High confining pressure blasting model test method and system for simulating single free surface clamping effect

Abstract

The invention relates to the technical field of data processing, and discloses a high-confining-pressure blasting model test method and system for simulating a single-free-surface clamping effect. The method comprises the following steps: preparing a single-free-surface model sample with a preset blast hole by regulating and controlling the proportion of cement mortar, performing three-way constraint loading by utilizing a hydraulic confining pressure system to form a stable clamping state, and filling trace explosive and blocking and sealing the hole to construct a complete blasting model. Multi-parameter synchronous monitoring is adopted to collect vibration waveform and confining pressure change data, and a time-frequency domain analysis algorithm is applied to identify a single free surface clamping effect and obtain blasting failure mode parameters under the influence of confining pressure. The technical problem that an existing blasting model test cannot accurately simulate the single-free-surface clamping constraint condition of underground engineering and cannot quantitatively recognize the influence rule of confining pressure on a blasting failure mode is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a high confining pressure blasting model test method and system for simulating the clamping effect of a single free surface. Background Art

[0002] Existing blasting model test methods primarily utilize free-space or multi-free-surface test conditions, simulating blasting effects by setting up multiple open surfaces around the specimen. The test setup typically consists of a simple clamping device and a conventional pressure-loading system. Traditionally, the preparation of surrounding rock-like materials often utilizes a fixed-ratio cement mortar. Data acquisition during the test primarily relies on monitoring a single parameter, such as vibration signals or pressure changes. Data analysis primarily relies on time-domain analysis, evaluating blasting effects by observing the temporal characteristics of the waveform.

[0003] However, existing technologies have significant shortcomings: first, multi-free surface test conditions cannot truly reflect the actual constraint state of a single free surface in underground tunnel excavation faces, resulting in a large deviation between the test results and the actual engineering; second, similar materials with fixed ratios cannot be adjusted according to different surrounding rock conditions, affecting the similarity accuracy of the model; third, single parameter monitoring cannot fully capture the multi-dimensional response characteristics during the blasting process, and conventional confining pressure loading systems cannot accurately control complex three-dimensional constraint states; finally, traditional time domain analysis methods cannot effectively identify the impact mechanism of confining pressure changes on blasting effects, and lack the ability to quantitatively analyze the single free surface constraint effect.

[0004] Based on an in-depth analysis of the above technical status, the core technical problems faced by existing technologies show progressive characteristics: how to establish an accurate surrounding rock material ratio system through similarity theory to ensure the physical similarity of the model, how to construct a three-dimensional loading control system that can accurately simulate the single free surface constraint conditions, how to establish a multi-parameter synchronous monitoring network to comprehensively capture the transient response characteristics of the blasting process, and how to quantitatively identify the influence of the single free surface clamping effect on the blasting failure mode through time-frequency domain analysis algorithms. The solution to these technical problems is directly related to whether the blasting model test can truly reflect the actual blasting conditions of underground engineering. Summary of the Invention

[0005] The present application provides a high confining pressure blasting model test method and system for simulating the clamping effect of a single free surface, which solves the technical problems that the existing blasting model test cannot accurately simulate the clamping constraint conditions of a single free surface in underground engineering and quantitatively identify the influence of confining pressure on blasting failure mode.

[0006] In the first aspect, the present application provides a high confining pressure blasting model test method for simulating the clamping effect of a single free surface, and the high confining pressure blasting model test method for simulating the clamping effect of a single free surface includes: preparing and processing similar materials of the surrounding rock by adjusting the cement mortar ratio to obtain a single free surface model specimen with a preset blast hole; performing three-dimensional constraint loading processing on the single free surface model specimen according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state; loading a trace amount of explosives into the preset blast hole of the clamped specimen for plugging and sealing, to obtain a complete blasting loading model; collecting and processing blasting data of the blasting loading model through multi-parameter synchronous monitoring to obtain a blasting response data set containing vibration waveforms and confining pressure changes; using a time-frequency domain analysis algorithm to identify the single free surface clamping effect on the blasting response data set to obtain blasting failure mode parameters under the influence of confining pressure.

[0007] In a second aspect, the present application provides a high confining pressure blasting model test system for simulating the clamping effect of a single free surface, the high confining pressure blasting model test system for simulating the clamping effect of a single free surface comprising:

[0008] The preparation module is used to prepare similar materials of surrounding rocks by adjusting the cement mortar ratio to obtain a single free-surface model specimen with a preset blasthole;

[0009] The loading module is used to perform three-dimensional constrained loading on the single-free surface model specimen according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state;

[0010] The plugging module is used to load a small amount of explosives into the preset blastholes of the clamped sample to plug and seal the holes, thus obtaining a complete blasting loading model;

[0011] The acquisition module is used to collect and process blasting data of the blasting loading model through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes;

[0012] The identification module is used to identify the single free surface clamping effect of the blasting response data set using a time-frequency domain analysis algorithm to obtain the blasting failure mode parameters under the influence of confining pressure.

[0013] In a third aspect, a high confining pressure blasting model test device simulating the clamping effect of a single free surface is provided, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the high confining pressure blasting model test device simulating the clamping effect of a single free surface executes the above-mentioned high confining pressure blasting model test method simulating the clamping effect of a single free surface.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned high confining pressure blasting model test method simulating the clamping effect of a single free surface.

[0015] In the technical solution provided by the present application, the precise preparation of materials similar to the surrounding rock is achieved by regulating the cement mortar ratio. The material formula system established based on the similarity theory can simultaneously meet the dual requirements of strength similarity ratio and density similarity ratio, ensuring that the model specimen maintains a high degree of consistency with the actual surrounding rock in terms of physical and mechanical properties. The precise positioning of the preset blastholes lays a geometric foundation for subsequent charging and monitoring. The three-dimensional constrained loading processing of the hydraulic confining pressure system realizes the precise simulation of the single-free surface constraint conditions. The stability and uniformity of the confining pressure are ensured by the servo control algorithm. The clamped specimens under the stable confining pressure state truly reflect the actual stress environment of the tunnel excavation surface in the underground engineering. The precise loading of trace explosives and the plugging and sealing treatment ensure the effective utilization and directional release of blasting energy. The complete blasting loading model provides a reliable experimental platform for studying the blasting mechanism under single-free surface conditions. The multi-parameter synchronous monitoring system realizes the comprehensive capture of multi-dimensional responses such as vibration, stress, and displacement. The high-frequency sampling and synchronous triggering mechanism ensure the complete recording of the blasting transient process, providing rich raw data for subsequent data analysis. The application of time-frequency domain analysis algorithms enables in-depth analysis of complex blasting signals. Through the comprehensive use of advanced algorithms such as wavelet transform, frequency domain filtering and pattern recognition, the characteristic parameters reflecting the clamping effect of a single free surface are successfully extracted, and a quantitative relationship model between confining pressure and blasting failure mode is established.

[0016] In the specific application area of underground engineering blasting and excavation, the core contribution of the time-frequency domain analysis algorithm lies in its ability to accurately identify characteristic frequency components related to the single-free-surface constraint effect from complex multidimensional blasting response signals. The wavelet transform algorithm achieves precise positioning in the time-frequency domain through multi-scale decomposition. The adaptive design of the frequency domain filter ensures the accurate extraction of the confining pressure-sensitive frequency band. The failure pattern recognition algorithm achieves effective conversion from signal characteristics to physical parameters through parameter inversion calculation. This algorithm combination not only solves the technical problem of traditional time-domain analysis methods' inability to quantify the influence of confining pressure, but also provides a reliable technical means for establishing a predictive model linking blasting effects and confining pressure conditions. It has important engineering practical value, especially in the optimization of blasting parameters and construction safety assessment under complex geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of an embodiment of a high confining pressure blasting model test method for simulating the clamping effect of a single free surface in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of an embodiment of a high confining pressure blasting model test system for simulating the clamping effect of a single free surface in an embodiment of the present application;

[0020] Figure 3 It is a schematic structural diagram of a high confining pressure blasting model test device simulating a single free surface clamping effect in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present application provide a high confining pressure blasting model test method and system that simulates the clamping effect of a single free surface. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or apparatus.

[0022] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of a high confining pressure blasting model test method simulating the clamping effect of a single free surface includes:

[0023] Step S101: preparing surrounding rock-like materials by adjusting the cement mortar ratio to obtain a single-free-surface model specimen with a preset blasthole;

[0024] Step S102: performing a three-dimensional constrained loading process on the single-free surface model specimen according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state;

[0025] Step S103: loading a small amount of explosive into the preset blasthole of the clamped sample to block and seal the hole, thereby obtaining a complete blasting loading model;

[0026] Step S104: performing blasting data acquisition and processing on the blasting loading model through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes;

[0027] Step S105: using a time-frequency domain analysis algorithm to perform single-free-surface clamping effect identification processing on the blasting response data set to obtain blasting failure mode parameters under the influence of confining pressure.

[0028] It is understandable that the execution subject of this application can be a high confining pressure blasting model test system simulating the clamping effect of a single free surface, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, the cement mortar ratio control is based on similarity theory calculations to determine the mass ratio parameters of cement, sand, and water. The similarity theory calculations involve the comprehensive optimization of geometric similarity ratios, density similarity ratios, and strength similarity ratios. The formula of similar materials for the surrounding rock is homogenized and mixed through a vibration stirring system. The vibration frequency control eliminates the bubble stratification phenomenon in the slurry and ensures the uniformity of the material. The preset blastholes are formed by pre-buried pipes during the fixed casting process. The blasthole diameter maintains a specific proportional relationship with the model scale. The single-free surface model specimen completes strength development under a standard curing environment. The precise control of curing temperature and humidity directly affects the final physical and mechanical parameters.

[0030] Three-way constrained loading achieves spatial positioning through a rigid constraint frame, with the free surface remaining completely open and the remaining five surfaces subject to varying degrees of constraint. The servo-hydraulic control algorithm, based on the principle of feedback control, monitors the deviation between the loading force and the target confining pressure in real time and adjusts the hydraulic system output via a PID controller. A stepped loading mode is used for graded confining pressure application, with the ratio of each confining pressure increment to the previous one remaining constant to avoid stress concentration caused by sudden loading. The bidirectionally uniformly distributed lateral constraint stress field is monitored in real time via multi-point pressure sensors to ensure that the uniformity error of the stress distribution is within a preset range.

[0031] Similarity scaling is based on the geometric relationship between the model and the prototype, scaling down the actual project charge to the range of trace explosives. The standard charge dosage is measured using a precision electronic balance, and the measurement accuracy directly affects the reproducibility of the blasting effect. During the formation of the charge segment structure, the ratio of the charge column length to the total length of the blasthole ensures the effective transfer of explosive energy. Sand plugging is performed using a layered compaction method. The density of each layer is determined through standard compaction tests. The density of the plugging section directly affects the effectiveness of the explosive pressure containment.

[0032] The high-frequency sampling trigger mechanism uses the voltage jump of the detonation signal as the trigger source, and the trigger delay time is controlled at the microsecond level. The multi-parameter monitoring network includes accelerometers, pressure sensors, and displacement sensors. The layout of each sensor is optimized according to the stress wave propagation path. The data synchronization acquisition system uses a unified clock source, and the time synchronization error between channels is controlled at the nanosecond level. During high-speed data recording, the sampling frequency is set to more than ten times the main blasting frequency to ensure complete signal capture. The data preprocessing algorithm includes three steps: low-pass filtering, high-pass filtering, and band-pass filtering. The cutoff frequency of the filter is dynamically adjusted according to the signal spectrum characteristics.

[0033] The time-frequency domain analysis algorithm uses wavelet transforms to achieve time-frequency decomposition of the signal, and the choice of wavelet basis function directly affects the time-frequency resolution. The energy distribution characteristic spectrum is obtained by squaring the wavelet coefficients. The energy distribution in different frequency bands reflects the energy transfer pattern during the blasting process. The frequency domain filter adopts an adaptive bandpass filter design, and the filter passband range is dynamically adjusted according to the confining pressure-sensitive frequency band. The extraction of confining pressure-sensitive frequency bands is based on spectral density analysis. The sensitive frequency bands are identified by calculating the correlation coefficient between the energy of each frequency band and the confining pressure change. Stress wave propagation theory establishes a mathematical relationship between the waveform distortion coefficient and the degree of confining pressure constraint. The waveform distortion coefficient is obtained by cross-correlating the original waveform with the ideal waveform. The failure pattern recognition algorithm is based on the principle of pattern matching. The measured failure pattern is compared and analyzed with the standard failure pattern library, and the recognition results are output as a similarity score.

[0034] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0035] Based on similarity theory, the mass ratio parameters of cement, sand and water are optimized to obtain a formula of similar materials for surrounding rock that meets the requirements of strength similarity ratio and density similarity ratio;

[0036] The formula of the surrounding rock-like material is input into the vibration stirring system for homogenization and mixing to obtain a uniform slurry mixture without bubbles and stratification;

[0037] The uniform slurry mixture is shaped and poured according to the preset mold size to obtain a cubic specimen blank with a blasthole channel of specified diameter in the center;

[0038] The cubic specimen blank was subjected to strength development treatment under a standard curing environment to obtain a single free-surface model specimen with physical and mechanical parameters meeting the standards.

[0039] Specifically, accurate simulation of the surrounding rock material is achieved through mathematical modeling based on similarity theory. Similarity theory calculations first establish the constraint relationship between the geometric similarity ratio CL, the density similarity ratio Cρ, and the strength similarity ratio Cσ. The geometric similarity ratio is determined to be 1:50 to 1:100 based on laboratory space limitations and test accuracy requirements. The density similarity ratio is achieved by adjusting the mass ratio of cement to sand. The cement density is approximately 3.1 g / cm 3 , the density of quartz sand is about 2.65g / cm 3 By changing the ratio of the two, the 3 The density of the mixture can be adjusted within a certain range. Controlling the strength similarity ratio relies on precise adjustment of the water-cement ratio. As the water-cement ratio increases from 0.4 to 0.6, the compressive strength of concrete exhibits an exponential decay trend. The optimization process for mass mix parameters utilizes multiple regression analysis, with the density similarity ratio and strength similarity ratio as objective functions and cement content, sand content, and water content as design variables. The optimal mix ratio is determined using the Lagrange multiplier method.

[0040] The homogenized mixing of the vibration mixing system is based on the principle of uniform distribution of shear stress. The vibration frequency is set to 50-80Hz, the amplitude is controlled in the range of 2-5mm, and the mixing time is calculated as 3-5 minutes per cubic decimeter according to the volume of the slurry. The shear rate during the mixing process is determined by the product of the rotational speed and the geometric parameters of the agitator. The uniformity of the shear rate directly affects the consistency of the cement hydration reaction. Bubble elimination is achieved by a combination of negative pressure extraction and ultrasonic vibration. The negative pressure is controlled at -0.08 to -0.1MPa, and the ultrasonic frequency is set to 40kHz. The uniformity of the slurry mixture is verified by rheological parameter testing, including apparent viscosity, yield stress and thixotropy indicators. A qualified uniform slurry mixture should have the characteristics of a Newtonian fluid or a quasi-Newtonian fluid.

[0041] The shaping casting process adopts a process that combines layered casting and mechanical compaction. The preset mold is made of steel, the inner wall smoothness is controlled within Ra0.8, and the mold size accuracy reaches ±1mm. The blasthole channel is formed by pre-buried PVC pipes. The pipe diameter is determined according to the similarity ratio, usually 8-12mm. The center line of the pipe coincides with the geometric center of the mold, and the deviation is controlled within ±2mm. The casting process adopts a continuous casting method, and the casting speed is controlled at a rising height of no more than 20mm per minute to avoid slurry separation and bubble entrainment. Mechanical compaction uses an inserted vibrator with a vibration frequency of 12,000 times / minute. The action time of each vibration point is 10-15 seconds, and the distance between vibration points does not exceed 1.5 times the action radius of the vibrator.

[0042] Strength development in a standard curing environment is strictly carried out in accordance with concrete curing specifications. The curing temperature is controlled at 20±2°C, and the relative humidity is maintained above 95%. The temperature and humidity fluctuations during curing do not exceed ±1°C and ±3%, respectively. A film is applied to maintain moisture within the initial 24 hours of curing to prevent surface dehydration and cracking, after which the material is transferred to a standard curing room for wet curing. The hydration degree during strength development is monitored by differential thermal analysis. When the hydration degree reaches above 70%, the material strength is essentially stable. Verification of compliance with physical and mechanical parameters includes compressive strength, tensile strength, and elastic modulus, measured through cube compression tests, splitting tests, and static elastic modulus tests. The ratio of compressive strength to the prototype strength of the surrounding rock should be equal to the strength similarity ratio, with an error within ±5%. The elastic modulus similarity ratio should be consistent with the strength similarity ratio, and the deviation between the density test result and the design density should not exceed ±2%.

[0043] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0044] The single-free-surface model specimen is placed in a rigid constraint frame for positioning and installation, obtaining an initial loading state in which the free-surface direction is open and the other five surfaces are constrained.

[0045] Based on the servo hydraulic control algorithm, graded confining pressure is applied in two horizontal directions perpendicular to the free surface to obtain a bidirectionally uniformly distributed lateral restraint stress field.

[0046] The lateral restraint stress field is subjected to constant pressure stabilization control according to the preset confining pressure target value to obtain a quasi-static loading environment in which stress relaxation is completed.

[0047] The deformation stability of the model specimens under quasi-static loading environment was detected by the stress-strain monitoring system, and the clamped specimens under stable confining pressure state with fully adjusted internal stress were obtained.

[0048] Specifically, the positioning and installation of the rigid constraint frame utilizes precision mechanical positioning principles to achieve precise control of all six degrees of freedom in space. The constraint frame is constructed of high-strength alloy steel, with a stiffness coefficient exceeding 10^8 N / m, ensuring negligible deformation during loading. During positioning and installation, the single-free-surface model specimen is positioned using a three-point positioning method. Two positioning points on the bottom and one on the side provide a stable geometric constraint. The orientation of the free-surface is determined by measuring the distance from each specimen surface to the frame's reference plane using a laser rangefinder, with a measurement accuracy of 0.01 mm. Constraints on the remaining five surfaces are achieved using adjustable constraint plates. The contact between the constraint plate and the specimen surface is established using a flexible cushioning layer with a thickness of 3-5 mm and an elastic modulus approximately 1 / 10 of the specimen material. Establishing the initial loading state requires pre-tightening the constraint plate until it contacts the specimen surface. The pre-tightening force is controlled within the range of 50-100 N, and the pre-tightening force is monitored in real time by a force sensor. The servo-hydraulic control algorithm utilizes a dual closed-loop control structure, with an outer pressure control loop and an inner flow control loop. The setpoint of the pressure control loop is derived from the preset confining pressure target, and the feedback signal comes from a pressure sensor mounted on the loading plate. The controller utilizes a PID algorithm, with the proportional coefficient Kp set to 0.8-1.2, the integral coefficient Ki to 0.1-0.3, and the differential coefficient Kd to 0.05-0.15. The flow control loop regulates the hydraulic system's flow output to ensure a smooth loading rate. A stepped loading curve is used for graded confining pressure application. The number of loading stages is determined by the target confining pressure, typically 5-8 levels, with each stage loading 15%-20% of the target value. The two horizontal directions perpendicular to the free surface correspond to the X- and Y-direction loading systems, respectively. These two systems utilize independent hydraulic circuits and coordinate loading via a synchronous controller. Bidirectional uniform distribution is achieved by real-time monitoring of the pressure differential between the X and Y directions, which is controlled within ±2% of the target confining pressure.

[0049] Constant pressure stability control is based on the disturbance suppression principle of feedback control theory. The preset confining pressure target value is determined according to the ground stress conditions of the actual project, usually in the range of 30-100MPa. The control system adopts an adaptive control algorithm to dynamically adjust the control parameters according to the deformation response characteristics of the specimen. The stability of the lateral constraint stress field is achieved through multi-point pressure monitoring. 3-5 pressure sensors are arranged on each loading surface, and the standard deviation of the pressure value at the monitoring point does not exceed 3% of the average value. The stress relaxation phenomenon is described by the Maxwell viscoelastic model, and the relaxation time constant is determined according to the material properties of the specimen, usually 10-30 minutes. The criterion for judging the quasi-static loading environment is that the pressure fluctuation amplitude is less than 1% of the target value within 10 consecutive minutes, and the loading rate is reduced to less than 0.1MPa / min.

[0050] The stress and strain monitoring system utilizes a distributed sensor network for comprehensive monitoring. Deformation monitoring is achieved using strain gauges and displacement sensors installed on the specimen surface. The strain gauges utilize 120Ω resistive strain gauges with a sensitivity coefficient of 2.0-2.2, while the displacement sensors utilize LVDTs with a measurement range of ±10mm and a resolution of 0.001mm. The monitoring data acquisition frequency is set at 10Hz to ensure that transient changes during stress adjustment are captured. Deformation stability is determined based on statistical analysis of continuous monitoring data, calculating the coefficient of variation for the most recent 100 data points. Deformation stability is considered achieved when the coefficient of variation is less than 2%. Adequate internal stress adjustment is verified by monitoring strain coordination at each point on the specimen, ensuring that the strain difference between adjacent measuring points does not exceed 5% of the average strain. Final confirmation of a stable confining pressure state requires the simultaneous fulfillment of the three conditions of pressure stability, deformation stability, and strain coordination. The stabilization time typically occurs 30-60 minutes after the confining pressure is applied.

[0051] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0052] Based on similarity ratio conversion, the charge parameters of trace explosives are accurately measured and processed to obtain the standard charge dosage that matches the model scale;

[0053] The standard charge dosage is input into the bottom of the preset blasthole of the clamped sample for fixed-point filling, and a charge section structure is obtained in which the length of the charge column accounts for one third of the total length of the blasthole;

[0054] According to the energy sealing principle, the empty hole section above the charge section structure is filled with sand to obtain a sealing section structure with a blocking length twice the charge length.

[0055] The detonator initiation system is used to connect the detonator device to the top of the sealing section structure, and a complete blasting loading model with remote detonation capability is obtained.

[0056] Specifically, the similarity ratio conversion is based on the dimensional analysis method of blasting similarity theory to establish a quantitative relationship between charge amount and model scale. The similarity ratio of charge amount is determined according to the cube root relationship, that is, the charge similarity ratio is equal to the cube of the geometric similarity ratio. When the geometric similarity ratio is 1:50, the charge similarity ratio is 1:125000. The trace explosive uses a standard explosive with a TNT equivalent and a density of approximately 1.6g / cm 3, detonation velocity 7000m / s, and explosive heat 4520kJ / kg. Precise metering is performed using an analytical balance with an accuracy of 0.1mg. The ambient temperature is controlled at 20±1°C and the humidity at 50±5%. The charge parameter calculation process first determines a baseline value based on the single-hole charge of the prototype project. This is then converted using a similarity ratio, and finally, correction factors for explosive density and energy density are considered. The standard charge dose typically ranges from 0.1-1.0g, with the specific value determined based on the simulated blasting conditions and sample size.

[0057] The targeted charging process utilizes a dedicated micro-charging tool to precisely position the explosive. The charging tool consists of a slender charging tube, a push rod, and a positioning fixture. The inner diameter of the charging tube is slightly smaller than the borehole diameter, ensuring close contact between the explosive and the borehole wall. The bottom of the pre-set borehole is identified by accurately measuring the hole depth with a depth gauge, with an accuracy of ±0.5mm. The standard charge dosage is delivered to the bottom of the hole in batches, with each load controlled to 1 / 3-1 / 2 of the total volume. Gentle vibration is used to assist compaction during the loading process. Grain length is controlled by marking the charging tube with pre-marked length scales. During the formation of the charge segment structure, the explosive is compacted by light compaction, with a compaction force controlled within the range of 5-10N to ensure that the charge density reaches above 90% of the theoretical density. The ratio of charge length to total borehole length directly affects the efficiency of explosive energy utilization. A too small ratio results in energy waste, while a too large ratio compromises the plugging effect.

[0058] The sand filling process utilizes the energy containment principle from explosion mechanics to design the plugging parameters. This principle requires the plugging section to possess sufficient inertial mass and shear strength to withstand the impact of high-pressure gases generated by the explosion. Quartz sand with a particle size of 0.1-0.5 mm and a moisture content of 8-12% is used to increase the sand's cohesion and compaction properties. The sand filling of the remaining hole is compacted in layers, with each layer 5-8 mm thick. The compaction tool uses a round rod with a diameter slightly smaller than the hole diameter, and the compaction force is controlled at 20-30 N. The plugging density is determined through a standard compaction test and should exceed 95% of the maximum dry density. The 2:1 ratio of the plugging length to the charge length of the plugging section is determined based on an empirical formula. This ratio effectively prevents gas leakage during explosive detonation while also avoiding stress concentration caused by excessive plugging. The plugging effectiveness is verified by measuring the density distribution of the plugging section using a gamma-ray densitometer with an accuracy of ±0.02 g / cm. 3 .

[0059] The electric detonator detonation system utilizes a combination of instantaneous electric detonators and dedicated initiators. The electric detonators are No. 8 industrial detonators with an initiation capacity of 0.4-0.6g, an initiation current of no less than 2.5A, and a safety current of less than 0.18A. During the initiation process, the detonator leg wires are connected to the main detonating circuit via waterproof connectors, which are sealed with insulating tape. A space for the detonators is reserved at the top of the sealing section, with a depth equal to the detonator length plus a 5-10mm margin. The detonators are secured within the sealing section using fine sand filling to ensure the transmission distance between the detonator and the explosives is within a reasonable range. Remote detonation is achieved via wireless or wired initiators, with a detonation range of no less than 50m. The initiators incorporate multiple safety features, including a safety lock, double confirmation, and delayed detonation. After establishing a complete blasting loading model, the connectivity of the initiation circuit is verified through resistance testing. The circuit resistance should be within the specified range, and the insulation resistance should be no less than 0.5MΩ. The reliability of the initiation system is verified through simulated initiation tests, in which small current pulses are used to verify the system functionality without causing a real explosion.

[0060] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0061] Based on the high-frequency sampling trigger mechanism, the sensor array is arranged at multiple points on the surface of the complete blasting loading model to obtain a multi-parameter monitoring network covering vibration, stress and displacement.

[0062] The detonation signal is input into the data synchronization acquisition system for time base calibration processing to obtain multi-channel parallel acquisition control instructions under a unified time stamp;

[0063] According to the transient characteristics of blasting, high-speed data recording and processing is performed on multi-channel parallel acquisition control instructions to obtain the original multi-dimensional sensor data stream covering the entire blasting process;

[0064] The original multi-dimensional sensor data stream is subjected to noise filtering and feature extraction through data preprocessing algorithms to obtain a blasting response dataset containing vibration waveforms and confining pressure changes.

[0065] Specifically, the high-frequency sampling trigger mechanism achieves millisecond-level response speeds based on the principle of voltage transition detection. The core of the trigger mechanism is a Schmitt trigger circuit. When the detonation signal voltage transitions from a low level to a high level, the trigger outputs a trigger pulse to initiate data acquisition. The trigger threshold is set at 50% of the detonator output voltage, typically 12-15V, and the trigger delay is controlled within the range of 0.1-0.5 milliseconds. The sensor array utilizes a layered and partitioned spatial layout strategy. Vibration sensors are placed at key points on the specimen surface, including the center of the free surface, around the blasthole, and at the specimen corners. Sensor spacing is determined by the stress wave wavelength, typically 1 / 4-1 / 2 of the wavelength. Stress sensors are mounted at the interface between the loading plate and the specimen to monitor changes in confining pressure. The sensor range covers 0-150 MPa, with an accuracy level of 0.5. A laser displacement sensor is positioned 50-100 mm in front of the free surface to measure free surface deformation and fragment ejection trajectory. The multi-parameter monitoring network is connected through a star topology, with the central data collector serving as a network node. Each sensor is connected to the collector through a shielded cable, and the cable length is controlled within 10m to reduce signal attenuation.

[0066] Time base calibration utilizes GPS clock synchronization technology to achieve nanosecond-level time accuracy. The data synchronization acquisition system utilizes a distributed architecture, with a master controller responsible for time base management and slave controllers responsible for data acquisition of specific channels. The detonation signal is input to the master controller via an optoelectronic isolator, whose response time is less than 1 microsecond, ensuring real-time signal transmission. Timestamp generation is based on a high-precision crystal oscillator clock with a clock frequency of 100 MHz and a time resolution of 10 nanoseconds. A unified timestamp is synchronized to each slave controller via a clock distribution network, with network latency compensated for by round-trip time measurement. Multi-channel parallel acquisition control instructions include parameters such as sampling frequency, sampling duration, trigger mode, and data format. The sampling frequency is set to 5-10 times the main frequency of the measured signal, typically within the range of 100 kHz to 1 MHz, based on the frequency characteristics of the measured signal. Control instructions are transmitted to each acquisition channel via a high-speed digital bus with a bandwidth of 1 Gbps, ensuring real-time command delivery.

[0067] High-speed data recording is based on a storage strategy that combines hardware caching and software buffering. The transient characteristics of blasting are characterized by large signal amplitude, high frequency, and short duration. The stress wave propagation speed is approximately 3000-5000 m / s, and the entire blasting process is completed within tens of microseconds. Multi-channel parallel acquisition uses an FPGA chip to achieve hardware-level parallel processing. Each channel is equipped with an independent ADC converter with 16-bit conversion accuracy and a conversion rate of 1MSPS. A circular buffer structure is used during data recording. The buffer capacity is calculated based on the sampling frequency and recording duration, typically 64MB-256MB. The raw data is stored in binary format, and each sampling point contains information such as timestamp, channel number, value, and status flag. The synchronization of the multi-dimensional sensor data stream is guaranteed by the hardware clock, and the time deviation between channels is controlled within one sampling cycle. Data integrity is verified by a CRC checksum to ensure no data loss or errors during transmission.

[0068] The data preprocessing algorithm combines multi-stage filtering with adaptive threshold detection. Noise filtering first removes low-frequency drift using a high-pass filter, with a cutoff frequency set to 1 / 10 of the signal's main frequency, typically 1-10 Hz. A bandpass filter then extracts the effective frequency band. The passband range is determined based on the spectral characteristics of the blast signal, typically 100 Hz-50 kHz. The filter adopts a Butterworth design with an order of 4-6 to ensure a flat amplitude-frequency characteristic within the passband. Feature extraction is achieved through a combination of time-domain and frequency-domain analysis. Time-domain features include peak value, effective value, and zero-crossing rate, while frequency-domain features include center frequency, bandwidth, and power spectral density. Feature extraction of the vibration waveform focuses on the arrival time, amplitude, and frequency characteristics of the P and S waves, identifying the waveform's starting point using the STA / LTA algorithm. Feature extraction of confining pressure changes highlights the transient characteristics of pressure changes through differential operations, with the differential step size determined by the sampling frequency and signal change rate. The construction of the blast response dataset is achieved through the combination of feature vectors. The response state at each moment is represented by a vector containing multiple feature parameters, and the vector dimension is usually 20-50 dimensions.

[0069] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0070] The vibration waveform signal in the blasting response data set is decomposed into time and frequency based on the wavelet transform algorithm to obtain the energy distribution characteristic spectrum in different frequency bands.

[0071] The energy distribution characteristic spectrum is input into the frequency domain filter to extract the confining pressure sensitive frequency band and obtain the characteristic frequency component reflecting the single free surface constraint effect;

[0072] Based on the stress wave propagation theory, the correlation analysis between the characteristic frequency components and the confining pressure variation data was carried out to obtain a quantitative relationship model between the confining pressure constraint degree and the waveform distortion coefficient.

[0073] The quantitative relationship model is processed by parameter inversion calculation through the failure pattern recognition algorithm to obtain the blasting failure mode parameters under the influence of confining pressure, including the crack propagation directionality and the fragmentation range definition.

[0074] Specifically, the wavelet transform algorithm uses the Morlet mother wavelet function to achieve time-frequency decomposition of the vibration waveform signal. The Morlet wavelet has excellent time-frequency localization properties. The center frequency parameter in its mathematical expression is set to 5-8 Hz, and the bandwidth parameter is set to 1-2. The time-frequency decomposition process is achieved through the continuous wavelet transform, with the scale parameter set from 0.1 to 100, corresponding to a frequency range from 10 kHz to 100 Hz. The scale step size is logarithmically spaced to improve low-frequency resolution. The wavelet coefficients are calculated through convolution operations. The original signal is convolved with the wavelet function at different scales to obtain complex coefficients in the time-frequency domain. The energy distribution is calculated by calculating the square of the wavelet coefficient modulus, that is, the energy density is equal to the sum of the square of the real and imaginary parts of the wavelet coefficients. The energy distribution in different frequency bands is obtained by integrating the energy density within the corresponding scale range. The frequency band is divided into 8-12 sub-bands using an octave method. The characteristic spectrum is constructed by time-averaging the energy distribution of each frequency band, resulting in an energy distribution characteristic spectrum with frequency as the horizontal axis and energy density as the vertical axis.

[0075] The frequency domain filter uses an adaptive design to accurately extract the confining pressure-sensitive frequency band. The filter design is based on Wiener filtering theory, and the filter coefficients are optimized using the minimum mean square error criterion. The identification of confining pressure-sensitive frequency bands is based on correlation analysis between the energy distribution characteristic spectrum and the confining pressure change. The Pearson correlation coefficient between the energy and the confining pressure change in each frequency band is calculated. Frequency bands with correlation coefficients greater than 0.7 are identified as sensitive frequency bands. The frequency domain filter uses an FIR structure, and the filter length is determined based on the required frequency resolution, typically 512-1024 points. The filter passband is set to the identified sensitive frequency band, with ripple within the passband controlled within ±0.1 dB and stopband attenuation greater than 40 dB. Characteristic frequency components are obtained through spectral analysis of the filtered signal, and the power spectral density is calculated using the FFT algorithm. The frequency resolution is determined by the signal length and sampling frequency. The single free surface constraint effect manifests itself in the characteristic frequency components as energy enhancement or suppression at specific frequencies. This effect is quantified by comparing the spectra under constrained and unconstrained conditions.

[0076] The stress wave propagation theory establishes the physical relationship between the waveform distortion coefficient and the degree of confining pressure constraint. The propagation speed of stress waves in elastic media is related to the elastic modulus and density of the medium. The increase in confining pressure will increase the equivalent elastic modulus of the medium, thereby changing the wave speed. The correlation analysis between the characteristic frequency component and the confining pressure change data is achieved through multivariate linear regression, with the amplitude and phase of the characteristic frequency as independent variables and the confining pressure change as the dependent variable. The least squares method is used to solve the regression coefficient in the regression analysis. The significance of the regression equation is verified by the F test. The correlation coefficient R 2 Generally, a value greater than 0.8 indicates a strong linear relationship. The waveform distortion coefficient is defined as the relative deviation between the measured waveform and the theoretical waveform, and is calculated as: the root mean square of the difference between the measured and theoretical waveforms divided by the root mean square of the theoretical waveform. The degree of confining pressure constraint is represented by the ratio of the confining pressure value to the uniaxial compressive strength, and the ratio range is usually between 0.1 and 2.0. The quantitative relationship model adopts the form of an exponential function, that is, the waveform distortion coefficient is linearly related to the logarithm of the confining pressure constraint degree, and the model parameters are determined by nonlinear least squares fitting.

[0077] The damage pattern recognition algorithm utilizes a combination of pattern matching and machine learning. Parameter inversion calculations are based on Bayesian inference theory, using a quantitative relationship model as prior knowledge and updating the posterior probability distribution of model parameters using observed data. The inversion process employs the Markov Chain Monte Carlo method to achieve random sampling of the parameter space, with the sampling chain length set between 10,000 and 50,000 times to ensure convergence. Crack propagation directionality is determined by analyzing the energy distribution of signals received by sensors in different directions, with the main crack direction corresponding to the direction with the highest energy density. Fragmentation range definition is achieved through signal attenuation analysis, with the fragmentation boundary inferred based on the attenuation pattern of sensor signal amplitude at different distances. Explosive damage pattern parameters include quantitative indicators such as crack density, fragment size distribution, and damage variable. Crack density is expressed as the total crack length per unit volume, fragment size distribution is described using the Weibull distribution function, and the damage variable is defined as the ratio of the volume of the damaged region to the total volume. These parameters are obtained by comparing the inversion results with a database of known damage patterns, and the degree of match is quantitatively assessed using Euclidean distance or cosine similarity.

[0078] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0079] Based on the spectrum density analysis, the main frequency band of the energy distribution characteristic spectrum is identified and a set of candidate frequency intervals containing the confining pressure response information is obtained;

[0080] The candidate frequency interval set is input into an adaptive bandpass filter for frequency band separation processing to obtain multiple sub-band energy components divided by frequency range;

[0081] The sensitivity weights of multiple sub-band energy components are calculated according to the amplitude of confining pressure change to obtain a sensitivity coefficient matrix that quantifies the contribution of each sub-band to the confining pressure response.

[0082] The sensitivity coefficient matrix is processed by threshold screening algorithm to extract high-sensitive frequency bands, and characteristic frequency components reflecting the single-free surface constraint effect whose sensitivity coefficient exceeds the preset threshold are obtained.

[0083] Specifically, spectral density analysis uses power spectral density estimation to identify the dominant frequency band in the energy distribution signature spectrum. The power spectral density is calculated using the Welch method, segmenting the signal into 1024 samples per segment. The overlap between segments is set to 50%, and a Hanning window is used for windowing to reduce spectral leakage. Main frequency band identification is based on a peak detection algorithm, which locates the dominant frequency component by finding the local maximum of the power spectral density. The peak detection threshold is set at 10%-20% of the maximum power spectral density value, and the minimum frequency interval between adjacent peaks is set at 1 / 10 of the fundamental frequency to avoid false peaks. Confining pressure response information is identified by comparing the power spectral density under different confining pressure conditions. Frequency drift and amplitude changes caused by confining pressure changes are used as indicators of confining pressure sensitivity. Candidate frequency bins are identified by setting a frequency window around the dominant frequency component. The window width is set based on the frequency resolution and signal bandwidth, typically 5%-10% of the dominant frequency. The frequency bin set consists of 3-8 candidate bins, each with a center frequency corresponding to a dominant frequency band. The bin boundaries are determined using the 3dB bandwidth criterion.

[0084] The adaptive bandpass filter utilizes an elliptical filter design to achieve highly selective frequency band separation. The filter's passband boundaries are dynamically adjusted based on the candidate frequency interval, keeping passband ripple within ±0.5dB and stopband attenuation at least 40dB. This adaptive feature is achieved by monitoring the input signal's spectral characteristics. When spectral changes are detected, the filter coefficients are automatically updated to maintain optimal frequency response. The frequency band separation process utilizes a parallel filtering structure, with each candidate frequency interval corresponding to an independent bandpass filter channel. The filter bank design ensures complete frequency response with no overlap between channels. The transition band width between adjacent channels is set to 20%-30% of the center frequency difference. The energy components of multiple subbands are obtained by calculating the instantaneous power of the filtered signal, which is equal to the square of the signal amplitude. The energy components are then smoothed using a sliding average filter to determine their time-varying characteristics. The energy components are normalized by dividing by the total energy, ensuring that the sum of the energy components in each band equals 1, facilitating subsequent sensitivity analysis.

[0085] Sensitivity weight calculation quantifies the correlation between subband energy components and confining pressure variations based on mutual information theory. The magnitude of confining pressure variations is obtained by differencing the confining pressure time series. The differencing step size is determined by the time scale of the confining pressure variations and is typically 10-50 times the sampling period. Mutual information is calculated using kernel density estimation, estimating the joint probability density function of the subband energy components and the confining pressure variation using a Gaussian kernel function. The sensitivity weight is defined as the ratio of the mutual information value to the maximum possible mutual information value, ranging from 0 to 1, with higher values indicating greater sensitivity. The weight calculation considers time delay effects, calculating the mutual information at different time delays to find the optimal delay corresponding to the maximum value. The sensitivity coefficient matrix is constructed using the frequency band number as the row index and the time window as the column index. The matrix elements represent the sensitivity weights of the corresponding frequency bands in a specific time window. Matrix normalization is achieved by row-wise normalization to ensure that the sum of the weights for each frequency band is equal to 1, eliminating the influence of energy level differences between different frequency bands.

[0086] The threshold screening algorithm uses an adaptive threshold determination method to extract highly sensitive frequency bands. The preset threshold is determined by statistically analyzing the sensitivity coefficient matrix. The mean and standard deviation of all sensitivity coefficients are calculated, and the threshold is set to the mean plus 1-2 times the standard deviation. Adaptive characteristics are achieved by monitoring the distribution characteristics of the sensitivity coefficients. When the distribution exhibits a clear bimodal or multimodal characteristic, the maximum inter-class variance method is used to determine the optimal threshold. Highly sensitive frequency bands are screened by comparing the average sensitivity coefficient of each frequency band with the threshold. Bands exceeding the threshold are marked as highly sensitive. The screening results are verified using a cross-validation method. The data is divided into training and test sets. The threshold is determined on the training set, and the screening effect is verified on the test set. The final determination of the characteristic frequency components takes into account the continuity and stability of the frequency bands. Continuous highly sensitive frequency bands are merged into a single characteristic frequency component, and discontinuous frequency bands are selected based on their physical significance. The manifestation of the single free surface constraint effect in the characteristic frequency components is verified by comparing the frequency response differences between the free surface direction and the constraint direction. Frequency components with significant differences are identified as characteristic frequencies reflecting the constraint effect.

[0087] The above describes the high confined pressure blasting model test method for simulating the clamping effect of a single free surface in the embodiment of the present application. The following describes the high confined pressure blasting model test system for simulating the clamping effect of a single free surface in the embodiment of the present application. Figure 2 In one embodiment of the present application, a high confining pressure blasting model test system for simulating the clamping effect of a single free surface includes:

[0088] The preparation module is used to prepare similar materials of surrounding rocks by adjusting the cement mortar ratio to obtain a single free-surface model specimen with a preset blasthole;

[0089] The loading module is used to perform three-dimensional constrained loading on the single-free surface model specimen according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state;

[0090] The plugging module is used to load a small amount of explosives into the preset blastholes of the clamped sample to plug and seal the holes, thus obtaining a complete blasting loading model;

[0091] The acquisition module is used to collect and process blasting data of the blasting loading model through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes;

[0092] The identification module is used to identify the single free surface clamping effect of the blasting response data set using a time-frequency domain analysis algorithm to obtain the blasting failure mode parameters under the influence of confining pressure.

[0093] above Figure 2 From the perspective of modular functional entities, the high confining pressure blasting model test system for simulating the clamping effect of a single free surface in an embodiment of the present invention is described in detail. Below, from the perspective of hardware processing, the high confining pressure blasting model test equipment for simulating the clamping effect of a single free surface in an embodiment of the present invention is described in detail.

[0094] Reference Figure 3 In an embodiment of the present invention, a high confined pressure blasting model test device simulating the clamping effect of a single free surface is further provided. The high confined pressure blasting model test device simulating the clamping effect of a single free surface can be a server, and its internal structure can be as follows: Figure 3 As shown. The high confined pressure blasting model test equipment simulating the clamping effect of a single free surface includes a processor, a memory, a display screen, an input device, a network interface and a database connected via a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the high confined pressure blasting model test equipment simulating the clamping effect of a single free surface includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the high confined pressure blasting model test equipment simulating the clamping effect of a single free surface is used to store the corresponding data in this embodiment. The network interface of the high confined pressure blasting model test equipment simulating the clamping effect of a single free surface is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0095] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the high confining pressure blasting model test equipment for simulating the clamping effect of a single free surface to which the solution of the present invention is applied.

[0096] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the high confining pressure blasting model test method simulating the clamping effect of a single free surface.

[0097] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a high-confined pressure blasting model test device (which can be a personal computer, server, or network device, etc.) that simulates the clamping effect of a single free surface to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A high confining pressure blasting model test method simulating the clamping effect of a single free surface, characterized in that: The method comprises: The surrounding rock-like material was prepared by adjusting the cement mortar ratio to obtain a single-free-surface model specimen with a preset blasthole. The single free surface model specimen was subjected to three-dimensional constraint loading according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state. A small amount of explosives was loaded into the preset blastholes of the clamped specimens to block and seal the holes, thus obtaining a complete blasting loading model. The blasting data of the blasting loading model is collected and processed through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes; The time-frequency domain analysis algorithm is used to identify the single free surface clamping effect of the blasting response data set, and the blasting failure mode parameters under the influence of confining pressure are obtained.

2. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 1 is characterized in that: The method of preparing similar surrounding rock materials by adjusting the cement mortar ratio to obtain a single free-surface model specimen with a preset blasthole includes: Based on similarity theory, the mass ratio parameters of cement, sand and water are optimized to obtain a formula of similar materials for surrounding rock that meets the requirements of strength similarity ratio and density similarity ratio; The formula of the surrounding rock-like material is input into the vibration stirring system for homogenization and mixing to obtain a uniform slurry mixture without bubbles and stratification; The uniform slurry mixture is shaped and poured according to the preset mold size to obtain a cubic specimen blank with a blasthole channel of specified diameter in the center; The cubic specimen blank was subjected to strength development treatment under a standard curing environment to obtain a single free-surface model specimen with physical and mechanical parameters meeting the standards.

3. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 1 is characterized in that: The method of performing three-dimensional constraint loading on a single-free-surface model specimen according to a hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state includes: The single-free-surface model specimen is placed in a rigid constraint frame for positioning and installation, obtaining an initial loading state in which the free-surface direction is open and the other five surfaces are constrained. Based on the servo hydraulic control algorithm, graded confining pressure is applied in two horizontal directions perpendicular to the free surface to obtain a bidirectionally uniformly distributed lateral restraint stress field. The lateral restraint stress field is subjected to constant pressure stabilization control according to the preset confining pressure target value to obtain a quasi-static loading environment in which stress relaxation is completed. The deformation stability of the model specimens under quasi-static loading environment was detected by the stress-strain monitoring system, and the clamped specimens under stable confining pressure state with fully adjusted internal stress were obtained.

4. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 1 is characterized in that: The method of filling a small amount of explosive into the preset blasthole of the clamped sample to block and seal the hole is to obtain a complete blasting loading model, including: Based on similarity ratio conversion, the charge parameters of trace explosives are accurately measured and processed to obtain the standard charge dosage that matches the model scale; The standard charge dosage is input into the bottom of the preset blasthole of the clamped sample for fixed-point filling, and a charge section structure is obtained in which the length of the charge column accounts for one third of the total length of the blasthole; According to the energy sealing principle, the empty hole section above the charge section structure is filled with sand to obtain a sealing section structure with a blocking length twice the charge length. The detonator initiation system is used to connect the detonator device to the top of the sealing section structure, and a complete blasting loading model with remote detonation capability is obtained.

5. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 1 is characterized in that: The blasting data acquisition and processing of the blasting loading model is performed through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes, including: Based on the high-frequency sampling trigger mechanism, the sensor array is arranged at multiple points on the surface of the complete blasting loading model to obtain a multi-parameter monitoring network covering vibration, stress and displacement. The detonation signal is input into the data synchronization acquisition system for time base calibration processing to obtain multi-channel parallel acquisition control instructions under a unified time stamp; According to the transient characteristics of blasting, high-speed data recording and processing is performed on multi-channel parallel acquisition control instructions to obtain the original multi-dimensional sensor data stream covering the entire blasting process; The original multi-dimensional sensor data stream is subjected to noise filtering and feature extraction through data preprocessing algorithms to obtain a blasting response dataset containing vibration waveforms and confining pressure changes.

6. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 1 is characterized in that: The time-frequency domain analysis algorithm is used to identify the single free surface clamping effect of the blasting response data set to obtain the blasting failure mode parameters under the influence of confining pressure, including: The vibration waveform signal in the blasting response data set is decomposed into time and frequency based on the wavelet transform algorithm to obtain the energy distribution characteristic spectrum in different frequency bands. The energy distribution characteristic spectrum is input into the frequency domain filter to extract the confining pressure sensitive frequency band and obtain the characteristic frequency component reflecting the single free surface constraint effect; Based on the stress wave propagation theory, the correlation analysis between the characteristic frequency components and the confining pressure variation data was carried out to obtain a quantitative relationship model between the confining pressure constraint degree and the waveform distortion coefficient. The quantitative relationship model is processed by parameter inversion calculation through the failure pattern recognition algorithm to obtain the blasting failure mode parameters under the influence of confining pressure, including the crack propagation directionality and the fragmentation range definition.

7. The high confining pressure blasting model test method for simulating the clamping effect of a single free surface according to claim 6, characterized in that: The energy distribution characteristic spectrum is input into the frequency domain filter to extract the confining pressure sensitive frequency band to obtain the characteristic frequency component reflecting the single free surface constraint effect, including: Based on the spectrum density analysis, the main frequency band of the energy distribution characteristic spectrum is identified and a set of candidate frequency intervals containing the confining pressure response information is obtained; The candidate frequency interval set is input into an adaptive bandpass filter for frequency band separation processing to obtain multiple sub-band energy components divided by frequency range; The sensitivity weights of multiple sub-band energy components are calculated according to the amplitude of confining pressure change to obtain a sensitivity coefficient matrix that quantifies the contribution of each sub-band to the confining pressure response. The sensitivity coefficient matrix is processed by threshold screening algorithm to extract high-sensitive frequency bands, and characteristic frequency components reflecting the single-free surface constraint effect whose sensitivity coefficient exceeds the preset threshold are obtained.

8. A high confining pressure blasting model test system simulating the clamping effect of a single free surface, characterized by: A high confining pressure blasting model test method for simulating a single free surface clamping effect according to any one of claims 1 to 7, wherein the high confining pressure blasting model test system for simulating a single free surface clamping effect comprises: The preparation module is used to prepare similar materials of surrounding rocks by adjusting the cement mortar ratio to obtain a single free-surface model specimen with a preset blasthole; The loading module is used to perform three-dimensional constrained loading on the single-free surface model specimen according to the hydraulic confining pressure system to obtain a clamped specimen under a stable confining pressure state; The plugging module is used to load a small amount of explosives into the preset blastholes of the clamped sample to plug and seal the holes, thus obtaining a complete blasting loading model; The acquisition module is used to collect and process blasting data of the blasting loading model through multi-parameter synchronous monitoring to obtain a blasting response data set including vibration waveforms and confining pressure changes; The identification module is used to identify the single free surface clamping effect of the blasting response data set using a time-frequency domain analysis algorithm to obtain the blasting failure mode parameters under the influence of confining pressure.

9. A high confining pressure blasting model test equipment simulating the clamping effect of a single free surface, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the high confining pressure blasting model test method simulating the clamping effect of a single free surface as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the high confining pressure blasting model test method simulating the clamping effect of a single free surface according to any one of claims 1 to 7.

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