A Method for Predicting Joint Roughness of Concrete Cutoff Walls Based on Aggregate Gradation Characteristics

Abstract

This invention discloses a method for predicting the roughness of joints in concrete cutoff wall trenches based on aggregate gradation characteristics, belonging to the field of concrete cutoff wall engineering technology. This method constructs a multi-dimensional aggregate gradation characterization system encompassing "gradation fractal characteristics, particle morphology, and physical parameters," introduces dynamic construction influencing factors, uses multi-directional sampling to define the roughness range, establishes a unified nonlinear coupled prediction model, and achieves accurate roughness prediction after experimental calibration. Based on the prediction results, the aggregate gradation is optimized. This invention resolves the ambiguity in the characterization of three-dimensional trench joint roughness, achieves advanced prediction using conventional parameters without excavation and scanning, and forms a "prediction-optimization" closed loop. It reduces costs and improves construction efficiency while meeting seepage prevention requirements, providing scientific guidance for cutoff wall design and construction.

Description

Technical Field

[0001] This invention relates to the field of concrete cutoff wall engineering technology, specifically to a method for predicting the roughness of the joints in the trench section of a concrete cutoff wall based on aggregate gradation characteristics. This method is applicable to infrastructure projects in fields such as water conservancy, municipal engineering, and transportation that rely on concrete cutoff walls for seepage control. Background Technology

[0002] Concrete cutoff walls are core seepage-proof components that block underground seepage and ensure the stability of engineering structures. Their construction requires a segmented casting process, inevitably resulting in joints at the contact surfaces of adjacent segments. The roughness of these joints (usually characterized by the joint roughness coefficient) directly determines the contact characteristics and seepage-proof performance of the joint surface, representing a critical weak point in the cutoff wall. Existing technologies for controlling the roughness of these joints have four major limitations:

[0003] Influencing factor focus bias: It focuses only on the impact of construction technology (such as pouring speed and slurry viscosity) on roughness, ignoring the essential controlling role of concrete aggregate gradation (particle size distribution, particle shape, packing characteristics) as the concrete skeleton, resulting in a lack of fundamental material basis for prediction.

[0004] Ambiguity in three-dimensional characterization: Using a single direction or discrete points to detect roughness cannot cover the anisotropic roughness characteristics of three-dimensional groove joints, and it is easy to miss local weak areas in seepage prevention, leading to evaluation bias.

[0005] Delayed detection timeliness: Relying on post-construction excavation scanning or drilling for detection, if the roughness is found to be substandard, secondary roughening and grouting are required to remedy the situation, which greatly increases the construction period and cost;

[0006] Without optimization and closed-loop: It only stays at the level of roughness "detection or prediction" without establishing a connection between the prediction results and aggregate gradation design, and cannot achieve the synergistic goal of "roughness compliance - cost optimization - construction efficiency".

[0007] Therefore, there is an urgent need to build a complete technical system with aggregate gradation as the core, integrating multi-dimensional influencing factors, covering three-dimensional characterization, and connecting prediction and optimization, so as to solve the fundamental defects of existing technologies. Summary of the Invention

[0008] In view of this, the purpose of this invention is to overcome the defects of the prior art and provide a method for predicting and optimizing the roughness range of aggregate gradation-concrete anti-seepage wall trench joints based on multi-factor coupling. This method constructs a nonlinear prediction model with a unified structure, incorporating key factors such as particle morphology, aggregate orientation, and construction dynamics, to provide a basis for anti-seepage wall construction.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for predicting the roughness of joints in concrete anti-seepage wall trenches based on aggregate gradation characteristics includes the following steps:

[0011] S1. Construct a multi-dimensional aggregate gradation characterization system, and obtain core parameters through experimental measurement and theoretical derivation. The core parameters include at least gradation fractal characteristic parameters, particle morphology and orientation parameters, and physical parameters.

[0012] The gradational fractal feature parameters include at least the gradational fractal dimension. Particle size distribution uniformity coefficient curvature coefficient ,in These are the particle sizes corresponding to cumulative volume fractions of 10%, 30%, and 60%, respectively.

[0013] The particle morphology and orientation parameters include at least a comprehensive coefficient for angular sharpness based on a projection method. and aggregate orientation distribution coefficient ;

[0014] The physical parameters include at least aggregate bulk density. ;

[0015] S2. Introduce key construction parameters that affect aggregate packing morphology to construct dynamic influencing factors:

[0016]

[0017] in, For speed, For mud density, For the verticality deviation of the slot sidewall, This is a dimensionless correction factor, with units of 1 kg * h / m. 4 , Characterization and The effect of synergistic effect on aggregate deposition morphology;

[0018] S3. Define a method for characterizing the range of multi-directional roughness:

[0019] Multi-directional roughness range characterization: Two-dimensional profiles are taken along at least three non-coincident characteristic directions on the surface of the three-dimensional groove segment. The measured roughness values ​​of each profile are calculated, and the minimum value is taken. and maximum value ,by As the roughness range of the three-dimensional groove segment;

[0020] S4, Establish and Coupled prediction model:

[0021]

[0022] Where A, B, and C are uniform regression coefficients. The fractal index is the gradation index. and The values ​​are 3.8 and 6.3 or -3.2 and -8.4. These are the geometric constraint coefficients; This is the random error term; The width of the anti-seepage wall trench section;

[0023] S5. Model Tests and Parameter Calibration: Model tests will be conducted using at least two different aggregate gradations. Measurements will be taken using a scanning device with an accuracy of at least 0.1 mm. and Measured values ​​are used to calibrate model parameters, which in turn determine the model's fit coefficients. ;

[0024] S6, Aggregate gradation optimization: with and Constrained by meeting design requirements and ensuring that aggregate gradation conforms to specifications, and aiming at minimizing concrete costs and maximizing construction efficiency, the aggregate gradation parameters are adjusted to output the optimal solution.

[0025] Furthermore, in step S1, the gradation fractal dimension The calculation method is as follows:

[0026]

[0027] in, Let i be the volume fraction of the i-th particle size range. The median particle size in the interval. For the maximum particle size, This is the minimum particle size.

[0028] Furthermore, in step S1, the comprehensive coefficient of edge sharpness... The calculation method is as follows:

[0029]

[0030] in, This represents the number of three-dimensional edges of the particle. The average height of the edge (mm). The average particle size is (mm). This represents the actual perimeter of the particle's three-dimensional projection. It is the circumference of a circle with the same area.

[0031] Further, in step S1, the aggregate orientation distribution coefficient The uniformity of aggregate arrangement along the longitudinal direction of the anti-seepage wall trench is characterized by the combined control of aggregate stress state and spatial constraints. Its calculation method is as follows:

[0032]

[0033] in, The apparent density of concrete, It is the acceleration due to gravity. The viscosity coefficient of concrete (Pa·s). The result of the balance between the driving force and resistance to the directional arrangement of aggregates, when When the driving force equals the resistance force, it indicates a non-directional distribution. At that time, the driving force equals 3 times the resistance, indicating a strong directional distribution; It represents the ratio of the total dynamic density of directional drive to the drag density of directional drive. The larger the value, the more dominant the dynamic drive and the stronger the directional drive. Characterizing the matching degree between aggregate size and channel space, the closer the particle size is to the channel width, the greater the spatial restriction on particle arrangement and the more significant the orientation.

[0034] Furthermore, in step S4, Characterizes the synergistic interlocking effect of gradation complexity and edge sharpness; Due to the density saturation effect, when the relative density is >0.85, the tanh function approaches 1, and the interlocking effect reaches its limit.

[0035] Furthermore, in step S4, The synergistic effect of uniformity and curvature characterizes the roughness distribution; the higher the uniformity and the more reasonable the curvature, the more stable the roughness distribution. Geometric constraints are characterized by larger particle size and narrower grooves, which restrict particle arrangement and suppress roughness.

[0036] Further, in step S4, the directional adjustment term The calculation method is as follows:

[0037]

[0038] in, The directional term is of the weakening type because the direction of maximum roughness needs to break through the limitation of directional arrangement; The orientation term is a compensation type, because the minimum roughness direction needs to rely on the orientation arrangement to reduce the extremely smooth area.

[0039] Furthermore, in step S4, the mechanistic constraints of the model are:

[0040] (1) ;

[0041] (2) This indicates that the stronger the orientation, the lower the maximum roughness, because the particles are more neatly arranged along a certain direction.

[0042] (3) This indicates that the stronger the orientation, the higher the minimum roughness, because the roughness in the weak direction is compensated by the orientation arrangement.

[0043] Furthermore, in step S5, the roughness JRC is calculated as follows:

[0044]

[0045] in, It is the cross-sectional length of the groove section. It refers to the elevation of each point on the cross-section. It is the distance between adjacent points. N is the number of points taken on the cross-section, and N is the number of cross-sections taken on the groove joint surface.

[0046] Furthermore, in the roughness calculation method, 0.5 mm is used as the sampling distance for each profile and the distance between each profile on the surface.

[0047] The beneficial effects of this invention are as follows:

[0048] Resolving Ambiguities in 3D Representation: Defining Representations Through Multi-directional Sampling and The range fully covers roughness in all directions, avoiding weak or missed areas in seepage prevention or deviations in construction assessment caused by a single value, thus ensuring the reliability of seepage prevention.

[0049] Predictive logic innovation: A multi-dimensional coupled system is constructed with aggregate gradation as the core, achieving for the first time "based on conventional parameters (such as...)". "Pre-construction roughness prediction" eliminates the need for excavation and scanning, avoiding the cost of delayed remediation;

[0050] The model has strong universality: it uses a unified structural model to predict simultaneously. and It adapts to different roughness extreme values ​​only by subtle differences in parameters, and is compatible with various aggregate types such as continuous gradation and discontinuous gradation, making it widely applicable in engineering.

[0051] Full-process optimization closed loop: The prediction results are directly linked to the aggregate gradation design, so as to achieve the best cost and efficiency while meeting the seepage prevention requirements, and provide accurate guidance for the design and construction of seepage prevention walls;

[0052] In summary, this invention, by constructing a multi-dimensional aggregate gradation characterization system and construction dynamic influencing factors, pioneered a multi-directional roughness range characterization method and established a unified structural nonlinear coupling model, achieving accurate prediction of the roughness of concrete anti-seepage wall groove joints before construction. At the same time, it forms a "prediction-optimization" closed loop, which solves the ambiguity of three-dimensional joint surface roughness characterization and avoids the cost of delayed detection and remediation, while meeting anti-seepage requirements and achieving the lowest concrete cost and the highest construction efficiency. Attached Figure Description

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0054] Figure 1 A flowchart of the method for predicting the roughness of the joint in the concrete anti-seepage wall groove provided by the present invention;

[0055] Figure 2 (a) is a dimensional schematic diagram of the mold involved in this invention;

[0056] Figure 2 (b) is a schematic diagram of concrete pouring according to the present invention;

[0057] Figure 3 This refers to the particle size distribution curve of continuously graded soil used in the embodiments of the present invention.

[0058] Figure 4 The particle size distribution curve of discontinuously graded soil used in the embodiments of the present invention;

[0059] Figure 5 Roughness parameters in embodiments of the present invention A schematic diagram of the calculation method;

[0060] Figure 6 This is a schematic diagram illustrating the transformation from a curved surface model to a planar model in an embodiment of the present invention;

[0061] Figure 7 This is a schematic diagram of a typical three-dimensional seam model in an embodiment of the present invention;

[0062] Figure 8 (a) is a comparison image of the actual picture and the three-dimensional scanning result of the concrete underfill continuous gradation in an embodiment of the present invention;

[0063] Figure 8 (b) is a comparison image of the actual concrete intermittent top gradation of the present invention and the three-dimensional scanning results;

[0064] Figure 8 (c) is a comparison image of the actual picture and the three-dimensional scanning result of the clay concrete with continuous gradation under the embodiment of the present invention;

[0065] Figure 8 (d) is a comparison image of the actual picture and the three-dimensional scanning result of the clay concrete intermittent top gradation in the embodiment of the present invention;

[0066] Figure 9 (a) is the prediction result of the model predicting the minimum roughness of the interface in the embodiment of the present invention;

[0067] Figure 9 (b) is the prediction result of the model predicting the maximum roughness of the interface in the embodiment of the present invention;

[0068] Figure 10 (a) is a comparison diagram of the predicted result of the minimum roughness of the roughness model interface and the actual roughness in the embodiment of the present invention;

[0069] Figure 10 (b) is a comparison diagram of the predicted result of the maximum roughness of the roughness model interface and the actual roughness in the embodiment of the present invention;

[0070] Figure 11 The roughness model gradation fractal dimension in the embodiments of the present invention. Sensitivity analysis;

[0071] Figure 12 The roughness model edge sharpness comprehensive coefficient in the embodiments of the present invention. Sensitivity analysis. Detailed Implementation

[0072] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0073] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images, and should not be construed as limiting the invention. It is understandable that some well-known structures and their descriptions may be omitted in the drawings for those skilled in the art.

[0074] The following is in conjunction with the appendix Figure 1-12 This embodiment takes the concrete anti-seepage wall of a water conservancy project as the application scenario. The anti-seepage wall of this project is designed to be 90m deep and 2.0m wide. The dam foundation cover layer is mainly composed of silty clay and gravel, and the groundwater is abundant, which imposes strict requirements on the anti-seepage performance of the trench joints.

[0075] like Figure 1 As shown, the method for predicting the roughness of the joints in the concrete anti-seepage wall groove used in this embodiment has the following specific steps:

[0076] S1. Construct a multi-dimensional aggregate gradation characterization system, and obtain core parameters through experimental measurement and theoretical derivation, specifically including:

[0077] (1) Characteristic parameters of gradational fractals: gradational fractal dimension Particle size distribution uniformity coefficient curvature coefficient ,in These are the particle sizes corresponding to cumulative volume fractions of 10%, 30%, and 60%, respectively.

[0078] (2) Particle morphology and orientation parameters: Comprehensive coefficient of edge sharpness based on projection method and aggregate orientation distribution coefficient ;

[0079] (3) Physical parameters: aggregate bulk density ;

[0080] S2. Pouring speed, slurry density, and verticality deviation are three key construction parameters affecting the aggregate packing morphology in concrete. Pouring speed dominates the horizontal dispersion of the aggregate, slurry density controls its suspension stability, and verticality deviation affects the lateral constraint of the trench walls on the aggregate. These three factors work together to influence the spatial distribution of aggregate in the concrete mixture, thus determining the final roughness of the trench joints. Based on the force balance theory of aggregate during pouring, its motion is comprehensively regulated by buoyancy, drag force, gravity, and fluid kinetic energy. Therefore, by introducing the above construction parameters, a mechanical influencing factor reflecting the dynamic changes in aggregate packing morphology can be constructed. :

[0081]

[0082] in, For speed (m / h), The density of the mud (kg / m³) 3 ), This refers to the verticality deviation of the slot sidewall. Dimensionless correction factor (1kg*h / m) 4 ); Characterizing the synergistic effect of the two on the aggregate accumulation morphology, when the pouring speed... As the density of the slurry increases, the fluid kinetic energy increases, and the aggregate is prone to deviating from a uniform packing state due to excessive diffusion; when the slurry density increases... As the size increases, the buoyancy support is enhanced, the risk of aggregate settlement is reduced, and the uniformity of packing is improved; based on the theory of lateral confined stress distribution in the trench wall, the verticality deviation of the trench sidewall is reduced. It will change the distribution of lateral pressure on the concrete mixture from the trench wall, when When the pressure increases, the contact area between the tank wall and the mixture becomes uneven, leading to localized lateral pressure concentration. This causes the aggregate to shift towards the low-pressure zone under the pressure difference, disrupting uniform packing. Therefore, a linear term is used in the formula. Characterize its impact.

[0083] S3. Define a method for characterizing the range of multi-directional roughness:

[0084] (1) Sampling direction design: On the three-dimensional groove segment surface, two-dimensional cross-sections are cut along 6 characteristic directions ( =0°, 30°, 60°, 90°, 120°, 150° (The angle between the cross-section and the pouring direction).

[0085] (2) Roughness range definition: Calculate the measured roughness values ​​in 6 directions and take the minimum value. and maximum value As the roughness range of the three-dimensional seam surface, it characterizes the roughness coverage area in all directions;

[0086] S4. Based on the theory of oriented particle packing and the mechanism of multi-directional roughness formation, the derivation is... and Coupled prediction model:

[0087]

[0088] Among them, A, B, and C are uniform regression coefficients, which need to be determined by fitting; For the gradation fractal index, and The recommended values ​​are 3.8 and 6.3 respectively; For geometric constraint coefficients, and The recommended values ​​are -3.2 and -8.4 respectively; For random error term This is caused by experimental errors and environmental factors; The width of the anti-seepage wall trench section;

[0089] S5. Concrete Cutoff Wall Model Tests and Formula Calibration: Model tests of concrete cutoff walls were conducted using different aggregate gradations to generate cutoff wall groove surfaces with different roughnesses. A high-precision laser scanner was used to scan profiles in six directions, and the roughness in each direction was determined using the Barton standard curve to obtain... and The model parameters were calibrated based on the experimental results.

[0090] S6. Based on the model prediction results, the concrete aggregate is optimized to meet the predetermined requirements for the roughness of the concrete channel joints and the standard design conditions for aggregate gradation, so as to achieve the goal of the lowest concrete cost and the highest construction efficiency.

[0091] Based on the above prediction methods, preparations were made for the corresponding concrete trench segment model test and the determination of basic parameters. Figure 2 (a) is a schematic diagram of the mold involved in this embodiment. The mold measures 200mm × 200mm × 500mm. To simulate segmented construction, a PVC pipe with an inner diameter of 200mm is used to divide the interior into three parts. Each side of the sample chamber can accommodate a sample with dimensions of 200mm × 200mm × 200mm (length × width × height). According to the "General Specification for Concrete Structures" GB55008-2021, it can meet the testing requirements of concrete materials with aggregate particle sizes ranging from 0.075mm to 20mm.

[0092] Six sets of sample molds were set up during the experiment to allow for the simultaneous fabrication and testing of multiple samples. Each set of molds was made of high-quality wood and securely assembled with iron nails to ensure the mold maintained its structural rigidity and stability during concrete pouring. The molds were assembled using iron nails to ensure a tight connection between each mold. After the samples were fully formed, a claw hammer was used to assist in demolding. Furthermore, hot melt adhesive was applied to the junction between the inner side of the mold and the bottom wooden board, especially at the junction with the PVC material. This design was primarily to prevent concrete slurry leakage due to gaps during concrete pouring. Figure 2 (b) is a schematic diagram of concrete pouring in this embodiment.

[0093] Figure 3 , Figure 4 To implement the continuous and discontinuous gradation curves for soil used in the case study, soil from a dam overburden layer was selected as the coarse aggregate for concrete. During aggregate selection, the sand and gravel were graded, screened, and proportioned according to different particle size distribution curves. Since the original aggregate particle size was less than 20 mm, which met the instrument's particle size requirements, there was no need to scale down the original design gradation during the experiment.

[0094] To further investigate the influence of aggregate gradation on the frictional force and shear characteristics of the contact surface of the cutoff wall, a control group with discontinuous gradation was added for comparative testing. The test materials were divided into two types: continuous gradation and discontinuous gradation. The original gradation, upper envelope, and lower envelope were selected for testing, respectively.

[0095] In the test described in this example, the sand selection process was divided into four groups of samples, totaling 12 samples, including 3 continuously graded clay samples, 3 continuously graded concrete samples, 3 discontinuously graded clay samples, and 3 discontinuously graded concrete samples; the concrete design strength grade was C25, and the apparent density was... viscosity coefficient .

[0096] Aggregate gradation parameters were determined by standard sieve analysis, using the original gradation of continuous concrete gradation as a reference. The particle size corresponding to 10%, 30%, and 60% of the cumulative volume fraction was determined. The uniformity coefficient is calculated accordingly. curvature coefficient Aggregate bulk density was determined using the drainage method. The maximum packing density was determined by standard vibration compaction test. The relative packing density was 0.823. The three-dimensional morphological parameters of the particles were determined visually: average particle size... The number of three-dimensional edges is n=4, and the average height of the edges is... Actual perimeter of particle 3D projection Circumference of a circle with the same area Substitute into the formula for the comprehensive coefficient of edge sharpness .

[0097] Based on the construction plan, the key construction parameters are determined as follows: pouring and rising speed. mud density Verticality deviation of the slot sidewall Substitute into the formula for dynamic influence factor of pouring Combined with the maximum particle size of the aggregate Substitute into the formula for aggregate orientation distribution coefficient It falls within the value range of 1.0 to 1.5.

[0098] Subsequently, a high-precision laser scanner (scanning accuracy 0.01mm) was used to scan the groove section surface, along... Two-dimensional profiles are taken along six characteristic directions: 0°, 30°, 60°, 90°, 120°, and 150°. The distance between points on each profile is 0.5 mm, and the profile length L = 200 mm. 1000 measuring points are taken in each direction. Since the groove surface in this embodiment is curved, it is necessary to... Figure 6 The method shown transforms the groove joint surface into a three-dimensional plane.

[0099] Figure 5 For parameters The calculation method is based on The roughness of the groove section can be obtained from the calculation results; Figure 7 This embodiment presents a three-dimensional model of the test groove joint. Figure 8 (a) is a comparison between the actual image and the 3D scanning result of the continuous gradation of the concrete under the concrete in this embodiment; Figure 8 (b) is a comparison image of the actual concrete intermittent top gradation and the 3D scanning results in this embodiment; Figure 8 (c) is a comparison image of the actual picture and the three-dimensional scanning result of the clay concrete with continuous gradation under this embodiment; Figure 8 (d) is a comparison image of the actual image and the 3D scanning result of the intermittent top gradation of clay concrete in this embodiment.

[0100] Next, the prediction model was calibrated. This embodiment designed a total of 12 groups of specimens with different aggregate gradations, covering both continuous and discontinuous gradations. The formula for the aggregate orientation distribution coefficient was then determined. The value is 1.16. Other model parameters and simulation results for each sample are shown in Table 1 below. It should be noted that parameters are not considered at this stage. The impact.

[0101] Table 1 compares the roughness of the test groove section with the model calculation results in this embodiment:

[0102]

[0103] The following is about the aggregate orientation distribution coefficient. The calculation process will be explained in detail:

[0104] Aggregate directional distribution coefficient The uniformity of aggregate arrangement along the longitudinal direction of the anti-seepage wall trench is characterized by the combined control of aggregate stress state and spatial constraints. Its calculation method is as follows:

[0105]

[0106] in, apparent density of concrete (kg / m³) 3 ), It is the acceleration due to gravity. The viscosity coefficient of concrete (Pa·s); when aggregates are poured into the concrete mixture, The orientation of aggregates is determined by the balance between the traction effect of fluid flow and the resistance effect of their own forces. The stronger the traction effect and the weaker the resistance effect, the higher the orientation of the aggregates.

[0107] Furthermore, when At this point, the driving force and resistance are in equilibrium, corresponding to the spatially non-directional distribution characteristics of the system; when At that time, the driving force reaches three times the resistance, and the system exhibits a significant spatially oriented distribution pattern.

[0108] Furthermore, the first term "1" in the expression is the baseline term, corresponding to the state of non-directional segregation of aggregate under uniform casting;

[0109] Furthermore, the second term in the expression represents the total dynamic density of directional drive. With directional drag density The larger the ratio, the more dominant the driving force and the stronger the directionality; among which Density is the driving term characterizing the effect of gravity on the directional distribution of aggregates. Larger, larger particle size The larger the size, the more easily gravity-driven settlement disrupts directionality; The inertial force characterizing aggregates is the driving factor for their directional distribution. The greater the density, particle size, and pouring speed, the more easily the inertia causes the aggregates to deviate from the flow direction. The viscosity coefficient characterizes the inhibitory effect of viscous resistance. Larger, faster pouring The faster the flow, the stronger the fluid resistance, and the better it can suppress directional deviation caused by gravitational settling.

[0110] Furthermore, the third term in the expression Characterizing the matching degree between aggregate size and channel space, the closer the particle size is to the channel width, the greater the spatial restriction on particle arrangement and the more significant the orientation.

[0111] Parameter calibration was then performed, and data fitting was conducted using Python code. The final regression coefficients were A=1.57, B=0.31, and C=19.33. Model fit coefficient of determination , Figure 9 (a) is the prediction result of the model predicting the minimum roughness of the interface in this embodiment; Figure 9 (b) is the prediction result of the model predicting the maximum roughness of the interface in this embodiment; Figure 10 (a) is a comparison chart of the predicted minimum roughness of the roughness model interface and the actual roughness in this embodiment; Figure 10 (b) is a comparison diagram of the predicted roughness of the roughness model interface and the actual roughness in this embodiment.

[0112] Figure 11 , Figure 12 Sensitivity analysis of the model parameters was performed. This analysis revealed the fractal dimension of the gradation. right and The pattern of differentiated, phased regulation is evident: Follow The increase initially rises slowly to a peak, then falls slightly. exist The situation remained stable at that time. Then it decays significantly. This reveals the threshold effect of gradation complexity, low At that time, the simplicity of the gradation makes the particle packing protrusion feature more pronounced as the gradation complexity increases; when After exceeding the critical threshold (approximately 3.0), the excessive complexity of the gradation disrupts the uniformity of particle packing. The minimum roughness direction decreases sharply due to the regularization of the protrusion arrangement or the overfilling of voids. The maximum roughness direction remains at a high level due to local extreme protrusions, but it slightly decreases due to the disruption of uniformity.

[0113] In addition, the sharpness coefficient of the edges right and All showed monotonically increasing sensitivity, and The growth rate is significantly higher than This indicates that the sharpness of the edges is the core morphological factor controlling the surface roughness; the sharper the edges of the particles, the stronger the interlocking and protrusion effect between particles; among them, the direction of maximum roughness is affected by the formation of extreme protrusions. More sensitive, the minimum roughness direction is synchronously enhanced due to the presence of residual interlocking, reflecting the synergistic enhancement mechanism of particle morphology parameters on the anisotropic roughness of the seam surface, and the sharpness of the edges plays a dominant role in shaping the extreme roughness features.

[0114] Aggregate gradation optimization is performed based on the calibrated model; it is assumed that the project requires concrete channel joints. , Regarding the aforementioned continuous gradation of concrete The result is slightly below design requirements, necessitating parameter optimization. Model predictive analysis can be used to reduce the fractal dimension of the gradation. Can be improved Adjusting aggregate gradation The value was reduced from 3.03 to 2.85, while the corner sharpness coefficient was appropriately increased. The optimized prediction reached 0.75. , It meets the design requirements.

[0115] This embodiment fully presents the implementation process of the method of the present invention through experimental verification and practical application in a specific engineering scenario. From the determination of basic parameters, the actual measurement of roughness in multiple directions, model calibration to parameter optimization, all steps have clear operational standards and data support. The experimental results show that this method can accurately predict the roughness of the trench joint without excavation and scanning using conventional parameters such as aggregate gradation, effectively solving the problem of ambiguous characterization of three-dimensional joint surface roughness. The optimized aggregate gradation can stably meet the engineering seepage prevention and construction requirements, providing a scientific and feasible technical solution for the control of the roughness of the trench joint of concrete seepage prevention wall.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting the roughness of joints in concrete anti-seepage wall trenches based on aggregate gradation characteristics, characterized in that, Includes the following steps: S1. Construct a multi-dimensional aggregate gradation characterization system, and obtain core parameters through experimental measurement and theoretical derivation. The core parameters include at least gradation fractal characteristic parameters, particle morphology and orientation parameters, and physical parameters. The gradational fractal feature parameters include at least the gradational fractal dimension. Particle size distribution uniformity coefficient curvature coefficient ,in These are the particle sizes corresponding to cumulative volume fractions of 10%, 30%, and 60%, respectively. The particle morphology and orientation parameters include at least a comprehensive coefficient for angular sharpness based on a projection method. and aggregate orientation distribution coefficient ; The physical parameters include at least aggregate bulk density. ; S2. Introduce key construction parameters that affect aggregate packing morphology to construct dynamic influencing factors: in, For speed, For mud density, For the verticality deviation of the slot sidewall, This is a dimensionless correction factor, with units of 1 kg * h / m. 4 , Characterization and The effect of synergistic effect on aggregate deposition morphology; S3. Define a method for characterizing the range of multi-directional roughness: Multi-directional roughness range characterization: Two-dimensional profiles are taken along at least three non-coincident characteristic directions on the surface of the three-dimensional groove segment. The measured roughness values ​​of each profile are calculated, and the minimum value is taken. and maximum value ,by As the roughness range of the three-dimensional groove segment; S4, Establish and Coupled prediction model: Where A, B, and C are uniform regression coefficients. The fractal index is the gradation index. and The values ​​are 3.8 and 6.3 or -3.2 and -8.

4. These are the geometric constraint coefficients; This is the random error term; The width of the anti-seepage wall trench section; S5. Model Tests and Parameter Calibration: Model tests will be conducted using at least two different aggregate gradations. Measurements will be taken using a scanning device with an accuracy of at least 0.1 mm. and Measured values ​​are used to calibrate model parameters, which in turn determine the model's fit coefficients. ; S6, Aggregate gradation optimization: with and Constrained by meeting design requirements and ensuring that aggregate gradation conforms to specifications, and aiming at minimizing concrete costs and maximizing construction efficiency, the aggregate gradation parameters are adjusted to output the optimal solution.

2. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S1, the gradation fractal dimension The calculation method is as follows: in, Let i be the volume fraction of the i-th particle size range. The median particle size in the interval. For the maximum particle size, This is the minimum particle size.

3. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S1, the comprehensive coefficient of edge sharpness The calculation method is as follows: in, This represents the number of three-dimensional edges of the particle. The average height of the edge (mm). The average particle size is (mm). This represents the actual perimeter of the particle's three-dimensional projection. It is the circumference of a circle with the same area.

4. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S1, the aggregate orientation distribution coefficient The uniformity of aggregate arrangement along the longitudinal direction of the anti-seepage wall trench is characterized by the combined control of aggregate stress state and spatial constraints. Its calculation method is as follows: in, The apparent density of concrete, It is the acceleration due to gravity. The viscosity coefficient of concrete, The result of the balance between the driving force and resistance to the directional arrangement of aggregates, when When the driving force equals the resistance force, it indicates a non-directional distribution. At that time, the driving force equals 3 times the resistance, indicating a strong directional distribution; It represents the ratio of the total dynamic density of directional drive to the drag density of directional drive. The larger the value, the more dominant the dynamic drive and the stronger the directional drive. Characterizing the matching degree between aggregate size and channel space, the closer the particle size is to the channel width, the greater the spatial restriction on particle arrangement and the more significant the orientation.

5. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S4, Characterizes the synergistic interlocking effect of gradation complexity and edge sharpness; Due to the density saturation effect, when the relative density is >0.85, the tanh function approaches 1, and the interlocking effect reaches its limit.

6. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S4, The synergistic effect of uniformity and curvature characterizes the roughness distribution; the higher the uniformity and the more reasonable the curvature, the more stable the roughness distribution. Geometric constraints are characterized by larger particle size and narrower grooves, which restrict particle arrangement and suppress roughness.

7. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S4, the directional adjustment term The calculation method is as follows: in, The directional term is of the weakening type because the direction of maximum roughness needs to break through the limitation of directional arrangement; The orientation term is a compensation type, because the minimum roughness direction needs to rely on the orientation arrangement to reduce the extremely smooth area.

8. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S4, the mechanistic constraints of the model are: （1） ； (2) This indicates that the stronger the orientation, the lower the maximum roughness, because the particles are more neatly arranged along a certain direction. (3) This indicates that the stronger the orientation, the higher the minimum roughness, because the roughness in the weak direction is compensated by the orientation arrangement.

9. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 1, characterized in that, In step S5, the roughness JRC is calculated as follows: in, It is the cross-sectional length of the groove section. It refers to the elevation of each point on the cross-section. It is the distance between adjacent points. N is the number of points taken on the cross-section, and N is the number of cross-sections taken on the groove joint surface.

10. The method for predicting the roughness of concrete anti-seepage wall grooves based on aggregate gradation characteristics according to claim 9, characterized in that, In the roughness calculation method, 0.5 mm is used as the sampling distance for each profile and the distance between each profile on the surface.

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