Optimized arrangement method of concrete vibrorammer for prefabricating UHPC (Ultra High Performance Concrete) cover beam shell

Abstract

The invention is applicable to the technical field of bridge construction, and provides an optimal arrangement method of concrete vibrators for prefabricating a UHPC (Ultra High Performance Concrete) cover beam shell, which comprises the following steps of: setting a plurality of vibrating schemes; according to each vibrating scheme, honeycombs in concrete in a prefabricated UHPC bent cap shell are removed through vibrators, and the number of the vibrators corresponding to the various vibrating schemes is different; aiming at each vibration scheme, optimizing the arrangement positions of the plurality of vibrators on the prefabricated UHPC cover beam shell in the vibration scheme by utilizing a whale optimization algorithm to obtain the optimal arrangement positions of the plurality of vibrators corresponding to the vibration scheme; according to all the optimal arrangement positions, a concrete vibrator arrangement scheme is constructed; and arranging vibrators on the prefabricated UHPC bent cap shell by utilizing the concrete vibrator arrangement scheme. The layout of the vibrorammer can be optimized, and the compactness of concrete is improved under the condition that the vibrorammer is limited.

Description

Technical Field

[0001] This application belongs to the technical field of bridge construction, and particularly relates to an optimized arrangement method for concrete vibrators for prefabricated UHPC capping beam shells. Background Art

[0002] At present, with the continuous improvement of production efficiency, due to the development of transportation tools and transportation needs, the construction of large bridge projects is imperative. The capping beam plays a crucial role in bridge projects. The capping beam is a force-transferring structure at the top of the pier and is mainly used to support the upper structure of the bridge. Currently, the construction of capping beams generally adopts traditional construction methods, that is, the capping beam structure is prefabricated in the factory first, and then transported to the construction site for hoisting. This method is not applicable to the construction of large bridge projects, and it has a certain impact on the nearby traffic during construction. For the capping beams of large bridges, they are characterized by large size and heavy weight. Therefore, it is required that the transportation tools have strong load-bearing capacity and stable transportation performance, and there are extremely high requirements for the accuracy, efficiency, and stability of construction equipment, which greatly increases the construction cost and construction difficulty and affects the construction efficiency.

[0003] With the development needs of productivity, the traditional construction method will no longer be applicable to large bridge projects. For large bridges, some scholars have proposed using ultra-high-performance concrete (UHPC) as the capping beam shell material, making its hollow thin-walled structure as a permanent structure formwork, and then hoisting it onto the pier top support or cantilever support, and finally pouring the core concrete and applying permanent prestress to complete the construction of the giant structure. In the actual construction process, in order to improve the density of concrete, it is necessary to use vibrators to remove the honeycombs in the concrete. However, at present, the vibrators are generally arranged on the formwork by construction workers according to their own experience, resulting in poor density of the concrete. Summary of the Invention

[0004] The embodiment of this application provides an optimized arrangement method for concrete vibrators for prefabricated UHPC capping beam shells, which can solve the problem of poor density of concrete.

[0005] The embodiment of this application provides an optimized arrangement method for concrete vibrators for prefabricated UHPC capping beam shells, including:

[0006] Setting a variety of vibration schemes; each vibration scheme is to use multiple vibrators to remove the honeycombs in the concrete inside the prefabricated UHPC capping beam shell, and the number of vibrators corresponding to the multiple vibration schemes is different from each other;

[0007] For each vibration scheme, the whale optimization algorithm is used to optimize the layout positions of multiple vibrators in the vibration scheme on the outer shell of the precast UHPC cover beam, and the optimal layout positions of the multiple vibrators corresponding to the vibration scheme are obtained;

[0008] According to all the optimal layout positions, a concrete vibrator layout scheme is constructed; the concrete vibrator layout scheme includes the corresponding relationship between the number of vibrators, the optimal layout positions, and the degree of concrete compaction;

[0009] The vibrators are arranged on the outer shell of the precast UHPC cover beam by using the concrete vibrator layout scheme.

[0010] Optionally, using the whale optimization algorithm to optimize the layout positions of multiple vibrators in the vibration scheme on the outer shell of the precast UHPC cover beam, and obtaining the optimal layout positions of the multiple vibrators corresponding to the vibration scheme, including:

[0011] Create n initial populations; each initial population includes multiple whale individuals, and each whale individual is the layout position of a vibrator in the vibration scheme on the outer shell of the precast UHPC cover beam, and the multiple whale individuals correspond one by one to the multiple vibrators in the vibration scheme;

[0012] Calculate the fitness of each initial population; the fitness of each initial population is the degree of concrete compaction under the layout positions of the vibrators corresponding to the initial population;

[0013] Based on the fitness of each initial population, each initial population is iteratively updated multiple times, and the multiple whale individuals in the optimal population obtained by the iterative update are correspondingly used as the optimal layout positions of the multiple vibrators corresponding to the vibration scheme.

[0014] Optionally, in the t-th iterative update, a random probability value is generated, and it is judged whether the random probability value is greater than or equal to the probability threshold. If the random probability value is greater than or equal to the probability threshold, the population obtained by the previous iterative update is iteratively updated in the way of simulating a whale hunting prey. If the random probability value is less than the probability threshold, it is judged whether the absolute value of the coefficient A is greater than or equal to the coefficient threshold. If the absolute value of the coefficient A is greater than or equal to the coefficient threshold, the population obtained by the previous iterative update is iteratively updated in the way of simulating a whale searching for prey. If the absolute value of the coefficient A is less than the coefficient threshold, the population obtained by the previous iterative update is iteratively updated in the way of simulating a whale surrounding prey.

[0015] Optionally, the calculation formula of the coefficient A is: A = 2ar1 - a, r1 is a random number between 0 and 1, T max is the preset maximum number of iterations, and t is the number of iterations.

[0016] Optionally, when t = 1, the population obtained by the previous iteration update is the initial population; when t > 1, the population obtained by the previous iteration update is the population obtained by the (t - 1)-th iteration update.

[0017] Optionally, after the t-th iteration update, calculate the fitness of each population obtained by the t-th iteration update. The fitness of each population is the degree of concrete compaction under the arrangement positions of the vibrators corresponding to the population for the concrete.

[0018] Optionally, the optimal population is the population with the minimum fitness among the n populations obtained by the T-th iteration update. max -th iteration update.

[0019] Optionally, the optimal population is the population corresponding to the fitness when meeting the preset fitness requirement. The preset fitness requirement is that the fitness is less than the preset fitness value, or the change in fitness is less than the preset threshold.

[0020] Optionally, the fitness of the initial population and the fitness of the population obtained by the t-th iteration update are both obtained through the CFD simulation model.

[0021] Optionally, the CFD simulation model loads the propagation of the vibration force inside the concrete through the following formula:

[0022]

[0023] where F(t) is the vibration force, A' is the amplitude value defined according to the physical characteristics of the vibrator, f is the vibration frequency of the concrete, t' is the time, is the phase difference, and λ is the time decay coefficient.

[0024] The above solution of the present application has the following beneficial effects:

[0025] In the embodiment of the present application, the whale optimization algorithm is used to study the optimal arrangement positions of different numbers of vibrators, construct the concrete vibrator arrangement schemes under different numbers of vibrators, and determine the relationship between the number of vibrators and the degree of concrete compaction, so that the construction party can arrange an appropriate number of vibrators on the precast UHPC cover beam shell based on this relationship and the actual construction requirements, achieving the effect of improving the compaction degree of concrete under limited vibrators.

[0026] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0027] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of an optimized layout method for a concrete vibrator used for a precast UHPC bent cap shell provided by an embodiment of the present application;

[0029] Figure 2 It is an optimized flowchart of a whale optimization algorithm provided by an embodiment of the present application;

[0030] Figure 3 It is a flowchart of numerical simulation in the related art;

[0031] Figure 4 It is a structural schematic diagram of a bent cap shell in the related art. Detailed implementation manners

[0032] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0033] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0034] It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" according to the context.

[0036] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0037] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0038] Aiming at the problem of poor compactness of current concrete, the embodiment of the present application provides an optimized layout method for concrete vibrators for precast UHPC bent cap shells. The optimized layout method of the concrete vibrator studies the optimal layout positions of different numbers of vibrators through the whale optimization algorithm, constructs a layout plan of the concrete vibrator under different numbers of vibrators, and determines the relationship between the number of vibrators and the degree of concrete compactness, so that the construction party can arrange an appropriate number of vibrators on the precast UHPC bent cap shell based on this relationship and actual construction requirements, achieving the effect of improving the compactness of concrete under limited vibrators.

[0039] The following is an exemplary description of the optimized layout method for concrete vibrators for precast UHPC bent cap shells provided by the present application in combination with specific embodiments.

[0040] As Figure 1 shown, the optimized layout method for concrete vibrators for precast UHPC bent cap shells provided by the embodiment of the present application includes the following steps:

[0041] Step 11, setting multiple vibration schemes; each vibration scheme is to use multiple vibrators to eliminate honeycombs in the concrete in the precast UHPC bent cap shell, and the numbers of vibrators corresponding to the multiple vibration schemes are different from each other.

[0042] In some embodiments of the present application, various vibration schemes can be set in combination with the actual project scale. For example, it is set that 100 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 90 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 80 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 70 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 60 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 50 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 40 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 30 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, 20 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell, and 10 vibrators are used to eliminate honeycombing in the concrete inside the precast UHPC cover beam shell. These different numbers of vibrators represent different strategies from high-density layout to low-density layout, aiming to explore the relationship between the number of vibrators and the degree of concrete compaction by optimizing the layout of vibrators under different numbers.

[0043] Among them, the above-mentioned vibrators can adopt common vibrators, such as vibrators with a diameter of 25 cm, a height of 15 cm, including eccentric flywheels, servo motors, high-strength magnetic bases, bolt fixing holes, and cable ties, and a vibration frequency of 100 Hz - 300 Hz.

[0044] Step 12, for each vibration scheme, use the Whale Optimization Algorithm (WOA) to optimize the layout positions of multiple vibrators on the precast UHPC cover beam shell, and obtain the optimal layout positions of the multiple vibrators corresponding to the vibration scheme.

[0045] The Whale Optimization Algorithm (WOA) is a bionic heuristic algorithm, which is inspired by the foraging behavior of humpback whales, especially the bubble net strategy of whales surrounding prey. WOA solves optimization problems by simulating this strategy and has advantages such as strong global search ability and high search space coverage. In this algorithm, whale individuals represent candidate solutions to the problem, and their positions are continuously updated to approach the optimal solution until the optimal solution is found or the convergence condition is met.

[0046] In some embodiments of the present application, for any vibration scheme, the specific implementation method of using the Whale Optimization Algorithm to optimize the layout positions of multiple vibrators on the precast UHPC cover beam shell (i.e., the formwork) and obtaining the optimal layout positions of the multiple vibrators corresponding to the vibration scheme includes the following steps:

[0047] Step 12.1: Create n initial populations; each initial population includes multiple whale individuals, and each whale individual is the layout position of a vibrator on the outer shell of the precast UHPC cover beam in the vibration scheme. The multiple whale individuals correspond one-to-one to the multiple vibrators in the vibration scheme. Here, n can be adjusted according to actual needs. Exemplarily, n can be set to 4.

[0048] Step 12.2: Calculate the fitness of each initial population; the fitness of each initial population is the degree of concrete compaction under the layout positions of the vibrators corresponding to the initial population. Exemplarily, the fitness of the initial population can be obtained through a CFD simulation model.

[0049] Step 12.3: Based on the fitness of each initial population, perform multiple iterative updates on each initial population, and use the multiple whale individuals in the optimal population obtained by the iterative update as the optimal layout positions of the multiple vibrators corresponding to the vibration scheme one-to-one.

[0050] Among them, as Figure 2 shown, in the t-th iterative update, generate a random probability value P, and determine whether the random probability value P is greater than or equal to the probability threshold. If the random probability value P is greater than or equal to the probability threshold, then use the method of simulating whales hunting prey to perform iterative update on the population obtained from the previous iterative update. If the random probability value P is less than the probability threshold, then determine whether the absolute value of the coefficient A is greater than or equal to the coefficient threshold. If the absolute value of the coefficient A is greater than or equal to the coefficient threshold, then use the method of simulating whales searching for prey to perform iterative update on the population obtained from the previous iterative update. If the absolute value of the coefficient A is less than the coefficient threshold, then use the method of simulating whales surrounding prey to perform iterative update on the population obtained from the previous iterative update. It should be noted that when t = 1, the population obtained from the previous iterative update is the initial population; when t > 1, the population obtained from the previous iterative update is the population obtained from the (t - 1)-th iterative update.

[0051] Furthermore, after the t-th iterative update, calculate the fitness of each population obtained from the t-th iterative update. The fitness of each population is the degree of concrete compaction under the layout positions of the vibrators corresponding to the population. Similar to the initial population, the fitness of the population obtained from the t-th iterative update can also be obtained through a CFD simulation model.

[0052] In some embodiments of the present application, both the above probability threshold and coefficient threshold can be set according to actual situations. Exemplarily, the probability threshold can be set to 0.5, and the coefficient threshold can be set to 1.

[0053] Specifically, as Figure 2As shown, after the t-th (t is an integer greater than or equal to 1) iterative update, calculate the fitness of each population obtained from the t-th iterative update, and determine whether the iterative termination condition is met (such as the number of iterations reaches the preset maximum number of iterations). If it is met, take the population with the minimum fitness among the n populations obtained from the t-th iterative update as the optimal population, output the optimal population, and use each whale individual in the optimal population as the optimal layout position of the corresponding vibrator on the outer shell of the precast UHPC cover beam, and terminate the iterative update. If it is not met, generate a random probability value P. If the random probability value P is greater than or equal to 0.5, update the whale individuals in the population obtained from the previous (i.e., the t-th) iterative update in the way of hunting prey (that is, perform the (t + 1)-th iterative update by simulating whales hunting prey: simulate whales capturing prey through a spiral trajectory, and the algorithm selects global search or local search through probability, so as to continuously adjust the position of the vibrator in the search space). If the random probability value P is less than 0.5, continue to judge the magnitude of the absolute value of the coefficient A and 1. If the absolute value of the coefficient A is greater than or equal to 1, update the whale individuals in the population obtained from the previous (i.e., the t-th) iterative update in the way of simulating whales searching for prey (that is, perform the (t + 1)-th iterative update by searching for prey: the individual expands the entire solution space through random search to ensure finding the optimal solution globally). If the absolute value of the coefficient A is less than 1, update the whale individuals in the population obtained from the previous (i.e., the t-th) iterative update in the way of simulating whales surrounding prey (that is, perform the (t + 1)-th iterative update by surrounding prey: the individuals gather around the optimal solution, similar to whales surrounding and hunting prey, and the individuals gradually approach the optimal solution through position update).

[0054] It should be noted that the above iterative termination condition can be set according to the actual situation. For example, it can be set that the number of iterations reaches the preset maximum number of iterations, there is a fitness less than the preset fitness value, or the change in the minimum fitness (referring to the minimum value among the fitnesses of the n populations after the t-th iterative update) is less than the preset threshold. The change in the minimum fitness refers to the change in the minimum value among the fitnesses of the n populations after the t-th iterative update relative to the minimum value among the fitnesses of the n populations obtained from the previous iteration.

[0055] Correspondingly, the above optimal population can be the population with the minimum fitness among the n populations obtained from the T max -th iterative update, or the population corresponding to the fitness when the preset fitness requirement is met. The preset fitness requirement is that the fitness is less than the preset fitness value, or the change in the fitness is less than the preset threshold. T max is the preset maximum number of iterations.

[0056] In the related technologies of the whale optimization algorithm, there are coefficient A and coefficient C during the optimization process. In some embodiments of the present application, the calculation formula for coefficient A is: A = 2ar1 - a, where r1 is a random number between 0 and 1, T max is the preset maximum number of iterations, and t is the number of iterations (i.e., the current number of iterations); the calculation formula for coefficient C is: C = 2r2, where r2 is a random number between 0 and 1. Among them, a is called the convergence primer, and its value is related to the maximum number of iterations; referring to the behavior of whales in nature when hunting prey, their behavior pattern is generally: in the early stage (the initial stage of algorithm iteration), when no prey is found, whales wander randomly and quickly in the ocean, searching the entire sea area quickly, hoping to find the target prey; when the prey is just found, they quickly approach the prey; afterwards (in the later stage of algorithm iteration), they wander in a small range near the prey to drive it away, constantly making fine adjustments, approaching the prey, and finally hunting. In the traditional whale algorithm, the value of the convergence primer a linearly decreases from 2 to 0, that is, a = 2(1 - t / T max ), where T max represents the maximum number of iterations. This means that the update speed of the population during algorithm iteration also linearly decays from the initial stage to 0 in the final stage. This linear decay often causes the iteration speed in the early stage of the algorithm to decay too fast, resulting in an incomplete search for feasible solutions; while in the later stage when fine adjustments should be made, the iteration speed of the algorithm decays too slowly, resulting in too low efficiency in finding the population with the optimal fitness. The present application proposes an improved iteration strategy, using the formula to replace the linear decay in the original algorithm. This function decays very slowly in the early stage of iteration and decays rapidly in the middle and later stages of the algorithm. Therefore, it can effectively increase the range of searching for the optimal solution in the early stage of the algorithm and improve the convergence efficiency in the later stage of the algorithm.

[0057] The following gives an exemplary description of the updates of surrounding prey, hunting prey, and searching for prey in the related technologies.

[0058] Surrounding prey: Individuals gather around the optimal solution, similar to whales surrounding prey. Individuals gradually approach the optimal solution through position updates;

[0059]

[0060] where t is the current number of iterations, A and C are the above coefficients, X * (t) is the position of the optimal whale population so far (i.e., the population with the smallest fitness among the n populations obtained in the t-th iteration), X(t) represents the position vector of the current whale (i.e., the population obtained in the t-th iteration), and X(t + 1) represents the population obtained in the (t + 1)-th iteration.

[0061] Prey capture: Simulate a whale capturing prey through a spiral trajectory. The algorithm probabilistically selects global search or local search to continuously adjust the position of the vibrator in the search space;

[0062]

[0063] where d' = |X * (t) - X(t)| represents the distance between the whale and the prey, and b and l control the shape of the spiral logarithmic line and the forward speed of the whale. The prey refers to the optimal arrangement of all vibrators (a theoretical value, the final output of the method), and the whale refers to the optimal arrangement currently found by the algorithm (among various populations, find the population with the highest current concrete compaction degree, which is the position information of all vibrators in that population).

[0064] Search for prey: Individuals expand the entire solution space through random search to ensure finding the optimal solution globally. In this application, the whale individuals represent the spatial layout of the vibrators, and the fitness function is based on the concrete compaction degree obtained from numerical simulation under the corresponding layout. The algorithm iteratively updates the positions of each individual to gradually approach the layout corresponding to the optimal concrete compaction degree.

[0065]

[0066] where X rand (t) represents a random population at the current iteration. When |A| ≥ 1, a search individual is randomly selected, and the positions of other whales are updated according to the randomly selected whale position, forcing the whales to deviate from the prey, thereby finding a more suitable prey, which can enhance the exploration ability of the algorithm and enable the WOA algorithm to perform global search.

[0067] Step 13, construct a concrete vibrator layout plan according to all the optimal layout positions; the concrete vibrator layout plan includes the correspondence between the number of vibrators, the optimal layout positions, and the concrete compaction degree.

[0068] In some embodiments of this application, after obtaining the optimal layout positions of different numbers of vibrators, the correspondence between the number of vibrators, the optimal layout positions, and the concrete compaction degree can be established to obtain the corresponding relationships for different numbers of vibrators. Taking 10 vibrators as an example, the established correspondence is for 10 vibrators, the optimal layout positions of each of the 10 vibrators on the precast UHPC cover beam shell, and the corresponding concrete compaction degree. Among them, the concrete compaction degree is obtained through a CFD simulation model.

[0069] Step 14, arrange the vibrators on the precast UHPC cover beam shell using the concrete vibrator layout plan.

[0070] In some embodiments of the present application, by optimizing the layout under different numbers of vibrators, the optimal layout scheme and the corresponding concrete compaction degree under each working condition (i.e., different numbers of vibrators) can be obtained. According to the optimal layout results of different working conditions (numbers of vibrators), a corresponding relationship curve between the number of vibrators and the minimum concrete compaction degree is generated. This curve can intuitively reflect the influence of the number of vibrators on the concrete compaction degree, provide a reference for actual projects, and help engineering personnel reasonably arrange the layout of vibrators on the premise of meeting quality requirements (such as concrete compaction degree requirements), thus avoiding resource waste. For example, if the concrete compaction degree corresponding to 10 vibrators meets the requirements, the engineering personnel can arrange these 10 vibrators according to the optimal layout positions of 10 vibrators on the outer shell of the precast UHPC cover beam.

[0071] In the related art, the concrete of the outer shell of the precast UHPC cover beam has high strength, small structural size, small internal space, and dense steel bars, without vibration space, and conventional vibration equipment cannot be put in. When pouring concrete components mixed by a concrete mixer, it is necessary to eliminate the honeycombs in them, carry out tamping, make the concrete compactly combined, and eliminate the honeycombed and pitted surfaces of the concrete, etc., so as to improve its strength and ensure the quality of concrete components. If the vibration is not dense, the strength is difficult to meet the expected requirements. Therefore, an external attached micro-vibrating instrument (i.e., vibrator) is adopted.

[0072] It is worth mentioning that the number of vibrators is significantly related to the vibration effect. Although covering the formwork surface with vibrators can achieve a good effect of eliminating honeycombs in concrete, it will also greatly increase the construction cost and may cause waste. The present application provides the concrete compaction degree under different numbers of vibrators and the corresponding layout positions of vibrators, so that construction personnel can arrange an appropriate number of vibrators according to the construction requirements (such as concrete compaction degree requirements) according to the optimal layout positions, thereby achieving the effect of improving the compaction degree of concrete under limited vibrators.

[0073] The simulation process of the CFD simulation model is exemplarily described below.

[0074] In some embodiments of the present application, a traditional CFD simulation process is adopted, except that in the present application, the CFD simulation model loads the propagation of the vibration force inside the concrete through the following formula:

[0075]

[0076] where F(t) is the vibration force, A' is the amplitude value defined according to the physical characteristics of the vibrator, f is the vibration frequency of the concrete, t' is the time, is the phase difference, and λ is the time decay coefficient.

[0077] The simulation process includes three aspects: constructing a vibration model, embedding the UDF program into the simulation environment, and simulation analysis and parameter adjustment.

[0078] (1) Constructing a vibration model

[0079] Write a multi-variable composite vibration function to construct a mathematical model of the vibration force changing with time during the concrete vibration process. By introducing parameters such as amplitude, frequency, phase difference, and time decay coefficient, accurately simulate the propagation of the vibration force inside the concrete:

[0080]

[0081] A is an appropriate amplitude value defined according to the physical characteristics of the vibrator; f is the vibration frequency, which can be adjusted according to the viscosity and fluidity of the concrete to ensure that the vibration frequency matches the curing characteristics of the concrete; is the phase difference, which is set according to the different positions of the vibration equipment layout; λ is the time decay coefficient, which is used to simulate the gradual weakening of the vibration force with time and reflects the physical characteristics of the actual vibration equipment.

[0082] (2) Embedding the UDF program into the simulation environment

[0083] Establish a geometric model of the precast capping beam shell and perform spatial discretization on it through Cartesian grid adaptive technology. Then, based on the Bingham rheological model, accurately simulate the non-linear flow characteristics of the concrete. Finally, embed the above custom UDF program (i.e., the above vibration model into the user-defined function function in the simulation software) into the simulation framework, periodically apply the vibration force field, and simulate the actual working effect of the vibrator inside the concrete. Through the real-time calculation of the UDF function, dynamically generate waveforms to ensure the effect of vibration compaction inside the concrete. Among them, UDF is a general data format for communication, which can send data to different systems and applications in a unified format.

[0084] (3) Simulation analysis and parameter adjustment

[0085] Through the analysis of the simulation results, analyze the propagation path and compaction effect of the vibration force in the concrete, verify the rationality of the vibration model and macro parameters, and ensure that the expected concrete vibration compaction effect is achieved in actual construction. The specific numerical simulation process is as Figure 3 shown. Figure 3 The optimization algorithm in is the whale optimization algorithm mentioned above.

[0086] The specific simulation process is as follows:

[0087] In this numerical simulation process, first, an accurate three-dimensional geometric model of the precast bent cap shell is constructed, and the Cartesian grid adaptation technology is used for spatial discretization to ensure both the accuracy of mesh division and computational efficiency within the model area. In terms of physical model settings, the Bingham rheological model is used to describe the non-linear flow characteristics of concrete, ensuring that the yield stress and viscosity changes of concrete can be accurately captured during the simulation process. Next, boundary conditions are set, including the periodic vibration force applied by the vibrator and the no-slip boundary at the contact surface between the concrete and the mold, to ensure the effective transmission of the vibration force in the concrete. A complex composite vibration force function is written using a custom UDF program and embedded into the ANSYS Fluent simulation framework to periodically apply a vibration force field and simulate the actual working state of the vibrator inside the concrete. This vibration force function accurately simulates the mechanical changes during the concrete vibration process by adjusting parameters such as amplitude, frequency, phase difference, and attenuation coefficient. During the simulation run, by monitoring the flow velocity, pressure distribution, and stress field changes inside the concrete in real time, the propagation path of the vibration force and the densification process inside the concrete are observed. Particular attention is paid to the elimination of honeycombing and the formation of the concrete densification effect. After the simulation is completed, the post-processing function is used to analyze the data, and the results such as the concrete densification degree, flow velocity field, and pressure field are visually displayed to verify the promoting effect of the vibration force on the uniform densification of the concrete.

[0088] In summary, the bent cap construction method based on the concrete vibrator optimization layout method provided in the embodiments of this application has the following advantages:

[0089] First, when using UHPC, the bent cap shell as shown in Figure 4 (this structure is a reinforced structure, with a reinforcement structure arranged approximately every 2 to 3 m, which is used to prevent the collapse of both sides of the bent cap shell during the pouring of the inner core concrete. The middle hole is for better flow of the concrete during pouring to ensure the pouring density) serves both as a structure and a formwork, saving the time for installing and removing the supports and formwork compared to traditional construction, reducing the construction cost, and improving the construction efficiency;

[0090] Second, the use of UHPC makes the construction process lightweight, has low requirements for transportation tools and construction equipment, is less restricted by the construction site, reduces the construction difficulty. At the same time, compared with the traditional construction process, lightweight construction avoids long-term traffic control near the construction site and alleviates traffic pressure;

[0091] Third, using the external attached type of micro-vibrating instrument for vibration solves the problem that the internal steel bars of the UHPC precast bent cap shell are dense, there is no space for vibration, and conventional vibration equipment cannot be put in;

[0092] IV. The whale algorithm is adopted to solve the optimal layout problem of the attached micro-vibrator, reduce the number of vibrators arranged, and improve the vibration efficiency;

[0093] V. The calculation method of the coefficient in the whale algorithm can effectively improve the range of searching for the optimal solution in the early stage of the algorithm and improve the convergence efficiency in the later stage of the algorithm, thereby improving the position optimization efficiency of the vibrator;

[0094] VI. The numerical simulation technology is adopted to simulate the inside of the vibration, reveal the flow mechanism of concrete inside the UHPC precast capping beam shell, and provide a scientific basis for the optimization of the vibration scheme;

[0095] VII. Based on the numerical simulation, the position optimization is realized. Compared with the traditional test verification method, the optimization cost is greatly reduced and the optimization efficiency is improved.

[0096] The above are the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle described in the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. An optimized layout method for concrete vibrators used in precast UHPC capping beam shells, characterized in that, Including: Setting multiple vibration schemes; Each vibration scheme is to use multiple vibrators to eliminate honeycombing in the concrete inside the precast UHPC bent cap shell, and the number of vibrators corresponding to the multiple vibration schemes is different from each other; For each vibration scheme respectively, using the whale optimization algorithm to optimize the arrangement positions of the multiple vibrators in the vibration scheme on the precast UHPC bent cap shell, and obtaining the optimal arrangement positions of the multiple vibrators corresponding to the vibration scheme; According to all the optimal arrangement positions, constructing a concrete vibrator arrangement scheme; the concrete vibrator arrangement scheme includes the corresponding relationship between the number of vibrators, the optimal arrangement positions, and the degree of concrete compaction; Using the concrete vibrator arrangement scheme to arrange vibrators on the precast UHPC bent cap shell.

2. The optimized layout method of the concrete vibrator according to claim 1, characterized in that The using the whale optimization algorithm to optimize the arrangement positions of the multiple vibrators in the vibration scheme on the precast UHPC bent cap shell and obtaining the optimal arrangement positions of the multiple vibrators corresponding to the vibration scheme includes: Creating n initial populations; each initial population includes multiple whale individuals, and each whale individual is the arrangement position of a vibrator in the vibration scheme on the precast UHPC bent cap shell, and the multiple whale individuals correspond one by one to the multiple vibrators in the vibration scheme; Calculating the fitness of each initial population; the fitness of each initial population is the degree of concrete compaction of the concrete under the arrangement positions of the vibrators corresponding to the initial population; Based on the fitness of each initial population, performing multiple iterative updates on each initial population, and corresponding the multiple whale individuals in the optimal population obtained by the iterative update one by one as the optimal arrangement positions of the multiple vibrators corresponding to the vibration scheme.

3. The method for optimizing the layout of the concrete vibrator according to claim 2, characterized in that, In the t-th iterative update, generating a random probability value, and judging whether the random probability value is greater than or equal to the probability threshold. If the random probability value is greater than or equal to the probability threshold, then use the way of simulating a whale hunting prey to perform iterative update on the population obtained by the previous iterative update. If the random probability value is less than the probability threshold, then judge whether the absolute value of the coefficient A is greater than or equal to the coefficient threshold. If the absolute value of the coefficient A is greater than or equal to the coefficient threshold, then use the way of simulating a whale searching for prey to perform iterative update on the population obtained by the previous iterative update. If the absolute value of the coefficient A is less than the coefficient threshold, then use the way of simulating a whale surrounding prey to perform iterative update on the population obtained by the previous iterative update.

4. The optimized layout method of the concrete vibrator according to claim 3, characterized in that, The calculation formula for the coefficient A is: A = 2ar1 - a, where r1 is a random number between 0 and 1, max T is the preset maximum number of iterations, and t is the number of iterations.

5. The method for optimizing the layout of the concrete vibrator according to claim 4, wherein, When t = 1, the population obtained by the previous iterative update is the initial population; when t > 1, the population obtained by the previous iterative update is the population obtained by the (t - 1)-th iterative update.

6. The method for optimizing the layout of the concrete vibrator according to claim 5, wherein After the t-th iterative update, calculating the fitness of each population obtained by the t-th iterative update, and the fitness of each population is the degree of concrete compaction of the concrete under the arrangement positions of the vibrators corresponding to the population.

7. The method for optimizing the layout of the concrete vibrator according to claim 6, characterized in that, The optimal population is the population with the smallest fitness among the n populations obtained by the T max -th iterative update.

8. The method for optimizing the layout of the concrete vibrator according to claim 6, wherein, The optimal population is the population corresponding to the fitness when meeting the preset fitness requirement, and the preset fitness requirement is that the fitness is less than the preset fitness value, or the change in fitness is less than the preset threshold.

9. The method for optimizing the layout of the concrete vibrator according to claim 6, characterized in that The fitness of the initial population and the fitness of the population obtained by the t-th iterative update are both obtained through the CFD simulation model.

10. The method for optimizing the layout of a concrete vibrator according to claim 9, characterized in that The CFD simulation model loads the propagation of the vibration force inside the concrete through the following formula: Among them, F(t) is the vibration force, A' is the amplitude value defined according to the physical characteristics of the vibrator, f is the vibration frequency of the concrete, t' is the time, is the phase difference, and λ is the time decay coefficient.

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