A method for identifying pipe pile defects based on swarm intelligence algorithm

Abstract

The present invention relates to the technical field of quantitative identification using low-strain methods, and in particular to a method for identifying pipe pile defects based on a swarm intelligence algorithm. The method mainly comprises obtaining measured velocity time-history response curve data V of a pipe pile; constructing a pipe pile defect identification program, inputting the measured velocity time-history response curve data, empirical learning parameters, model fixed parameters, and model variable parameters into the pipe pile defect identification program; and the pipe pile defect identification program performs iterative calculations to obtain an optimal candidate solution. The present invention quantitatively identifies the defect locations of prestressed concrete pipe piles under homogeneous soil cover conditions, and provides quantitative identification data, including the excitation force amplitude F, pulse width L, reference integrity coefficient β, pile-soil friction coefficient μ, and pile-soil damping ratio ξ. This method improves the accuracy and reliability of traditional low-strain methods and addresses the shortcoming that traditional methods can only qualitatively determine the integrity of pipe piles.

Description

Technical Field

[0001] The present invention relates to the technical field of low-strain quantitative identification, and in particular to a method for identifying pipe pile defects based on a swarm intelligence algorithm. Background Art

[0002] Pile foundations are a crucial component of building structures. Pile defects can pose a serious safety hazard to structural operation. Accurately assessing pile foundation damage is essential for ensuring the safe operation, maintenance, and management of structures. The low-strain reflection wave method applies a transient impact dynamic load to the pile top, generating stress waves that propagate downward along the pile shaft. Reflected waves are generated where significant impedance interfaces (such as the pile bottom, fractures, segregation, or mud inclusions) or cross-sectional area changes (such as necking or expansion) occur within the pile shaft. Using specialized instruments, through reception, amplification, filtering, and data processing, the reflected information from various locations on the pile shaft can be identified. Analysis and calculation of this reflected information allows for the determination of pile damage and pile integrity. However, limited research in China on the quantitative analysis and reliability of low-strain reflection wave damage identification for piles has limited its further application. Rapid and accurate quantitative identification of pile damage through nondestructive dynamic testing is a hot topic for many scholars and engineers both domestically and internationally.

[0003] To address the above issues, the present invention combines an advanced nonlinear brute force algorithm—a swarm intelligence optimization algorithm—for quantitative identification of pipe pile defects, improving the accuracy and reliability of the low-strain method. Swarm intelligence algorithms are a type of intelligent optimization method based on swarm search, implemented by simulating a certain behavior of a biological population. Their advantages include strong global search capabilities, no need for sensitivity information, and insensitivity to initial values. Common algorithms include the particle swarm optimization algorithm (PSO), the cloud model fruit fly algorithm (CMFOA), the JAYA algorithm, the squirrel search algorithm, and the experience-based learning algorithm. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a pipe pile defect identification method based on a swarm intelligence optimization algorithm, which can quantitatively identify the defect locations of prestressed concrete pipe piles under homogeneous covering conditions, improve the accuracy and reliability of the traditional low-strain method, and solve the shortcoming that the traditional method can only qualitatively judge the integrity of the pipe piles.

[0005] To achieve the above objectives, the present invention can be implemented through the following technical solutions:

[0006] A method for identifying pipe pile defects based on a swarm intelligence algorithm, the method comprising:

[0007] Obtaining the measured velocity time history response curve data V of the pipe pile;

[0008] Inputting the measured velocity time history response curve data V, experience learning parameters, model fixed parameters and model variable parameters into the pipe pile defect recognition program;

[0009] The pipe pile defect identification program performs iterative calculations to obtain the optimal candidate solution.

[0010] Furthermore, the experience learning parameters include the maximum number of iterations Iter max , the maximum population size Np, the upper bound of the decision variable X max , the lower bound of the decision variable X min and the maximum number of dimensions n, the upper bound of the decision variable X max and the lower bound of the decision variable X min The setting is performed according to the value range of the model change parameter.

[0011] Furthermore, the fixed parameters of the model include the radius R of the soil around the pile, the buried depth h of the soil around the pile, the diameter d of the pipe pile, the wall thickness t of the pipe pile, the pile length l, the number of components N of the pile body, the elastic modulus E1 of the pile body material, the elastic modulus E2 of the soil around the pile, the Poisson's ratio v1 of the pile body material, the Poisson's ratio v2 of the soil around the pile, the density ρ1 of the pile body material, the density ρ2 of the soil around the pile, the cohesion c, the friction angle φ, the longitudinal wave velocity C1 of the pile body material, and the longitudinal wave velocity C2 of the soil around the pile. The fixed parameters of the model do not change with the iterative calculation.

[0012] Furthermore, the model variation parameters include the exciting force amplitude F, the exciting force pulse width L, the pipe pile integrity coefficient set β, the pile-soil friction coefficient μ and the pile-soil damping ratio ξ, and the model variation parameters are optimized with iterative calculation.

[0013] Furthermore, the iterative calculation includes:

[0014] The algorithm population is initialized to obtain the initialized candidate solution X p,q ;

[0015] According to the initialized candidate solution X p,q Start iterative calculation;

[0016] Each iteration iterates and calculates the candidate solution according to the number of individuals p and the number of dimensions q in the population;

[0017] Iterate until the maximum number of iterations Iter is reached max , and obtain the optimal candidate solution.

[0018] Furthermore, each iteration iteratively calculates the candidate solutions according to the number of individuals p in the population and the number of dimensions q, including:

[0019] Let the number of individuals in the population p = 1;

[0020] According to the candidate solution X through matlabp,q Modify the model change parameters in the command stream file;

[0021] Abaqus calls the modified command stream file to generate the pile-soil model and processes the operation to obtain the calculated velocity time history response curve data.

[0022] According to the measured speed time history response curve data V and the calculated speed time history response curve data Calculate candidate solution X p,q The fitness value f;

[0023] For candidate solution X p,q , randomly select another candidate solution X from the population (p≠1) l,q , used to generate potential search directions according to the candidate solution X p,q The distance between the old and new positions is used to update the new position of the candidate solution through the model;

[0024] Iterate in sequence until the number of individuals p in the population reaches the maximum population size Np;

[0025] Entering the spatial enhanced dimension search phase, iteratively calculates the number of dimensions q to obtain better candidate solutions;

[0026] Iterate in sequence until the number of dimensions q reaches the maximum number of dimensions n.

[0027] Furthermore,

[0028] The candidate solution X is initialized according to the following formula p,q :

[0029] X p,q =X min +rand·(X max -X min ),p=1,2,…,Np,q=1,2,…,n (1)

[0030] X p,q =(F, L, β, μ, ξ); β = (β1, β2,…,β N ) T ;

[0031] Among them, X max is the upper bound of the decision variable, X min is the lower bound of the decision variable, rand is a uniformly distributed random number in the range of [0, 1], p is the number of individuals in the population, q is the number of dimensions, F is the amplitude of the exciting force, L is the pulse width of the exciting force, β is the set of pile integrity coefficients, μ is the pile-soil friction coefficient, and ξ is the pile-soil damping ratio.

[0032] Furthermore, the candidate solution X is calculated according to the following formulap,q The fitness value f:

[0033]

[0034] in, is the calculated speed time history response curve data, and V is the measured speed time history response curve data.

[0035] Furthermore, for the candidate solution X p,q , randomly select another candidate solution X from the population (p≠1) l,q , used to generate potential search directions according to the candidate solution X p,q The distance between the old and new positions is updated using the following two models:

[0036] Mode 1: Candidate solution X p,q The new position directly to the candidate solution X l,q Learning, the exploration radius changes dynamically with iteration:

[0037]

[0038] Mode 2: Based on candidate solution X l,q Experience, generate candidate solutions X near the original position p,q New location:

[0039]

[0040] Among them, rand is a function that takes a random value between [0,1], Iter is the current number of iterations, Iter max is the maximum number of iterations;

[0041] In each iteration, in order to balance the exploration and exploitation capabilities of the algorithm during the search process, one of the two modes is randomly called;

[0042] If the fitness value of the new position is better than the old position, the position of the candidate solution is updated according to the following formula, otherwise the old position is retained:

[0043]

[0044] Furthermore, entering the spatial enhanced dimension search phase and iteratively calculating the number of dimensions q to obtain a better candidate solution includes the following steps:

[0045] Find the best candidate solution X among all candidate solutions iteratively calculated with the number of individuals in the population p = 1, 2, ..., Np best,q and the worst candidate solution X worst,q ;

[0046] According to the best candidate solution X best,q and the worst candidate solution Xworst,q , and changing the value of one dimension while maintaining the values ​​of other dimensions to generate new candidate solutions

[0047] Compare the fitness values ​​of the new candidate solutions with the original candidate solutions and retain the better candidate solutions;

[0048] The new candidate solution is generated by the following formula:

[0049]

[0050] Among them, X best,q is the best candidate solution, X worst,q is the worst candidate solution, Iter is the current iteration number, Iter max is the maximum number of iterations, rand is a uniformly distributed random number in the range [0, 1], and q is the number of dimensions.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention quantitatively identifies defect locations of prestressed concrete pipe piles under homogeneous covering soil conditions and provides quantitative identification data, including the exciting force amplitude F, pulse width L, benchmark integrity coefficient β, pile-soil friction coefficient μ, and pile-soil damping ratio ξ. This improves the accuracy and reliability of traditional low-strain methods and overcomes the shortcoming that traditional methods can only qualitatively determine the integrity of pipe piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 It is a schematic diagram of establishing pile-soil model;

[0055] Figure 3 It is a schematic diagram of the defect types of the model pile;

[0056] Figure 4 It is the speed time history curve fitting comparison, algorithm iteration curve, and integrity coefficient curve. DETAILED DESCRIPTION

[0057] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Terms such as "upper," "inner," "middle," "left," "right," and "one" used in this specification are for ease of description and are not intended to limit the scope of the present invention. Changes or adjustments to these terms, without substantially altering the technical content, are considered within the scope of the present invention.

[0058] The pipe pile defect identification method based on swarm intelligence algorithm of the present invention is as follows: Figure 1 As shown, it includes the following stages:

[0059] Phase 1: Experimental Data Collection

[0060] Obtain the measured velocity time history response curve data V of the pipe pile.

[0061] Step 1: Install the accelerometer on the top of the pile;

[0062] Step 2: Set the sampling time interval Δt and sampling frequency f s ;

[0063] Step 3: Select a suitable excitation hammer, strike the pile top to measure the velocity time history response curve data V, and import the data into the pipe pile defect identification program.

[0064] This embodiment uses the Euro-American Earth PIT-QFV low-strain pile integrity tester to collect the measured velocity time-history response curve data V of the pipe pile. The test system mainly consists of a signal acquisition instrument, an accelerometer, and an exciting hammer. The instrument technical parameters are shown in Table 1.

[0065] Table 1 Technical parameters of test instruments

[0066]

[0067] Phase 2: Construct a pipe pile defect recognition program, and input the measured velocity time history response curve data V, experience learning parameters, model fixed parameters and model variable parameters into the pipe pile defect recognition program.

[0068] A pipe pile defect recognition program based on the empirical learning algorithm was constructed using a MATLAB program. The empirical learning parameters, fixed model parameters, and variable model parameters were input into the MATLAB program. During the algorithm's iterative calculations, the program invoked Abaqus software for each calculation. The variable model parameters within the Abaqus Python command stream were modified based on the candidate solutions, automating modeling and pre- and post-processing.

[0069] Step 4: Input the experience learning parameters (Iter max , Np, X max , X min , n), the main control parameters of the experience learning algorithm include the maximum number of iterations Iter max , the maximum population size Np, the upper bound of the decision variable X max , the lower bound of the decision variable X minAnd the maximum number of dimensions n. These parameters are the initialization parameters of the experience learning algorithm. The algorithm needs to pre-set the value and value range of the initialization parameters. The upper bound Xmax of the decision variable is the set of the upper limit of the value interval of all model change parameters; the lower bound Xmin of the decision variable is the set of the lower limit of the value interval of all model change parameters. max and the lower bound of the decision variable X min Set according to the value range of the model change parameter.

[0070] Step 5: Enter the Abaqus model fixed parameters (R, h, d, t, l, N, E1, E2, v1, v2, ρ1, ρ2, c, φ, C1, C2). These include the pile radius R, pile depth h, pile diameter d, pile wall thickness t, pile length l, number of pile components N, pile material elastic modulus E1, pile soil elastic modulus E2, pile material Poisson's ratio v1, pile soil Poisson's ratio v2, pile material density ρ1, pile soil density ρ2, cohesion c, friction angle φ, pile material longitudinal wave velocity C1, and pile soil longitudinal wave velocity C2. These fixed parameters do not change during the iterative calculation. They are set based on the soil lithology and pile size parameters from the field test and are consistent with the field test construction data from Phase 1. The Poisson's ratio v2 of the soil around the pile, the density of the pile body material ρ1, the density of the soil around the pile ρ2, the cohesion c, and the friction angle φ are set according to the longitudinal wave velocity C1 of the pile body material, and the longitudinal wave velocity C2 of the soil around the pile is set according to experience.

[0071] For example: the pipe pile is concrete C80, the diameter of the pipe pile is 500mm, the pile length is 10m, and the soil around the pile is clay.

[0072] The initial fixed parameters of Abaqus are R = 5m, h = 10m, d = 0.5m, t = 0.125m, l = 10m, N = 20. Other model material property parameters are shown in Table 2.

[0073] Table 2 Model material property parameters

[0074]

[0075] Step 6: Input the Abaqus model variation parameters (F, L, β, μ, ξ). The model variation parameters include the excitation force amplitude F, the excitation force pulse width L, the pile integrity coefficient set β, and the pile-soil friction coefficient μ. The pile-soil damping ratio ξ. The model variation parameters are set to an initial value based on field test experience and soil conditions. During the iterative calculation process, they are continuously optimized to find the optimal value within the value range. The optimal candidate solution required by the present invention is the model variation parameters (F, L, β, μ, ξ).

[0076] Table 3 Algorithm parameter value settings

[0077]

[0078] Phase 3: Iterative Calculation

[0079] The algorithm population candidate solution X sought by the present invention p,q That is, the model change parameters (F, L, β, μ, ξ), the candidate solution X p,q is a set of numbers, the number of individuals in the population is p, and the number of dimensions is q;

[0080]

[0081] Among them, p is the number of individuals in the population, q is the number of dimensions, F is the amplitude of the exciting force, L is the pulse width of the exciting force, β is the set of pile integrity coefficients, μ is the pile-soil friction coefficient, and ξ is the pile-soil damping ratio.

[0082] Step 7: Initialize the algorithm population. The initial population position is randomly generated. The initialized candidate solution X is obtained according to the following formula: p,q :

[0083] X p,q =X min +rand·(X max -X min ),p=1,2,…,Np,q=1,2,…,n (1)

[0084] X p,q =(F, L, β, μ, ξ); β = (β1, β2,…,β N ) T ;

[0085] Among them, X max is the upper bound of the decision variable, X min is the lower bound of the decision variable, rand is a uniformly distributed random number in the range [0, 1], p is the number of individuals in the population, q is the number of dimensions, F is the amplitude of the exciting force, L is the pulse width of the exciting force, β is the set of pile integrity coefficients, μ is the pile-soil friction coefficient, and ξ is the pile-soil damping ratio. In the first iterative calculation, the candidate solution X is initialized. p,q to start the calculation.

[0086] The fitness value of the candidate solution f=(f1,f2,…,f Np ) is calculated according to the following objective function:

[0087] f p =obj(X p,1 ,X p,2 ,…,X p,n ),p=1,2,…,Np (2)

[0088] The fitness value represents the degree of fit between the calculated candidate solution and the actual situation. The smaller the fitness value, the more accurate the fit, and the more consistent the calculated candidate solution is with the actual situation.

[0089] Step 8: Start iterative calculation, the number of iterations iter = 1.

[0090] Step 9: Let the number of individuals in the population p = 1;

[0091] Step 10: Call the Python interpreter built into the Abaqus program and calculate the candidate solution X. p,q Modify the modeling command flow (modify the model change parameters, and keep other parameters unchanged), automatically generate the pile-soil model through the command flow, and perform processing operations to obtain the calculated speed time history response curve data

[0092] Finite element modeling was performed using Abaqus software. Pre- and post-modeling processing was performed using command streams executed via the built-in Python interpreter. The model soil (foundation soil) was assumed to be a viscoelastic Voigt solid. The constitutive model for the pile concrete was a linear elastic model.

[0093] Use Abaqus finite element software to build a three-dimensional pile-soil model, and the main modeling process is shown. Figure 2 . Establish a three-dimensional pile-soil model and perform processing operations to obtain the calculated velocity time history response curve data. The steps are as follows:

[0094] (1) Establishing the model entity components: using eight-node reduced-integral linear hexahedral units to divide the unit grid; establishing the model entity components based on the seven parameters of pile radius R, pile soil burial depth h, pile diameter d, pile wall thickness t, pile length l, and the number of pile body components N.

[0095] (2) Define material properties: Assign material properties to different components of the single-layer soil and pipe pile based on the elastic modulus E1 of the pile body material, the elastic modulus E2 of the soil around the pile, the Poisson's ratio v1 of the pile body material, the Poisson's ratio v2 of the soil around the pile, the density ρ1 of the pile body material, the density ρ2 of the soil around the pile, the cohesion c, the friction angle φ, the longitudinal wave velocity C1 of the pile body material, the longitudinal wave velocity C2 of the soil around the pile, and the pile-soil damping ratio ξ.

[0096] (3) Modify the material density properties of different units of the pile according to the pile integrity coefficient set β. Simulating pile body defects will cause impedance changes. Pile body defects will cause the cross-sectional wave impedance to decrease. The wave impedance calculation formula is Z = ρcA. In order to simulate the changes in pile body defects, the defects are numerically modeled. According to formula (3), the pile defects are equivalent to the reduction of material density:

[0097]

[0098] Where β is the integrity coefficient of the pile, Z d is the wave impedance of the medium after the stress wave passes through the defect interface, ρ d is the density of the medium after the stress wave passes through the defect interface, Z0 is the wave impedance of the medium before the stress wave passes through the defect interface, and ρ0 is the density of the medium after the stress wave passes through the defect interface. The model of this embodiment uses density reduction as the damage model.

[0099] When modeling, the pile foundation is divided into N = 20 parts in equal proportion, and the initial integrity coefficient β of each part is set. N =1, N=1, 2…20, input the complete density ρ, and modify the component unit material mass density ρ N =ρ×β N Thus changing the cross-sectional wave impedance Z N =ρ N cA.

[0100] (4) The pile-soil interaction is defined using the surface-to-surface contact of Abaqus. The tangential contact model is selected as penalty friction, and the normal contact model is selected as hard contact (the pile bottom is considered as a free end without contact with the soil);

[0101] (5) Apply an excitation force load p(t) to the top of the pile. The excitation force load p(t) can be obtained according to the formula corresponding to the F and L in the model variation parameters. The boundary conditions of the soil around the pile are that the two sides constrain the lateral displacement and the bottom constrains the vertical displacement. The vibration source of the low-strain reflection wave method can be equivalent to a transient pulse signal, and its characteristic indicators mainly include parameters such as the excitation force amplitude and pulse width. The waveform of the pulse load excited by the excitation hammer is close to the shape of the raised cosine pulse wave function and is represented by the raised cosine pulse function. The calculation method of the excitation force load p(t) is as follows:

[0102]

[0103] Where t is the time variable, p(t) is the pulse load, F is the exciting force amplitude, and L is the pulse width. The two parameters F and L can be calculated from the characteristic points of the measured vibration time domain curve.

[0104] (6) Dynamic analysis was used, with the analysis time set to 13 ms and the integration step to 0.01 ms.

[0105] (7) After the calculation is completed, the time-history response curve data of the pile top calculation velocity of the pipe pile model is extracted. The calculation results include time-history data such as velocity, acceleration, stress and strain of all nodes, which can be output as needed and set through command streams.

[0106] Step 11: Combine and calculate the speed time history response curve data With the measured speed time history response curve data V, we can construct the following objective function, that is, calculate the standard deviation of the measured speed time history response curve data, and calculate the candidate solution X through the following objective function p,q The fitness value f:

[0107]

[0108] in, is the calculated speed time history response curve data, and V is the measured speed time history response curve data.

[0109] Candidate solution X p,q is a set of numbers, which contains F, L, β, u and ξ. In each iteration, the fitness value of the candidate solution must be calculated, that is, the fitness value is calculated by formula (5).

[0110] Where: f is the candidate solution X of the experience learning algorithm p,q The fitness value of X p,q represents the candidate solution in the empirical learning algorithm; F is the exciting force amplitude, L is the exciting force pulse width, β is the set of pile integrity coefficients, μ is the pile-soil friction coefficient, and ξ is the pile-soil damping ratio.

[0111] Step 12: Generate a new position of the population. Based on the learning strategy based on the experience of other solutions, the population candidate solution is considered to move to a better position around its initial position. p,q , randomly select another candidate solution X from the population (p≠1) l,q , X l,q is the candidate solution of the qth dimension of the lth individual in the population obtained in the previous iteration, l is a number randomly selected from 2, 3, ..., Np to generate potential search directions. In the first iteration, select X in the initialized population l,q According to the candidate solution X p,q The distance between the old and new positions is updated using the following two models:

[0112] Mode 1: Candidate solution X p,q The new position directly to the candidate solution X l,q Learning, the exploration radius changes dynamically with iteration:

[0113]

[0114] Mode 2: Based on candidate solution X l,q Experience, generate candidate solutions X near the original position p,q New location:

[0115]

[0116] Among them, rand is a function that takes a random value between [0,1], Iter is the current number of iterations, Iter max is the maximum number of iterations.

[0117] Of these two modes, Mode 1 aims to explore candidate solutions in the feasible space, thereby improving the algorithm's exploration capabilities and avoiding being trapped in local optima. Mode 2 improves the algorithm's convergence performance, thereby enhancing its exploitation capabilities. During each iteration, one of these two modes is randomly invoked to balance the algorithm's exploration and exploitation capabilities during the search process.

[0118] If the fitness value of the new position is better than the old position, the position of the candidate solution is updated, otherwise the old position is retained:

[0119]

[0120] Step 13: Determine if the number of individuals in the population p < Np. If so, p = p + 1 and return to step 10. If p = Np, enter the spatial search enhancement phase.

[0121] Spatial Search Enhancement Phase: During the search process of the empirical learning algorithm, all dimensions of each candidate solution are updated simultaneously. However, changes in one dimension may negatively impact variables in other dimensions, leading to poor convergence performance in each dimension. To further enhance the in-depth search in each dimension, the following steps are taken in each iteration:

[0122] (1) Find the best candidate solution X among all candidate solutions iteratively calculated with the number of individuals in the population p = 1, 2, ..., Np best,q and the worst candidate solution X worst,q ;

[0123] (2) According to the best candidate solution X best,q and the worst candidate solution X worst,q , and changing the value of one dimension while maintaining the values ​​of other dimensions to generate new candidate solutions

[0124] The new candidate solution is generated by the following formula:

[0125]

[0126] Among them, rand is a function that takes random values ​​between [0,1], X best,q is the best candidate solution, X worst,q is the worst candidate solution, Iter is the current iteration number, Iter max is the maximum number of iterations, and q is the number of dimensions.

[0127] (3) Compare the fitness value of the new candidate solution with that of the original candidate solution and retain the better candidate solution, that is, the candidate solution with the smaller fitness value;

[0128] (4) Repeat steps (1) and (3) for other dimensions.

[0129] Step 14: Determine the condition Iter <Iter max , if it is established, return to step 9. If Iter=Iter max Output current This is the optimal candidate solution and the iteration ends.

[0130] Step 15: Draw the fitting comparison curve, integrity coefficient set β curve and iteration curve.

[0131] The present invention is based on a pipe pile defect identification method of a swarm intelligence algorithm, and the identification results include the excitation force amplitude F, pulse width L, integrity coefficient set β, pile-soil friction coefficient μ, and pile-soil damping ratio ξ. The pipe pile defect identification method based on the experience learning algorithm can obtain quantitative identification data, has high inversion accuracy, and fast convergence speed. It can accurately invert the position range and degree of the model pile defects, realize the quantitative identification of foundation pile defects under a single homogeneous covering soil, improve the accuracy and reliability of the low-strain method, and provide a technical reference for further research on the quantitative identification of foundation pile defects under complex geological conditions in actual engineering. According to the identification results, a comparative fitting curve, an integrity coefficient β curve, and an iteration curve can also be drawn. From the fitting curve, it can be seen whether the calculation result is close to the actual situation and the degree of fitting. From the integrity system β curve, it can be seen that the specific defect degree of the pipe pile (i.e., quantitative identification) can be seen. From the iteration curve, it can be seen how the convergence situation is and whether the iterative calculation has been completed. At the same time, relevant parameters such as the maximum number of iterations can be provided for the next identification inversion calculation.

[0132] In order to study the influence and effect of the low strain method on the integrity judgment of a single homogeneous covered pipe pile, and to compare the recognition effect of the traditional method and the quantitative identification program of pile defects based on the swarm intelligence algorithm, the present invention designed an indoor comparative test of the low strain method for pipe piles. The nylon hammer was used to test the time history curve data V of the pile top velocity, and the curve was analyzed to summarize the law of the low strain method for single and double defect integrity judgment. According to the test requirements, 10 model piles (C80 hollow prestressed concrete pipe piles) were designed and manufactured. The design parameters of the model piles are shown in Table 4. The diameter is 500mm (wall thickness 125mm), of which 2 piles are 8.0m long and 8 piles are 10.0m long. The defect types are divided into 2, 3, and 4 types of defects, and the integrity coefficients are β 2 =93.6%, β 3 =66.9%, β 4 =50.0%, the pile impedance is changed by cutting the defect, and the model pile defect is shown. Figure 3The soil around the pile is clay soil, and the pile bottom is tested in a free pile state.

[0133] Table 4 Model pile design parameters

[0134]

[0135]

[0136] The pipe pile defect recognition program based on the experience learning algorithm was used to identify and invert 10 model defect piles. The parameter settings are shown in Table 3. The initial parameters of the experience learning algorithm are set to Iter max =100, Np=500, X max and X min The Abaqus model variable parameters are set according to their value range; the initial Abaqus model variable parameters are F = 2.6 kN and L = 1.7 ms, β n =1, μ=0.5, ξ=0.1; the initial Abaqus fixed parameters are R=5m, h=10m, d=0.5m, t=0.125m, l=10m, N=20. Other model material property parameters are shown in Table 2.

[0137] Finally, the inversion curve fitting comparison, iteration curve, and unit integrity coefficient curve of 10 model piles obtained by iterative calculation of the pipe pile defect identification program are shown in Figure 4 The algorithm identification parameters are shown in Table 5. As can be seen from the figure, the measured curves of the 10 model piles and the calculated curves from the finite element model inversion are basically fit. The algorithm iteration curves basically converged at the 60th iteration, and the fitness value was close to 0, indicating a fast convergence speed. The unit integrity coefficient β curve basically corresponds to the location of the pile design defects, and the values ​​are close, with an error of no more than 10%. Whether it is a complete pile, a single-defect pile, or a double-defect pile, the defect location and extent can be accurately identified, providing accurate quantitative analysis results. The inversion method has high accuracy and fast convergence speed. Considering the influence of measurement error, noise, model production error, and pile heterogeneity, the inversion results basically reflect the actual situation of the indoor model. The quantitative identification of pile defects under a single homogeneous cover soil is basically achieved, providing a reference for further research on the quantitative identification of pile defects under complex geological conditions in actual engineering.

[0138] Table 5 Summary of algorithm recognition parameter results

[0139]

[0140]

[0141] The embodiments of the present invention are not limited to these. According to the above contents of the present invention, by utilizing common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, the present invention can also make other various forms of modification, replacement or combination, all of which fall within the scope of protection of the present invention.

Claims

1. A method for identifying pipe pile defects based on swarm intelligence algorithm, characterized in that: The method comprises: Obtaining the measured velocity time history response curve data V of the pipe pile; Inputting the measured velocity time history response curve data V, experience learning parameters, model fixed parameters and model variable parameters into the pipe pile defect recognition program; The experience learning parameters include the maximum number of iterations , maximum population size , the upper bound of the decision variable , the lower bound of the decision variable and the maximum number of dimensions n, the upper bound of the decision variables and the lower bound of the decision variable Setting according to the value range of the model change parameter; The fixed parameters of the model include the radius of the soil around the pile , soil burial depth around piles , pipe pile diameter , pipe pile wall thickness , pile length , Number of pile body parts , elastic modulus of pile material , elastic modulus of soil around pile , Poisson's ratio of pile material , Poisson's ratio of soil around pile , pile material density , soil density around pile , cohesion , friction angle , longitudinal wave velocity of pile material and longitudinal wave velocity of soil around pile , the fixed parameters of the model do not change with iterative calculations; The model change parameters include the excitation force amplitude , Excitation force pulse width , Pipe pile integrity coefficient set , pile-soil friction coefficient and pile-soil damping ratio , the model variation parameters are optimized with iterative calculation; The pipe pile defect identification program performs iterative calculation to obtain the optimal candidate solution; The iterative calculation includes: The algorithm population is initialized to obtain the initialized candidate solution ; According to the initial candidate solution Start iterative calculation; Each iteration iterates and calculates the candidate solution according to the number of individuals p and the number of dimensions q in the population; Iterate until the maximum number of iterations is reached , get the optimal candidate solution; Each iteration is iteratively calculated according to the number of individuals p in the population and the number of dimensions q to obtain candidate solutions, including: Let the number of individuals in the population p = 1; According to the candidate solution through matlab Modify the model change parameters in the command stream file; Abaqus calls the modified command stream file to generate the pile-soil model and processes the operation to obtain the calculated velocity time history response curve data. ; According to the measured speed time history response curve data and calculate the speed time history response curve data Calculate candidate solutions The fitness value f; For candidate solutions , from the population Randomly select another candidate solution from , used to generate potential search directions, according to the candidate solutions The distance between the old and new positions is used to update the new position of the candidate solution through the model; Iterate until the number of individuals p in the population reaches the maximum population size ; Entering the spatial enhanced dimension search phase, iteratively calculates the number of dimensions q to obtain better candidate solutions; Iterate in sequence until the number of dimensions q reaches the maximum number of dimensions n; The candidate solution is initialized according to the following formula : in, is the upper bound of the decision variable, is the lower bound of the decision variable, is a random number uniformly distributed in the range [0, 1], p is the number of individuals in the population, q is the number of dimensions, is the exciting force amplitude, is the excitation force pulse width, is the set of integrity coefficients of the pipe piles, is the pile-soil friction coefficient, is the pile-soil damping ratio; The candidate solution is calculated according to the following formula The fitness value f: in, is the calculated speed time history response curve data, and V is the measured speed time history response curve data.

2. The method for identifying pipe pile defects according to claim 1, characterized in that: For candidate solutions , from the population Randomly select another candidate solution from , used to generate potential search directions, according to the candidate solutions The distance between the old and new positions is updated using the following two modes: Mode 1: Candidate Solution The new position directly to the candidate solution Learning, the exploration radius changes dynamically with iteration: Mode 2: Based on candidate solutions Experience, generate candidate solutions near the original position New location: Among them, rand is a function that takes a random value between [0,1], Iter is the current number of iterations, is the maximum number of iterations; In each iteration, in order to balance the exploration and exploitation capabilities of the algorithm during the search process, one of the two modes is randomly called; If the fitness value of the new position is better than the old position, the position of the candidate solution is updated according to the following formula, otherwise the old position is retained: 。 3. The method for identifying pipe pile defects according to claim 1, characterized in that: Entering the spatial enhanced dimension search phase, iteratively calculating the number of dimensions q to obtain a better candidate solution includes the following steps: The number of individuals in the population Find the best candidate solution among all candidate solutions calculated iteratively and the worst candidate solution ; According to the best candidate solution and the worst candidate solution , and changing the value of one dimension while maintaining the values ​​of other dimensions to generate new candidate solutions ; Compare the fitness values ​​of the new candidate solutions with the original candidate solutions and retain the better candidate solutions; The new candidate solution is generated by the following formula: in, is the best candidate solution, is the worst candidate solution, Iter is the current iteration number, Iter max is the maximum number of iterations, is a uniformly distributed random number in the range [0, 1], and q is the number of dimensions.

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