A new type of green model soil for large-scale shaking table test surrounding rock and its preparation method and application

Abstract

This invention belongs to the field of geotechnical engineering physical model testing technology, and particularly relates to a novel green model soil for large-scale shaking table tests, its preparation method, and its application. The composition of the novel green model soil for large-scale shaking table tests, calculated by mass percentage, includes: 30-45% river sand, 60-45% fly ash, 8-10% machine oil, and 0-2% barite powder. Using these methods, this invention provides a novel green model soil preparation scheme suitable for Class IV surrounding rock in large-scale shaking table tests. This scheme allows for precise control of multiple parameters of the model soil, exhibits excellent long-term dynamic stability and performance consistency, and features a simple, controllable, green, and economical preparation process.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering physical model testing technology, and particularly relates to a new type of green model soil for large-scale shaking table tests of surrounding rock, its preparation method and application. Background Technology

[0002] In geotechnical engineering, shaking table testing has become an important tool for studying soil-structure dynamic interactions and a key method for investigating the mechanical behavior and stability of underground structures such as tunnels and slopes under complex geological conditions. Physical model tests for weak and fractured Class IV and V surrounding rock are particularly important. These types of surrounding rock have low strength and poor self-stabilizing ability, making them prone to instability and failure under dynamic loads such as construction disturbances, groundwater seepage, and earthquakes. However, for large-scale shaking table tests, the core challenge lies in the need for the model soil to withstand long-term, cyclic dynamic loads, and the strict guarantee of long-term coordinated stability of multiple parameters such as density, modulus, and strength under anhydrous conditions.

[0003] Currently, the preparation methods for model soils used to simulate Class IV and V surrounding rock are mainly based on artificially mixed materials. By adjusting the proportions of various aggregates, binders, and modifiers, the target density, strength, and deformation parameters can be controlled. Commonly used material systems mainly include the following categories: First, a mixed system using barite powder as aggregate and gypsum, machine oil, paraffin wax, or petrolatum as binders, with the addition of quartz sand, iron powder, etc., to adjust the bulk density and elastic modulus; second, using sand and gypsum as the main materials, supplemented with other additives; third, using barite powder and quartz sand as aggregates, molten petrolatum as binder, and adding liquid paraffin wax as a modifier to improve the material's waterproofness and stability; fourth, incorporating lightweight materials such as sawdust and foam particles into the soil base material to adjust the dynamic shear modulus for dynamic test simulation. In addition, in recent years, a new intelligent preparation method has emerged, using water-soluble PVA material 3D printed into structural reinforcement layers, achieving a controllable decrease in strength through water dissolution.

[0004] Existing simulation approaches, such as those involving residual aqueous phases and performance dispersion due to thermal melting processes, are precisely the major pitfalls of dynamic testing, failing to meet the stringent requirements of large-scale dynamic models for material property consistency, extremely low dispersion, and stability. In model tests targeting Class IV surrounding rock, especially those involving complex conditions such as dynamic coupling, the following significant shortcomings still exist: 1) Similarity relationships are difficult to fully coordinate: Especially in soil-structure coupling systems, the similarity constants of model soil and prototype surrounding rock in terms of density, elastic modulus, shear modulus, etc., often cannot be satisfied at the same time. The model soil has a large error in the test and it is difficult to accurately reproduce the soil-structure response of the prototype.

[0005] 2) Inappropriate material configuration: Most model soils use an aqueous phase in their composition, resulting in poor volume stability and making it difficult to simulate the long-term effects on rock mass strength. Although some studies have used hydrophobic binders such as petrolatum and paraffin to improve water resistance, the evolution of the mechanical properties of these materials under dynamic loading differs significantly from that of real rock masses.

[0006] 3) Complex preparation process and low controllability: Existing methods mostly rely on experience for trial preparation, and the processes of material mixing, compaction, and curing are sensitive to temperature and humidity, resulting in large performance dispersion when preparing large quantities. In particular, when using hot melt binders (such as petrolatum and paraffin), the temperature control requirements are high, the operation is inconvenient, and it is not conducive to rapid on-site preparation and adjustment.

[0007] 4) Insufficient environmental friendliness and feasibility: Some formulation systems use materials such as lead oxides, which are not only costly but also pose certain toxicity risks, posing potential threats to the health of test personnel and the environment.

[0008] In summary, in order to more accurately simulate soil-structure interaction and seismic response characteristics in actual engineering, it is urgent to improve model soil preparation technology in terms of material innovation, preparation method optimization, and refinement of similarity evaluation system, so as to better serve dynamic model tests of complex geotechnical engineering and improve the realism and reliability of test simulation. Summary of the Invention

[0009] The purpose of this invention is to provide a novel green model soil for large-scale shaking table tests, its preparation method, and its application, in order to solve the above-mentioned problems.

[0010] To achieve the above objectives, the present invention provides the following solution: A novel green model soil for large-scale shaking table tests is provided. The composition of the model soil, calculated by mass percentage, includes: 30-45% river sand, 60-45% fly ash, 8-10% machine oil, and 0-2% barite powder.

[0011] Preferably, the composition of the model soil, calculated by mass percentage, includes: 34-41% river sand, 56-49% fly ash, 8-10% machine oil, and 0-2% barite powder.

[0012] Preferably, the composition of the model soil, calculated by mass percentage, includes: 37% river sand, 53% fly ash, 9% engine oil, and 1% barite powder.

[0013] A method for preparing a novel green model soil for large-scale shaking table tests includes: S1. Pre-treat the raw materials; S2. Pre-dispersion dry mixing: Weigh the pretreated river sand, fly ash and barite powder according to the proportion, place them in the mixing container, and dry mix at a speed of 20-30 rpm for 5-8 minutes until the material color is uniform. S3, Gradient Wet Mixing: Add the pretreated engine oil to the dry-mixed material in three parts; add 50% of the total engine oil in the first part and mix at 40-60 rpm for 3 minutes; add the remaining 30% of the engine oil in the second part and continue to mix at 40-60 rpm for 3 minutes; finally add the remaining engine oil and increase the speed to 70-90 rpm and mix for 5-7 minutes until a loose agglomerate with uniform color and texture is formed; S4. Homogenization curing: Place the gradient wet-mixed material in a sealed container and let it stand for 24 hours at room temperature of 20±5℃ to allow the machine oil to fully penetrate and stably distribute, thus obtaining the model soil.

[0014] Preferably, the pretreatment process of the raw materials in step S1 is as follows: the river sand is sieved through a 5mm sieve, the fly ash is selected as grade II fly ash, the barite powder is selected as 325 mesh, the river sand, fly ash and barite powder are placed in a dryer and dried at 200℃ for 1 hour, and the machine oil is selected as No. 40 industrial machine oil.

[0015] Preferably, in step S2, the rotation speed of the pre-dispersed dry mixing is 25 rpm, and the mixing time is 6 minutes.

[0016] Preferably, the medium-speed mixing speed of the gradient wet mixing is 50 rpm, the high-speed mixing speed is 80 rpm, and the high-speed mixing time is 6 minutes.

[0017] Preferably, the homogenization curing is carried out at room temperature of 22°C.

[0018] The application of a novel green model soil for large-scale shaking table tests of surrounding rock includes: applying the novel green model soil for large-scale shaking table tests of surrounding rock of surrounding rock to large-scale shaking table tests of the interaction between civil, transportation, water conservancy and marine engineering structures and soil.

[0019] Compared with the prior art, the present invention has the following advantages and technical effects: Accurate Similarity of Model Parameters and Performance Prediction: Addressing the challenge of comprehensively coordinating similarity relationships, a regression model was established using response surface methodology based on experimental data, relating elastic modulus, cohesion, and internal friction angle to the components. Model analysis revealed a significant "antagonistic" relationship between river sand and engine oil in reducing cohesion. Multi-objective optimization was then performed based on this model, providing a solution to the problem of tuning multi-parameter similarity relationships.

[0020] Stability and Consistency: This invention uses a completely anhydrous system of "fly ash-river sand-machine oil," eliminating strength degradation and volume instability caused by water sensitivity. Combined with a unique three-stage gradient mixing and homogenization curing process, the batch-to-batch dispersion coefficient of key mechanical parameters can be controlled below 3%, significantly superior to conventional processes (dispersion coefficient > 6%), ensuring extremely high reliability and repeatability of large-scale, repetitive test results.

[0021] A simple and highly controllable method for mass production is provided: the three-stage gradient mixing process is carried out at room temperature, eliminating the need for complex hot-melt temperature control equipment and avoiding material property dispersion caused by temperature fluctuations. This process, through the stepwise gradient addition of machine oil and subsequent sealing and curing, ensures uniform coating of solid particles by the cementing medium, making it suitable for rapid, large-scale on-site preparation and filling of model soil.

[0022] It possesses outstanding environmental friendliness and economic efficiency: all components are made from non-toxic, low-cost industrial by-products (fly ash) or common mineral materials, avoiding the environmental and health risks associated with the use of toxic substances such as lead oxides in some methods. Utilizing fly ash as the main admixture realizes the resource utilization of solid waste, significantly reducing raw material costs while ensuring performance, which aligns with the green and low-carbon engineering concept.

[0023] With a wide range of applications, it is especially suitable for large-scale dynamic tests: The model soil prepared by this method can not only accurately match the similarity requirements of Class IV surrounding rock in terms of static mechanical parameters, but its waterless phase characteristics and stability also make it particularly suitable for large-scale shaking table dynamic model tests. It can realistically simulate the nonlinear response and cumulative damage of surrounding rock under complex dynamic loads such as strong earthquakes, and provide a physical simulation basis for seismic research of underground engineering.

[0024] By utilizing these methods, this invention provides a novel green model soil preparation scheme suitable for Class IV surrounding rock in large-scale shaking table tests. This scheme enables precise control of multiple parameters of the model soil, possesses excellent long-term dynamic stability and performance consistency, and features a simple, controllable, green, and economical preparation process. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 The triaxial test results are for the ratio of river sand: fly ash: machine oil: barite powder = 37:53:9:1. Figure 2 This is the main effect diagram of the surface regression of the internal friction angle response. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example 1 A method for preparing model soil for Class IV surrounding rock with a geometric similarity ratio of 1:24 includes the following steps: Weigh out 3.0 kg of river sand that has passed through a 5 mm sieve, 6.0 kg of secondary fly ash, and 0.3 kg of 325 mesh barite powder. After drying at 200 degrees Celsius for one hour, put them into a centrifugal mixer and dry mix at a low speed of 25 rpm for 6 minutes.

[0029] Add 0.35 kg of No. 40 industrial machinery oil (50% of the total oil volume of 0.7 kg), adjust the speed to 50 rpm, and wet mix for 3 minutes.

[0030] Add another 0.21kg of engine oil (30% of the remaining amount) and continue mixing at 50 rpm for 3 minutes.

[0031] Add the remaining 0.14 kg (20% of the remaining amount) of machine oil, increase the speed to 80 rpm, and mix for 6 minutes until the material is uniform.

[0032] The mixture is placed in a sealed container and left to cure at room temperature of 22°C for 24 hours to form a model soil for Class IV surrounding rock with a geometric similarity ratio of 1:24.

[0033] Example 2 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 37:53:9:1, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0034] Example 3 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 45:45:10:0, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0035] Example 4 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 34:56:8:2, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0036] Example 5 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 41:49:10:0, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0037] Example 6 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 30:60:9:1, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0038] Example 7 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 37:53:7:3, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0039] Example 8 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 41:49:8:2, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0040] Example 9 The only difference between this embodiment and Embodiment 1 is that: River sand: fly ash: machine oil: barite powder = 34:56:10:0, wherein the proportion of machine oil added in stages is the same as in Example 1.

[0041] Experimental Example 1: Verification Test of Soil Mix Design Validity and Similarity Ratio Matching for Class IV Surrounding Rock Model The purpose of this experiment is to verify whether the model soils with different mix proportions in Examples 1-9 match the physical and mechanical parameter requirements of the 1:24 geometric similarity ratio for Class IV surrounding rock in the "Railway Tunnel Design Code" (TB10003-2016), to determine the optimal mix proportion, and to verify the effectiveness of the model soil mix proportion system of this invention.

[0042] This experimental example involves curing the model soils from Examples 1-9, followed by sample preparation according to specifications, and then performing density measurements using the ring cutter method and triaxial tests (consolidated undrained). The model soil prepared in Example 2 was found to have the best performance, with average properties of: density 2.12 g / cm³, elastic modulus 36.67 MPa, cohesion 20.36 kPa, and internal friction angle 30.18°. These results are highly consistent with the target values ​​for the model shown in Table 1.

[0043] Based on the relevant content of the "Railway Tunnel Design Code" (TB10003-2016), the physical and mechanical parameters of Class IV surrounding rock and similar materials were determined and summarized in Table 1.

[0044] Table 1. Physical and Mechanical Properties of Class IV Surrounding Rock

[0045] Table 2. Mechanical properties of model soils used in experiments with different mix proportions (i.e., Examples 1-9)

[0046] Among them, the triaxial test results of river sand: fly ash: machine oil: barite powder = 37:53:9:1 in Example 2 are closest to the physical and mechanical properties of the surrounding rock model, such as Figure 1 As shown, the stress-strain curve and Mohr's circle of the consolidated undrained triaxial test of the soil of this mix design model are highly matched with the mechanical response characteristics of the Class IV surrounding rock model. The test data of the measured mechanical indicators are all within the reasonable range of the model target values ​​in Table 1, which proves that the above embodiment is basically consistent with the theoretical values ​​required by similar theory and verifies the correctness of the technical solution of the present invention.

[0047] The results of this experiment show that the mechanical parameters of the model soil prepared under the mix proportion of Example 2 are highly consistent with the target values ​​of the Class IV surrounding rock model in Table 1, which verifies the correctness of the mix proportion system of the present invention. This mix proportion is the optimal mix proportion under a geometric similarity ratio of 1:24. The prepared model soil can be applied to large-scale shaking table surrounding rock model tests, and the mix proportion range of the present invention can be adapted to a geometric similarity ratio range of 1:15 to 30.

[0048] Experimental Example 2: Validation Experiment of Interaction Mechanism of Model Soil Components and Quantitative Regression Model The purpose of this experiment is to reveal the quantitative influence of core components such as river sand and engine oil on the mechanical properties of model soil and the interaction between components, based on the measured data of 9 sets of mix proportions in Experiment 1, and to establish a regression model of mechanical parameters and component content, so as to provide a theoretical basis for the precise control of model soil parameters.

[0049] Based on the measured mechanical properties of the nine groups of model soils with different mix proportions in Table 2 of Experiment Example 1, regression analysis was performed using the response surface methodology: Using river sand and engine oil as core variables (considering the complementary relationship between fly ash and river sand and the relatively small proportion of barite powder), a quadratic regression model was established for elastic modulus (E), cohesion (c), and internal friction angle (φ).

[0050] The final regression equation obtained is as follows (where the variables represent their mass percentages): Elastic modulus E (MPa) model:

[0051] Cohesion c (kPa) model:

[0052] Model of internal friction angle φ (°):

[0053] The quantitative relationship between the aforementioned internal friction angle and river sand and engine oil can be intuitively represented by the main effect diagram of response surface regression, such as... Figure 2 As shown, the influence of the mass percentage changes of river sand and engine oil on the internal friction angle exhibits a significant nonlinear response characteristic. The main effect trends of the two are consistent with the coefficients of the first-order term of the regression equation, and the interaction characteristics of the surface directly reflect the antagonistic relationship between the two on the regulation of the internal friction angle.

[0054] Analysis shows that: In terms of main effects: the coefficients of the first-order terms in the equation accurately quantify the influence of single factors. For cohesion, both river sand (-34.0) and engine oil (-92.2) show strong negative effects, jointly confirming that they are the most effective control methods to achieve the target of low cohesion. For elastic modulus, the negative effect of river sand (-9.82) is dominant, indicating that it is the key to controlling the modulus.

[0055] Regarding interaction effects: the coefficients of interaction terms (such as river sand * engine oil) reveal the complex interactions between components.

[0056] In the cohesion model, the coefficient of this interaction term is a significant positive value (+3.51). This indicates that river sand and engine oil have a resistant effect on reducing cohesion; that is, when both are present, their combined effect on reducing cohesion is weaker than the simple superposition of their individual effects. This finding implies that a precise balance of proportions is necessary to achieve the target low value.

[0057] In the elastic modulus model, the coefficient of the interaction term is (+1.279), indicating that there is a weak synergistic effect between the two on modulus adjustment.

[0058] In the internal friction angle model, the coefficient of the interaction term is negative (-0.138), indicating the presence of resistance.

[0059] These directional and varying interaction effects together constitute a complex response system, making it impossible to find the optimal ratio through empirical deduction; it must rely on system modeling and optimization.

[0060] Nonlinear response and the existence of extrema: The quadratic terms (river sand², engine oil²) are significant in all models, clearly indicating that the performance parameters have a nonlinear relationship with the change of component content, and there are extrema (maximum or minimum values). This mathematically explains why there is a clear "optimal ratio interval" rather than a monotonic relationship.

[0061] The regression equation not only describes the degree of influence of each component on performance, but also reveals the complex direction-specific interaction between river sand and engine oil (which is manifested as resistance in the cohesion model).

[0062] Experiment Example 3: Comparative Verification Experiment on the Effectiveness of Three-Stage Gradient Mixing and Homogenized Curing Process The purpose of this experiment is to verify, through a single-factor control experiment, the superiority of the three-stage gradient mixing + 24h sealed curing process described in this invention over the conventional mixing process in improving the uniformity and performance consistency of the model soil, based on the optimal mix ratio (37:53:9:1) determined in Experiment Example 1. Experimental Examples 1 and 2 verified the effectiveness of the proportioning system and the theoretical basis for parameter control of the present invention. To verify the independent technical effect of the special mixing and curing process of the present invention, the following control experiment was designed: An experimental group and a control group were set up. The experimental group was the optimal ratio of Example 2 of the present invention, and the control group was the model soil that was prepared directly for testing by using the "one-time mixing process", that is, all the engine oil was added to the dry material at one time, and the mixture was mixed for the same total time. After mixing, no sealing and curing was performed. Three parallel samples were prepared for both the experimental group and the control group.

[0063] Testing and Calculation: Consolidated undrained triaxial tests were performed on all samples to measure cohesion (c), internal friction angle (φ), and elastic modulus (E). The average value, standard deviation, and coefficient of variation (COP) of each parameter were calculated, and the results are shown in Tables 3 and 4.

[0064] Table 3. Data Recording Table for Parallel Samples in the Gradient Mixing Process Control Test

[0065] Table 4. Examples of Mechanical Parameter Dispersion Analysis in Comparative Experiments of Gradient Mixing Process

[0066] Conclusion: As shown in Table 4, the model soil prepared using the gradient mixing process of this invention exhibits significantly lower coefficients of variation for all key mechanical parameters compared to conventional one-time mixing processes. This indicates that the process of this invention can greatly improve the uniformity of material mixing and the consistency of performance within batches, thereby ensuring the reliability and repeatability of large-scale shaking table test results.

[0067] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A novel green model soil for large-scale shaking table tests of surrounding rock, characterized in that, The composition of the model soil, calculated by mass percentage, includes: 30-45% river sand, 60-45% fly ash, 8-10% engine oil, and 0-2% barite powder.

2. The novel green model soil for large-scale shaking table test surrounding rock as described in claim 1, characterized in that, The composition of the model soil, calculated by mass percentage, includes: 34-41% river sand, 56-49% fly ash, 8-10% machine oil, and 0-2% barite powder.

3. The novel green model soil for large-scale shaking table tests of surrounding rock as described in claim 1, characterized in that, The composition of the model soil, calculated by mass percentage, includes: 37% river sand, 53% fly ash, 9% engine oil, and 1% barite powder.

4. A method for preparing a novel green model soil for large-scale shaking table tests as described in any one of claims 1-3, characterized in that, include: S1. Pre-treat the raw materials; S2. Pre-dispersion dry mixing: Weigh the pretreated river sand, fly ash and barite powder according to the proportion, place them in the mixing container, and dry mix at a speed of 20-30 rpm for 5-8 minutes until the material color is uniform. S3, Gradient Wet Mixing: Add the pretreated engine oil to the dry-mixed material in three parts; add 50% of the total engine oil in the first part and mix at 40-60 rpm for 3 minutes; add the remaining 30% of the engine oil in the second part and continue to mix at 40-60 rpm for 3 minutes; finally add the remaining engine oil and increase the speed to 70-90 rpm and mix for 5-7 minutes until a loose agglomerate with uniform color and texture is formed; S4. Homogenization curing: Place the gradient wet-mixed material in a sealed container and let it stand for 24 hours at room temperature of 20±5℃ to allow the machine oil to fully penetrate and stably distribute, thus obtaining the model soil.

5. The method for preparing a novel green model soil for large-scale shaking table tests according to claim 4, characterized in that, The pretreatment process of the raw materials in step S1 is as follows: the river sand is passed through a 5mm sieve, the fly ash is selected as grade II fly ash, the barite powder is selected as 325 mesh, the river sand, fly ash, and barite powder are placed in a dryer and dried at 200℃ for 1 hour, and No. 40 industrial machine oil is selected as the machine oil.

6. The method for preparing a novel green model soil for large-scale shaking table tests according to claim 4, characterized in that, In step S2, the rotation speed of the pre-dispersed dry mixing is 25 rpm, and the mixing time is 6 minutes.

7. The method for preparing a novel green model soil for large-scale shaking table tests according to claim 4, characterized in that, The gradient wet mixing process involves a medium-speed mixing speed of 50 rpm, a high-speed mixing speed of 80 rpm, and a high-speed mixing time of 6 minutes.

8. The method for preparing a novel green model soil for large-scale shaking table tests according to claim 4, characterized in that, The homogenization curing was carried out at room temperature of 22°C.

9. The application of a novel green model soil for surrounding rock in large-scale shaking table tests, characterized in that... include: The novel green model soil for large-scale shaking table tests described in any one of claims 1-3 is applied to large-scale shaking table tests of the interaction between soil and structures in civil, transportation, water conservancy, and marine engineering.

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