collection module: used to collect relevant data of tunnel blasting projects, wherein the relevant data includes geological and topographical information, boundary condition information and blasting design parameters.

[0018] Similar blasting project blasting information acquisition module: used to acquire relevant data of various historical tunnel blasting projects, further filter historical similar blasting projects of tunnel blasting projects, and obtain blasting information of historical similar blasting projects. The blasting information includes seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information.

[0019] The tunnel blasting model blasting information acquisition module is used to construct a numerical simulation three-dimensional model of the tunnel blasting project based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, and to simulate the tunnel blasting to obtain the blasting information of the simulated blasting in the tunnel blasting simulation model.

[0020] The module for predicting the impact of blasting on structural safety is used to simulate the seismic effects, vibration, stress and strain, tunnel structural damage, and flyrock fragments of blasting based on historical similar blasting projects and tunnel blasting simulation models. It analyzes the impact coefficient of tunnel blasting projects on structural safety and provides feedback.

[0021] The module for predicting the impact of blasting on ground deformation is used to simulate ground damage information from blasting based on historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation, and provide feedback.

[0022] The module for predicting the impact of blasting on the surrounding environment is used to analyze the impact coefficient of tunnel blasting projects on the surrounding environment based on the air shock wave information, sound wave information and air quality information simulated by similar historical blasting projects and tunnel blasting simulation models, and to provide feedback.

[0023] Database: Used to store blasting operation records for each historical tunnel blasting project.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention obtains blasting information from similar historical blasting projects and simulated blasting in tunnel blasting simulation models, and predicts and analyzes the impact of tunnel blasting. It can combine multiple prediction methods for comprehensive analysis, thereby improving the reliability and accuracy of the prediction results of the impact of tunnel blasting.

[0025] 2. This invention, by predicting the impact of tunnel blasting on structural safety, ground deformation, and the surrounding environment, helps to comprehensively understand the potential impacts of tunnel blasting, thereby facilitating the optimization of blasting design to reduce adverse effects and ensure the safety and efficiency of operations. Attached Figure Description

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 Schematic diagram of the method of the present invention.

[0028] Figure 2 This is a system module connection diagram of the present invention.

[0029] Figure 3 This is a diagram of the tunnel blasting impact prediction and analysis architecture of the present invention. Detailed Implementation

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] Please see Figure 1 and Figure 3 As shown, the first aspect of the present invention provides a method for predicting and analyzing the impact of tunnel blasting based on machine vision, comprising the following steps: Step 1, collecting relevant data on tunnel blasting: collecting relevant data on tunnel blasting projects, wherein the relevant data includes geological and topographical information, boundary condition information and blasting design parameters.

[0032] As a preferred option, the specific analysis process of step one is as follows: obtain the geological report, topographic map and real-scene image of the tunnel blasting project area to obtain the geological and topographic information and boundary condition information of the tunnel blasting project. The geological and topographic information includes rock type, stratum structure, groundwater level, geostress state and soil type. The boundary condition information includes geometric boundary conditions, mechanical boundary conditions, blast boundary conditions, material properties and time-related boundary conditions.

[0033] Obtain the blasting design parameters for the tunnel blasting project. These parameters include the type of explosive, explosive properties, single-hole charge, total charge, blasting hole layout, detonation sequence, and time interval. Explosive properties include the density, detonation velocity, and explosive energy of the explosive. The blasting hole layout includes the depth, diameter, spacing between holes, and row spacing of the blasting holes.

[0034] As a preferred option, the stratigraphic structure includes features such as bedding, joints, and faults.

[0035] As a preferred approach, the geostress state includes self-weight stress and tectonic stress.

[0036] In one specific embodiment, the geometric boundary condition is a free surface.

[0037] In one specific embodiment, the mechanical boundary conditions include fixed boundaries, stress boundaries, and displacement boundaries.

[0038] In one specific embodiment, the explosion boundary conditions are the location, energy, and type of the explosion source.

[0039] In one specific embodiment, the material properties include the physical and mechanical properties of the rock and the explosive properties, wherein the physical and mechanical properties of the rock include, but are not limited to, elastic modulus, Poisson's ratio, compressive strength, tensile strength, density, and fracture toughness.

[0040] In one specific embodiment, the time-dependent boundary conditions are dynamically loaded.

[0041] Step 2: Obtaining Blasting Information for Similar Blasting Projects: Obtain relevant data for each historical tunnel blasting project, further filter historical similar blasting projects, and obtain blasting information for historical similar blasting projects. The blasting information includes seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information.

[0042] For example, the specific analysis process of step two is as follows: extract the blasting operation records of each historical tunnel blasting project stored in the database to obtain the relevant data and blasting information of each historical tunnel blasting project.

[0043] By comparing the relevant data of each historical tunnel blasting project with the relevant data of the tunnel blasting project, the number of matching sub-items in geological and topographical information, boundary condition information and blasting design parameters of each historical tunnel blasting project and the tunnel blasting project is obtained, and the similarity coefficient between each historical tunnel blasting project and the tunnel blasting project is analyzed.

[0044] As a preferred approach, the similarity coefficients between historical tunnel blasting projects and current tunnel blasting projects are analyzed. Specifically, the number of matching sub-items between each historical tunnel blasting project and current tunnel blasting project in terms of geological and topographical information, boundary condition information, and blasting design parameters is obtained and denoted as follows: 'a' represents the number of the 'a'th historical tunnel blasting project, where a = 1, 2, ..., b.

[0045] By analyzing the formula The similarity coefficient δ between each historical tunnel blasting project and the current tunnel blasting project was obtained. a ,in ε1, ε2, and ε3 represent the influence factors corresponding to the unit quantity matching sub-items in the preset geological and topographic information, boundary condition information, and blasting design parameters, respectively. ε1, ε2, and ε3 represent the weight factors of the preset geological and topographic information, boundary condition information, and blasting design parameters, respectively. ε1 + ε2 + ε3 = 1.

[0046] As a preferred approach, the weighting factors for geological and topographical information, boundary condition information, and blasting design parameters are set based on the correlation between the similarity of these three factors and the similarity between the tunnel projects being evaluated. In one specific embodiment, the weighting factors for geological and topographical information, boundary condition information, and blasting design parameters are 0.4, 0.2, and 0.4, respectively.

[0047] The similarity coefficients of each historical tunnel blasting project and the tunnel blasting project are compared. The historical tunnel blasting project with the largest similarity coefficient is recorded as the historical similar blasting project of the tunnel blasting project.

[0048] Based on the blasting information of various historical tunnel blasting projects, blasting information of similar historical blasting projects was obtained.

[0049] As a preferred approach, if the sub-item is a feature-based sub-item, such as rock type or explosive type, then the sub-items are considered to be the same; if the sub-item is a numerical sub-item, such as charge amount or blast hole depth, then the sub-items are considered to be the same if the difference between them is less than a set threshold.

[0050] As a preferred approach, blasting operation records of historical tunnel blasting projects can be obtained using measuring equipment such as accelerometers, seismometers, sound level meters, pressure sensors, and vibration monitors, as well as monitoring methods such as ground monitoring stations, GPS positioning systems, radar interferometry, laser scanning technology, and traditional topographic surveying.

[0051] Step 3: Obtaining blasting information from the tunnel blasting model: Based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, a numerical simulation three-dimensional model of the tunnel blasting project is constructed using numerical simulation software. The tunnel blasting is simulated to obtain the blasting information of the simulated blasting in the tunnel blasting simulation model.

[0052] In one specific embodiment, numerical simulation software, such as FLAC3D, is used to establish a three-dimensional model of the tunnel blasting to simulate the blasting process and its impact on the surrounding structures. The numerical simulation methods involved include, but are not limited to, the finite element method, the finite difference method, the discrete element method, and smoothed particle hydrodynamics.

[0053] In another specific embodiment, specialized blasting prediction software, such as BLAST, is used to simulate and obtain key data such as vibration, shock wave, and ground deformation during tunnel blasting.

[0054] As a preferred option, using numerical simulation software to simulate tunnel blasting is a relatively mature existing technology, which will not be elaborated here.

[0055] Step 4: Prediction of the impact of blasting on structural safety: Based on historical similar blasting projects and tunnel blasting simulation models, the seismic effect information, vibration information, stress and strain information, tunnel structure damage information, and flyrock fragment information of the blasting are simulated. The impact coefficient of the tunnel blasting project on structural safety is analyzed and feedback is provided.

[0056] For example, the specific analysis process of step four includes: obtaining the intensity, peak ground acceleration, and peak velocity of the seismic waves from historical similar blasting projects based on the seismic effect information, and denoting them as c, d, and v respectively, and then analyzing them using the formula... The seismic effect influence factor φ1 of similar historical blasting projects was obtained, where c represents the correction coefficient for the preset seismic effect influence factor.设 d 设 v 设 These represent the preset threshold values ​​for seismic wave intensity, peak acceleration, and peak velocity, respectively.

[0057] As a preferred approach, the threshold values ​​for seismic wave intensity, peak ground acceleration, and peak velocity represent the limits that a tunnel can withstand in a tunnel blasting project. Exceeding these limits will risk tunnel collapse. The threshold values ​​for seismic wave intensity, peak ground acceleration, and peak velocity can be obtained through field investigation, simulation modeling, and reference to similar or historical tunnel blasting experience.

[0058] As a preferred option, seismographs can be used to measure the seismic effects of similar historical blasting projects.

[0059] Based on vibration information from similar historical blasting projects, the vibration velocity, vibration frequency, and ground acceleration caused by the blasting are obtained and denoted as v1, f, and d1, respectively. The formulas are then analyzed. The vibration influence factor φ2 of similar historical blasting projects was obtained, where This represents the correction coefficient for the preset vibration influence factor, v 1设 f 设 d 1设 These represent the preset threshold values ​​for vibration velocity, vibration frequency, and ground acceleration caused by blasting.

[0060] As a preferred approach, the threshold values ​​for vibration velocity, vibration frequency, and surface acceleration caused by blasting represent the ultimate limits that a tunnel can withstand in a tunnel blasting project. Exceeding these limits will pose a risk of tunnel collapse. The threshold values ​​for vibration velocity, vibration frequency, and surface acceleration caused by blasting can be obtained through on-site investigation, simulation modeling, and reference to similar or historical tunnel blasting experience.

[0061] Based on stress and strain information from similar historical blasting projects, the maximum stress and maximum strain values ​​of similar historical blasting projects are obtained and denoted as F and s, respectively. 变 By analyzing the formula The stress-strain influence factor φ3 of similar historical blasting projects was obtained, where γ represents the correction coefficient for the preset stress-strain influence factor. ΔF ,γ′ Δs These represent the influence factors corresponding to the preset unit stress value and unit strain value, respectively.

[0062] For example, the specific analysis process in step four further includes: based on the tunnel structure damage information of similar historical blasting projects, obtaining the maximum crack length and deformation degree of the tunnel main structure and tunnel support structure in similar historical blasting projects, and denoting them as l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16, l17, l18, l19, l10, l11, l12, l13, l14 ...2, l13, l14, l16, l17, l18, l19, l18, l19, l19, l12, l13, l18, l19, l l2, By analyzing the formula The tunnel structure damage impact factor φ4 of similar historical blasting projects was obtained, among which... This represents the correction coefficient for the preset tunnel structure damage impact factor, e represents the natural constant, and l 1设 , l 2设 , These represent the preset thresholds for crack length and deformation degree of the main tunnel structure and the thresholds for crack length and deformation degree of the tunnel support structure, respectively.

[0063] As a preferred approach, the threshold values ​​for crack length and deformation of the main tunnel structure and the tunnel support structure represent the ultimate limits that the tunnel can withstand in a tunnel blasting project. Exceeding these limits will pose a risk of tunnel collapse. The threshold values ​​for crack length and deformation of the main tunnel structure and the tunnel support structure can be obtained through on-site investigation, simulation modeling, and reference to similar or historical tunnel blasting experience.

[0064] As a preferred approach, the maximum crack length of the tunnel's main structure and support structure in similar historical blasting projects can be obtained by acquiring images of the tunnel's main structure and support structure before and after blasting using visual sensors. By comparing the images of the tunnel's main structure and support structure before and after blasting, the maximum crack length of the tunnel's main structure and support structure in similar historical blasting projects can be obtained.

[0065] As a preferred approach, to obtain the deformation degree of the tunnel main structure and tunnel support structure in similar historical blasting projects, one can obtain spatial models of the tunnel main structure and tunnel support structure before and after blasting, fit the spatial models of the tunnel main structure and tunnel support structure before and after blasting, and analyze the overlap degree to further obtain the deformation degree of the tunnel main structure and tunnel support structure in similar historical blasting projects.

[0066] As a preferred option, the deformation of the tunnel structure includes tilting, displacement, and torsion.

[0067] Based on flyrock fragment information from similar historical blasting projects, the average flyrock size, average fragment diameter, blast pile volume, and flyrock dispersion area of ​​similar historical blasting projects are obtained, and denoted as d. 石均 d 片均 、V 堆 s 散 .

[0068] By analyzing the formula The impact factor φ5 of flyrock fragments from similar historical blasting projects was obtained, among which... d′ represents the correction coefficient for the preset impact factor of flying rock fragments. 石均 ,d′ 片均 V′ 堆 ,s′ 散 These represent the preset thresholds for flyrock size, fragment diameter, explosion volume, and scattering area, respectively.

[0069] As a preferred approach, the threshold values ​​for flyrock size, fragment diameter, blast pile volume, and scattering area represent the limits that a tunnel can withstand in a tunnel blasting project. Exceeding these limits will risk tunnel collapse. The threshold values ​​for flyrock size, fragment diameter, blast pile volume, and scattering area can be obtained through field surveys, simulation models, and reference to similar or historical tunnel blasting experience.

[0070] As a preferred option, the average size of the flystone can be its area, volume, or particle size.

[0071] For example, the specific analysis process of step four also includes: analyzing the formula The impact coefficient η of similar historical blasting projects on structural safety was obtained. 历史 , where κ1, κ 2 κ1, κ2, κ3, κ4, and κ5 represent the preset weights of the seismic effect influence factor, vibration influence factor, stress-strain influence factor, tunnel structure damage influence factor, and flyrock fragment influence factor, respectively, with κ1+κ2+κ3+κ4+κ5=1.

[0072] As a preferred embodiment, the weights of the seismic effect influence factor, vibration influence factor, stress-strain influence factor, tunnel structure damage influence factor, and flyrock fragment influence factor are set based on the degree of threat posed by seismic effects, vibration, stress-strain, tunnel structure damage, and flyrock fragments to the safety of the tunnel structure. In one specific embodiment, the weights of the seismic effect influence factor, vibration influence factor, stress-strain influence factor, tunnel structure damage influence factor, and flyrock fragment influence factor are 0.2, 0.2, 0.2, 0.2, and 0.2, respectively.

[0073] The impact coefficient of simulated blasting on structural safety was analyzed using a tunnel blasting simulation model.

[0074] As a preferred approach, the method for analyzing the impact coefficient of tunnel blasting simulation models on structural safety is based on the same principle as the method for analyzing the impact coefficient of similar historical blasting projects on structural safety.

[0075] The weighted average of the impact coefficients of similar historical blasting projects and tunnel blasting simulation models on structural safety is calculated to obtain the impact coefficient of tunnel blasting projects on structural safety, and this result is fed back to the construction supervision department of the tunnel blasting project.

[0076] As a preferred option, the weights of the impact coefficients of historical similar blasting projects and tunnel blasting simulation models on structural safety are set values, and the sum of these values ​​is 1.

[0077] Step 5: Prediction of the impact of blasting on ground deformation: Based on the ground damage information simulated by historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation and provide feedback.

[0078] For example, the specific analysis process of step five includes: obtaining ground displacement information, ground settlement information, and ground crack information of similar historical blasting projects based on ground damage information of similar historical blasting projects.

[0079] Based on ground displacement information from similar historical blasting projects, the horizontal and vertical displacements of the ground after blasting in similar historical blasting projects are obtained, and the ground displacement influencing factors of similar historical blasting projects are analyzed.

[0080] As a preferred approach, the horizontal and vertical displacements of the ground after blasting in similar historical blasting projects are substituted into a preset relationship function between the horizontal and vertical displacements of the ground and the ground displacement influence factor to obtain the ground displacement influence factor for similar historical blasting projects.

[0081] Based on ground settlement information from similar historical blasting projects, the ground settlement depth, settlement rate, and settlement area of ​​similar historical blasting projects are obtained, and the ground settlement influencing factors of similar historical blasting projects are analyzed.

[0082] As a preferred approach, the settlement depth, settlement rate, and settlement area of ​​the ground in similar historical blasting projects are substituted into a preset relationship function between the settlement depth, settlement rate, and settlement area and the ground settlement influence factor to obtain the ground settlement influence factor for similar historical blasting projects.

[0083] As a preferred option, ground settlement depth refers to the amount of ground subsidence relative to the area before blasting.

[0084] As a preferred option, the settlement rate refers to the rate at which the ground descends per unit time.

[0085] As a preferred option, the settlement area refers to the area of ​​the surface region affected by the blasting.

[0086] Based on ground crack information from similar historical blasting projects, the maximum length, maximum width, maximum depth, and density of ground cracks in similar historical blasting projects are obtained, and the influencing factors of ground cracks in similar historical blasting projects are analyzed.

[0087] As a preferred approach, the maximum length, maximum width, maximum depth, and density of ground cracks from similar historical blasting projects are substituted into a preset relationship function between the maximum length, maximum width, maximum depth, and density of ground cracks and the ground crack influence factor to obtain the ground crack influence factor for similar historical blasting projects.

[0088] As a preferred option, crack density refers to the number of cracks per unit area.

[0089] The influence coefficient of similar historical blasting projects on ground deformation is obtained by calculating the weighted average of the ground displacement influence factor, ground settlement influence factor, and ground crack influence factor.

[0090] As a preferred option, the weights of the ground displacement influence factor, ground settlement influence factor, and ground crack influence factor are set values, and their sum is 1.

[0091] For example, the specific analysis process in step five also includes: analyzing the influence coefficient of the tunnel blasting simulation model on the ground deformation.

[0092] As a preferred approach, the method for analyzing the influence coefficient of tunnel blasting simulation model on ground deformation is based on the same principle as the method for analyzing the influence coefficient of similar historical blasting projects on ground deformation.

[0093] The influence coefficient of tunnel blasting projects on ground deformation was calculated by weighted averaging the influence coefficients of similar historical blasting projects and tunnel blasting simulation models.

[0094] As a preferred option, the weights of the impact coefficients of historical similar blasting projects and tunnel blasting simulation models on ground deformation are set values, and the sum of these values ​​is 1.

[0095] Step Six: Prediction of the Impact of Blasting on the Surrounding Environment: Based on the air shock wave information, sound wave information, and air quality information of similar historical blasting projects and tunnel blasting simulation models, analyze the impact coefficient of the tunnel blasting project on the surrounding environment and provide feedback.

[0096] For example, the specific analysis process of step six includes: obtaining the overpressure and overpressure duration of the air shock wave ...

[0097] As a preferred approach, the overpressure and overpressure duration of air shock waves from similar historical blasting projects are substituted into a preset relationship function between the overpressure and overpressure duration of air shock waves and the air shock wave influence factor to obtain the air shock wave influence factor of similar historical blasting projects.

[0098] Based on the acoustic wave information of similar historical blasting projects, the sound pressure level, frequency, duration, and sound level fluctuation of the acoustic waves from similar historical blasting projects are obtained, and the acoustic wave influence factors of similar historical blasting projects are analyzed.

[0099] As a preferred approach, the sound pressure level, frequency, duration, and sound level fluctuation of the sound waves from similar historical blasting projects are substituted into a preset relationship function between the sound pressure level, frequency, duration, sound level fluctuation, and sound wave influence factor to obtain the sound wave influence factor of similar historical blasting projects.

[0100] Based on air quality information from similar historical blasting projects, the dust concentration and emission concentration of various harmful gases generated by blasting in similar historical blasting projects are obtained, and the air quality influencing factors of similar historical blasting projects are analyzed.

[0101] As a preferred approach, the dust concentration and emission concentration of various harmful gases generated by blasting in similar historical blasting projects are substituted into a preset relationship function between dust concentration, emission concentration of various harmful gases and air quality impact factors to obtain the air quality impact factors of similar historical blasting projects.

[0102] The impact coefficient of similar historical blasting projects on the surrounding environment is obtained by calculating the weighted average of the air shock wave impact factor, sound wave impact factor, and air quality impact factor.

[0103] As a preferred option, the weights of the air shock wave impact factor, the sound wave impact factor, and the air quality impact factor are set values, and their sum is 1.

[0104] For example, the specific analysis process in step six also includes: analyzing the impact coefficient of the tunnel blasting simulation model on the surrounding environment.

[0105] As a preferred approach, the method for analyzing the impact coefficient of tunnel blasting simulation models on the surrounding environment is based on the same principle as the method for analyzing the impact coefficient of similar historical blasting projects on the surrounding environment.

[0106] The impact coefficient of tunnel blasting projects on the surrounding environment is obtained by calculating the weighted average of the impact coefficients of similar historical blasting projects and tunnel blasting simulation models.

[0107] As a preferred option, the weights of the impact coefficients of historical similar blasting projects and tunnel blasting simulation models on the surrounding environment are set values, and the sum is 1.

[0108] In this embodiment, the present invention obtains blasting information from historical similar blasting projects and simulated blasting using tunnel blasting simulation models to predict and analyze the impact of tunnel blasting. It can combine multiple prediction methods for comprehensive analysis, thereby improving the reliability and accuracy of the predicted impact of tunnel blasting.

[0109] In this embodiment, the present invention predicts the impact of tunnel blasting on multiple aspects such as structural safety, ground deformation, and the surrounding environment. This helps to comprehensively understand the potential impact of tunnel blasting, thereby facilitating the optimization of blasting design to reduce adverse effects and ensure the safety and efficiency of operations.

[0110] See Figure 2 As shown, a second aspect of the present invention provides a machine vision-based tunnel blasting impact prediction and analysis system, including a tunnel blasting-related data collection module, a similar blasting project blasting information acquisition module, a tunnel blasting model blasting information acquisition module, a blasting impact prediction module on structural safety, a blasting impact prediction module on ground deformation, a blasting impact prediction module on the surrounding environment, and a database.

[0111] The module for acquiring blasting information for similar blasting projects is connected to the module for collecting data related to tunnel blasting and the module for acquiring blasting information for tunnel blasting models. The module for acquiring blasting information for tunnel blasting models is connected to the modules for predicting the impact of blasting on structural safety, the impact of blasting on ground deformation, and the impact of blasting on the surrounding environment. The database is connected to the module for acquiring blasting information for similar blasting projects.

[0112] The tunnel blasting related data collection module is used to collect relevant data for tunnel blasting projects, including geological and topographical information, boundary condition information, and blasting design parameters.

[0113] The module for acquiring blasting information of similar blasting projects is used to acquire relevant data of various historical tunnel blasting projects, further filter historical similar blasting projects of tunnel blasting projects, and acquire blasting information of historical similar blasting projects. The blasting information includes seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information.

[0114] The tunnel blasting model blasting information acquisition module is used to construct a numerical simulation three-dimensional model of the tunnel blasting project based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, and to simulate the tunnel blasting to obtain the blasting information of the simulated blasting in the tunnel blasting simulation model.

[0115] The module for predicting the impact of blasting on structural safety is used to simulate the seismic effects, vibrations, stress and strain, tunnel structural damage, and flyrock fragments of blasting based on historical similar blasting projects and tunnel blasting simulation models. It then analyzes the impact coefficient of tunnel blasting projects on structural safety and provides feedback.

[0116] The module for predicting the impact of blasting on ground deformation is used to simulate ground damage information from blasting based on historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation, and provide feedback.

[0117] The module for predicting the impact of blasting on the surrounding environment is used to analyze the impact coefficient of the tunnel blasting project on the surrounding environment based on the air shock wave information, sound wave information and air quality information simulated by similar historical blasting projects and tunnel blasting simulation models, and to provide feedback.

[0118] The database is used to store blasting operation records for each historical tunnel blasting project.

[0119] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for predicting and analyzing the impact of tunnel blasting based on machine vision, characterized in that, The steps include: Step 1: Data Collection for Tunnel Blasting: Collect relevant data for the tunnel blasting project, including geological and topographical information, boundary condition information, and blasting design parameters; Step 2: Obtaining Blasting Information for Similar Blasting Projects: Obtain relevant data for each historical tunnel blasting project, further filter historical similar blasting projects for tunnel blasting projects, and obtain blasting information for historical similar blasting projects. The blasting information includes seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information. Step 3: Obtaining blasting information from the tunnel blasting model: Based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, a numerical simulation three-dimensional model of the tunnel blasting project is constructed using numerical simulation software to simulate the tunnel blasting and obtain the blasting information of the simulated blasting in the tunnel blasting simulation model; Step 4: Prediction of the impact of blasting on structural safety: Based on historical similar blasting projects and tunnel blasting simulation models, analyze the seismic effects, vibration, stress-strain, tunnel structure damage, and flyrock fragment information of the simulated blasting, and provide feedback; The specific analysis process in step four includes: based on the seismic effect information of similar historical blasting projects, obtaining the intensity, peak ground acceleration, and peak velocity of the seismic waves from similar historical blasting projects, and recording them as follows: By analyzing the formula Obtain the seismic effect influencing factor of similar historical blasting projects. ,in This represents the correction coefficient for the preset seismic effect influence factor. These represent the preset threshold values ​​for seismic wave intensity, peak ground acceleration, and peak velocity, respectively. Based on vibration information from similar historical blasting projects, the vibration velocity, vibration frequency, and surface acceleration caused by the blasting are obtained and denoted as follows: By analyzing the formula Vibration influence factors of similar historical blasting projects were obtained. ,in This represents the correction factor for the preset vibration influence factor. These represent the preset threshold values ​​for vibration velocity, vibration frequency, and surface acceleration caused by the blasting, respectively. Based on stress and strain information from similar historical blasting projects, the maximum stress and maximum strain values ​​from similar historical blasting projects are obtained and denoted as follows: By analyzing the formula Stress-strain influence factors of similar historical blasting projects were obtained. ,in This represents the correction coefficient for the preset stress-strain influence factor. These represent the influencing factors corresponding to the preset unit stress value and unit strain value, respectively; Step 5: Prediction of the impact of blasting on ground deformation: Based on the ground damage information simulated by historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation and provide feedback; Step Six: Prediction of the Impact of Blasting on the Surrounding Environment: Based on the air shock wave information, sound wave information, and air quality information of similar historical blasting projects and tunnel blasting simulation models, analyze the impact coefficient of the tunnel blasting project on the surrounding environment and provide feedback.

2. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 1, characterized in that: The specific analysis process for step two is as follows: Extract blasting operation records of each historical tunnel blasting project stored in the database to obtain relevant data and blasting information for each historical tunnel blasting project; The relevant data of each historical tunnel blasting project are compared with the relevant data of the tunnel blasting project to obtain the number of matching sub-items in geological and topographical information, boundary condition information and blasting design parameters between each historical tunnel blasting project and the tunnel blasting project, and the similarity coefficient between each historical tunnel blasting project and the tunnel blasting project is analyzed. The similarity coefficients of each historical tunnel blasting project and the tunnel blasting project are compared with each other, and the historical tunnel blasting project with the largest similarity coefficient is recorded as the historical similar blasting project of the tunnel blasting project. Based on the blasting information of various historical tunnel blasting projects, blasting information of similar historical blasting projects was obtained.

3. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 2, characterized in that: The specific analysis process in step four also includes: Based on tunnel structural damage information from similar historical blasting projects, the maximum crack length and deformation degree of the tunnel main structure and tunnel support structure in similar historical blasting projects were obtained, and denoted as follows: By analyzing the formula Obtain the impact factor of tunnel structural damage in similar historical blasting projects. ,in This represents the correction coefficient for the preset tunnel structure damage impact factor. Represents the natural constant. These represent the preset thresholds for crack length and deformation degree of the main tunnel structure and the thresholds for crack length and deformation degree of the tunnel support structure, respectively. Based on flyrock fragment information from similar historical blasting projects, the average flyrock size, average fragment diameter, blast pile volume, and flyrock dispersion area of ​​similar historical blasting projects were obtained, and these were denoted as follows: ; By analyzing the formula Obtain the impact factor of flyrock fragments from similar historical blasting projects. ,in This represents the correction coefficient for the preset impact factor of flying rock fragments. These represent the preset thresholds for flyrock size, fragment diameter, explosion volume, and scattering area, respectively.

4. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 3, characterized in that: The specific analysis process in step four also includes: By analyzing the formula Obtain the impact coefficient of similar historical blasting projects on structural safety. ,in These represent the preset weights for the seismic effect influence factor, vibration influence factor, stress-strain influence factor, tunnel structure damage influence factor, and flyrock fragment influence factor, respectively. ; Analyze the impact coefficients of simulated blasting on structural safety using a tunnel blasting simulation model; The weighted average of the impact coefficients of similar historical blasting projects and tunnel blasting simulation models on structural safety is calculated to obtain the impact coefficient of tunnel blasting projects on structural safety, and this result is fed back to the construction supervision department of the tunnel blasting project.

5. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 1, characterized in that: The specific analysis process in step five includes: Based on ground damage information from similar historical blasting projects, we obtain ground displacement information, ground settlement information, and ground crack information for similar historical blasting projects. Based on ground displacement information from similar historical blasting projects, the horizontal and vertical displacements of the ground after blasting in similar historical blasting projects are obtained, and the ground displacement influencing factors of similar historical blasting projects are analyzed. Based on ground settlement information from similar historical blasting projects, the ground settlement depth, settlement rate, and settlement area of ​​similar historical blasting projects are obtained, and the ground settlement influencing factors of similar historical blasting projects are analyzed. Based on ground crack information from similar historical blasting projects, the maximum length, maximum width, maximum depth, and density of ground cracks in similar historical blasting projects are obtained, and the influencing factors of ground cracks in similar historical blasting projects are analyzed. The influence coefficient of similar historical blasting projects on ground deformation is obtained by calculating the weighted average of the ground displacement influence factor, ground settlement influence factor, and ground crack influence factor.

6. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 5, characterized in that: The specific analysis process in step five also includes: Analyze the influence coefficients of tunnel blasting simulation model on ground deformation; The influence coefficient of tunnel blasting projects on ground deformation was calculated by weighted averaging the influence coefficients of similar historical blasting projects and tunnel blasting simulation models.

7. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 1, characterized in that: The specific analysis process in step six includes: Based on the air shock wave information of similar historical blasting projects, the overpressure and overpressure duration of the air shock waves of similar historical blasting projects are obtained, and the air shock wave influencing factors of similar historical blasting projects are analyzed. Based on the acoustic wave information of similar historical blasting projects, the sound pressure level, frequency, duration, and sound level fluctuation of the acoustic waves of similar historical blasting projects are obtained, and the acoustic wave influencing factors of similar historical blasting projects are analyzed. Based on air quality information from similar historical blasting projects, the dust concentration and emission concentration of various harmful gases generated by blasting in similar historical blasting projects are obtained, and the air quality influencing factors of similar historical blasting projects are analyzed. The impact coefficient of similar historical blasting projects on the surrounding environment is obtained by calculating the weighted average of the air shock wave impact factor, sound wave impact factor, and air quality impact factor.

8. The method for predicting and analyzing the impact of tunnel blasting based on machine vision according to claim 6, characterized in that: The specific analysis process in step six also includes: Analyze the impact coefficients of tunnel blasting simulation models on the surrounding environment; The impact coefficient of tunnel blasting projects on the surrounding environment is obtained by calculating the weighted average of the impact coefficients of similar historical blasting projects and tunnel blasting simulation models.

9. A machine vision-based tunnel blasting impact prediction and analysis system, used to execute the steps of the machine vision-based tunnel blasting impact prediction and analysis method as described in any one of claims 1-8, characterized in that, include: Tunnel blasting related data collection module: used to collect relevant data for tunnel blasting projects, including geological and topographical information, boundary condition information, and blasting design parameters; Similar blasting project blasting information acquisition module: used to acquire relevant data of various historical tunnel blasting projects, further filter historical similar blasting projects of tunnel blasting projects, and acquire blasting information of historical similar blasting projects, including seismic effect information, vibration information, stress and strain information, tunnel structure damage information, flyrock fragment information, ground damage information, air shock wave information, sound wave information, and air quality information; The tunnel blasting model blasting information acquisition module is used to construct a numerical simulation three-dimensional model of the tunnel blasting project based on the geological and topographical information, boundary condition information, and blasting design parameters of the tunnel blasting project, and to simulate the tunnel blasting to obtain the blasting information of the simulated blasting in the tunnel blasting simulation model. The module for predicting the impact of blasting on structural safety is used to simulate the seismic effects, vibration, stress and strain, tunnel structural damage, and flyrock fragments of blasting based on historical similar blasting projects and tunnel blasting simulation models. It analyzes the impact coefficient of tunnel blasting projects on structural safety and provides feedback. The module for predicting the impact of blasting on ground deformation is used to simulate ground damage information from blasting based on historical similar blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on ground deformation, and provide feedback. The module for predicting the impact of blasting on the surrounding environment is used to simulate the air shock wave information, sound wave information, and air quality information of blasting based on similar historical blasting projects and tunnel blasting simulation models, analyze the impact coefficient of tunnel blasting projects on the surrounding environment, and provide feedback. Database: Used to store blasting operation records for each historical tunnel blasting project.

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