A method for detecting integrity and strength of grouting reinforced anti-seepage wall of earth-rock dam

Abstract

The present invention discloses a method for detecting the integrity and strength of an earth-rock dam grouting reinforced anti-seepage wall, and the steps are as follows: collecting relevant data of the earth-rock dam; establishing the relationship between the resistivity and strength of the anti-seepage wall; using the grouting hole as the detection hole, making a PVC electrode tube and lowering it into the detection hole; measuring the potential value between the detection holes, and filtering and denoising the field detection data using the variational mode-wavelet algorithm; establishing a geoelectric finite element model to calculate the potential value; establishing an objective function of the measured potential value and the calculated potential value, and using the egret swarm intelligent optimization bionic algorithm to invert the resistivity; drawing an inversion resistivity graph, and analyzing the integrity and strength of the anti-seepage wall. The present invention enhances the signal-to-noise ratio of the field detection potential value; improves the inversion calculation efficiency, enhances the optimization capability, and improves the inversion accuracy. A new method is provided for quickly, conveniently, and accurately evaluating the integrity and strength of the anti-seepage wall of an earth-rock dam, which can be promoted and applied in the detection of anti-seepage walls.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent detection of water conservancy projects, and relates to a method for detecting the integrity and strength of a grouting-reinforced anti-seepage wall of an earth-rock dam. Background Art

[0002] Leakage is the main cause of embankment collapse, and most earth-rock dam collapses are caused by leakage. Especially under the influence of long-term operation and natural environment, earth-rock dams will have leakage, cracks, collapse and other problems, which seriously threaten the safety and stability of water conservancy projects. Therefore, in order to ensure the safe operation of earth-rock dams, the country has begun to carry out quality upgrades and renovations on earth-rock dam projects. The most direct and effective method is to reinforce earth-rock dams by grouting. After grouting, a cement soil anti-seepage wall is formed inside the earth-rock dam, which can effectively prevent leakage and damage to the earth-rock dam. However, the construction quality of the anti-seepage wall directly affects the anti-seepage performance of the earth-rock dam.

[0003] The quality inspection of earth-rock dam grouting anti-seepage wall is an important means to ensure the construction quality. At present, the traditional drilling coring can only represent the reinforcement quality around the inspection hole. It is a destructive sampling test and cannot evaluate the integrity of the anti-seepage wall. In addition, it is difficult to obtain a complete core sample for the cement soil anti-seepage wall in the actual project, which seriously affects the quality inspection of the anti-seepage wall. Therefore, it is urgent to develop a method for the integrity and strength inspection of the earth-rock dam grouting reinforcement anti-seepage wall to improve the comprehensiveness, accuracy, timeliness and operability of on-site inspection. Summary of the invention

[0004] The purpose of the present invention is to provide a low-cost, high-efficiency, simple to operate, accurate and reliable detection result of the earth-rock dam grouting reinforcement anti-seepage wall integrity and strength detection method, the method through the earth-rock dam anti-seepage wall on-site resistivity detection inversion results, and combined with the established anti-seepage wall with different ratios of cement stone body resistivity and compressive strength of the relationship between the establishment, so as to achieve the anti-seepage wall integrity identification and strength detection.

[0005] To achieve the above object, the present invention is implemented by the following technical scheme: a method for detecting the integrity and strength of an earth-rock dam grouting reinforcement anti-seepage wall, comprising the following steps:

[0006] Step S1, according to the designed mix ratio of grouting reinforcement for earth-rock dam, the relationship between hardened slurry resistivity and compressive strength is tested by indoor test;

[0007] Step S2, drilling and grouting on site of the earth-rock dam to complete the grouting reinforcement of the anti-seepage wall;

[0008] Step S3, selecting two grouting holes as test holes for the integrity and strength of the anti-seepage wall, and performing slurry wall protection on the test holes;

[0009] Step S4, lowering the PVC pipe with the copper electrode into the detection hole, and backfilling the detection hole with grouting;

[0010] Step S5, conduct on-site inspection on the inspection hole after grouting backfilling is completed, connect the electrode wire to the resistance tester, and use the free running electrode method to test the measurement potential set U of the inspection area. m ;

[0011] Step S6, using the variational mode VMD-wavelet filtering algorithm to measure the potential set U detected on site m Perform filtering to improve the measurement potential set U m signal-to-noise ratio;

[0012] Step S7: compile the geoelectric finite element model of the corresponding detection area and calculate the calculated potential set U of each node c ;

[0013] Step S8, using the Egret Eagle Swarm intelligent optimization bionic algorithm to perform resistivity inversion calculation to obtain the resistivity of the detection area;

[0014] Step S9, resistivity drawing inversion image, analyzing the integrity of the earth-rock dam anti-seepage wall;

[0015] Step S10, determining the strength of the earth-rock dam anti-seepage wall according to the relationship between the resistivity and the compressive strength in step S1.

[0016] Further, in step S1, the relationship between the slurry hardening resistivity and the compressive strength is calculated as follows:

[0017] (1);

[0018] Where: H c It indicates the compressive strength of hardened slurry; It represents the resistivity of slurry hardening; and They are the solution coefficients, that is, the correlation coefficients that need to be determined by fitting the experimental data.

[0019] Furthermore, the free running electrode method in step S5 is optimized on the Wenner quadrupole ABMN, where A and B are power supply electrodes and M and N are measurement electrodes, specifically:

[0020] Step S51, the grouting hole is used as a detection hole, the two detection holes are respectively a first hole and a second hole, and the detection range is the area between the two detection holes;

[0021] Step S52, the distance between the electrodes of the PVC tube of the copper electrode is 20 cm, each electrode is connected to a wire and numbered, the first hole is numbered as electrode 1#, electrode 2#, ..., electrode L#, and the second hole is numbered as electrode (L+1)#, electrode (L+2)#, ..., electrode 2L#;

[0022] Step S53, the wires of the electrodes of the first hole are integrated into a first cable, and the wires of the electrodes of the second hole are integrated into a second cable;

[0023] Step S54, the measuring electrode N is fixed between the two detection holes, and the ABM runs freely;

[0024] Step S55, power supply electrodes AB are combined, power supply electrode A is connected to electrode 1#, power supply electrode B is connected to 2L-1 electrodes after power supply electrode A, power supply electrode A is connected to 2#, 3#, …, (2L-1)# in sequence, power supply electrode B is connected to 2L-2, 2L-3, …, 1 electrodes in sequence, and there are a total of 2L-1+2L-2+…+1=L*(2L-1) running electrode combinations; Step S56, in the power supply electrode AB running electrode mode, the measuring electrode M is connected to 2L-2 electrodes except the power supply electrode AB, and L*(2L-1)*(2L-2) potential values ​​are obtained each time the on-site measurement is performed.

[0025] Furthermore, the variational mode VMD-wavelet filtering algorithm in step S6 is specifically as follows: step S61, using variational mode VMD to measure the potential set U in step S5 m The potential value is decomposed into n signal components IMFs;

[0026] Step S62, analyzing the noise of the signal component IMFs according to the characteristics of the signal component IMFs;

[0027] Step S63, using a wavelet decomposition algorithm to filter and reduce noise on the noisy signal component IMFs;

[0028] Step S64, reconstructing the filtered and denoised signal components IMFs and the noise-free signal components IMFs.

[0029] Furthermore, in step S7, a geoelectric finite element model of the corresponding detection area is compiled to calculate the calculated potential set U of each node. c , specifically:

[0030] Step S71, using scientific computing language MATLAB software to compile a geoelectric finite element model, and establishing a geoelectric finite element model corresponding to the detection area;

[0031] Step S72, selecting four-node rectangular units for meshing and assigning mesh parameter attributes;

[0032] Step S73, when solving the point source finite element boundary problem, convert the point source finite element boundary problem into a plane partial differential equation for solution;

[0033] Step S74: The forward calculation method is consistent with the field detection method, and the final calculation node potential set is U c .

[0034] Furthermore, in step S8, the egret swarm intelligent optimization bionic algorithm is used to perform resistivity inversion calculation to obtain the resistivity of the detection area; specifically, step S81, the egret swarm population is initialized, as shown in formula (2);

[0035] (2);

[0036] In the formula, The candidate resistivity values ​​representing the number of i egret eagle populations and j candidate parameter dimensions; Represents a random number between 0 and 1; , Respectively represent the maximum and minimum values ​​of candidate resistivity values; i=1,2, …,Z, j=1,2, …,W; Z is the number of egret populations, and W is the dimension of candidate parameters;

[0037] Based on the geoelectric finite element model, the computational potential set U of each node is calculated. c ;

[0038] Step S82, using the geoelectric finite element model to calculate the potential set U of each node c The potential value and the measured potential set U m The potential value of is used to establish the objective function of the inversion analysis, as shown in formula (3) and formula (4);

[0039] Object[F( )]= (3);

[0040] (4);

[0041] In the formula, Object[F( )] is the objective function of resistivity inversion, , A candidate resistivity value representing a population of egrets and a candidate parameter dimension, The candidate resistivity values ​​representing two egret populations and one candidate parameter dimension, represents the candidate resistivity value of i egret populations and 1 candidate parameter dimension, is the potential value obtained by forward calculation of the i-th egret population, The potential value measured by the free-running pole method for the i-th egret population is: and Respectively represent the lower and upper limits of the resistivity value;

[0042] Step S83, calculating the fitness value corresponding to each egret population through the objective function in formula (3), as shown in formula (5);

[0043] F( )= (5);

[0044] In the formula, F( ) is the fitness value calculated for each egret population;

[0045] Step S84, the egret begins to look for prey, and the egret finds that the position of the prey is constantly updated, as shown in formula (6) and formula (7);

[0046] While t<1 / 3T, (6);

[0047] (7);

[0048] In the formula, While represents judgment; t represents the number of iterations; T represents the maximum number of iterations; is the latest updated value of the i-th egret population and the j-th candidate parameter dimension; It indicates the first stage of searching for prey; represents the value of the i-th egret population and the j-th candidate parameter dimension variable; and They represent the first and second candidate solutions randomly generated in the initial stage, Indicates the optimal position of the current egret; is the latest updated status position of the i-th egret population; is the latest objective function value of the i-th egret population, is the function value of the number of the i-th egret population; Indicates a judgment statement;

[0049] Step S85, the egret eagle finds the prey and consumes the prey's physical strength and endurance. The random motion model of the egret eagle is shown in formula (8) and formula (9);

[0050] While (8);

[0051] (9);

[0052] In the formula, represents the optimal solution of the current iteration, and e represents the exponential function;

[0053] Step S86, when the prey is exhausted, the egret eagle quickly attacks and captures the prey, and the egret eagle's position update mathematical model is shown in formula (10) and formula (11);

[0054] While (10);

[0055] (11);

[0056] In the formula, represents the weighting of Levy flight;

[0057] Where RL=0.5×L(D), L(D) is calculated as shown in formula (12);

[0058] (12);

[0059] Where L(D) represents the Levy flight function, and are coefficients, and the coefficient values ​​are 0.01 and 1.5 respectively. and is a random number in [0,1], As shown in formula (13);

[0060] (13);

[0061] In the formula, σ is the standard deviation, represents the gamma function; is a coefficient with a value of 1.5;

[0062] Step S87: The egret eagle mainly avoids hunting by its natural enemies by camouflage and escape. In order to improve the search capability of the intelligent optimization bionic algorithm of the egret eagle, a dynamic disturbance factor is introduced. , as shown in formula (14) and formula (15);

[0063] (14);

[0064] (15);

[0065] In the formula, P represents the state of the egret eagle's escape strategy; represents the random candidate solution at the current iteration; C1 and C2 represent camouflage and escape respectively, r i=0.5, R2 represents a randomly generated number in a normal distribution, K represents a random 1 or 2, K=round(1+rand(1,1)), round means that the function rounds the number to a given number of digits; rand(1,1) represents a random number generated randomly between (0,1).

[0066] The present invention has the following beneficial effects: the present invention is based on the advanced egret swarm intelligent optimization algorithm, and a finite element geoelectric model is compiled through the scientific computing language MATLAB software to calculate the potential value of each node. On this basis, the present invention constructs the objective function of resistivity inversion, and through continuous iterative optimization, finite element numerical calculation is performed to minimize the objective function value, thereby obtaining the optimal resistivity value. The present invention does not require re-drilling, but uses the grouting hole as the detection hole. The field measurement adopts the free running pole method, which can obtain more measurement data than the traditional resistivity measurement, and provide rich measurement data for inversion imaging, which can greatly improve the accuracy of inversion analysis, thereby realizing the detection of the integrity and strength of the anti-seepage wall of the earth-rock dam. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of on-site detection of the present invention.

[0068] Figure 2 It is a cross-sectional view of the anti-seepage wall detection of the present invention.

[0069] Figure 3 It is a relationship diagram between resistivity and strength of the cement soil anti-seepage wall of the present invention.

[0070] In the figure, 1 is a long earth-rock dam section, 2 is an anti-seepage wall, 3 is a dam top, 4 is a first PVC pipe, 5 is a second PVC pipe, 6 is an electrode, 7 is a high-density resistivity instrument, 8 is a first cable, 9 is a second cable, 10 is a first cable interface, and 11 is a second cable interface. DETAILED DESCRIPTION

[0071] The following is a detailed and complete description of the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, and the description is for explanation rather than limitation of the present invention. It is obvious that other embodiments obtained by relevant technicians in the technical field without innovative results all belong to the protection scope of the present invention.

[0072] The present invention is achieved through the following technical solutions: A method for detecting the integrity and strength of a grouting reinforced anti-seepage wall of an earth-rock dam, comprising the following steps:

[0073] Step S1, according to the designed mix ratio of grouting reinforcement for earth-rock dam, the relationship between hardened slurry resistivity and compressive strength is tested by indoor test;

[0074] Step S2, drilling and grouting on site of the earth-rock dam to complete the grouting reinforcement of the anti-seepage wall;

[0075] Step S3, selecting two grouting holes as detection holes for the integrity and strength of the anti-seepage wall, and performing mud wall protection on the detection holes to prevent the collapse of the detection holes from affecting the lowering of the PVC pipe for measuring the copper electrode;

[0076] Step S4, lowering the PVC pipe with the copper electrode into the detection hole, and backfilling the detection hole with grouting;

[0077] Step S5, conduct on-site inspection on the inspection hole after grouting backfilling is completed, connect the electrode wire to the resistance tester, and use the free running electrode method to test the measurement potential set U of the inspection area. m ;

[0078] Step S6, using the variational mode VMD-wavelet filtering algorithm to measure the potential set U detected on site m Perform filtering to improve the measurement potential set U m signal-to-noise ratio;

[0079] Step S7: compile the geoelectric finite element model of the corresponding detection area and calculate the calculated potential set U of each node c ;

[0080] Step S8, using the Egret Eagle Swarm intelligent optimization bionic algorithm to perform resistivity inversion calculation to obtain the resistivity of the detection area;

[0081] Step S9, resistivity drawing inversion image, analyzing the integrity of the earth-rock dam anti-seepage wall;

[0082] Step S10, determining the strength of the earth-rock dam anti-seepage wall according to the relationship between the resistivity and the compressive strength in step S1.

[0083] Further, in step S1, the relationship between the slurry hardening resistivity and the compressive strength is calculated as follows:

[0084] (1);

[0085] Where: H c It indicates the compressive strength of hardened slurry; It represents the resistivity of slurry hardening; and They are the solution coefficients, that is, the correlation coefficients that need to be determined by fitting the experimental data.

[0086] Furthermore, the free running electrode method in step S5 is optimized on the Wenner quadrupole ABMN, where A and B are power supply electrodes and M and N are measurement electrodes, specifically:

[0087] Step S51, the grouting hole is used as a detection hole, the two detection holes are respectively a first hole and a second hole, and the detection range is the area between the two detection holes;

[0088] Step S52, the distance between the electrodes of the PVC tube of the copper electrode is 20 cm, each electrode is connected to a wire and numbered, the first hole is numbered as electrode 1#, electrode 2#, ..., electrode L#, and the second hole is numbered as electrode (L+1)#, electrode (L+2)#, ..., electrode 2L#;

[0089] Step S53, the wires of the electrodes of the first hole are integrated into a first cable, and the wires of the electrodes of the second hole are integrated into a second cable;

[0090] Step S54, the measuring electrode N is fixed between the two detection holes, and the ABM runs freely;

[0091] Step S55, the power supply electrodes AB are combined, the power supply electrode A is connected to electrode 1#, the power supply electrode B is connected to the 2L-1 electrodes after the power supply electrode A, the power supply electrode A is connected to 2#, 3#, ..., (2L-1)# in sequence, the power supply electrode B is connected to 2L-2, 2L-3, ..., 1 electrode in sequence, and there are a total of 2L-1+2L-2+...+1=L*(2L-1) running electrode combinations; Step S56, in the power supply electrode AB running electrode mode, the measuring electrode M is connected to 2L-2 electrodes except the power supply electrode AB, and L*(2L-1)*(2L-2) potential values ​​are obtained each time the on-site measurement is performed.

[0092] Furthermore, the variational mode VMD-wavelet filtering algorithm in step S6 is specifically as follows: step S61, using variational mode VMD to measure the potential set U in step S5 m The potential value is decomposed into n signal components IMFs;

[0093] Step S62, analyzing the noise of the signal component IMFs according to the characteristics of the signal component IMFs;

[0094] Step S63, using a wavelet decomposition algorithm to filter and reduce noise on the noisy signal component IMFs;

[0095] Step S64, reconstructing the filtered and denoised signal components IMFs and the noise-free signal components IMFs.

[0096] Furthermore, in step S7, a geoelectric finite element model of the corresponding detection area is compiled to calculate the calculated potential set U of each node. c , specifically:

[0097] Step S71, using scientific computing language MATLAB software to compile a geoelectric finite element model, and establishing a geoelectric finite element model corresponding to the detection area;

[0098] Step S72, selecting four-node rectangular units for meshing and assigning mesh parameter attributes;

[0099] Step S73, when solving the point source finite element boundary problem, convert the point source finite element boundary problem into a plane partial differential equation for solution;

[0100] Step S74: The forward calculation method is consistent with the field detection method, and the final calculation node potential set is U c .

[0101] Furthermore, in step S8, the egret swarm intelligent optimization bionic algorithm is used to perform resistivity inversion calculation to obtain the resistivity of the detection area; specifically, step S81, the egret swarm population is initialized, as shown in formula (2);

[0102] (2);

[0103] In the formula, The candidate resistivity values ​​representing the number of i egret eagle populations and j candidate parameter dimensions; Represents a random number between 0 and 1; , Respectively represent the maximum and minimum values ​​of candidate resistivity values; i=1,2, …,Z, j=1,2, …,W; Z is the number of egret populations, and W is the dimension of candidate parameters;

[0104] Based on the geoelectric finite element model, the computational potential set U of each node is calculated. c ;

[0105] Step S82, using the geoelectric finite element model to calculate the potential set U of each node c The potential value and the measured potential set U m The potential value of is used to establish the objective function of the inversion analysis, as shown in formula (3) and formula (4);

[0106] Object[F( )]= (3);

[0107] (4);

[0108] In the formula, Object[F( )] is the objective function of resistivity inversion, , A candidate resistivity value representing a population of egrets and a candidate parameter dimension, The candidate resistivity values ​​representing two egret populations and one candidate parameter dimension, represents the candidate resistivity value of i egret populations and 1 candidate parameter dimension, is the potential value obtained by forward calculation of the i-th egret population, The potential value measured by the free-running pole method for the i-th egret population is: and Respectively represent the lower and upper limits of the resistivity value;

[0109] Step S83, calculating the fitness value corresponding to each egret population through the objective function in formula (3), as shown in formula (5);

[0110] F( )= (5);

[0111] In the formula, F( ) is the fitness value calculated for each egret population;

[0112] Step S84, the egret begins to look for prey, and the egret finds that the position of the prey is constantly updated, as shown in formula (6) and formula (7);

[0113] While t<1 / 3T, (6);

[0114] (7);

[0115] In the formula, While represents judgment; t represents the number of iterations; T represents the maximum number of iterations; is the latest updated value of the i-th egret population and the j-th candidate parameter dimension; It indicates the first stage of searching for prey; represents the value of the i-th egret population and the j-th candidate parameter dimension variable; and They represent the first and second candidate solutions randomly generated in the initial stage, Indicates the optimal position of the current egret; is the latest updated status position of the i-th egret population; is the latest objective function value of the i-th egret population, is the function value of the number of the i-th egret population; Indicates a judgment statement;

[0116] Step S85, the egret eagle finds the prey and consumes the prey's physical strength and endurance. The random motion model of the egret eagle is shown in formula (8) and formula (9);

[0117] While (8);

[0118] (9);

[0119] In the formula, represents the optimal solution of the current iteration, and e represents the exponential function;

[0120] Step S86, when the prey is exhausted, the egret eagle quickly attacks and captures the prey, and the egret eagle's position update mathematical model is shown in formula (10) and formula (11);

[0121] While (10);

[0122] (11);

[0123] In the formula, represents the weighting of Levy flight;

[0124] Where RL=0.5×L(D), L(D) is calculated as shown in formula (12);

[0125] (12);

[0126] Where L(D) represents the Levy flight function, and are coefficients, and the coefficient values ​​are 0.01 and 1.5 respectively. and is a random number in [0,1], As shown in formula (13);

[0127] (13);

[0128] In the formula, σ is the standard deviation, represents the gamma function; is a coefficient with a value of 1.5;

[0129] Step S87: The egret eagle mainly avoids hunting by its natural enemies by camouflage and escape. In order to improve the search capability of the intelligent optimization bionic algorithm of the egret eagle, a dynamic disturbance factor is introduced. , as shown in formula (14) and formula (15);

[0130] (14);

[0131] (15);

[0132] In the formula, P represents the state of the egret eagle's escape strategy; represents the random candidate solution at the current iteration; C1 and C2 represent camouflage and escape respectively, ri =0.5, R2 represents a randomly generated number in a normal distribution, K represents a random 1 or 2, K=round(1+rand(1,1)), round means that the function rounds the number to a given number of digits; rand(1,1) represents a random number generated randomly between (0,1).

[0133] Specific examples are given below.

[0134] like Figure 1 , Figure 2 As shown, the project overview: A county river and lake shoreline ecological protection and restoration (Phase I) PPP project, the total length of the heightened and thickened embankment is 102.946km, with 69.572km of prefabricated slope protection, 2.55km of cast-in-place concrete slope protection, 8.63km of riprap revetment, 0.775km of pressure immersion embankment, 63.336km of deep mixing anti-seepage wall embankment, the anti-seepage wall mix ratio is designed to be 0.8 water-cement ratio, using No. 42.5 ordinary Portland cement, and the design strength of the anti-seepage wall is 3MPa.

[0135] Step S1, reinforcement of the earth-rock dam anti-seepage wall, wherein the anti-seepage wall is essentially cement soil, according to the design, the grouting slurry ratio of the anti-seepage wall is 0.8, the cement is PO32.5 ordinary Portland cement, the design strength at 28 days is 3Mpa, and combined with the on-site grouting experience, the cement content in the cement soil of the anti-seepage wall is determined to be 25%; according to the mix ratio and cement content, 3 samples of 550 mm×150 mm×150 mm are prepared, and the resistivity change of the samples is measured every day; then 84 samples of 70 mm×70 mm×70 mm are prepared, and the compressive strength of the samples is measured every day until the age of 28 days, 3 samples are taken for each compressive strength test, and the compressive strength values ​​are averaged; finally, a relationship curve between compressive strength and resistivity is drawn, as shown in FIG. Figure 3 The specific relationship is as follows:

[0136] (1); Step S2, according to the anti-seepage design results of the earth-rock dam grouting, survey and layout are carried out on site of the earth-rock dam to determine the location of the grouting holes, drill holes on site, mix the grouting slurry, and grout on site. After the maximum grouting design pressure is reached, the grouting of the anti-seepage wall of the earth-rock dam is completed on site.

[0137] Step S3, select a 100m long earth-rock embankment section 1 to implement integrity and strength testing of the anti-seepage wall 2, use two grouting holes after on-site grouting as testing holes, the depth of the two testing holes is 8m, and when the grouting is completed, the grouting holes are protected by mud walls to prevent hole collapse. The testing area is 50m×8m.

[0138] Step S4, make electrodes to detect the first PVC tube 4 and the second PVC tube 5. The inner diameter of the PVC tube is 16mm, the outer diameter is 20mm, and the length of a single tube is 4m. Drill holes at intervals of 20cm on the single PVC tube, connect the electrode 6 to the wire and insert it into the hole, and stick the electrode firmly with universal glue. Two 4m long PVC tubes are connected on site, and the connection is tightened and fixed with screws to make the first PVC tube 4 and the second PVC tube 5 in turn. Then the first PVC tube 4 and the second PVC tube 5 with electrodes are gradually lowered to the bottom of the detection hole. The electrodes on the first PVC tube 4 are numbered 1#, 2#, ..., 20#, and the electrodes on the second PVC tube 5 are numbered (20+1)#, (20+2)#, ..., 40#. Then the electrode wires in the first PVC tube 4 and the second PVC tube 5 are respectively integrated into the first cable 8 and the second cable 9, and then the hole is sealed and backfilled. Finally, the cable is led from the top of the embankment 3 to the outside of the embankment, and then the top of the embankment road is poured, and finally the cable joint is installed with a protective device.

[0139] Step S5, on the 28th day after the grouting is completed, the potential value of the earth-rock dam anti-seepage wall is measured on site using a WDJD-4 high-density resistivity instrument 7.

[0140] Step S51, firstly connect the first cable 8 to the first cable interface 10, connect the second cable 9 to the second cable interface 11, and at the same time fix the measuring electrode N in the middle of the two detection holes for grounding.

[0141] Step S52, then perform ABM free running electrode measurement of potential values. First, power supply electrode A is connected to electrode 1#, power supply electrode B is sequentially connected to electrode 2#, electrode 3#, ..., electrode 39# after power supply electrode A, and measuring electrode M is sequentially connected to other electrodes except power supply electrode A and power supply electrode B. Power supply electrode A completes running electrode measurement in turn to obtain 1482 potential values ​​in total. Power supply electrode A has 20 running electrode modes, and finally obtains 29640 potential values. The potential set is U m1 .

[0142] Step S6, using the variational mode VMD-wavelet filtering algorithm to measure the potential set U detected on site m Perform filtering to improve the measurement potential set U m Step S61, using variational mode VMD to measure the potential set U in step S5 m The potential value is decomposed into 13 signal components IMFs.

[0143] Step S62: Analyze the noise of the signal component IMFs according to the characteristics of the signal component IMFs.

[0144] Step S63: Filter and reduce noise on the noisy signal components IMFs using a wavelet decomposition algorithm.

[0145] Step S64, reconstruct the signal components IMFs after filtering and noise reduction and the signal components IMFs without noise to obtain the filtered field detection potential set U m .

[0146] Step S7: compile the geoelectric finite element model of the corresponding detection area and calculate the calculated potential set U of each node c .

[0147] Step S71, the geoelectric finite element model is written using the computing language MATLAB software to establish a geoelectric finite element model corresponding to the detection area, with a model size of 50m×8m.

[0148] Step S72: Select four-node rectangular units for meshing, assign mesh parameter attributes, and set the initial resistivity to 200Ω·m and the density to 3200kg / m 3 .

[0149] Step S73, when solving the point source finite element boundary problem, convert the point source finite element boundary problem into a plane partial differential equation for solution.

[0150] Step S74: The forward calculation method is consistent with the field detection method, and 29640 calculated potential values ​​corresponding to the measured values ​​are obtained. The calculated potential set of the final calculation node is recorded as U c .

[0151] Step S8, use the Egret Swarm Intelligent Optimization Bionic Algorithm to perform resistivity inversion calculation, obtain the resistivity of the detection area, set the maximum number of iterations T=100, the population size Z=200, and the minimum value of the candidate resistivity value =100Ω·m, the maximum value of the candidate resistivity value =1000Ω·m, the dimension of candidate parameters is W=8.

[0152] Furthermore, in step S8, the resistivity inversion calculation is performed using the egret swarm intelligent optimization bionic algorithm to obtain the resistivity of the detection area; specifically:

[0153] Step S81, initializing the egret population, as shown in formula (2);

[0154] (2);

[0155] In the formula, The candidate resistivity values ​​representing the number of i egret eagle populations and j candidate parameter dimensions; Represents a random number between 0 and 1; , Respectively represent the maximum and minimum values ​​of candidate resistivity values; i=1,2, …,200, j=1,2, …,8;

[0156] Geoelectric finite element model, calculate the potential set U of each node c ;

[0157] Step S82, using the geoelectric finite element model to calculate the potential set U of each node c The potential value and the measured potential set U m The potential value of is used to establish the objective function of the inversion analysis, as shown in formula (3) and formula (4);

[0158] Object[F( )]= (3);

[0159] (4);

[0160] In the formula, Object[F( )] is the objective function of resistivity inversion, , A candidate resistivity value representing a population of egrets and a candidate parameter dimension, The candidate resistivity values ​​representing two egret populations and one candidate parameter dimension, represents the candidate resistivity value of i egret populations and 1 candidate parameter dimension, is the potential value obtained by forward calculation of the number of the i-th egret population, is the measured potential value of the free-running pole mode of the egret population, and Respectively represent the lower and upper limits of the resistivity value;

[0161] Step S83, calculating the fitness value corresponding to each egret population through the objective function in formula (3), as shown in formula (5);

[0162] F( )= (5);

[0163] In the formula, F( ) is the fitness value calculated for each egret population;

[0164] Step S84, the egret begins to look for prey, and the egret finds that the position of the prey is constantly updated, as shown in formula (6) and formula (7);

[0165] While t<1 / 3T, (6);

[0166] (7);

[0167] In the formula, While represents judgment; t represents the number of iterations; T represents the maximum number of iterations; is the latest updated value of the i-th egret population and the j-th candidate parameter dimension; It indicates the first stage of searching for prey; represents the value of the i-th egret population and the j-th candidate parameter dimension variable; and They represent the first and second candidate solutions randomly generated in the initial stage, Indicates the optimal position of the current egret; is the latest updated status position of the i-th egret population; is the latest objective function value of the i-th egret population, is the function value of the number of the i-th egret population; Indicates a judgment statement.

[0168] Step S85, the egret eagle finds the prey and consumes the prey's physical strength and endurance. The random motion model of the egret eagle is shown in formula (8) and formula (9);

[0169] While (8);

[0170] (9);

[0171] In the formula, represents the optimal solution of the current iteration, and e represents the exponential function;

[0172] Step S86, when the prey is exhausted, the egret eagle quickly attacks and captures the prey, and the egret eagle's position update mathematical model is shown in formula (10) and formula (11);

[0173] While (10);

[0174] (11);

[0175] In the formula, represents the weighting of Levy flight;

[0176] Where RL=0.5×L(D), L(D) is calculated as shown in formula (12);

[0177] (12);

[0178] Where L(D) represents the Levy flight function, and are coefficients, and the coefficient values ​​are 0.01 and 1.5 respectively. and is a random number in [0,1], As shown in formula (13);

[0179] (13);

[0180] In the formula, σ is the standard deviation, represents the gamma function; is a coefficient with a value of 1.5;

[0181] Step S87: The egret eagle mainly avoids hunting by its natural enemies by camouflage and escape. In order to improve the search capability of the intelligent optimization bionic algorithm of the egret eagle, a dynamic disturbance factor is introduced. , as shown in formula (14) and formula (15);

[0182] (14);

[0183] (15);

[0184] In the formula, P represents the state of the egret eagle's escape strategy; represents the random candidate solution at the current iteration; C1 and C2 represent camouflage and escape respectively, r i =0.5, R2 represents a randomly generated number in a normal distribution, K represents a random 1 or 2, K=round(1+rand(1,1)), round represents the function rounding the number to a given number of digits; rand(1,1) represents a random number generated randomly between (0,1).

Claims

1. A method for detecting the integrity and strength of a grouting reinforced anti-seepage wall of an earth-rock dam, characterized in that: The following steps are involved: Step S1, according to the designed mix ratio of grouting reinforcement for earth-rock dam, the relationship between hardened slurry resistivity and compressive strength is tested by indoor test; Step S2, drilling and grouting on site of the earth-rock dam to complete the grouting reinforcement of the anti-seepage wall; Step S3, selecting two grouting holes as test holes for the integrity and strength of the anti-seepage wall, and performing slurry wall protection on the test holes; Step S4, lowering the PVC pipe with the copper electrode into the detection hole, and backfilling the detection hole with grouting; Step S5, conduct on-site inspection on the inspection hole after grouting backfilling is completed, connect the electrode wire to the resistance tester, and use the free running electrode method to test the measurement potential set U of the inspection area. m ; Step S6, using the variational mode VMD-wavelet filtering algorithm to measure the potential set U detected on site m Perform filtering to improve the measurement potential set U m signal-to-noise ratio; Step S7: compile the geoelectric finite element model of the corresponding detection area and calculate the calculated potential set U of each node c ; Step S8, using the Egret Eagle Swarm intelligent optimization bionic algorithm to perform resistivity inversion calculation to obtain the resistivity of the detection area; Step S9, resistivity drawing inversion image, analyzing the integrity of the earth-rock dam anti-seepage wall; Step S10, determining the strength of the earth-rock dam anti-seepage wall according to the relationship between the resistivity and the compressive strength in step S1; In step S1, the relationship between the hardened resistivity of the slurry and the compressive strength is calculated as follows: （1）； Where: H c It indicates the compressive strength of slurry hardening; It represents the resistivity of slurry hardening; and They are the solution coefficients, i.e., the correlation coefficients that need to be determined by fitting the experimental data; The free-running electrode method in step S5 is optimized on the Wenner quadrupole ABMN, where A and B are power supply electrodes and M and N are measurement electrodes, specifically: Step S51, the grouting hole is used as a detection hole, the two detection holes are respectively a first hole and a second hole, and the detection range is the area between the two detection holes; Step S52, the distance between the electrodes of the PVC tube of the copper electrode is 20 cm, each electrode is connected to a wire and numbered, the first hole is numbered as electrode 1#, electrode 2#, ..., electrode L#, and the second hole is numbered as electrode (L+1)#, electrode (L+2)#, ..., electrode 2L#; Step S53, the wires of the electrodes of the first hole are integrated into a first cable, and the wires of the electrodes of the second hole are integrated into a second cable; Step S54, the measuring electrode N is fixed between the two detection holes, and the ABM runs freely; Step S55, power supply electrodes AB are combined, power supply electrode A is connected to electrode 1#, power supply electrode B is connected to 2L-1 electrodes after power supply electrode A, power supply electrode A is connected to 2#, 3#, ..., (2L-1)# in sequence, power supply electrode B is connected to 2L-2, 2L-3, ..., 1 electrode in sequence, and there are 2L-1+2L-2+...+1=L*(2L-1) running electrode combinations in total; Step S56, in the running electrode mode of power supply electrode AB, measuring electrode M is connected to 2L-2 electrodes except power supply electrode AB, and L*(2L-1)*(2L-2) potential values ​​are obtained in each on-site measurement; The variational mode VMD-wavelet filtering algorithm in step S6 is specifically as follows: step S61, using variational mode VMD to measure the potential set U in step S5 m The potential value is decomposed into n signal component IMFs; Step S62, analyzing the noise of the signal component IMFs according to the characteristics of the signal component IMFs; Step S63, using a wavelet decomposition algorithm to filter and reduce noise on the noisy signal component IMFs; Step S64, reconstructing the filtered and denoised signal components IMFs and the noise-free signal components IMFs; In step S7, a geoelectric finite element model corresponding to the detection area is compiled to calculate the calculated potential set U of each node. c , specifically: Step S71, using scientific computing language MATLAB software to compile a geoelectric finite element model, and establishing a geoelectric finite element model corresponding to the detection area; Step S72, selecting four-node rectangular units for meshing and assigning mesh parameter attributes; Step S73, when solving the point source finite element boundary problem, convert the point source finite element boundary problem into a plane partial differential equation for solution; Step S74: The forward calculation method is consistent with the field detection method, and the final calculation node potential set is U c .

2. A method for detecting the integrity and strength of a grouting reinforced anti-seepage wall of an earth-rock dam according to claim 1, characterized in that: In step S8, the egret swarm intelligent optimization bionic algorithm is used to perform resistivity inversion calculation to obtain the resistivity of the detection area; specifically, step S81, the egret swarm population is initialized, as shown in formula (2); （2）； In the formula, represents the number of heron eagle populations and j candidate resistivity values ​​for candidate parameter dimensions; Represents a random number between 0 and 1; , Respectively represent the maximum and minimum values ​​of candidate resistivity values; i=1,2, …,Z, j=1,2, …,W; Z is the number of egret populations, and W is the dimension of candidate parameters; Based on the geoelectric finite element model, the computational potential set U of each node is calculated. c ; Step S82, using the geoelectric finite element model to calculate the potential set U of each node c The potential value and the measured potential set U m The potential value of is used to establish the objective function of the inversion analysis, as shown in formula (3) and formula (4); （3）； （4）； In the formula, Object[ F ( )] is the objective function of resistivity inversion, , A candidate resistivity value representing a population of egrets and a candidate parameter dimension, The candidate resistivity values ​​representing two egret populations and one candidate parameter dimension, represents the candidate resistivity value of i egret populations and 1 candidate parameter dimension, is the potential value obtained by forward calculation of the i-th egret population, The potential value measured by the free-running pole method for the i-th egret population is: and Respectively represent the lower and upper limits of the resistivity value; Step S83, calculating the fitness value corresponding to each egret population through the objective function in formula (3), as shown in formula (5); （5）； In the formula, F ( ) is the fitness value calculated for each egret population; Step S84, the egret begins to look for prey, and the egret finds that the position of the prey is constantly updated, as shown in formula (6) and formula (7); （6）； （7）； In the formula, While means judgment; t Indicates the number of iterations; T Indicates the maximum number of iterations; For the i The number of egret eagle populations and j The latest updated values ​​of candidate parameter dimensions; It indicates the first stage of searching for prey; Indicates i The number of egret eagle populations and j The values ​​of the candidate parameter dimension variables; and They represent the first and second candidate solutions randomly generated in the initial stage, Indicates the optimal position of the current egret; For the i The latest updated status and location of the Eurasian Hawk population; For the i The latest objective function value of the heron eagle population, For the i The function value of the number of egret eagle populations; Indicates a judgment statement; Step S85, the egret eagle finds the prey and consumes the prey's physical strength and endurance. The random motion model of the egret eagle is shown in formula (8) and formula (9); （8）； （9）； In the formula, represents the optimal solution of the current iteration, and e represents the exponential function; Step S86, when the prey is exhausted, the egret eagle quickly attacks and captures the prey, and the egret eagle's position update mathematical model is shown in formula (10) and formula (11); （10）； （11）； In the formula, represents the weighting of Levy flight; in RL =0.5× L ( D ), L ( D ) The calculation method is shown in formula (12); （12）； In the formula, L ( D ) represents the Lévy flight function, and are coefficients, and the coefficient values ​​are 0.01 and 1.5 respectively. and is a random number in [0,1], As shown in formula (13); （13）； In the formula, σ is the standard deviation, represents the gamma function; is a coefficient with a value of 1.5; Step S87: The egret eagle mainly avoids hunting by its natural enemies by camouflage and escape. In order to improve the search capability of the intelligent optimization bionic algorithm of the egret eagle, a dynamic disturbance factor is introduced. , as shown in formula (14) and formula (15); （14）； （15）； In the formula, P The state when indicating the egret eagle's escape strategy; represents the random candidate solution at the current iteration; C 1 and C 2 means disguise and escape respectively, r i =0.5, R 2 represents a randomly generated number from a normal distribution, K Indicates a random 1 or 2, K =round(1+rand(1,1)), round means that the function rounds the number to a given number of digits; rand(1,1) means a random number randomly generated between (0,1).

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