Bridge concrete strength detection method based on pigeon inspired algorithm and inversion theory

Abstract

The invention provides a bridge concrete strength detection method based on a pigeon inspired algorithm and an inversion theory, which comprises the following steps: establishing a two-dimensional concrete slab model of a railway overpass, and carrying out discretization modeling by adopting a finite element method; on the premise that initial parameters and boundary conditions are known, numerical analysis is conducted on the model, and the temperature field distribution situation of the concrete structure is obtained; carrying out optimization inversion on the temperature field data through a pigeon inspired algorithm, and constructing an objective function in combination with the existing concrete temperature field data, so as to invert the temperature distribution in the concrete; and evaluating the strength of the concrete based on the internal temperature data of the concrete, the cement hydration heat release characteristic and the transmissivity curve. The method has the beneficial effects that nondestructive testing of the internal temperature of the concrete is realized, so that the slump is accurately judged, the strength is accurately evaluated, and the problems that in the prior art, only the strength can be roughly judged, and damage to different degrees of a concrete structure cannot be effectively recognized are solved.

Description

Technical Field

[0001] The invention belongs to the field of detection technology, and in particular relates to a bridge concrete strength detection method based on pigeon flock algorithm and inversion theory. Background Art

[0002] Concrete plays a key role in the construction and design of railway overpasses. Concrete is used in major load-bearing structures such as piers, abutments, and bridge decks. Its high compressive strength and durability ensure the stability and safety of the bridge, and it can withstand long-term use by heavy-loaded trains. The durability of concrete materials enables it to resist erosion from the natural environment, such as rain, weathering, and freeze-thaw cycles. In view of this, it is necessary to strengthen the inspection of concrete quality in actual engineering construction, and increase the supervision, control, and assessment of the construction industry. At the same time, analyzing the influence of concrete mix ratio and non-destructive testing method related parameters on compressive strength is an important way to improve concrete quality, ensure the normal service life of the project, and support the sustainable development of the national economy. Through these measures, the safety and reliability of concrete engineering can be ensured, laying a solid foundation for the long-term development of the construction industry.

[0003] There are many methods for testing concrete strength. Among them, the core drilling method is a direct test method for testing the compressive strength of concrete. By drilling a core sample inside the concrete component and then conducting a compressive strength test on the core sample, the overall compressive strength of the concrete can be inferred. The core drilling method can directly obtain the actual compressive strength of concrete. The results are accurate and reliable and are not affected by surface damage or deterioration. However, the core drilling process may cause local damage to the concrete component, especially for thinner components, which may affect its structural integrity. Compared with other non-destructive testing methods, the cost is usually higher, including equipment rental, material and labor costs. In general, the core drilling method is a more accurate concrete strength test method, especially for important structures or projects that require accurate assessment of concrete quality. However, due to its destructive characteristics, the pros and cons should be weighed when using it, and appropriate repair and reinforcement measures should be taken when necessary.

[0004] The rebound method is a commonly used non-destructive method for testing the compressive strength of concrete. The compressive strength is estimated by measuring the surface hardness of concrete. The rebound method will not cause permanent damage to concrete components and can be tested without destroying the integrity of the structure. Compared with destructive testing methods such as the core drilling method, the rebound method is less expensive and suitable for strength testing of large-scale concrete structures. However, defects such as wear, looseness, and pockmarks on the concrete surface may affect the accuracy of the rebound test. It is greatly affected by ambient temperature, humidity and other conditions, and needs to be tested under suitable environmental conditions.

[0005] The ultrasonic rebound method is a non-destructive testing method for concrete strength that combines ultrasonic testing and rebound testing. It uses the propagation speed of ultrasonic waves and the rebound value of concrete to infer the compressive strength of concrete. The ultrasonic rebound method will not cause permanent damage to the concrete structure and can maintain the integrity and function of the structure. This method combines the ultrasonic propagation characteristics and the rebound value to comprehensively evaluate the performance of concrete. It is suitable for various types of concrete structures, including concrete of different strength grades and degrees of carbonization. However, for concrete structures with large thickness or serious internal defects, the propagation of ultrasonic waves may be limited, affecting the accuracy of the test results. The accuracy of the ultrasonic rebound method depends on the calibration of the instrument and the technical level of the operator.

[0006] The compressive strength test method measures the strength of concrete by applying pressure on the concrete test block. This method is simple, fast and economical, and can provide accurate concrete strength data. However, this method requires sampling and making test blocks, which involves more manpower and time costs. In addition, the specimen method can only reflect the strength of the concrete itself, but cannot fully reflect the actual quality of the concrete structure after construction. Summary of the invention

[0007] In view of this, the present invention aims to propose a bridge concrete strength detection method based on pigeon flock algorithm and inversion theory to at least solve one problem in the background technology.

[0008] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0009] A bridge concrete strength detection method based on pigeon flock algorithm and inversion theory, comprising:

[0010] S1. Establish a two-dimensional concrete slab model of the railway overpass and use the finite element method for discretization modeling;

[0011] S2. Under the premise of knowing the initial parameters and boundary conditions, the model is numerically analyzed to obtain the temperature field distribution of the concrete structure;

[0012] S3, optimizing and inverting the above temperature field data by pigeon flock algorithm, and combining the existing concrete temperature field data to construct an objective function, so as to invert the temperature distribution inside the concrete;

[0013] S4. Evaluate the strength of concrete based on the internal temperature data of concrete, cement hydration heat release characteristics and transmittance curve.

[0014] Furthermore, in step S2, the temperature field distribution of the concrete is obtained through model building and discrete solution; in step S3, the pigeon flock algorithm is introduced and combined with the existing temperature field data to perform inversion analysis, thereby obtaining the temperature distribution inside the concrete.

[0015] Furthermore, in step S2, when the initial parameters and boundary conditions are known, a numerical analysis is performed on the model, specifically including:

[0016] Construct a non-steady-state heat conduction forward problem model for the horizontal direction of the concrete of the railway overpass and preset the initial temperature inside the concrete;

[0017] Based on the known initial parameters and conditions, the model is numerically solved to obtain the distribution results of the temperature field.

[0018] Furthermore, in step S3, the pigeon flock algorithm is introduced, and the aforementioned temperature field data is combined to establish an objective function for inversion. The specific steps are as follows:

[0019] The temperature of the concrete outer wall of the railway overpass is calculated based on the temperature field distribution obtained from the unsteady-state heat conduction forward problem model.

[0020] Construct the objective function, compare the measured outer wall temperature with the model solution, use the pigeon flock algorithm to optimize the inversion process, and obtain the temperature difference;

[0021] Set the convergence condition. When the temperature difference does not meet the convergence condition, continue to iteratively adjust the temperature distribution of the concrete until the convergence condition is met, thereby obtaining the final temperature inversion result.

[0022] Furthermore, in step S1, the finite element method is used to discretize the calculation domain, which specifically includes:

[0023] The computational domain is divided into numerous small units, each unit contains a set of discretized nodes, and the interpolation function in the unit is constructed through the nodes;

[0024] A three-node triangular linear element is selected, and the temperature T in the element is assumed to be a linear function of x and y;

[0025] The expression of temperature gradient is derived through geometric relationship and shape function is defined;

[0026] The shape function is used to describe the temperature field change, which is substituted into the heat transfer differential equation, and the weighted residual method is used to analyze and obtain the discretized control equation.

[0027] According to Fourier's law of heat conduction and energy conservation equation, a two-dimensional unsteady-state direct problem model of heat conduction in the horizontal direction of railway overpass concrete is established.

[0028] Furthermore, the step S3 specifically includes:

[0029] The objective function is constructed based on the inverse estimated temperature of the concrete outer wall of the railway overpass, the real temperature of the outer wall from the solution of the forward problem model, and the corresponding time series sequence;

[0030] When the value of the objective function is too large, the ambient temperature is iteratively updated until the objective function meets the convergence condition. In each iteration, the update of the ambient temperature is calculated using the pigeon flock optimization algorithm.

[0031] Furthermore, the pigeon flock optimization algorithm includes a map compass operator navigation phase and a landmark operator navigation phase, wherein:

[0032] In the map compass operator navigation phase, the population speed and position are initialized. When the number of iterations reaches the preset threshold, the phase of landmark operator navigation is entered;

[0033] In the landmark operator navigation phase, the center position of the population is calculated and updated. In each iteration, individuals with poor fitness are discarded according to the fitness value, so that the population size is halved until the convergence condition is reached.

[0034] Furthermore, the present solution discloses an electronic device, including a processor and a memory that is communicatively connected to the processor and is used to store executable instructions of the processor, wherein the processor is used to execute a method for detecting the concrete strength of a bridge based on a pigeon flock algorithm and inversion theory.

[0035] Furthermore, the present solution discloses a server comprising at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a method for detecting bridge concrete strength based on a pigeon flock algorithm and inversion theory.

[0036] Furthermore, the present solution discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for detecting the concrete strength of a bridge based on a pigeon flock algorithm and inversion theory.

[0037] Compared with the prior art, the bridge concrete strength detection method based on pigeon flock algorithm and inversion theory described in the present invention has the following beneficial effects:

[0038] (1) The method for detecting the strength of bridge concrete based on the pigeon flock algorithm and inversion theory described in the present invention realizes non-destructive detection of the internal temperature of concrete based on the temperature inversion theory, thereby accurately judging the slump and accurately evaluating the strength, solving the problem that the prior art can only roughly judge the strength and cannot effectively identify the different degrees of damage to the concrete structure;

[0039] (2) The method for detecting the strength of bridge concrete based on the pigeon flock algorithm and the inversion theory described in the present invention avoids any physical damage to the concrete structure, especially for the key structural parts that cannot withstand any damage, which plays an important protective role;

[0040] (3) The method for detecting the concrete strength of a bridge based on the pigeon swarm algorithm and inversion theory described in the present invention ensures the integrity and safety of the structure by avoiding destructive testing. It can also effectively avoid safety issues that may be caused by destructive testing, such as structural damage or personal injury, which is particularly critical for engineers and technicians working in high-risk environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 It is a flowchart of the implementation of the railway overpass concrete strength detection method based on the pigeon flock algorithm and temperature inversion theory of the present invention;

[0043] Figure 2 The concrete slab model of the railway overpass of the present invention;

[0044] Figure 3 A schematic diagram of grid division in an experimental example of the present invention;

[0045] Figure 4 It is a three-dimensional temperature change curve diagram of the experimental example of the present invention;

[0046] Figure 5 It is a logic block diagram of the iterative algorithm in the experimental example of the present invention;

[0047] Figure 6 It is a comparison between the measured temperature and the inverted temperature in the experimental example of the present invention;

[0048] Figure 7 is a transmittance curve diagram of an experimental example of the present invention;

[0049] Figure 8 This is a temperature change curve diagram of an experimental example of the present invention. DETAILED DESCRIPTION

[0050] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0051] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0052] In order to overcome the limitations of existing concrete strength detection methods, the present invention proposes a new detection method based on pigeon flock algorithm and temperature inversion theory, which is suitable for railway overpass concrete. This method realizes non-destructive detection of the internal temperature of concrete, so that the slump can be accurately determined and the concrete strength can be accurately evaluated.

[0053] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0054] A bridge concrete strength detection method based on pigeon flock algorithm and inversion theory includes the following steps:

[0055] S1. Firstly, a two-dimensional concrete slab model of the railway overpass is established and modeled using finite element discretization;

[0056] S2. With the initial parameters and conditions known, the model is numerically analyzed to obtain the temperature field distribution of the model;

[0057] S3, by using the pigeon flock algorithm, combining it with the existing concrete temperature field data, constructing an objective function to achieve inversion of the internal temperature distribution of the concrete;

[0058] S4. With the help of concrete internal temperature data, cement hydration heat release characteristics and transmittance curve, the strength of concrete can be evaluated. Through this method, non-destructive testing of the internal temperature of concrete is achieved, so that the slump of concrete can be accurately determined and its strength can be accurately evaluated.

[0059] Furthermore, in the step S1, a two-dimensional concrete slab model of the railway overpass is first established, and the model is discretely modeled using finite elements;

[0060] Furthermore, in step S2, the thermophysical parameters of the railway overpass concrete are collected, and the temperature field is obtained by discrete solution. In step S3, the pigeon flock algorithm is introduced to invert the internal temperature of the concrete.

[0061] Furthermore, in step S1, a railway overpass concrete rectangular plate model is established, and the heat transfer temperature field of the concrete model is obtained by discrete solution using the finite element method, specifically including: using the finite element method to divide the calculation domain into countless small units. Each unit has a set of discretized nodes, through which the interpolation function in the entire unit can be constructed. This method uses triangular linear units for meshing.

[0062] For a three-node triangular linear element, it is usually assumed that the temperature T on the element is a linear function of x and y, and its expression is:

[0063] T=a 1 +a 2 x+a 3 y (1)

[0064] Where a 1 、a 2 、a 3 is a constant to be determined.

[0065] Through the change

[0066]

[0067] Where A is the area of ​​the triangular linear unit.

[0068] Define the shape function:

[0069]

[0070] In the formula, l=i,j,k.

[0071] This method uses shape functions to approximately describe the change of temperature field and applies them to the heat transfer differential equation to obtain the expression of the residual term:

[0072]

[0073] The weighted residual method is used for analysis:

[0074]

[0075] Using the Galerkin method to analyze and sort out, we can get:

[0076]

[0077] Where [C] is the heat capacity matrix, [K T ] is the heat transfer matrix, {F T} is the boundary heat flux load matrix. From the analysis of the triangular linear unit, the global control equation can be inferred:

[0078]

[0079] The unsteady-state heat conduction forward problem model of the railway overpass concrete in the horizontal direction is constructed. The internal temperature of the railway overpass concrete is preset. According to Fourier's heat conduction law and energy conservation equation, a two-dimensional unsteady-state heat transfer forward problem model along the horizontal direction of the railway overpass concrete is established. The control equation is:

[0080]

[0081] Where α is the thermal diffusivity.

[0082] The boundary conditions are:

[0083]

[0084]

[0085] Where, L 1 and L 2 is a type of boundary condition in region Ω, indicating an adiabatic boundary; L 4 and L 3 It is the third type of boundary condition (convection heat transfer). f is the ambient temperature, and h is the surface heat transfer coefficient.

[0086] Initial conditions:

[0087] T=T 0 (∈Ω,t>t 0 ) (14)

[0088] Where, T 0 is the initial temperature.

[0089] Based on the surface temperature and thermophysical property parameters of the railway overpass concrete obtained in step S2, solving the horizontal temperature field of the railway overpass concrete;

[0090] Furthermore, the step S3 introduces the pigeon flock algorithm and combines it with the previously obtained temperature field data.

[0091] Establish the objective function and invert to get the internal temperature of concrete, which includes the following steps:

[0092] The temperature of the concrete outer wall of the railway overpass obtained by solving the unsteady-state heat conduction forward problem and the temperature of the concrete outer wall of the railway overpass measured by the temperature measuring probe are substituted into the objective function used to solve the inverse problem to determine whether the obtained solution meets the convergence standard. If so, the concrete temperature inversion result is output.

[0093] The inverse problem of heat conduction is to construct the minimum optimization objective function of the difference between known parameters and unknown parameters, and obtain the inverse value of the position parameters through a certain optimization algorithm. The objective function is:

[0094]

[0095] T Est,n represents the estimated temperature of the concrete outer wall of the railway overpass, T Exp,n is the true temperature of the outer wall from the solution of the direct problem model, and n represents the corresponding time series sequence.

[0096] When the value of the objective function J is small enough, it indicates that the concrete temperature distribution obtained by the inverse problem of heat conduction is closest to the true solution, and a sufficiently small number μ is given as the convergence criterion. f ) 0Substitute the calculated value of the concrete outer wall temperature of the railway overpass under the condition into the inverse problem objective function to determine whether the value of the objective function meets the convergence condition.

[0097] If the value of the objective function is too large and does not meet the convergence condition, it is necessary to adjust T f Iterate and use a new round of T f Substitute the value into the positive problem to obtain a new recalculated objective function value, and each iteration T f The value of is calculated using the gorilla algorithm. The pigeon flock flight process mainly consists of the map compass operator navigation stage and the landmark operator navigation stage, the specific contents are as follows.

[0098] (1) Map compass operator

[0099] When the pigeon swarm optimization algorithm uses the map compass operator as a search tool, it first randomly initializes the speed and position of the population, and updates the position and speed of all individuals in the population as the number of iterations increases. Suppose the number of individuals in the current population is N, the dimension is dim, and the initialization speed v of the pigeon swarm is k =[v k1 ,v k2 ,v k3 ,...,v kdim ], position x k =[x k1 ,x k2 ,x k3 ,...,x kdim ], where k = 1, 2, 3, ..., N. That is, the displacement update formula of the pigeon flock according to the map and compass operator is:

[0100]

[0101] In the above formula, t represents the current iteration number; R represents the map and compass operator, and its value range is (0,1); rand represents a random number, and its value range is between 0 and 1; x best It represents the position of the global optimal solution found by the population before the tth iteration process. When the number of iterations t reaches the maximum number of iterations using the map and compass operators, the pigeon flock uses the landmark operator for navigation.

[0102] (2) Landmark operator

[0103] After the pigeon flock uses equations (16) and (17) to iterate and update the speed and position for multiple times, it enters the landmark operator navigation stage. In this stage, the pigeon flock optimizes its own flight direction and speed by finding the surrounding landmarks. In order to further reduce the algorithm's optimization time, each iteration needs to abandon individuals with poor positions in the population, and the population size becomes half of the previous iteration size; after the population size is halved, the center position of the population is calculated, and the center position is used as the basis for flight to update the individual flight direction. The specific expression is as follows:

[0104]

[0105] where x c represents the center position of the pigeon flock; f is the function to obtain the fitness value. According to the different solution directions, it can be divided into the following two expressions, where ε represents an infinitesimal value.

[0106]

[0107] The algorithm flow is as follows:

[0108] Step 1: Initialize relevant parameters of the population;

[0109] Step 2: Initialize the population position and speed, traverse all individuals in the population, and calculate the fitness values ​​of all individuals. By comparison, the optimal value f of the current population is obtained. best and the optimal position x best ;

[0110] Step 3: Use the map and compass operator to update the position and speed of the pigeons in the population according to equations (16) and (17), and update the population optimal solution f by comparing the updated individual fitness values. best and the optimal solution position x best ;

[0111] Step 4: Compare t and T 1 If the current number of iterations is greater than T 1 , enter the landmark operator navigation phase Step5; otherwise, go to Step3;

[0112] Step 5: Sort the pigeon population according to the fitness value of each pigeon, and discard the pigeons with poor fitness values ​​in the current population according to formula (19);

[0113] Step 6: Calculate the center position of the current population according to formula (18);

[0114] Step 7: Update the position of the pigeon according to formula (20), optimize the optimal solution of the algorithm and the position of the optimal solution;

[0115] Step8: Determine whether the current number of iterations is greater than the maximum number of iterations T. If the current number of iterations is greater, the algorithm ends, otherwise go to Step5.

[0116] In the specific implementation process:

[0117] Experimental Example 1

[0118] like Figure 1 —8, this experimental example provides a bridge concrete strength detection method based on pigeon swarm algorithm and inversion theory. The process is as follows:

[0119] Constructing an objective function to measure the temperature and solve the temperature of the concrete outer wall of the railway overpass, introducing a pigeon flock algorithm to perform inverse solution, and obtaining a temperature difference;

[0120] A convergence condition is preset, and when the temperature difference does not meet the convergence condition, the concrete temperature of the railway overpass is iterated until the convergence condition is met, thereby obtaining an inversion result of the concrete temperature of the railway overpass;

[0121] Furthermore, in the step (S4), the strength of the concrete can be evaluated by using the concrete internal temperature data, cement hydration heat release characteristics and transmittance curve. Specifically, it includes:

[0122] The humidity of concrete can be detected using a microwave humidity sensor. Next, an infrared thermometer is used to measure the temperature of the concrete. Then, an adiabatic temperature rise meter is used to measure the surface temperature rise of the concrete at a specific temperature to calculate its thermal conductivity. Furthermore, the thermal diffusivity of the material is directly measured by the laser flash method. In addition, the volume and weight of the concrete sample are measured by direct weighing to calculate its density. Finally, the density of the concrete is determined using an experimental determination method.

[0123] The hydration process of concrete can be divided into five stages: initial hydrolysis period, induction period, acceleration period, decay period and stabilization period. The first stage occurs about 15-30 minutes after the concrete is mixed with water. At this time, the concrete has not yet been poured, and the hydration reaction only affects the initial pouring temperature, causing it to rise by 1-2°C. The second stage occurs during the mixing, transportation and pouring of concrete and lasts for 1-3 hours. The third stage occurs after the concrete is poured and lasts for 3-12 hours. After the peak of concrete heat release, the hydration heat release rate begins to gradually decrease and eventually stabilizes. In the actual theoretical and computational research on concrete hydration heat release, the first two stages occur before concrete pouring and last for a short time, and the hydration heat release is small, so they are often ignored. The general calculation model starts from the third stage (i.e., the accelerated hydration stage after concrete pouring), which simplifies the problem. Studies have shown that the Sauer formula is used internationally to calculate concrete strength, and the formula is:

[0124]

[0125] In the formula, M is maturity (℃h), T is concrete temperature (℃), and Δt is the interval between two temperature measurements (h). Studies have shown that when the surface temperature of concrete rises to 38℃~39.5℃ (assuming the ambient temperature is 30℃) and the transmittance reaches 0.1%~0.2%, the slump can be considered qualified and the concrete strength meets the standard. By comprehensively considering the concrete surface temperature data and transmittance, the slump and strength can be evaluated more accurately.

[0126] Table 1 Inversion results of different algorithms at 2 h

[0127]

[0128] It can be seen from Table 1 that by combining the concrete surface temperature data and transmittance, the purpose of accurately determining the slump and accurately evaluating the strength can be achieved.

[0129] Experimental Example 2

[0130] Concrete structures play a key role in construction. Their strength has a significant impact on the stability and performance of the structure, so it has become an important control indicator in engineering. The detection methods for concrete strength are now quite diverse, including the rebound method, the core drilling method, etc. Compared with the traditional rebound method, the core drilling method, and the test block method, the new method proposed in this study selected concrete samples of four different strength grades, C30, C40, C50, and C60, for testing, and analyzed the relationship between them.

[0131] In the experimental preparation work, firstly, the concrete proportion is carried out, the specimen specifications are selected, and the instruments required for the experiment are determined. The experimental process includes using the rebound method, the core drilling method, the test block method and the new method proposed by the present invention to test the strength of the specimen. Through these experiments, the specific values ​​of different concrete strength grades will be obtained, and the relationship between them will be deeply analyzed.

[0132] Table 2 Comparison of concrete strength testing methods

[0133]

[0134]

[0135] After many experiments, the conclusions are shown in Table 2. It was found that the strength values ​​obtained by the rebound method are generally lower than those obtained by the core drilling method, and as the concrete strength grade increases, this difference becomes more significant; in general, the measured values ​​obtained by the rebound method are generally lower than the actual strength of the concrete. The rebound strength, core drilling strength and compressive strength of the standard cube test block are different, among which the strength results of the rebound method and the test block method are quite different, while the difference between the rebound method and the core drilling method is slightly smaller. Therefore, the use of the rebound method to analyze the concrete entity strength may produce a large deviation, because the rebound method mainly detects the concrete surface and lacks the ability to reflect the overall strength of the concrete. In contrast, the method proposed in the present invention can stably reflect the actual strength of the concrete.

[0136] These experiments verified the stability and superiority of the railway overpass concrete strength detection method based on the pigeon flock algorithm and temperature inversion theory, providing a reliable reference and theoretical basis for similar projects.

[0137] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A bridge concrete strength detection method based on pigeon flock algorithm and inversion theory, characterized in that: include: S1. Establish a two-dimensional concrete slab model of the railway overpass and use the finite element method for discretization modeling; S2. Under the premise of knowing the initial parameters and boundary conditions, the model is numerically analyzed to obtain the temperature field distribution of the concrete structure; S3, optimizing and inverting the above temperature field data by pigeon flock algorithm, and combining the existing concrete temperature field data to construct an objective function, so as to invert the temperature distribution inside the concrete; S4. Evaluate the strength of concrete based on the internal temperature data of concrete, cement hydration heat release characteristics and transmittance curve.

2. The method for detecting the strength of bridge concrete based on pigeon flock algorithm and inversion theory according to claim 1 is characterized in that: In step S2, the temperature field distribution of the concrete is obtained by model building and discrete solution; in step S3, the pigeon flock algorithm is introduced and combined with the existing temperature field data to perform inversion analysis to obtain the temperature distribution inside the concrete.

3. The bridge concrete strength detection method based on pigeon flock algorithm and inversion theory according to claim 1 is characterized by: In step S2, when the initial parameters and boundary conditions are known, the model is numerically analyzed, specifically including: Construct a non-steady-state heat conduction forward problem model for the horizontal direction of the concrete of the railway overpass and preset the initial temperature inside the concrete; Based on the known initial parameters and conditions, the model is numerically solved to obtain the distribution results of the temperature field.

4. The bridge concrete strength detection method based on pigeon flock algorithm and inversion theory according to claim 1 is characterized by: In step S3, the pigeon flock algorithm is introduced, and the aforementioned temperature field data is combined to establish the objective function for inversion. The specific steps are as follows: The temperature of the concrete outer wall of the railway overpass is calculated based on the temperature field distribution obtained from the unsteady-state heat conduction forward problem model. Construct the objective function, compare the measured outer wall temperature with the model solution, use the pigeon flock algorithm to optimize the inversion process, and obtain the temperature difference; Set the convergence condition. When the temperature difference does not meet the convergence condition, continue to iteratively adjust the temperature distribution of the concrete until the convergence condition is met, thereby obtaining the final temperature inversion result.

5. The bridge concrete strength detection method based on pigeon flock algorithm and inversion theory according to claim 1 is characterized by: In step S1, the finite element method is used to discretize the calculation domain, which specifically includes: The computational domain is divided into numerous small units, each unit contains a set of discretized nodes, and the interpolation function in the unit is constructed through the nodes; A three-node triangular linear element is selected, and the temperature T in the element is assumed to be a linear function of x and y; The expression of temperature gradient is derived through geometric relationship and shape function is defined; The shape function is used to describe the temperature field change, which is substituted into the heat transfer differential equation, and the weighted residual method is used to analyze and obtain the discretized control equation. According to Fourier's law of heat conduction and energy conservation equation, a two-dimensional unsteady-state direct problem model of heat conduction in the horizontal direction of railway overpass concrete is established.

6. The bridge concrete strength detection method based on pigeon flock algorithm and inversion theory according to claim 1 is characterized in that: The step S3 specifically includes: The objective function is constructed based on the inverse estimated temperature of the concrete outer wall of the railway overpass, the real temperature of the outer wall from the solution of the forward problem model, and the corresponding time series sequence; When the value of the objective function is too large, the ambient temperature is iteratively updated until the objective function meets the convergence condition. In each iteration, the update of the ambient temperature is calculated using the pigeon flock optimization algorithm.

7. The bridge concrete strength detection method based on pigeon flock algorithm and inversion theory according to claim 6 is characterized in that: The pigeon flock optimization algorithm includes a map compass operator navigation phase and a landmark operator navigation phase, wherein: In the map compass operator navigation phase, the population speed and position are initialized. When the number of iterations reaches the preset threshold, the phase of landmark operator navigation is entered; In the landmark operator navigation phase, the center position of the population is calculated and updated. In each iteration, individuals with poor fitness are discarded according to the fitness value, so that the population size is halved until the convergence condition is reached.

8. An electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, characterized in that: The processor is used to execute a bridge concrete strength detection method based on pigeon flock algorithm and inversion theory as described in any one of claims 1 to 7.

9. A server, characterized in that: It includes at least one processor and a memory that is communicatively connected to the processor, wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a bridge concrete strength detection method based on a pigeon flock algorithm and inversion theory as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements a bridge concrete strength detection method based on pigeon flock algorithm and inversion theory as described in any one of claims 1 to 7.

Images