A method for construction feed control optimization of earth-rock dam considering spatial variability of dam rockfill material gradation

Abstract

The application discloses a kind of earth-rock dam construction feed control optimization methods considering dam rockfill gradation spatial variability, comprising: using unmanned aerial vehicle to obtain the image of earth-rock dam surface, and the image is pretreated;Fractal dimension of rockfill filling warehouse surface image is obtained by "box" dimension method, and potential dense packing pore ratio and other information of in-situ gradation rockfill are predicted according to accumulation algorithm;The above steps are repeated at different positions and different elevations of earth-rock dam filling warehouse surface, and the state-dependent constitutive theory of rockfill is used to obtain the distribution of overall mechanical properties of earth-rock dam;According to the overall mechanical property distribution, the deformation law of dam body during construction is quickly predicted, and the subsequent construction feed control optimization scheme is made according to the set deformation threshold value.The application can quickly and non-destructively obtain the gradation information of rockfill and optimize the construction, reduce the economic and time cost, and improve the construction quality and stability of earth-rock dam.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of civil engineering, and specifically relates to a soil and rock dam construction feed control optimization method considering the spatial variability of dam construction rockfill gradation. BACKGROUND

[0002] As a widely used water conservancy structure, soil and rock dams are mainly used for water storage, flood control, power generation and other purposes. They are widely used in the world due to their economy, strong adaptability and simple construction.

[0003] Construction control is the key link to ensure the quality of soil and rock dams. The existing construction control technology mainly includes quality inspection of construction materials, real-time monitoring of construction process and strict management of construction technology. However, due to the large scale of soil and rock dam engineering and the complex and variable construction environment, the existing technology still faces many challenges in actual operation, such as the unevenness of construction materials, the uncertainty factors in the construction process, etc.

[0004] In the construction process of soil and rock dams, the gradation and spatial variability of rockfill have important influence on the stability and safety of soil and rock dams. Spatial variability may lead to uneven stress distribution, affecting the long-term stability and safety of the dam body. Traditional design methods often assume that the material is uniformly distributed, but this assumption is not always true in actual engineering. And the traditional rockfill gradation detection method mainly relies on field sampling and laboratory screening. However, the field sampling and laboratory screening process is complex, time-consuming and labor-intensive; traditional detection methods usually require destructive sampling and cannot achieve large-scale and continuous detection; due to the long detection period, the traditional detection and construction quality control method is difficult to provide real-time feedback on the influence of gradation changes in the construction process on the deformation and stability of the dam body.

[0005] With the rapid development of unmanned aerial vehicle technology and image analysis algorithms, it is possible to use unmanned aerial vehicles to collect soil and rock dam surface images and combine intelligent image analysis algorithms to obtain rockfill gradation information. Unmanned aerial vehicles can quickly cover the surface of soil and rock dams, collect high-resolution images, and significantly improve detection efficiency; image analysis technology does not require destructive sampling and can achieve large-scale, continuous non-destructive detection; combined with intelligent image analysis algorithms, the gradation information of rockfill can be obtained in real time, providing timely feedback for the construction process. SUMMARY

[0006] In view of the shortcomings of the prior art, the present application proposes a soil and rock dam construction feed control optimization method considering the spatial variability of dam construction rockfill gradation, which quickly and non-destructively obtains rockfill gradation information through unmanned aerial vehicle image collection, intelligent image analysis, rockfill state related constitutive theory and finite element numerical simulation, and performs construction optimization to ensure the construction quality and long-term stability of soil and rock dams.

[0007] To achieve the above object, the present application provides the following scheme:

[0008] A soil and rock dam construction feed control optimization method considering the spatial variability of dam rockfill gradation, comprising the following steps:

[0009] S1: using a UAV to take pictures of the surface of the earth and rock dam, and preprocessing the images;

[0010] S2: analyzing the preprocessed images by intelligent image analysis algorithm to obtain the gradation curve and fractal dimension of the rockfill;

[0011] S3: obtaining the potential dense packing porosity ratio and fine particle content related information of the rockfill through the gradation curve and fractal dimension of the rockfill;

[0012] S4: obtaining the state-dependent constitutive relationship of the rockfill through the dense packing porosity ratio and fine particle content related information and the triaxial test results of the calibrated graded rockfill sample, and predicting the mechanical properties of the rockfill in the image acquisition area based on the state-dependent constitutive relationship of the rockfill;

[0013] S5: obtaining the overall mechanical property distribution of the earth and rock dam by repeating S1 to S3 at different positions of the earth and rock dam;

[0014] S6: according to the distribution of the overall mechanical properties of the earth and rock dam, carrying out corresponding finite element numerical simulation to predict the deformation law of the dam body during construction, and realizing the construction feed control optimization of the earth and rock dam based on the deformation law.

[0015] Preferably, in S2, the method for analyzing the preprocessed images by intelligent image analysis algorithm to obtain the gradation curve and fractal dimension of the rockfill comprises:

[0016] Converting the collected RGB color image of the earth and rock dam surface into a gray scale image by floating point method;

[0017] Applying Otsu binarization method to the gray scale image for binarization processing to extract the boundary texture features of the rock particles;

[0018] Using "box" dimension method to analyze the binarized image to extract the image fractal dimension D i as an index representing the fine particle content of the rockfill;

[0019] Using the image fractal dimension D i to evaluate and calculate the gradation curve of the rockfill.

[0020] Preferably, the method for using "box" dimension method to analyze the binarized image to extract the image fractal dimension D i comprises:

[0021] According to the aspect ratio of the image, a rectangle "box" with a characteristic size of Δ is selected to cover the image, the covered pixels need to meet the pixel aspect ratio of the box consistent with the overall image, and the length and width of the image are greater than 1000 pixels;

[0022] The number N(Δ) of the boundaries of the particles contained in the "box" with a characteristic size of Δ is counted, and the value of Δ is changed to obtain the number N(Δ) covered by several boxes;

[0023] The relationship curve of lgN and lgΔ is drawn in the double logarithmic coordinate system, and the fractal dimension D of the filling bin image is taken as the negative number of the slope of the curve i =-k.

[0024] Preferably, the fractal dimension D i of the image is used to evaluate the method for calculating the grading curve of the rockfill material, which comprises:

[0025]

[0026] In the formula, d is the particle size of the particle, d M is the maximum particle size of the rockfill material; and D is the fractal dimension of the grading of the rockfill material.

[0027] Preferably, in S4, the method for predicting the mechanical properties of the rockfill material in the image collection area based on the state-related constitutive relation of the rockfill material comprises:

[0028]

[0029] η c =M g (ξ c Ψ+1)

[0030]

[0031] η g =M g (ξ g Ψ-1)

[0032] In the formula, p and q are the average effective stress and the generalized shear stress respectively, η is the stress ratio η=p / q; η c , η p and η g are the stress ratios of the zero expansion, the peak value and the critical state respectively; N is a material parameter related to the normal consolidation line, k f and n f are material parameters, and are the plastic strain and the plastic shear strain respectively, m g and M g are material parameters related to the plastic potential function, ξ c and ξg respectively, the state-dependent material parameters and the constitutive model parameters; H is the hardening parameter of the yield surface of the elastoplastic model; the expression of the state parameter of the model is Ψ = e - e cs where e is the current void ratio of the material, e cs is the void ratio at the critical state; p r and p' r are the reference stresses on the loading and unloading curves respectively, which are obtained according to the known void ratio e L and the corresponding normal stress p L at a certain time. and λ and κ are the parameters related to the plastic and elastic modulus of the normal consolidation line respectively.

[0033] Preferably, in S5, the method for obtaining the distribution of the overall mechanical properties of the earth-rockfill dam by repeating S1 to S3 on different positions of the earth-rockfill dam comprises:

[0034] image recognition, gradation analysis and void ratio prediction are performed on the rockfill materials at different positions of the earth-rockfill dam to obtain the mechanical property data of the rockfill materials at the positions;

[0035] the mechanical property data at the positions are integrated by Kriging interpolation to obtain the distribution of the overall mechanical properties of the earth-rockfill dam.

[0036] Preferably, S6: the method for realizing the construction feedback control optimization of the earth-rockfill dam based on the distribution of the overall mechanical properties of the earth-rockfill dam and the deformation law during the construction period of the dam body comprises:

[0037] the overall mechanical property distribution data of the earth-rockfill dam are collected and analyzed, and the area with a probability of 5% that the critical state void ratio e cs of a certain dam-filling rockfill material is greater than a set threshold value is taken as a filling warning area;

[0038] the deformation law during the construction period of the dam body is predicted according to the overall mechanical property distribution law and the finite element numerical simulation, and the ratio s / H of the settlement extreme value of the dam body to the current dam height is taken as a deformation warning index during the construction period of the dam body when the ratio is greater than a set threshold value;

[0039] construction quality feedback information is returned according to the filling warning area and the deformation warning index during the construction period of the dam body, and the dam-filling material source and the construction parameters in the construction process are dynamically adjusted to realize the construction feedback control optimization of the earth-rockfill dam.

[0040] Compared with the prior art, the method has the following beneficial effects:

[0041] Non-destructive and rapid acquisition of rockfill material gradation information: Using unmanned aerial vehicles and intelligent image analysis technology, the invention can quickly and non-destructively acquire the gradation information of rockfill material. Compared with the traditional on-site sampling and laboratory screening method, the detection efficiency is greatly improved, and the detection time is reduced. Moreover, the method of the invention realizes non-destructive detection of rockfill material gradation through image analysis technology, avoiding the influence of destructive sampling on the structure of the earth and rockfill dam in the traditional method, and is suitable for large-scale and continuous detection tasks.

[0042] Real-time feedback and optimization: Combined with real-time monitoring data and intelligent analysis algorithm, the invention can timely feedback the gradation information of rockfill material, and dynamically adjust the construction scheme according to the detection results to ensure the construction quality. Compared with the traditional method, the invention can realize real-time quality control and optimization during construction, significantly improving the construction efficiency and quality.

[0043] Accurate prediction of deformation law during construction period: Through finite element numerical simulation and overall mechanical property distribution analysis, the invention can accurately predict the deformation law of the dam body during the construction period, timely identify potential mechanical weak points and risk areas, and develop a scientific construction control optimization scheme to ensure the safety and stability of the earth and rockfill dam.

[0044] Dynamic adjustment of construction parameters: According to the filling warning area and deformation warning index, the invention can provide construction quality feedback information to the design and construction team, dynamically adjust the dam material source and construction parameters during construction, and ensure the construction quality and deformation stability of the earth and rockfill dam, further improving the safety and reliability of construction. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the present application, the following briefly introduces the drawings needed to be used in the embodiments. Obviously, the drawings described in the following only some embodiments of the present application, and for those skilled in the art, without creative labor, can also obtain other drawings according to these drawings.

[0046] Figure 1 For the construction control optimization method of the present application embodiment emphasizing the spatial variability of the gradation, the total flow chart is shown in the figure.

[0047] Figure 2 For the prediction flow chart of the related mechanical behavior of the earth and rockfill filling material gradation of the present application embodiment, the figure is shown.

[0048] Figure 3 For the apparent image of the rockfill material rolling warehouse surface obtained by the unmanned aerial vehicle of the present application embodiment, the figure is shown.

[0049] Figure 4 For the apparent image of the rockfill material after the gray-scale image is binarized and processed by the "box" dimension method of the present application embodiment, the figure is shown.

[0050] Figure 5 Fig. 1 is a schematic diagram of predicting the critical state void ratio according to the linear relationship between the minimum void ratio and the critical state void ratio according to an embodiment of the present application;

[0051] Figure 6 Fig. 4 is a schematic diagram of carrying out corresponding finite element numerical simulation according to the distribution of the overall mechanical properties of the dam according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0054] Embodiment one

[0055] The embodiment provides a soil and rock dam construction feed control optimization method considering spatial variability of dam construction rockfill gradation, and the overall process is as shown in the figure, including the following steps: Figure 1

[0056] Step 1: taking an image of the surface of the soil and rock dam by using a drone, and preprocessing the image;

[0057] Step 2: analyzing the preprocessed image by using an intelligent image analysis algorithm to obtain a gradation curve and a fractal dimension of the rockfill;

[0058] Step 3: obtaining potential dense packing void ratio and fine particle content related information of the rockfill by using the gradation curve and the fractal dimension of the rockfill;

[0059] Step 4: obtaining a rockfill state-dependent constitutive relationship by using the dense packing void ratio and fine particle content related information and triaxial test results of a calibrated graded rockfill sample, and predicting mechanical properties of the rockfill in an image acquisition area based on the rockfill state-dependent constitutive relationship;

[0060] Step 5: obtaining an overall mechanical property distribution of the soil and rock dam by repeating S1 to S3 at different positions of the soil and rock dam;

[0061] Step 6: carrying out corresponding finite element numerical simulation according to the distribution of the overall mechanical properties of the soil and rock dam, predicting a deformation law of the dam body during construction, and realizing soil and rock dam construction feed control optimization based on the deformation law. ​

[0062] Specifically, Step 1: UAV image capturing dam surface images

[0063] Select a UAV device with high-resolution camera and stable flight performance, such as DJI Phantom4RTK or similar models.

[0064] Ensure that the UAV battery is sufficient, the camera is clean, and the storage card capacity is sufficient.

[0065] Set flight parameters, including flight height, speed, angle, and shooting interval, to obtain clear and high-resolution images. The flight height is usually set to 5 meters, and the shooting angle is perpendicular to the dam surface.

[0066] According to the specific shape and area of the earth-rock dam, plan a reasonable flight path to ensure the comprehensiveness and coverage of image acquisition. The flight path should cover the entire earth-rock dam filling warehouse surface, especially the main and secondary rockfill areas. Use the "sliding window" method for multiple shooting to ensure that the image overlap rate is more than 60% for subsequent image stitching.

[0067] According to the predetermined path and parameters, fly the UAV and collect the RGB color images of the earth-rock dam surface. During the shooting process, ensure that the UAV flies stably to avoid image blur. Each warehouse surface image is stitched into a warehouse surface large image using the "sliding window" method.

[0068] Step 2: Obtain information such as rockfill gradation and fractal dimension through intelligent image analysis algorithm.

[0069] Convert the collected RGB color images (such as Figure 3 ) to grayscale images using the floating-point method to simplify the subsequent processing process.

[0070] Image preprocessing includes image denoising, contrast enhancement, and other operations to ensure image quality.

[0071] Apply Otsu binarization method or equivalent grayscale method to the grayscale image for binarization processing, as shown in Figure 4 , to extract the boundary texture features of the rock particles. The Otsu method automatically determines the best threshold for binarization by calculating the inter-class variance of the image, ensuring clear particle boundaries.

[0072] Use the "box" dimension method to analyze the binarized image and extract the image fractal dimension D i as an indicator of fine content of rockfill. The specific steps include: according to the aspect ratio of the image, select a rectangular "box" with feature size Δ to cover the image, as shown in Figure 4As shown, the covered pixels need to meet the pixel length-width ratio of the box consistent with the overall image, and the length and width of the selected image are greater than 1000 pixels. The number N(Δ) of particle boundaries contained in the "box" with a statistical feature size of Δ is counted, the value of Δ is changed, and the number N(Δ) covered by a number of boxes is obtained. In the double logarithmic coordinate system, the relationship curve of lgN and lgΔ is drawn, and the fractal dimension D of the filling warehouse surface image is taken as the negative number of the slope of the curve i = -k.

[0073] The fractal dimension D i of the rockfill is calculated, and the following formula is used to calculate the grading curve of the rockfill:

[0074]

[0075] In the formula, d is the particle size of the particle, d M is the maximum particle size of the rockfill; D is the fractal dimension of the rockfill grading. Among them, d M selects the maximum value of the distance between the two adjacent black pixels in the binary image; the value of D is linearly related to the fractal dimension D i of the image, and the linear parameters are determined by a number of particle analysis test results and image analysis results of the rockfill.

[0076] Step 3: Obtain the potential dense packing porosity of the rockfill through image information

[0077] The grading curve of the rockfill is determined by the above method, the three-dimensional particle accumulation is mapped to a one-dimensional line segment by using the dimension reduction mapping algorithm, and then the minimum porosity is obtained, and the potential dense packing density of the rockfill in the image area is obtained according to the particle specific gravity of the rockfill:

[0078]

[0079] In the formula, G s is the particle specific gravity of the rockfill, and e min is the minimum porosity.

[0080] Step 4: Obtain the mechanical properties of the rockfill through the porosity and other information

[0081] The steps of predicting the mechanical properties of the rockfill are as Figure 2 shown, a group of typical grading rockfills are carried out, the maximum dry density and triaxial test of the rockfill is carried out, and the parameters of the state-related elastoplastic constitutive model are determined according to the test results. The constitutive model adopts the following form:

[0082]

[0083] η c = M g (ξ c Ψ+1)

[0084]

[0085] η g =M g (ξ g Ψ-1)

[0086] In the formula, p and q are the mean effective stress and generalized shear stress, respectively, and η is the stress ratio η = p / q; η c η p and η g These are the stress ratios at zero expansion, peak value, and critical state, respectively; N is a material parameter related to the normal consolidation line, and k... f and n f These are the material parameters. and These are plastic volume strain and plastic shear strain, respectively, m g and M g It is a material parameter related to the plastic potential function, ξ c and ξ g These are the state-dependent material parameters and constitutive model parameters, respectively; H is the yield surface hardening parameter of the elastoplastic model; p r and p' r These are the reference stresses on the loading and unloading curves, respectively, based on the known porosity e at a certain moment. L and the corresponding normal stress p L get and λ and κ are parameters related to the plasticity and elastic modulus of a normal consolidation line, respectively.

[0087] Calculate the state parameters Ψ of the material:

[0088] Ψ=ee cs

[0089] Where e is the current porosity of the material, e cs It is the void ratio under the critical state (sufficiently large shear strain).

[0090] Determine the location of the critical state line:

[0091] e cs =Γ(p+p cs ) -λ

[0092]

[0093] In the formula, Γ represents the material parameter, and p cs Based on a known critical state line, the porosity e cs0 and its corresponding average stress p cs0 get

[0094] Wherein: critical void ratio e cs The relationship with minimum porosity is as follows: Figure 5 As shown, therefore, the minimum porosity can be obtained using step 3, and the critical porosity can be predicted based on the linear relationship between the minimum porosity and the critical porosity.

[0095] For arbitrarily graded rockfill without prior triaxial testing, the fractal dimension of the image is obtained using the above method, and the location of its maximum dry density and critical state line is predicted. Without changing the model parameters, by changing e... cs The new mechanical response of the riprap was obtained at the location.

[0096] Step 5: Obtain the overall mechanical property distribution of the dam by repeating the above steps at different locations on the dam.

[0097] Using the above method, image recognition, gradation analysis and void ratio prediction are performed on the rockfill material at different locations of the earth-rock dam to obtain the mechanical properties of the rockfill material at each location.

[0098] The earth-rock dam during construction was modeled using the finite element method, and the layered filling process was simulated. The constitutive model and parameters of the dam's rockfill were obtained using the above method.

[0099] By integrating the mechanical property data at various locations using Kriging interpolation, the overall mechanical property distribution of the earth-rock dam is obtained.

[0100] Step 6: Conduct corresponding finite element numerical simulations to predict the deformation patterns of the dam body during construction.

[0101] Based on the geometric characteristics and construction technology of the earth-rock dam, a finite element model is established. The model should include the dam body, foundation, and the soil layers surrounding the dam body. Figure 6 As shown, the dam body is divided into sections using an appropriate mesh generation method, and the mechanical properties of the rockfill material at each location are input into the finite element model. The main material parameters are void ratio, but also include elastic modulus, Poisson's ratio, and density. The Kriging interpolation method is used to integrate the mechanical property data at each location to ensure the accuracy of the model parameters. The layered filling process of the earth-rock dam is simulated. After each layer is filled, compaction and consolidation analyses are performed. The stress and deformation distribution during construction are calculated, and the settlement and lateral displacement of the dam body are predicted.

[0102] Based on the predicted deformation patterns during the dam construction period, a construction feedforward optimization scheme was developed.

[0103] Based on the finite element analysis results, the deformation patterns of the dam body during construction are analyzed. Deformation concentration areas and potential risk points are identified. The critical state porosity e of a certain dam-building rockfill material is determined. csThe area with a probability greater than 5% of a set threshold value is taken as a filling early warning area; the ratio s / H of the settlement extreme value of the dam body to the current dam height is taken as a dam body construction period deformation early warning index. According to the deformation early warning index and the filling early warning area, the area needing key monitoring and optimization is determined, and then according to the mechanical property distribution data and the deformation law, a specific construction optimization scheme is designed to dynamically adjust the dam construction parameters in the construction process, such as adjusting the filling sequence and compaction method of the rockfill material to ensure the uniformity and compactness of the rockfill material, ensure the construction quality and the deformation stability of the earth-rock dam. Finally, numerical simulation and optimization algorithm can be used to verify the feasibility and effectiveness of the optimization scheme.

[0104] Specifically, the dam material parameter optimization scheme includes:

[0105] 1) For the filling early warning area, the fractal dimension of the rockfill material gradation curve in the stockyard is improved, and for the gradation with a fractal dimension greater than 2.6, the content of fine materials in the rockfill material is reduced in the subsequent construction scheme; for the gradation with a fractal dimension less than 2.3, the content of fine materials in the rockfill material is increased in the subsequent construction scheme.

[0106] 2) For the deformation early warning index exceeding the threshold value, the number of compaction passes of the rockfill material layer is increased in the subsequent construction scheme.

[0107] The application provides a soil-rock dam construction feedback control optimization method considering spatial variability of dam rockfill material gradation, which realizes real-time feedback control of soil-rock dam deformation law during construction of the soil-rock dam by combining unmanned aerial vehicle image acquisition, intelligent image analysis, pore ratio analysis and mechanical property prediction, and combining finite element numerical simulation, and provides a scientific basis for construction quality and deformation control of the soil-rock dam.

[0108] The above-described embodiments are only descriptions of the preferred modes of the application, and do not limit the scope of the application, and various modifications and improvements to the technical solutions of the application made by those skilled in the art without departing from the design spirit of the application shall fall within the protection scope of the claims of the application.

Claims

1. A method for construction feed control optimization of earth-rockfill dam considering spatial variability of rockfill material gradation, characterized in that, It comprises the following steps: S1: taking an image of the earth-rock dam surface with a UAV and pre-processing the image; S2: analyzing the pre-processed image through an intelligent image analysis algorithm to obtain the gradation curve and fractal dimension of the rockfill material; S3: obtaining the potential dense packing porosity ratio and fine particle content related information of the rockfill material through the gradation curve and fractal dimension of the rockfill material; S4: obtaining the state-dependent constitutive relationship of the rockfill material through the dense packing porosity ratio and fine particle content related information and the triaxial test results of the calibrated graded rockfill sample, and predicting the mechanical properties of the rockfill material in the image collection area based on the state-dependent constitutive relationship of the rockfill material; S5: obtaining the overall mechanical property distribution of the earth-rock dam by repeating S1 to S3 at different positions of the earth-rock dam; S6: carrying out corresponding finite element numerical simulation according to the distribution of the overall mechanical properties of the earth-rock dam, predicting the deformation law during the dam construction period, and realizing the construction feedback control optimization of the earth-rock dam based on the deformation law; In S4, the method for predicting the mechanical properties of the rockfill material in the image collection area based on the state-dependent constitutive relationship of the rockfill material comprises: ； ； ； ； ； where p and q are the average effective stress and the generalized shear stress, respectively, h is the stress ratio h = p / q ; h c , and h g are the stress ratios for the zero dilatancy, peak and critical states, respectively; N is the material parameter related to the normal consolidation line, and are the material parameters, and are the plastic volumetric strain and plastic shear strain, respectively, and are the material parameters related to the plastic potential function, and are the state-dependent material parameters and the constitutive model parameters, respectively; H is the hardening parameter of the yield surface of the elastoplastic model; the expression of the state parameters of the model is where, e is the current void ratio of the material, is the void ratio at the critical state; and are the reference stresses on the loading and unloading curves, respectively, according to the known void ratio e L and the corresponding normal stress p L and are obtained; l and are the parameters related to the plastic and elastic moduli of the normal consolidation line, respectively.​ 2. The method for construction feedback control optimization of earth-rockfill dam considering spatial variability of dam rockfill material gradation according to claim 1, characterized in that, In S2, the method for obtaining the gradation curve and fractal dimension of the rockfill material by analyzing the pre-processed image through an intelligent image analysis algorithm comprises: Converting the collected RGB color image of the earth-rock dam surface into a grayscale image through a floating-point method; Applying an Otsu binarization method to perform binarization processing on the grayscale image and extracting the boundary texture features of the rock particles; The binary image is analyzed by using the box dimension method to extract the fractal dimension of the image as an index representing the fine content of the rockfill Utilizing image fractal dimension The grading curve of the rockfill material is calculated and evaluated.

3. The method for construction feedback control optimization of earth-rockfill dam considering spatial variability of dam rockfill material gradation according to claim 2, characterized in that, The binarized image is analyzed using the "box" dimension method to extract the image fractal dimension. The methods include: According to the aspect ratio of the image, a rectangular "box" with a feature size of is selected to cover the image. The covered pixels need to satisfy that the pixel aspect ratio of the box is consistent with the overall image, and the length and width of the image are greater than 1000 pixels. The statistical feature size is the number of boundaries of the particles contained within a "box" of , changing the value of the number of boxes covered by ; The relationship curve is plotted in a double logarithmic coordinate system and The fractal dimension of the fill storage surface image is the negative of the slope of the curve .

4. The method for construction feedback control optimization of earth-rockfill dam considering spatial variability of dam rockfill material gradation according to claim 2, characterized in that, Using image fractal dimension The method for evaluating the grading curve of the rockfill material calculated by the computer includes: ； wherein d is the particle size of the granules, is the maximum particle size of the rockfill material; D is the fractal dimension of the rockfill material grading.

5. The method for construction feed control optimization of earth-rockfill dam considering spatial variability of dam rockfill material gradation according to claim 1, characterized in that, In S5, the method for obtaining the overall mechanical property distribution of the earth-rock dam by repeating S1 to S3 at different positions of the earth-rock dam comprises: Performing image recognition, gradation analysis, and porosity ratio prediction on the rockfill material at different positions of the earth-rock dam to obtain the mechanical property data of the rockfill material at each position; Integrating the mechanical property data at each position through Kriging interpolation to obtain the overall mechanical property distribution of the earth-rock dam.

6. The method for construction feed control optimization of earth-rockfill dam considering spatial variability of dam rockfill material gradation according to claim 1, In S6, the method for carrying out corresponding finite element numerical simulation according to the distribution of the overall mechanical properties of the earth-rock dam to predict the deformation law during the dam construction period, and realizing the construction feedback control optimization of the earth-rock dam based on the deformation law comprises: The overall mechanical property distribution data of earth-rock dams are collected and analyzed, and the critical state void ratio of a dam filling material is greater than a set threshold value The region with a probability of 5% greater than the set threshold value is taken as a filling early warning region. According to the distribution law of overall mechanical characteristics and finite element numerical simulation, the deformation law of dam body during construction period is predicted, and the ratio of settlement extreme value of dam body to current dam height is counted greater than a set threshold value as a dam body construction period deformation early warning index According to the filling early warning area and the deformation early warning index during the dam construction period, returning the construction quality feedback information, dynamically adjusting the dam construction material source and construction parameters during the construction process, and realizing the construction feedback control optimization of the earth-rock dam.

Images