A dynamic design system and method for infill strength based on a three-dimensional geological model

Abstract

This invention belongs to the field of mining engineering technology, specifically relating to a dynamic design method and system for backfill strength based on a three-dimensional geological model. The system constructs and converts a three-dimensional geological model into a volumetric model, generating theoretical stress maps using numerical simulation. It then combines real-time monitoring data with dynamic coupling analysis and parameter inversion to correct the model. A dynamic decision-making module for backfill strength calculates the critical strength based on the corrected indicators and generates a mix design scheme. An execution module then issues instructions and performs closed-loop optimization. This application solves the problem that backfill strength design cannot respond in real-time to complex mining-induced stress fluctuations, realizing a shift from static to dynamic precision in strength design. While preventing backfill instability risks and enhancing safety, it significantly reduces support costs and resource waste.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a dynamic design system and method for the strength of backfill bodies based on a three-dimensional geological model. Background Technology

[0002] In the field of underground mining resource development, backfill mining is a key technology for achieving green mining and ground pressure management, playing a crucial role in ensuring mine production safety and controlling surface subsidence. The application of this technology focuses on filling the goaf with a slurry of a specific ratio, which hardens to form a backfill with a specified load-bearing capacity, thereby effectively supporting the surrounding rock and maintaining the long-term mechanical stability of the mining environment.

[0003] Among these, the scientific design of the backfill strength is a fundamental aspect of this technical system, directly affecting the effectiveness of stope support and the overall operating cost of the mine. A reasonable strength design requires comprehensive consideration of the ore mining sequence, stope geometric parameters, and the physical and mechanical properties of the backfill material. The aim is to determine the optimal backfill material ratio through precise stress calculations and simulation analysis, thereby achieving a synergistic improvement in mining safety and resource recovery rate.

[0004] Existing technologies for backfill strength design primarily rely on traditional empirical formulas such as Litvinishen's theory or offline static finite element analysis. These methods face significant challenges in handling the complex conditions of actual mining operations, especially as adjacent mining areas are continuously mined, leading to a dynamic redistribution of the surrounding rock stress field and a high likelihood of significant mining-induced stress concentration. For example, a method for constructing karst tunnels in coal mine goafs disclosed in patent application number CN202511848043.X cannot respond in real-time to such severe stress fluctuations using existing static design methods. This often results in instability and failure of the backfill body during peak stress periods due to insufficient design strength, or blindly increasing cement content due to overly conservative design schemes, leading to substantial economic waste. Furthermore, the lack of deep integration and logical coupling between three-dimensional geological models and real-time field monitoring data makes it difficult for the system to capture the subtle mechanical evolution characteristics during mining, resulting in a lack of specificity and timeliness in strength design.

[0005] Therefore, this invention provides a novel dynamic design system and method for backfill strength based on a three-dimensional geological model. It can comprehensively and accurately capture the dynamic changes of mining-induced stress and adjust the design strength of the backfill in real time to ensure that it is highly matched with the actual mechanical requirements. It can not only effectively prevent the occurrence of backfill instability accidents and ensure the safety of mining, but also avoid redundant support costs and improve the economic benefits of the mine. Summary of the Invention

[0006] The present invention aims to address the problem that the existing filling strength design scheme cannot respond in real time to the complex mining stress fluctuations during underground mining, resulting in a low degree of matching between the design strength of the filling body and the actual mechanical requirements, which in turn leads to the instability of the filling body or redundant support costs.

[0007] This invention provides a dynamic design system for the strength of infill bodies based on a three-dimensional geological model, comprising: The geological environment digital construction module extracts the original geological exploration data, tunnel measurement data and rock physical and mechanical experimental parameters of the area to be mined, and constructs a three-dimensional geological model in virtual space that includes stratigraphic boundaries, fault structures, ore body morphology and existing goaf areas. The numerical simulation module for the mining process dynamically simulates the stress release and transfer process of the surrounding rock caused by mining in a three-dimensional geological model based on the preset mining step distance, mining sequence and filling time node, and generates theoretical stress field distribution maps of the area to be filled and its adjacent structures under different mining stages. The real-time mechanical sensing and monitoring module acquires data on the surrounding rock pressure, internal stress and strain of the filling body, and surface subsidence displacement around the mining area to be filled. The dynamic coupling analysis and feedback module receives monitoring data from the real-time mechanical sensing and monitoring module, and performs multi-dimensional comparative analysis with the theoretical stress field distribution map output by the mining process numerical simulation module. It identifies the mechanical deviation between the theoretical model and the actual working conditions, corrects the rock mass physical and mechanical parameters in the three-dimensional geological model through parameter inversion technology, and outputs the corrected mining stability evaluation index. The dynamic decision-making module for filling strength calculates the critical bearing strength required for the area to be filled within a specific service cycle based on the revised stope stability evaluation index and the predicted impact of the next stage of mining. It dynamically generates a filling strength design scheme that includes cement content, aggregate gradation and slurry concentration. The instruction execution and quality feedback module transforms the filling strength design scheme into the proportion adjustment instruction of the filling station's automated control system, and performs post-filling sampling verification of the actual strength, feeding back the verification results to the dynamic coupling analysis and feedback module.

[0008] Preferably, when constructing a three-dimensional geological model, the geological environment digital construction module uses the Kriging interpolation algorithm to reconstruct the spatial surface of discrete borehole data; the volume element processing process divides the three-dimensional geological model into grid units with independent mechanical properties, the size of the grid units is adaptively adjusted according to the complexity of the mining area geometry, and grid densification is performed in stress concentration areas.

[0009] Preferably, the numerical simulation module for the mining process introduces a constitutive model based on the Mohr-Coulomb criterion to calculate the expansion range of the plastic zone of the stope roof and sides under mining disturbance when simulating the stress transfer process; the numerical simulation module for the mining process performs a full-field stress balance calculation once every 24 hours, and outputs a dynamic dataset containing principal stress directions, shear stress values, and deformation energy release rates; the number of grid cells in the simulation calculation is not less than 100,000, and the evolution of pore water pressure during the mining process is simulated through a fluid-structure interaction algorithm.

[0010] Preferably, the real-time mechanical sensing and monitoring module includes a multi-point displacement gauge buried inside the surrounding rock, a pressure pillow installed on the top of the mining area, and a fiber optic strain sensor implanted inside the filling body. The fiber optic strain sensor achieves quasi-distributed measurement through wavelength encoding characteristics, with a measurement range of -5000 microstrain to +5000 microstrain and a measurement accuracy better than 1 microstrain. The pressure pillow has a range of not less than 20 MPa. The data acquisition frequency of all sensors is set to once every 10 minutes.

[0011] Preferably, the dynamic coupling analysis and feedback module uses the least squares support vector machine algorithm to establish a nonlinear mapping relationship between monitoring data and model parameters. When the relative deviation between the measured stress value and the theoretical prediction value exceeds 15%, the parameter correction process is triggered to adjust the elastic modulus and cohesion parameters of the corresponding area in the three-dimensional geological model until the root mean square error between the two is reduced to below 5%. The parameter correction order is determined by sensitivity analysis, and is as follows: deformation modulus, cohesion, internal friction angle and Poisson's ratio.

[0012] Preferably, when calculating the critical bearing strength, the dynamic decision-making module for filling strength comprehensively considers the self-weight of the filling body, the pressure of the overlying strata, and the dynamic load generated by blasting in adjacent mining areas. Based on the time window of subsequent mining, the dynamic decision-making module for filling strength calculates the minimum strength standard that the filling slurry should reach at 3 days, 7 days, and 28 days. If it is predicted that the adjacent mining area will be mined in 15 days, the system sets the strength of the current filling body at 14 days to be more than 1.2 times the design safety factor.

[0013] Preferably, the instruction execution and quality feedback module communicates with the program logic controller of the filling station through an open database connection interface to monitor the feeding rate of the electronic belt scale and the reading of the flow meter in real time, so as to ensure that the deviation between the actual proportion and the dynamic design scheme is within 2%. The instruction execution and quality feedback module also performs online monitoring of the slump and fluidity of the slurry, and maintains the pumping performance by adjusting the addition ratio of high-efficiency water-reducing agent.

[0014] Preferably, it also includes a multi-criteria risk early warning unit, which is used to monitor the risk of instability in the mining area in real time based on the output of the dynamic coupling analysis and feedback module; when the predicted compressive stress of the filling body reaches 85% of its dynamic design strength, it issues an audible and visual alarm command.

[0015] This invention also provides a dynamic design method for the strength of infill bodies based on a three-dimensional geological model. The method, based on the aforementioned dynamic design system for the strength of infill bodies based on a three-dimensional geological model, includes the following steps: S1. Obtain basic geological data and material mechanical parameters, and generate an initial three-dimensional geological model in a computer system; S2. Set the initial mining plan, use numerical simulation to simulate the stress distribution in the stope after the first mining cycle, and determine the initial backfilling ratio; S3. Utilize a sensor network deployed at key mechanical feature points to continuously monitor the coordinated stress state of the surrounding rock and the backfill, and extract real-time data; S4. Feed the measured data back to the three-dimensional geological model, compare and analyze the difference between theoretical and actual values, and identify stress concentration areas and potential instability sites under the influence of mining. S5. Use a dynamic evolution algorithm to correct the model parameters and recalculate the required backfill strength index for the next mining cycle; S6. Adjust the filling material ratio based on the latest strength index and control the filling execution system in real time to complete the grout preparation and injection; S7. Repeat steps S3 to S6 until the mining of all ore bodies is completed.

[0016] In S4, the process for identifying stress concentration zones is as follows: the ratio of the maximum local stress in the zone to the original rock stress at that depth is calculated to obtain the stress concentration factor; when the stress concentration factor exceeds 1.5, the zone is defined as a key reinforcement zone, and the design strength level of the corresponding filling material in the zone is increased in the subsequent dynamic design.

[0017] The beneficial effects of this invention are as follows: This invention constructs an accurate three-dimensional geological model through a digital geological environment construction module, taking into account various geological factors to provide an accurate foundation for subsequent analysis. A numerical simulation module for the mining process dynamically simulates the mining process and generates a theoretical stress field distribution map. A real-time mechanical sensing and monitoring module acquires actual data, and a dynamic coupling analysis and feedback module compares and analyzes the measured data with theoretical values, identifies mechanical deviations, and corrects model parameters. This enables the design to respond in real-time to the complex stress fluctuations during underground mining, effectively solving the problem of low matching degree caused by the inability of existing filling strength design schemes to respond to stress fluctuations in real time.

[0018] The dynamic decision-making module for filling strength dynamically generates a design scheme for the strength of the filling body based on the corrected indicators and the predicted impact of the next stage of mining. This avoids redundant support costs due to excessively high design strength, while preventing instability of the filling body due to insufficient design strength, thus ensuring the safety of mining and achieving a balance between economic benefits and safety.

[0019] The digital geological environment construction module employs Kriging interpolation and adaptive volumetric element processing to improve the accuracy of the 3D geological model. The numerical simulation module for the mining process introduces a constitutive model based on the Mohr-Coulomb criterion, performs frequent full-field stress balance calculations, uses a large number of mesh elements, and simulates pore water pressure evolution through a fluid-structure interaction algorithm, making the simulation results closer to reality. The real-time mechanical sensing and monitoring module uses multiple high-precision sensors and sets appropriate data acquisition frequencies to ensure accurate and reliable real-time data. The dynamic coupling analysis and feedback module uses the least squares support vector machine algorithm to establish nonlinear mapping relationships and sets reasonable parameter correction trigger conditions and sequences to improve the accuracy of parameter correction. These measures collectively enhance the accuracy and reliability of the entire system.

[0020] The instruction execution and quality feedback module transforms the design scheme into proportion adjustment instructions, samples and verifies the actual strength after filling, and provides feedback on the results, forming a closed-loop control system. By continuously repeating the steps in the design method, the strength design scheme of the filling body is continuously optimized to adapt to the constantly changing geological conditions and mining impacts during the mining process. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall technical architecture of a dynamic design system for the strength of infill bodies based on a three-dimensional geological model. Figure 2 This is a schematic diagram illustrating the principle framework of closed-loop correction of filling strength based on real-time mechanical sensing. Figure 3 Logical flowchart for digital construction and model element processing of geological environment; Figure 4 A logical flowchart for generating numerical simulation and stress field distribution maps of the mining process; Figure 5 A schematic diagram of the multi-level interaction relationship and data flow of a real-time mechanical sensing and monitoring network and its data transmission. Figure 6 A logical flowchart for dynamic coupling analysis and physical and mechanical parameter inversion correction; Figure 7A logical flow diagram for generating instructions on dynamic decision-making for filling strength and automated proportioning adjustment. Figure 8 This is a flowchart illustrating the overall logical flow of a dynamic design method for the strength of infill bodies based on a three-dimensional geological model. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings. However, the described embodiments are only some embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Example 1

[0024] Combined with appendix Figure 1 This embodiment discloses a dynamic design system for backfill strength based on a three-dimensional geological model. This system is built upon the deep integration of safe mining and digital management in underground mines. The entire system integrates multi-source geological data processing, high-fidelity numerical simulation, real-time sensing IoT, and intelligent decision-making algorithms, achieving a fundamental shift from static, experience-based design to dynamic, precise design for backfill strength. The system consists of a geological environment digital construction module, a mining process numerical simulation module, a real-time mechanical sensing and monitoring module, a dynamic coupling analysis and feedback module, a backfill strength dynamic decision-making module, an instruction execution and quality feedback module, and a multi-criteria risk early warning unit. The modules seamlessly flow information and logically link through an industrial-grade high-reliability data bus.

[0025] Combined with appendix Figure 3The geological environment digitization module, serving as the geometric and physical benchmark for the entire system, is responsible for transforming complex underground geological entities into computer-recognizable and computable digital twins. First, the module extracts raw geological exploration data from the area to be mined through a data interface. This data includes the three-dimensional coordinates of mine boreholes, the strike and dip angle of rock strata, and rock physical and mechanical experimental parameters such as density, compressive strength, shear strength, and elastic modulus obtained through core sampling and laboratory testing. To ensure the accuracy of the tunnel spatial outline, the module also incorporates tunnel measurement data acquired by a 3D laser scanner. This data realistically reflects the geometric boundaries of the underground tunnels in point cloud form. When constructing a 3D geological model in virtual space, the module uses the Kriging interpolation algorithm to reconstruct the spatial surface of the discrete borehole data. By calculating the spatial correlation between known points, the Kriging algorithm can predict the distribution of stratigraphic properties in unknown areas, thereby generating continuous stratigraphic boundaries, fault structures, and ore body morphology models, ensuring that the fitting accuracy deviation of the ore body boundaries is strictly controlled within 0.5 meters. After completing the macroscopic modeling, the digital geological environment construction module performs volumetric processing on the 3D geological model, dividing it into hexahedral mesh elements with independent mechanical properties. The size of the mesh elements is not fixed but adaptively adjusted according to the complexity of the stope geometry and the rate of change of stress gradient. Specifically, at stope corners, fault intersections, and exposed roof areas where stress concentration is significant, the system performs mesh refinement processing to capture more subtle mechanical response characteristics, thereby providing a refined geometric topological basis for subsequent mechanical calculations.

[0026] Please refer to the attached document. Figure 4 The numerical simulation module for the mining process maintains a real-time connection with the digital geological environment construction module. Its core function is to pre-simulate the disturbance process of mining operations on the ground pressure balance in a virtual space. The mining process refers to a series of dynamic mechanical processes in underground mining activities, including the redistribution, deformation, and damage of surrounding rock stress caused by ore extraction. This process covers the entire time period from ore extraction to the change in the stable state of the surrounding rock, including but not limited to key aspects such as ore excavation, stress release and transfer of surrounding rock, deformation and damage of the roof and walls, and the formation and expansion of goaf.

[0027] The numerical simulation module for the mining process dynamically simulates the stress release and transfer in the surrounding rock caused by mining in a three-dimensional geological model, based on the pre-set mining step distance, mining sequence, and backfilling time nodes in the production plan. During the simulation calculation, the module fully considers the nonlinear rheological characteristics and plastic failure evolution of the surrounding rock. The system introduces a constitutive model based on the Mohr-Coulomb criterion to describe the failure behavior of the rock mass.

[0028]

[0029] In the above formula, Represents the maximum principal stress. Represents the minimum principal stress, and c represents the cohesion parameter of the rock mass. The internal friction angle of the rock mass is represented. Through real-time calculation using this criterion, the module can accurately identify the extent of plastic zone expansion in the stope roof and walls under mining disturbance and assess the stability of the rock mass. The simulation calculation not only covers mechanical loads but also integrates a fluid-structure interaction algorithm to simulate the evolution of groundwater pressure during mining and its impact on the mechanical behavior of the rock mass. The module is configured to perform a full-field stress balance calculation every 24 hours, covering the rock mass within at least twice the mining influence area of ​​the stope, with a grid number of no less than 100,000. The calculation results ultimately generate theoretical stress field distribution maps of the area to be filled and its adjacent structures under different mining stages. These maps include key dynamic datasets such as principal stress directions, shear stress values, and deformation energy release rates.

[0030] Combined with appendix Figure 5 The real-time mechanical sensing and monitoring module is deployed at various key stress points in the mining site, acting as the system's nerve endings. This module includes multi-point displacement gauges embedded in the surrounding rock, pressure pillows installed on the stope roof, and fiber optic strain sensors embedded within the filling material. The fiber optic strain sensors utilize wavelength encoding characteristics to achieve quasi-distributed measurements at multiple points along the optical fiber. Each measurement point has a range set from -5000 microstrain to +5000 microstrain, achieving a measurement accuracy of 1 microstrain level, capable of capturing extremely small shrinkage or compressive deformation within the filling material. The pressure pillow's range is set to no less than 20 MPa to ensure it can withstand the hydrostatic pressure of the grout in the initial filling stage and the intense surrounding rock compression caused by later mining activities. All sensors are connected to the mine's local area network via a moisture-proof and explosion-proof wireless gateway, with data acquisition frequency set to once every 10 minutes. This high-frequency real-time acquisition mechanism enables the system to capture abrupt changes in surface subsidence displacement, surrounding rock pressure, and infill strain in real time, and transmit massive amounts of monitoring data to the back-end server in real time through the industrial network.

[0031] Please refer to the attached document. Figure 2 With appendix Figure 6The dynamic coupling analysis and feedback module is the logical core of the system, responsible for verifying and offsetting theoretical predictions with engineering realities. This module receives monitoring data from the real-time mechanical sensing and monitoring module and performs multi-dimensional comparative analysis with the theoretical stress field distribution map output by the mining process numerical simulation module. Using the least squares support vector machine algorithm, the module establishes a nonlinear mapping relationship between the monitoring data and model parameters. When the relative deviation between the measured stress value and the theoretical prediction exceeds 15%, the system automatically triggers a parameter inversion procedure. During the inversion process, the system performs sensitivity analysis to determine the order of parameter correction, typically prioritizing the deformation modulus, which has the greatest impact on stability, followed by cohesion, internal friction angle, and Poisson's ratio. By continuously adjusting the physical and mechanical parameters of the corresponding areas in the 3D geological model, the system iteratively calculates until the root mean square error between the monitoring data and the model output is reduced to below 5%. This real-time data-driven parameter correction mechanism ensures that the 3D geological model can accurately reflect the evolution of the mechanical environment caused by mining damage or changes in geological conditions, thereby outputting corrected mining stability evaluation indicators.

[0032] Combined with appendix Figure 7 The dynamic decision-making module for backfill strength performs refined design of backfill strength based on the revised stope stability evaluation index and the predicted impact of the next stage of mining. This module comprehensively considers the self-weight of the backfill, the pressure of the overlying strata, and the dynamic load generated by blasting in adjacent stopes, calculating the critical bearing strength required for the area to be backfilled within a specific service period. The module has pre-set empirical formulas for the strength of backfill materials increasing with age, enabling it to accurately estimate the strength standards that the backfill grout should reach at 3 days, 7 days, and 28 days based on the time window of subsequent mining operations. For example, if it is predicted that an adjacent stope will enter the mining stage in 15 days, the system will automatically calculate the safe strength that the current backfill must reach at 14 days, requiring it to reach at least 1.2 times the design safety factor. Finally, the module dynamically generates a specific backfill strength design scheme that includes cement content, aggregate gradation, and grout concentration. For mining areas near fault fracture zones or high-stress areas, the scheme will automatically improve the stiffness and early strength of the backfill; while for the central area of ​​the ore body with extremely stable surrounding rock, the scheme tends to reduce backfilling costs by optimizing the cement content, while ensuring safety.

[0033] The instruction execution and quality feedback module communicates with the program logic controller of the filling station through an open database connection interface, transforming the strength design scheme of the filling body into proportioning adjustment instructions for the automatic control system. This module can monitor the feeding rate of the electronic belt scale, the speed of the screw conveyor, and the flow meter readings in real time, ensuring that the actual proportion deviation of aggregate and cement is controlled within 2%. During the filling process, the module also monitors the slump and fluidity of the slurry online in real time, dynamically adjusting the addition ratio of high-efficiency water-reducing agent to ensure that the slurry meets high-strength design requirements while possessing good pumpability. After filling is completed, the module automatically records the volume, coordinates, and timestamp of this filling, updating it in the three-dimensional geological model. Furthermore, the module connects to a laboratory automatic pressure machine to capture the subsequent compressive strength test data of the filling body sampling blocks and feeds it back to the dynamic coupling analysis and feedback module, forming a complete closed loop for quality control and design optimization.

[0034] Finally, the system also includes a multi-criteria risk early warning unit as the last line of defense for safe production. This unit extracts the output results of the dynamic coupling analysis and feedback module in real time and compares the ratio of the current compressive stress of the filling body to its dynamic design strength. When this ratio reaches the warning line of 85%, the unit will immediately trigger an audible and visual alarm and send emergency suggestions to the production scheduling center, such as suggesting that the filling station temporarily increase the amount of early strength agent or forcibly increase the cement ratio of the subsequent grout, thereby ensuring the overall stability of the stope structure.

[0035] Example 2

[0036] Please refer to the attached document. Figure 8 This embodiment, based on the hardware system described in Embodiment 1, elaborates in detail a dynamic design method for infill strength based on a three-dimensional geological model. This method decomposes the entire infill strength design process into the following seven steps.

[0037] Step 1: Basic Data Extraction and Initial Modeling. Technicians use geological exploration equipment to obtain raw borehole data from the mine, and a 3D laser scanner to acquire precise contour data of the tunnels, combined with rock mass physical parameters obtained from laboratory mechanical tests. This data is entered into a computer system, and an initial 3D geological model is generated through a digital geological environment construction module. The model not only includes the geometric boundaries of the ore body but also assigns specific initial physical and mechanical properties to each grid cell through volumetric processing.

[0038] Step 2: Initial Stress Simulation and Scheme Pre-setting. Before the official commencement of mining operations, the initial mining step distance and mining plan are set. The numerical simulation module of the mining process is used to simulate the stress distribution in the stope after the first mining cycle. Based on the stress field distribution map obtained from the simulation, the initial strength requirements of the backfill and the material mix design are determined.

[0039] Step 3: Dynamic Sensing and Real-Time Monitoring. As mining operations progress, the sensor network deployed at key feature points in the surrounding rock and inside the backfill begins high-frequency operation. Displacement gauges monitor the movement trend of the surrounding rock, pressure pillows capture changes in the roof load, and fiber optic sensors sense the internal strain of the backfill. After initial cleaning and verification, all data is transmitted to the dynamic coupling analysis and feedback module at 10-minute intervals.

[0040] Step 4: Stress Concentration Identification and Model Verification. The system feeds back the measured mechanical data into the three-dimensional geological model. By calculating the deviation between the measured and theoretical values, stress concentration zones caused by mining disturbances are identified. Specifically, the system locates risk areas by calculating stress concentration coefficients.

[0041]

[0042] In the above calculation logic, K represents the stress concentration factor, and σ max σ0 represents the maximum local stress within the area, and σ0 represents the original rock stress at that depth. When the stress concentration factor exceeds 1.5, the system automatically defines the area as a key reinforcement zone and specifically increases the design strength level of the filling material at that location in subsequent steps.

[0043] Step 5, Parameter Correction and Strength Index Iteration Stage. A dynamic evolution algorithm is used to correct the physical parameters of the 3D geological model in real time, enabling it to accurately simulate the current real-world surrounding rock condition. Based on this, a swarm intelligence optimization algorithm is used to find the optimal backfill material ratio. This optimization process uses the lowest backfill cost and highest stope stability as dual objective functions. Under the premise of meeting the critical bearing strength calculated in Step 4, the optimal mass ratio of cement to tailings is found through multiple iterations, thereby achieving a balance between economy and safety.

[0044] Step 6: Automated Execution and Dynamic Proportion Adjustment. The latest strength indicators are converted into automated control parameters for the filling station. The filling execution system adjusts the feed rate in real time according to instructions. During this process, the system monitors the flow characteristics of the slurry in the pipeline online to ensure that high-concentration slurry does not segregate or clog. Each batch of filling data generates a digital filling log, which is synchronized to the digital twin model in real time.

[0045] Step 7: Closed-loop feedback and continuous optimization phase. After the backfill body reaches the designed age, on-site sampling experiments are conducted. The actual strength values ​​obtained from the experiments are compared with the predicted strength from Step 5. If the deviation is within the allowable range, the next cycle of mining monitoring continues; if the deviation is too large, the parameter inversion process is re-triggered to deeply calibrate the model. The above process is repeated until the entire ore body mining task is successfully completed.

[0046] The method also includes an auxiliary process for long-term tracking and evaluation of the stability of the filling body. The system establishes an electronic archive for ground pressure monitoring throughout the entire lifecycle of the mine by recording the actual stress and deformation at each filling step in a three-dimensional geological model. This not only provides safety assurance for current production but also offers extremely valuable historical data for future development of deep resources and ground pressure control.

[0047] Through the system and method described in this invention, backfilling operations in underground mines are no longer blind, experience-based actions, but rather a precision engineering process driven by data, providing real-time feedback, and dynamically optimizing. The system can accurately determine the optimal strength of each volume of grout based on the actual mechanical evolution logic of the mine, thereby fundamentally resolving the contradiction between backfill instability and cost redundancy.

[0048] Furthermore, in specific engineering applications, the coordinate system used in the digital geological environment construction module enables real-time conversion from local stope coordinates to the overall mine coordinate system, ensuring the integrity of stress field calculations during multi-stope coordinated operations. In the numerical simulation module for the mining process, the calculation step size can be non-uniformly set according to changes in mining intensity. When large-scale blasting operations are carried out in the mine, the system automatically shortens the simulation calculation step size to 1 hour to capture the impact of transient dynamic disturbances on the early strength performance of the backfill.

[0049] During the operation of the real-time mechanical sensing and monitoring module, the system incorporates an automatic abnormal data filtering algorithm. When sensors generate outliers due to vibrations from mining machinery or electromagnetic interference, the algorithm can remove them using median filtering of time series data, ensuring that the data entering the dynamic coupling analysis and feedback module has extremely high reliability. Furthermore, the wireless gateway employs a multipath transmission protocol to ensure that the packet loss rate of signal transmission is less than 0.1% in the complex underground metal mining environment.

[0050] When performing parameter inversion, the dynamic coupling analysis and feedback module not only corrects the static mechanical parameters but also adjusts the rheological parameters of the rock mass based on the monitored deformation rate. This means that the system can predict the creep behavior of the backfill in the coming months or even years, thus providing a more scientific strength scheme for long-term permanent support backfill.

[0051] The dynamic decision-making module for filling strength also specifies the mixing time and feeding sequence of the slurry in detail. For high-strength filling schemes, the system will adjust the speed of the mixer to ensure that the cement slurry and tailings aggregate are fully combined, improving the uniformity of the slurry. In the instruction execution and quality feedback module, the system uses a closed-loop PID control algorithm of the electronic scale to improve the feeding accuracy to within 0.5 kg, minimizing the fluctuations in the mix ratio caused by human operation.

[0052] Throughout the system's lifecycle, the multi-criteria risk warning unit continuously learns from historical failure cases and optimizes alarm thresholds. In addition to single stress threshold alarms, this unit also introduces a sudden change warning logic based on displacement change rate. When the surrounding rock movement speed is detected to increase by more than twice the preset value within 24 hours, even if the current stress has not yet reached the design strength, the system will determine it as a potential instability risk, thereby triggering an emergency reinforcement command.

[0053] Furthermore, the three-dimensional geological model described in this invention employs advanced graphics rendering technology, enabling managers to intuitively present complex stress cloud maps and the evolution of infill bodies deep underground through various methods such as multi-dimensional slicing, isosurfaces, and transparent displays. This not only enhances the scientific rigor of engineering decisions but also provides a solid technical platform for the digital transformation of mine safety management.

[0054] In summary, this invention, through deep decoupling and logical reconstruction of geological modeling, numerical simulation, and real-time sensing, has created a dynamic design system for backfill strength with self-correcting capabilities. This system can adapt to the constantly changing geostress environment, uneven surrounding rock quality, and complex spatiotemporal constraints during mining operations. Through precise strength matching, it not only ensures the absolute safety of underground working spaces but also significantly reduces the ineffective consumption of backfill materials, providing core technical support for the construction of modern, intelligent, and green mines.

[0055] The digital geological environment construction module employs multi-source heterogeneous data fusion technology when processing raw geological exploration data. In addition to traditional borehole and mapping data, this module can also access location data from the mine microseismic monitoring system. The distribution density of microseismic events helps verify the activity and fracturing degree of fault zones, thus more realistically depicting the mechanical properties of weak surfaces in the 3D model. During Kriging interpolation, the system automatically calculates the variogram and dynamically adjusts the radius and major-minor axis ratio of the search ellipsoid based on the correlation differences of strata in different directions, making the generated 3D geological model more consistent with reality in terms of geological evolution logic.

[0056] The numerical simulation module for the mining process fully considers the roof contact ratio after backfilling during calculations. The system simulates the gap between the top surface of the backfill and the surrounding rock roof, as well as the subsequent collaborative stress process, by setting virtual contact units in the model. When the backfill is not tightly connected to the roof, the system automatically increases the weighting of the surrounding rock roof subsidence calculation and provides process suggestions in the subsequent dynamic decision-making module for backfill strength, such as increasing backfill pressure or improving slurry expansion performance, to ensure that the backfill can truly support the surrounding rock.

[0057] The pressure pillow in the real-time mechanical sensing and monitoring module adopts an all-stainless steel welded structure, internally filled with silicone oil with good temperature drift compensation characteristics, ensuring long-term stable operation in the humid and high-temperature environment underground. The fiber optic grating sensor uses a special armored protective sleeve to resist the direct erosion and wear of the sensing elements by the filling grout during the pouring process. All acquisition equipment is equipped with a backup power module, which can maintain at least 48 hours of continuous data acquisition in the event of an unexpected power outage in the mine, preventing the loss of critical mechanical data.

[0058] The dynamic coupling analysis and feedback module employs an online learning mode when using least squares support vector machines for computation. As mining data accumulates, the model automatically updates its internal weight coefficients, continuously enhancing its predictive ability for complex mechanical behaviors over time. When encountering entirely new geological structures, the system activates a rapid evolution mode, accelerating model convergence by increasing the local sampling frequency.

[0059] When generating the mix design, the dynamic decision-making module for backfill strength automatically retrieves the mine's current material inventory data. If there is a temporary shortage of tailings of a certain particle size, the module will use its built-in material replacement model to automatically adjust the cement ratio or other gradation components while ensuring strength, generating an alternative mix design to ensure the continuity of backfill production.

[0060] The instruction execution and quality feedback module boasts strong compatibility, supporting multiple standard industrial communication protocols. This module not only controls the mixing ratio but also acquires real-time data on the filling pump's oil pressure, outlet pressure, and discharge rate. Through comprehensive analysis of these pumping parameters, the system can indirectly assess the consistency changes of the slurry in the pipeline and use this as feedback signals to correct the water-reducing agent addition instructions in real time.

[0061] The multi-criteria risk early warning unit integrates multiple alarm methods, including sound, light, electricity, and mobile push notifications. When the risk level reaches the highest level, the system can directly link with the underground voice broadcasting system to automatically broadcast safe evacuation or reinforcement instructions. Simultaneously, all early warning information and processing results are permanently recorded for subsequent safety audits and experience summaries.

[0062] In the specific operation of Example 2, the stress concentration identification process mentioned in step 4 not only focuses on the stress value itself, but also introduces the concept of energy release rate. The system assesses the possibility of rockburst or severe dynamic instability by calculating the releasable elastic energy accumulated within a unit volume grid. When the energy release rate reaches a critical threshold, the system will force the addition of fiber reinforcement material in the current filling area to improve the impact toughness of the filling.

[0063] The dynamic evolution algorithm in step 5 employs a Pareto optimal solution selection mechanism when handling multi-objective optimization. The system seeks the optimal balance between safety margin and economic cost based on the mine's priorities at different production stages. For example, when near permanent facilities such as the main hoisting shaft, the system automatically increases the safety weight; while in general production areas, it appropriately favors cost optimization.

[0064] Step 6, the real-time control process, also covers the management of flushing water after filling. The system accurately calculates the flow rate and pressure of the flushing water to ensure that excessive flushing water does not enter the filling site and dilute the slurry or reduce the uniformity of the filling.

[0065] The seventh step, long-term tracking and evaluation, utilizes cloud computing technology. Massive amounts of ground pressure monitoring data are stored in the mine's cloud database. Through big data analytics, the system can extract stress evolution patterns at different depths and in different sections of the mine. These patterns are ultimately transformed into a priori knowledge base within the three-dimensional geological model, significantly improving the initial accuracy of subsequent modeling and design of new mining areas.

[0066] The system and method proposed in this invention completely eliminate the gray box area in filling design by digitizing and expanding every step. Every correction of mechanical parameters, every generation of proportioning schemes, and every issuance of early warning commands is traceable and verifiable. This not only significantly improves the quality stability of the filling body but also lays a solid mechanical design foundation for realizing fully unmanned and intelligent mining.

[0067] In future deep mining operations, the proactive adaptability of this system will become even more apparent as the ground pressure environment worsens. Through continuous digital twin evolution, the system will be able to anticipate extreme situations that may arise during mining and proactively adjust the strength of the backfill material to form a robust artificial support network within the rock mass, thereby ensuring the safe, efficient, and economical recovery of deep resources.

[0068] The digital geological environment construction module considers the heterogeneity of rock mass structure when processing 3D geological models into volumetric elements. By mapping the statistical distribution of mechanical parameters obtained from laboratory tests to each volumetric element, the system can simulate the influence of fine structures such as micro-fractures and joint surfaces within the rock mass on the overall mechanical strength. In stress concentration areas, the system not only performs geometric refinement but also defines the material properties of the volumetric elements in detail, such as introducing weak surface elements to simulate the shear slip characteristics of fault zones.

[0069] The numerical simulation module for the mining process also features a dynamic step size adjustment function. In the early stages of mining, when the surrounding rock deformation is relatively slow, the simulation step size can be set to 24 hours; however, when forced caving or mining reaches its final stage, and the surrounding rock stress fluctuates drastically, the module will automatically shorten the step size to 1 hour. This flexibility ensures that the system can provide sufficiently high time resolution at critical moments to capture transient signs of instability.

[0070] The sensor network of the real-time mechanical sensing and monitoring module has a self-diagnostic function. When a pressure chamber experiences signal drift or interruption due to line damage, the system can automatically identify the abnormal sensor and reconstruct the missing data using a spatial interpolation algorithm based on the values ​​of neighboring sensors, ensuring the continuity of the monitoring curve and preventing analysis interruptions due to a single node failure.

[0071] In the parameter inversion process, the dynamic coupling analysis and feedback module introduces a strategy combining global search and local fine-tuning. The system first uses a genetic algorithm to quickly locate the approximate range of mechanical parameters within a large parameter space, and then switches to the sensitivity matrix method for high-precision fine-tuning. This combined approach effectively solves the technical challenge of easily getting trapped in local optima during nonlinear inversion, resulting in highly realistic physical parameters for the corrected model.

[0072] The dynamic decision-making module for backfill strength fully utilizes the synergistic effect of backfill materials when calculating material proportions. The system dynamically recommends the amount of activator to add based on the chemical composition of the tailings, such as the content of reactive silica. Through this dual optimization of chemical and mechanical properties, cement consumption can be further reduced while achieving the same design strength for the backfill.

[0073] The instruction execution and quality feedback module is also linked to the mine's ventilation monitoring system. During backfilling operations, the system automatically suggests adjustments to the ventilation system's airflow in the corresponding stope based on the potential volatile gases in the slurry, ensuring air quality in the working environment. Simultaneously, the module records the slurry level during backfilling and compares it with the theoretical reservoir capacity in the 3D geological model to assess the actual effectiveness of the backfill connection.

[0074] The alarm logic of the multi-criteria risk early warning unit features multi-level nesting. The system monitors not only the absolute value of stress but also the acceleration of stress growth. If stress increases rapidly, even if the current value is within a safe range, the early warning unit will determine that there is a potential risk of sudden rockburst. Furthermore, the unit can automatically construct a failure probability model based on historical backfill damage cases, providing quantitative probabilistic support for mine safety decisions.

[0075] In the digital geological environment construction module, the system employs a versioned storage mechanism for managing the original geological exploration data. Every parameter correction and every local adjustment to the model retains a complete historical record. This allows technicians to revert to any mining stage at any time, compare and analyze the evolution of the geological model, and thus deepen their scientific understanding of the manifestation patterns of mine ground pressure.

[0076] The stress field distribution map output by the numerical simulation module of the mining process supports immersive display on virtual reality terminals. Mine managers can wear virtual reality headsets to directly enter the three-dimensional geological model and observe the stress concentration areas and plastic deformation areas around the mining area from a first-person perspective. This visual interactive method greatly facilitates the demonstration and optimization of production plans.

[0077] The real-time mechanical sensing and monitoring module also boasts strong scalability. In addition to the displacement, pressure, and strain measurements mentioned above, the system also includes interfaces for microseismic sensors and ultrasonic sensors. In the future, the monitoring dimensions can be upgraded as the mining depth increases, allowing for a more comprehensive perception of the rock mass's stability through acoustic emission and microseismic characteristics.

[0078] The algorithm library used by the dynamic coupling analysis and feedback module is updated regularly via a remote server. As more mining application data is aggregated to the cloud, the algorithm's generalization ability will continue to improve, enabling it to adapt to different types of ore bodies, such as gently dipping thick ore bodies and steeply dipping thin ore bodies, and other mining conditions with different geometries.

[0079] The dynamic decision-making module for filling strength also considers the exothermic effect of the filling grout during solidification in the design scheme. For large-volume filling bodies, the system predicts the internal temperature rise through thermodynamic simulation and adjusts the proportion of admixtures in the mix accordingly to prevent internal cracks caused by temperature stress, thus ensuring the integrity and long-term durability of the filling body.

[0080] The instruction execution and quality feedback module, integrated with the filling station's energy management system, can calculate the energy consumption index per ton of filling material in real time. By optimizing the operating conditions and mixing instructions of the filling pumps, the system achieves energy conservation and emission reduction in the filling production process while ensuring strength.

[0081] The alarm thresholds of the multi-criteria risk warning unit can be manually fine-tuned by senior engineers based on their specific engineering experience. The system automatically assesses the safety of the manually set values ​​and compares them with algorithmically recommended values. If the manually set values ​​are too risky, the system will display a risk warning, thus establishing a dual review mechanism of artificial intelligence and human experience.

[0082] In summary, the system and method provided by this invention form a rigorous, scientific, and intelligent engineering system, from the source acquisition of geological data to the dynamic driving of simulation models, and then to the real-time closed-loop feedback of sensor data and the automated implementation of decision commands. It not only solves the problem of dynamic adaptability in the design of backfill strength, but also promotes the advancement of underground mining towards precision and digitalization, providing comprehensive technical support for the safe development of deep resources.

[0083] The digital geological environment construction module can automatically identify interbedded rocks and structural fracture zones in the ore body and define their independent unit attributes during model building. This means the system can simulate the stress transmission characteristics of the backfill body when encountering interfaces with different media. In the numerical simulation module of the mining process, the system can simulate the stress superposition effect when multiple mining areas are simultaneously being mined, providing macroscopic data support for the support design of multi-mining areas.

[0084] The data transmission layer of the real-time mechanical sensing and monitoring module employs an industrial-grade encryption protocol to prevent interference or tampering of monitoring data during transmission. In the dynamic coupling analysis and feedback module, the system can simultaneously run multiple candidate models with different parameters. Through real-time data comparison, the best-performing model is selected as the current benchmark model, thereby further improving the system's robustness.

[0085] The instructions generated by the dynamic decision-making module for filling intensity also include the automatic calibration frequency requirements for the electronic scales at the filling station. The instruction execution and quality feedback module can automatically remind operators to zero or calibrate the sensors based on the intensity of the filling operation.

[0086] This invention has many applications, including but not limited to the following described scenarios: In underground mines with complex geological structures, including stratigraphic boundaries, fault structures, irregular ore body morphology, and existing goaf areas, the three-dimensional geological model constructed in this invention can accurately reflect the geological environment. The numerical simulation module can simulate the stress changes caused by mining under complex geological conditions, providing a reliable basis for the design of backfill strength. It is suitable for backfill design in the mining process of such mines.

[0087] For mines with extremely high safety requirements and strict control over support costs, such as metal mines and precious ore mines, this invention can ensure that the strength of the backfill body meets safety requirements through dynamic design, preventing instability accidents, while avoiding over-design that would increase costs, thus meeting the needs of such mines.

[0088] During mining operations, geological conditions may change significantly as mining progresses, such as alterations in the properties of the surrounding rock and expansion of the goaf. The dynamic design system and method of this invention can respond to these changes in real time. Through real-time monitoring and feedback analysis, it can promptly adjust the strength design scheme of the backfill body, making it suitable for mining scenarios with dynamically changing geological conditions.

[0089] For large mines that require long-term mining planning, the method of this invention can continuously revise the model and design scheme based on actual monitoring data as mining progresses, providing a scientific and reasonable design for the strength of the backfill body throughout the entire mining cycle, and ensuring the safety and economic benefits of long-term mining.

Claims

1. A system for dynamic design of a strength of a backfill based on a three-dimensional geological model, characterized by, include: The geological environment digital construction module extracts the original geological exploration data, tunnel measurement data and rock physical and mechanical experimental parameters of the area to be mined, and constructs a three-dimensional geological model in virtual space that includes stratigraphic boundaries, fault structures, ore body morphology and existing goaf areas. The numerical simulation module for the mining process dynamically simulates the stress release and transfer process of the surrounding rock caused by mining in a three-dimensional geological model based on the preset mining step distance, mining sequence and filling time node, and generates theoretical stress field distribution maps of the area to be filled and its adjacent structures under different mining stages. The real-time mechanical sensing and monitoring module acquires data on the surrounding rock pressure, internal stress and strain of the filling body, and surface subsidence displacement around the mining area to be filled. The dynamic coupling analysis and feedback module receives monitoring data from the real-time mechanical sensing and monitoring module, and performs multi-dimensional comparative analysis with the theoretical stress field distribution map output by the mining process numerical simulation module. It identifies the mechanical deviation between the theoretical model and the actual working conditions, corrects the rock mass physical and mechanical parameters in the three-dimensional geological model through parameter inversion technology, and outputs the corrected mining stability evaluation index. The dynamic decision-making module for filling strength calculates the critical bearing strength required for the area to be filled within a specific service cycle based on the revised stope stability evaluation index and the predicted impact of the next stage of mining. It dynamically generates a filling strength design scheme that includes cement content, aggregate gradation and slurry concentration. The instruction execution and quality feedback module transforms the filling strength design scheme into the proportion adjustment instruction of the filling station's automated control system, and performs post-filling sampling verification of the actual strength, feeding back the verification results to the dynamic coupling analysis and feedback module.

2. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, When constructing a three-dimensional geological model, the digital geological environment construction module uses the Kriging interpolation algorithm to reconstruct the spatial surface of discrete borehole data. The volume element processing process divides the three-dimensional geological model into grid cells with independent mechanical properties. The size of the grid cells is adaptively adjusted according to the complexity of the mining area geometry, and grid refinement is performed in stress concentration areas.

3. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, The numerical simulation module for the mining process introduces a constitutive model based on the Mohr-Coulomb criterion to calculate the expansion range of the plastic zone in the roof and sides of the stope under mining disturbance when simulating the stress transfer process. The numerical simulation module for the mining process performs a full-field stress balance calculation once every 24 hours and outputs a dynamic dataset containing the principal stress direction, shear stress value, and deformation energy release rate. The number of grid cells in the simulation calculation is no less than 100,000, and the evolution of pore water pressure during the mining process is simulated through a fluid-structure interaction algorithm.

4. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, The real-time mechanical sensing and monitoring module includes a multi-point displacement gauge embedded in the surrounding rock, a pressure pillow installed on the top of the mining area, and a fiber optic strain sensor implanted inside the filling body. The fiber optic strain sensor achieves quasi-distributed measurement through wavelength encoding characteristics, with a measurement range of -5000 microstrain to +5000 microstrain and a measurement accuracy better than 1 microstrain. The pressure pillow has a range of not less than 20 MPa. The data acquisition frequency of all sensors is set to once every 10 minutes.

5. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, The dynamic coupling analysis and feedback module uses the least squares support vector machine algorithm to establish a nonlinear mapping relationship between monitoring data and model parameters. When the relative deviation between the measured stress value and the theoretical prediction value exceeds 15%, the parameter correction process is triggered to adjust the elastic modulus and cohesion parameters of the corresponding area in the three-dimensional geological model until the root mean square error between the two is reduced to below 5%. The parameter correction order was determined through sensitivity analysis, and was as follows: deformation modulus, cohesion, internal friction angle, and Poisson's ratio.

6. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, When calculating the critical bearing strength, the dynamic decision-making module for filling strength comprehensively considers the self-weight of the filling body, the pressure of the overlying strata, and the dynamic load generated by blasting in adjacent mining areas. Based on the time window of subsequent mining, the dynamic decision-making module for filling strength calculates the minimum strength standard that the filling grout should reach at 3 days, 7 days, and 28 days. If it is predicted that the adjacent mining area will be mined in 15 days, the system sets the strength of the current filling body at 14 days to be more than 1.2 times the design safety factor.

7. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, The instruction execution and quality feedback module communicates with the program logic controller of the filling station through an open database connection interface to monitor the feeding rate of the electronic belt scale and the reading of the flow meter in real time, so as to ensure that the deviation between the actual ratio and the dynamic design scheme is within 2%. The instruction execution and quality feedback module also performs online monitoring of the slump and fluidity of the slurry, and maintains the pumping performance by adjusting the addition ratio of high-efficiency water-reducing agent.

8. The dynamic design system for infill strength based on a three-dimensional geological model according to claim 1, characterized in that, It also includes a multi-criteria risk early warning unit, which is used to monitor the risk of instability in the mining area in real time based on the output of the dynamic coupling analysis and feedback module; when the predicted compressive stress of the filling body reaches 85% of its dynamic design strength, it issues an audible and visual alarm command.

9. A dynamic design method for the strength of infill bodies based on a three-dimensional geological model, based on the dynamic design system for the strength of infill bodies based on a three-dimensional geological model as described in claim 1, characterized in that, Includes the following steps: S1. Obtain basic geological data and material mechanical parameters, and generate an initial three-dimensional geological model in a computer system; S2. Set the initial mining plan, use numerical simulation to simulate the stress distribution in the stope after the first mining cycle, and determine the initial backfilling ratio; S3. Utilize a sensor network deployed at key mechanical feature points to continuously monitor the coordinated stress state of the surrounding rock and the backfill, and extract real-time data; S4. Feed the measured data back to the three-dimensional geological model, compare and analyze the difference between the theoretical value and the actual value, and identify the stress concentration area and potential instability location under the influence of mining. S5. Use a dynamic evolution algorithm to correct the model parameters and recalculate the required backfill strength index for the next mining cycle; S6. Adjust the filling material ratio based on the latest strength index and control the filling execution system in real time to complete the grout preparation and injection; S7. Repeat steps S3 to S6 until the mining of all ore bodies is completed.

10. The method for dynamic design of infill strength based on a three-dimensional geological model according to claim 9, characterized in that, In S4, the process for identifying stress concentration zones is as follows: the ratio of the maximum local stress in the zone to the original rock stress at that depth is calculated to obtain the stress concentration factor; when the stress concentration factor exceeds 1.5, the zone is defined as a key reinforcement zone, and the design strength level of the corresponding filling material in the zone is increased in subsequent dynamic design.

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