Modular design system of assembled steel structure trestle transportation support

Abstract

The present application relates to structural engineering design technical field, specifically, it relates to a kind of modular design system of assembly type steel structure trestle transport support.It includes road condition characteristic module, stiffness matching module, dynamics modeling module, assembly sequence optimization module.The present application obtains road roughness data by road condition characteristic module and extracts main excitation band, calculates target stiffness interval and retrieves equivalent stiffness correction coefficient to correct connection interface tangent stiffness matrix to establish overall dynamics model, carries out full permutation design to transport support standard section, input overall dynamics model to permutation design scheme, output modal shape and calculate first-order bending modal frequency, then calculate modal separation degree, use genetic algorithm to maximize modal separation degree as target for global search, obtain optimal assembly sequence, can represent the microslip effect of connection interface, optimize modal distribution to avoid main excitation band, so as to improve the dynamics stability of transport support under complex road conditions.

Description

Technical Field

[0001] This invention relates to the field of structural engineering design technology, and more specifically, to a modular design system for prefabricated steel structure trestle transport supports. Background Technology

[0002] A modular design system for transport supports of prefabricated steel structure trestle bridges is mainly used to design and optimize the support structure of prefabricated steel structure trestle bridges during transportation, so as to ensure the structural integrity and transportation safety of prefabricated steel structure trestle bridges under complex road conditions.

[0003] In existing engineering practices, this field mainly adopts a simplified modeling method based on linear assumptions for design. Specifically, designers usually select the preload level of connecting bolts based on experience, simplify each standard section and connection interface of the transport support into an ideal linear elastic or rigid connection, and check the strength of the structure through conventional static calculations or linear dynamic analysis based on road surface grade and vehicle parameters. This method focuses on ensuring the basic load-bearing capacity of the transport support, that is, ensuring that when the transport support supports long steel beams, it will not fall apart due to the bolts being too loose, nor will it completely lose its elastic buffering effect due to the pursuit of rigidity alone.

[0004] However, existing simplified models often neglect the micro-slip effect at the connection interface of prefabricated steel structures. That is, they only consider the macroscopic range of preload and fail to accurately characterize the minute misalignments between the connectors caused by improper preload matching or complex excitation in the dynamic model. Because this micro-slip and the resulting nonlinear stiffness degradation are ignored, the dynamic model cannot truly reflect the impact of wear and stiffness changes on the overall structure during the wear process of the connection interface. This results in a large deviation between the established overall dynamic model and the actual transportation state, and the calculation results are not accurate enough.

[0005] In reality, when the overall dynamic model is distorted, it becomes difficult to effectively avoid complex resonance risks in the design. For example, when the main bump frequencies of the road surface (such as the frequency of speed bumps) happen to fall within the target stiffness range of the prefabricated steel structure trestle, an inaccurate overall dynamic model cannot accurately predict the dynamic response of the structure. At the same time, it is also difficult to solve the problem of coupled vibration caused by the torsional and bending frequencies of long steel beams being too close. This vibration will cause the steel beams to twist in a complex way like noodles, which will not only lead to beam deformation or support damage, but also amplify stress due to vibration and exacerbate micro-wear, thus seriously affecting transportation safety. Therefore, we provide a modular design system for the transportation support of prefabricated steel structure trestle. Summary of the Invention

[0006] The purpose of this invention is to provide a modular design system for prefabricated steel structure trestle transport supports to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a modular design system for prefabricated steel structure trestle transport supports, comprising:

[0008] The road condition feature module is used to acquire road surface roughness data of the transportation route and perform time-frequency domain conversion to obtain road surface roughness time domain signal, extract the main excitation frequency band in the road surface roughness time domain signal whose energy ratio exceeds the energy ratio threshold, and determine the road surface excitation frequency characteristics in the transportation process based on the main excitation frequency band.

[0009] The stiffness matching module calculates the target stiffness range required to avoid resonance based on the road excitation frequency characteristics, and retrieves the matching equivalent stiffness correction coefficient from a preset database according to the target stiffness range. The equivalent stiffness correction coefficient corresponds to the preload level of the connection interface.

[0010] The dynamic modeling module is used to treat the standard section of the transport support as a fixed interface substructure, and use the equivalent stiffness correction coefficient to update the tangent stiffness matrix of the connection interface between the substructures to characterize the micro-sliding effect, and then establish the overall dynamic model.

[0011] The assembly sequence optimization module is used to maximize the modal separation degree within the main excitation frequency bandwidth as a fitness function, construct a discrete design variable space for the standard section of the transport support at different final support point coordinates, and use a genetic algorithm to perform a global search for the arrangement design scheme in the discrete design variable space to obtain the optimal assembly sequence.

[0012] As a further improvement to this technical solution, the road condition feature module extracts the main excitation frequency band in the road surface roughness time domain signal whose energy proportion exceeds a preset energy proportion threshold, including the following method steps:

[0013] The wavelet packet decomposition algorithm is used to perform multi-scale decomposition of the road surface roughness time domain signal to obtain the frequency band reconstructed signal and calculate the energy ratio;

[0014] Set an energy percentage threshold, then sort the energy percentages in descending order and accumulate them. When the accumulated result is greater than or equal to the energy percentage threshold, the frequency band participating in the accumulation is defined as the main excitation frequency band.

[0015] As a further improvement to this technical solution, the road condition feature module also includes the following method steps:

[0016] Obtain the mass distribution matrix and center of gravity of the prefabricated steel structure trestle to be transported, and determine the preliminary coordinates of the support points in combination with the allowable axle load of the transport vehicle.

[0017] Calculate the moment of inertia tensor of the prefabricated steel structure trestle bridge in its transport posture, and correct the preliminary coordinates of the support points based on the principal axis direction of the moment of inertia tensor to obtain the final coordinates of the support points.

[0018] As a further improvement to this technical solution, the stiffness matching module calculates the target stiffness range required to avoid resonance, including the following steps:

[0019] Obtain the initial modal frequency and determine whether the initial modal frequency falls within the resonant bandwidth range of the road excitation frequency characteristics;

[0020] When the initial modal frequency falls within the resonant bandwidth range of the road excitation frequency characteristic, the peak frequency of the road excitation frequency characteristic is used as the reference.

[0021] The target frequency range is calculated based on the peak frequency, and the upper and lower limits of the target stiffness range are determined based on the target frequency range.

[0022] As a further improvement to this technical solution, the stiffness matching module retrieves a matching equivalent stiffness correction coefficient from a preset database based on the target stiffness range, including the following steps:

[0023] Based on the target stiffness range, the preload level is retrieved from the preset database, and then the hysteresis curve is obtained through the force and deformation of the connection interface.

[0024] Extract the slope of the hysteresis curve envelope, which is the equivalent stiffness under the preload level. Compare the equivalent stiffness with the linear stiffness value in the linear stiffness matrix to calculate the factor caused by micro-slip, which is the equivalent stiffness correction coefficient.

[0025] As a further improvement to this technical solution, the dynamic modeling module updates the tangent stiffness matrix of the connection interface between substructures using the equivalent stiffness correction coefficient, including the following steps:

[0026] Construct a parallel connection model containing multiple Jenkins units to describe the micro-sliding behavior of the connection interface;

[0027] The mapping relationship between normal contact pressure and tangential stiffness is obtained by querying the preset database according to the preload level.

[0028] The equivalent stiffness correction coefficient is used to adjust the sliding critical force parameter of the Jenkins unit, thereby updating the tangential stiffness matrix in real time and obtaining the updated tangential stiffness matrix.

[0029] As a further improvement to this technical solution, the method and steps for establishing the overall dynamic model in the dynamic modeling module are as follows:

[0030] Obtain the dynamic deflection and damping force of the transport vehicle's suspension system, and utilize... The hysteresis model is used for parameter identification, and piecewise hysteretic nonlinear differential equations are generated based on the parameters.

[0031] An overall dynamic model is established using the updated tangent stiffness matrix and the piecewise hysteretic nonlinear differential equation.

[0032] As a further improvement to this technical solution, the specific formula of the overall dynamic model is as follows:

[0033] ;

[0034] in, , and These refer to the displacement, velocity, and acceleration vectors in the physical coordinate system, respectively. This refers to a nonlinear force vector. This refers to external excitation load. This refers to damping force. This refers to the quality of prefabricated steel structure trestle bridges.

[0035] As a further improvement to this technical solution, the assembly sequence optimization module constructs a discrete design variable space for the standard section of the transport support at different final support point coordinates, including the following method steps:

[0036] The standard sections of the transport support are arranged in a full permutation design at different final support point coordinates to obtain the permutation design scheme, and then a discrete design variable space is constructed.

[0037] Calculate the mode shape for each arrangement design scheme, and extract the bending stiffness component and torsional stiffness component;

[0038] The first-order bending mode frequency and the first-order torsional mode frequency are obtained based on the bending stiffness component and the torsional stiffness component, respectively.

[0039] As a further improvement to this technical solution, the optimal assembly sequence in the assembly sequence optimization module includes the following method steps:

[0040] Based on the first-order bending mode frequency and the first-order torsional mode frequency, a fitness function is constructed with the goal of maximizing the modal separation degree.

[0041] Modal separation is defined as the ratio of the difference between the first-order bending mode frequency and the first-order torsional mode frequency to the bandwidth of the main excitation frequency band.

[0042] A genetic algorithm is used to iteratively search in the discrete design variable space to select the arrangement design scheme with a modality separation degree greater than the modality separation degree threshold as the optimal assembly sequence.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. In this modular design system for prefabricated steel structure trestle transport support, the stiffness matching module calculates the target stiffness range to avoid resonance based on the road surface excitation frequency characteristics, retrieves the hysteresis curve of the connection interface corresponding to the preload level from the database, determines the equivalent stiffness correction coefficient by extracting the envelope slope, and then uses the Jenkins unit parallel model to describe micro-slip through the dynamic modeling module. Combined with the equivalent stiffness correction coefficient of the stiffness matching module, the slip critical force parameter is adjusted, and the tangent stiffness matrix of the connection interface is updated in real time. This solves the model distortion problem caused by ignoring the micro-slip of the connection interface and nonlinear stiffness degradation, and can accurately characterize the macroscopic comprehensive stiffness of the structure, thereby significantly improving the accuracy of the transport dynamic response analysis of prefabricated steel structure trestle.

[0045] 2. In this modular design system for prefabricated steel structure trestle transport supports, the assembly sequence optimization module constructs a discrete design variable space for the standard sections of the transport support at different final support point coordinates, and calculates the first-order bending mode frequency and the first-order torsional mode frequency under each arrangement design scheme. Based on the first-order bending mode frequency and the first-order torsional mode frequency, the modal separation degree is calculated, and the modal separation degree is used as the fitness function of the genetic algorithm for iterative search. This solves the problem that existing assembly methods that rely on experience lead to dense structural modal frequencies and easy bending-torsional coupling resonance in the main excitation frequency band. By actively widening the distance between key modal frequencies, the system achieves active control of dynamic performance, effectively avoids resonance risks during transportation, and significantly improves transportation safety under complex road conditions. Attached Figure Description

[0046] Figure 1 This is a flowchart summarizing the invention.

[0047] Figure 2 This is a block diagram showing the module connection of the present invention.

[0048] The meanings of the labels in the diagram are as follows:

[0049] 1. Road condition feature module; 2. Stiffness matching module; 3. Dynamics modeling module; 4. Assembly sequence optimization module. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] This invention provides a modular design system for prefabricated steel structure trestle transport supports, comprising:

[0053] The road condition feature module 1 is used to acquire road surface roughness data of the transportation route and perform time-frequency domain conversion to obtain the road surface roughness time-domain signal. It extracts the main excitation frequency band in the road surface roughness time-domain signal where the energy proportion exceeds the energy proportion threshold, and determines the road surface excitation frequency characteristics during transportation based on the main excitation frequency band. The stiffness matching module 2 calculates the target stiffness range required to avoid resonance based on the road surface excitation frequency characteristics, and retrieves the matching equivalent stiffness correction coefficient from the preset database according to the target stiffness range. The equivalent stiffness correction coefficient corresponds to the preload level of the connection interface. The dynamic modeling module 3 is used to process the standard section of the transportation support as a fixed interface substructure, and uses the equivalent stiffness correction coefficient to update the tangent stiffness matrix of the connection interface between substructures to characterize the micro-sliding effect, and then establishes the overall dynamic model. The assembly sequence optimization module 4 is used to maximize the modal separation degree within the main excitation frequency band as a fitness function, construct the discrete design variable space of the standard section of the transportation support at different final support point coordinates, and use a genetic algorithm to perform a global search on the arrangement design scheme in the discrete design variable space to obtain the optimal assembly sequence.

[0054] Please refer to the following detailed explanation of the above modules. Figure 2 ;

[0055] Road condition feature module 1 first determines the transportation route, assuming the survey vehicle is a transport vehicle carrying prefabricated steel structure trestle bridges. The survey vehicle is used to conduct a preliminary survey based on the transportation route in advance to obtain road surface unevenness data in the spatial domain. Road surface unevenness data refers to the vertical height deviation of bumps and depressions relative to the road surface. Additionally, the surveying vehicle is programmed with a speed based on the specific transport sections of the route. This refers to the time axis (the horizontal coordinate axis that extends over time when collecting road surface roughness data in the spatial domain). When the survey vehicle travels at a preset speed, it uses a speed sensor to collect the actual travel speed. A Kalman filter is then used to preprocess the travel speed for noise reduction, resulting in the processed travel speed. To avoid high-frequency noise in the speed signal causing cumulative distance drift after integration, time integration is performed based on the processed driving speed to obtain the actual cumulative distance traveled by the survey vehicle. The road surface roughness data is converted into a time-domain signal of road surface roughness based on the processed driving speed using the integration method. ,in, This refers to the integral variable. This refers to the infinitesimal increment of time. The formula consists of two parts: the kinematic principle and the function mapping principle. Since the travel time and actual distance are not simply proportional in variable speed motion, time cannot be directly multiplied by speed. Therefore, the real travel distance obtained by real-time integration is used to map the road surface roughness data in the spatial domain. This ensures that the actual road surface position rolled by the wheel corresponds one-to-one with the road surface roughness time domain signal, achieving precise synchronization between the road surface undulation and the vehicle's driving state. This solves the problem of time sequence misalignment in traditional uniform speed transformation under variable speed conditions.

[0056] Setting the preset travel speed of the transport vehicle includes the following steps:

[0057] When the transport route is a highway, the speed limit is usually [missing information]. Considering the safety of transporting large items, the preset travel speed is: ;

[0058] When the transport route is a national highway or provincial highway, the speed limit is usually [missing information]. (For example, some national or provincial highways near schools or villages usually have a speed limit of...) Considering the complex road conditions, the preset driving speed is... ;

[0059] When the transport route is a temporary access road at the construction site, considering the safety of transporting large items, the preset travel speed is [missing information]. ;

[0060] Choose appropriate wavelet basis functions (e.g.) Wavelet series (due to their strong ability to capture transient signals) and number of decomposition levels (The number of decomposition layers depends on the sampling frequency and the required frequency resolution, typically 3 to 5 layers), performing decomposition on the time-domain signal of road surface unevenness. Layered wavelet packet decomposition is used to further divide the road surface unevenness time-domain signal into... There are three non-overlapping frequency bands, where a frequency band is a continuous frequency range. The first frequency band is extracted. The wavelet coefficients of all nodes in the layer are reconstructed to obtain the frequency band reconstructed signal. In this context, the frequency band reconstructed signal represents the portion of the oscillation waveform in the road surface roughness time domain signal that contains only a specific frequency range. Representing the Frequency band index below layer ( By reconstructing the data, we can isolate the mixed time-domain signals of road surface irregularities, allowing us to calculate the first-order road surface irregularity signal separately. Layer The total energy of the frequency band reconstructed signal over the entire time period Here, the entire time period refers to the duration for which the time-domain signal of the road surface unevenness is collected, and the calculation of the first... Layer The total energy of the frequency band reconstructed signal over the entire time period includes the following steps:

[0061] Calculate the first Layer The reconstructed signal of each frequency band on the time axis instantaneous amplitude The magnitude of the vibration displacement is calculated by removing the sign; then, the instantaneous amplitude is squared to obtain the squared value. ;

[0062] Integrate the squared value along the time axis to obtain the result of the integral, which is the result of the time step. Layer The total energy of the frequency band reconstructed signal over the entire time period According to Pasvald's theorem, the square integral of a time-domain signal equals the energy in the frequency domain, and the vibration intensity in that frequency band is quantified by integration.

[0063] When the first Layer When calculating the total energy of the frequency band reconstructed signal over the entire time period, the first frequency band is used to calculate the second frequency band reconstructed signal. Layer The total energy of the frequency band reconstructed signal accounts for the first frequency band during the entire time period. Total energy of reconstructed signal across all frequency bands in the layer The percentage, thus obtaining the energy percentage. The principle behind this formula is to use normalization to measure the relative importance of each frequency band.

[0064] Set energy percentage threshold The range of values ​​for the energy percentage threshold is: The energy percentages are then sorted from highest to lowest to obtain the sorted energy percentages. The energy percentages from the highest in the sorted order are accumulated until the accumulated result is greater than or equal to a preset energy percentage threshold. At this point, all frequency bands participating in the accumulation are defined as the main excitation frequency band, and the bandwidth of the main excitation frequency band is recorded. The main excitation bandwidth refers to the width of the frequency range where resonance occurs; please refer to Example 1.

[0065] Setting the energy percentage threshold involves the following steps:

[0066] The first Plot the cumulative energy percentage curve by using the number of frequency bands in the layer as the horizontal axis and the energy percentage as the vertical axis.

[0067] Observe the shape change of the energy accumulation percentage curve. The energy accumulation percentage curve usually rises sharply at first and then becomes flat.

[0068] The energy percentage threshold is set near the inflection point where the energy accumulation percentage curve begins to flatten. When the energy percentage reaches 85%, it indicates that the energy accumulation percentage curve has flattened, so the energy percentage threshold is 85%. When the energy percentage reaches 90%, the energy accumulation percentage curve is still rising steeply despite its flattening trend, indicating that the energy is relatively dispersed. In this case, the energy percentage threshold needs to be set higher to ensure sufficient coverage of the primary energy source. The primary energy source refers to the... The sum of the energy of the frequency band with the largest total energy among all reconstructed signal bands in the layer, i.e., the first... The total energy of the reconstructed signal across all frequency bands in the layer is multiplied by the energy percentage threshold. If the energy percentage threshold is set below 80%, key frequencies will be missed. If the energy percentage threshold is set above 95%, invalid noise will be introduced.

[0069] Example 1: Assume an energy percentage threshold Now the first The layer has 5 frequency bands:

[0070]

[0071] After determining the specific frequency bands included in the main excitation frequency band, it is necessary to further calculate the energy entropy of the frequency band reconstruction signal to determine the stationary excitation frequency band and the impulsive excitation frequency band in the frequency band reconstruction signal. Here, energy entropy refers to the degree of disorder in the distribution of the total energy of the frequency band reconstruction signal across different frequency bands. The energy entropy, stationary excitation frequency band, and impulsive excitation frequency band specifically include the following methods and steps:

[0072] First calculate the... Layer Relative energy probability of each frequency band Next, take the natural logarithm of the relative energy probability to obtain the corresponding natural logarithmic value. ;

[0073] The relative energy probability is then multiplied by the corresponding natural logarithm to obtain the first... The product of all frequency bands within the layer will be the result of the first... The product results of all frequency bands within the layer are accumulated to obtain the final accumulated result. Then, the sign of the final accumulated result is negative to obtain the nth... Layer energy entropy The principle of this formula originates from Shannon entropy in information theory. If the total energy of the frequency band reconstructed signal is concentrated in one frequency band (such as a single pothole), the relative energy probability distribution is extremely uneven, and the calculated energy entropy is small. If the total energy of the frequency band reconstructed signal is evenly distributed across all frequency bands (such as a random gravel road), the relative energy probability distribution is uniform, and the calculated energy entropy is large.

[0074] Set energy entropy threshold The energy entropy and energy entropy threshold are used to determine whether the frequency band reconstruction signal is a stationary excitation frequency band or an impulsive excitation frequency band; specific situations include:

[0075] Case 1: When the energy entropy is less than the energy entropy threshold, it indicates that the energy distribution of the reconstructed signal in this frequency band is highly concentrated. The reconstructed signal is defined as the impulse excitation frequency band; then the center frequency of the impulse excitation frequency band is calculated. The set of center frequencies of all impact excitation frequency bands is defined as the characteristic frequency of the main excitation frequency band. ;

[0076] Case 2: When the energy entropy is greater than the energy entropy threshold, it indicates that the energy distribution of the reconstructed signal in this frequency band is relatively dispersed, and the reconstructed signal is defined as a stable excitation frequency band.

[0077] Example scenario:

[0078] First, a survey vehicle travels at a preset speed (e.g., 60-80 km / h on highways). Kalman filtering removes speed noise, and combined with integral mapping technology, accurately and synchronously converts road surface irregularity data into a time-domain signal, completely solving the timing misalignment problem caused by vehicle speed fluctuations in traditional methods. Next, wavelet packet decomposition is used to segment the mixed road surface irregularity time-domain signal into multiple independent frequency bands. The energy percentage is then calculated and sorted. Based on the highest energy percentage, the main excitation frequency band with the highest energy percentage threshold is accumulated and selected, thus identifying the vibration component that has the greatest impact on the transport vehicle. Finally, energy entropy is used to determine the stable excitation frequency band and the impact excitation frequency band. If it is an impact excitation frequency band, it is determined that the transport vehicle will encounter speed bumps or potholes during transport according to the route, and the center frequency of the impact excitation frequency band is extracted as a characteristic frequency to guide vibration avoidance. If it is a stable excitation frequency band, it is determined that the transport vehicle will encounter ordinary gravel roads during transport according to the route.

[0079] In addition to extracting road surface excitation frequency features, which include the stable excitation frequency band or impact excitation frequency band, characteristic frequency, and main excitation bandwidth, the road condition feature module 1 is also responsible for calculating the optimal placement location of the transport support. The software obtains the mass distribution matrix and center of gravity position of the prefabricated steel structure trestle to be transported, and then obtains the allowable axle load (an inherent performance parameter; allowable axle load refers to the maximum vertical load that a single axle (or a single suspension point) of the transport vehicle is allowed to withstand during design and manufacturing). Using the mass distribution matrix and center of gravity position, combined with the allowable axle load, the preliminary coordinates of the support points are determined. ,in, This refers to the spatial coordinates of the i-th support point. This refers to the total number of support points; determining the preliminary coordinates of the support points can ensure that the reaction force at each support point is less than the allowable axle load, thus ensuring the safety of the foundation bearing capacity.

[0080] To further suppress vibration coupling, based on the initial coordinates of the support points, the rotational inertia tensor of the prefabricated steel structure trestle under transport posture is calculated. The calculation of the moment of inertia tensor includes the following steps:

[0081] Obtain the volume of the prefabricated steel structure trestle bridge And perform integration, at each volume infinitesimal element Record the volume micro coordinates at this location. and mass density ; Calculate the position vector of the support point ,in, This refers to the transpose operation, followed by calculating the square of the position vector magnitude of the support point. With the identity matrix The product of the two vectors is used to obtain the product. Then, the tensor product of the support point's position vector and itself is subtracted from the product to obtain the difference. Here, the identity matrix is ​​a... The diagonal matrix is ​​obtained, with diagonal elements set to 1 and the rest to 0. The difference is multiplied by the mass density of the support points and accumulated over the entire volume domain to finally obtain the moment of inertia tensor. The specific formula is as follows: The moment of inertia tensor refers to a... The matrix, It refers to the tensor product symbol. The moment of inertia tensor describes the moment of inertia and product of inertia of a prefabricated steel structure trestle when it rotates around different spatial axes, and reflects the ability of the prefabricated steel structure trestle to resist angular acceleration.

[0082] After calculating the moment of inertia tensor, in order to optimize the attitude of the prefabricated steel structure trestle bridge during transportation, the moment of inertia tensor is transformed into a diagonal matrix (i.e., only the upper left to lower right corners have values, and the rest are 0) by solving for the eigenvalues. The coordinate system direction of this new diagonal matrix is ​​the direction of the principal axes of inertia. The specific formula for solving for the eigenvalues ​​of the moment of inertia tensor is as follows: The principle behind this formula is the general definition of the tensor of the rotational inertia of a rigid body (referring to a prefabricated steel structure trestle bridge in this case) in physics; the core is based on the integration of the mass infinitesimal element and the expression of the parallel axis theorem in tensor form. It is an eigenvalue;

[0083] By correcting the initial coordinates of the support points along the main axis, the final coordinates of the support points are obtained, so that the line connecting the support points coincides with or is parallel to the inertial main axis of the prefabricated steel structure trestle.

[0084] Example scenario:

[0085] Using SolidWorks software, he clearly determined the center of gravity of the prefabricated steel structure trestle and the mass distribution of each steel plate in the trestle. He then checked the allowable axle load of the transport vehicle (i.e., the maximum weight that the axle or suspension can withstand). To prevent the prefabricated steel structure trestle from breaking the axle of the transport vehicle, he determined the initial optimal placement position of the transport support (the initial coordinates of the support points) based on the road excitation frequency characteristics and the transport vehicle information. This ensured that when the prefabricated steel structure trestle was pressed down, the force on each support point was within a safe range. However, this was not stable enough. To prevent the prefabricated steel structure trestle from swaying back and forth and side to side like jelly when the transport vehicle was in motion (to suppress vibration coupling), the prefabricated steel structure trestle was divided into countless tiny volume elements. The position vector and mass density of each fragment (volume element) were obtained. The resistance to rotation of all fragments was added together using an integral formula to calculate a moment of inertia tensor. The moment of inertia tensor can tell us which direction the prefabricated steel structure trestle is most difficult to turn in and which direction it is easiest to turn in.

[0086] The principal axis of inertia of the prefabricated steel structure trestle can also be obtained by using the rotational inertia tensor. The complex rotational inertia tensor is simplified into a diagonal matrix by using eigenvalues, thereby obtaining the direction of the principal axis. Finally, the initial optimal placement position of the transport support is finely adjusted according to the direction of the principal axis to obtain the final optimal placement position of the transport support. The line connecting the final optimal placement positions of the transport support coincides with or is parallel to the direction of the principal axis of inertia, thereby completely eliminating the risk of swaying during transportation.

[0087] Stiffness matching module 2 is used to receive the road surface excitation frequency characteristics from road condition characteristic module 1. When the impact excitation frequency band is received, the characteristic frequency in the road surface excitation frequency characteristics is regarded as the peak frequency. When a stable excitation frequency band is received, the characteristic frequency corresponding to the total energy of the highest frequency band reconstructed signal in the main excitation frequency band is regarded as the peak frequency. Peak frequency refers to the frequency value corresponding to the vibration component that has the greatest impact on transport vehicles in the road excitation frequency characteristics. That is, in the time-domain signal of road surface unevenness in the current transport section, it is the frequency point where the total energy of the frequency band reconstructed signal is most concentrated. The resonant bandwidth range is calculated using the characteristic frequency and the main excitation bandwidth in the road excitation frequency characteristics. This refers to the frequency range in which resonance occurs; simultaneously, a simplified finite element model of the frame-axle coupled system is established, and modal analysis is performed on the finite element model of the frame-axle coupled system to extract the initial modal frequencies. Among them, the finite element model of the frame-bridge coupling system is a digital simulation model that treats the transport frame (vehicle chassis) and the load-bearing bridge (prefabricated steel structure trestle bridge erected on the frame) as a unified whole and is established using the finite element method; the initial modal frequency refers to the inherent vibration frequency of the frame-bridge coupling system. Once the rhythm of road bumps hits it, the frame-bridge coupling system will resonate violently.

[0088] The initial modal frequency is used to determine whether it falls within the resonant bandwidth of the road excitation frequency characteristics. If the initial modal frequency falls within this bandwidth, the peak frequency of the road excitation frequency characteristics can be used as a benchmark to quickly identify safety hazards during transportation. Based on this, a safety margin value is set. Calculate the lower limit frequency of the target frequency range based on the peak frequency and the safety margin value. The target frequency range refers to the ideal frequency range within which the natural frequency of the prefabricated steel structure trestle is designed; the safety margin value refers to the difference between the natural frequency of the prefabricated steel structure trestle and the cutoff frequency of the road surface excitation energy. The safety margin value is determined based on the following methods and steps:

[0089] According to the theory of random vibration, in order to avoid the amplification of response caused by resonance, the natural frequency of the prefabricated steel structure trestle should avoid at least one frequency band of the main energy; a 20% safety margin can ensure that the prefabricated steel structure trestle is always in the dynamic response smooth zone under the conditions of dealing with driving speed fluctuations and road excitation frequency characteristics.

[0090] Because prefabricated steel structure trestle bridges and transport supports have a large number of bolted connections, their contact stiffness is highly nonlinear and there is irreversible fretting wear. The frequency of the actual physical system is usually lower than the value calculated by the finite element theory. Setting a safety margin of 20% ensures the safety and reliability of physical verification.

[0091] Based on ISO 8608:2016 Mechanical vibration—Road surface profile—Measurement data report, the spatial frequency range for highway pavement measurement is as follows: Considering the ground envelope characteristics of the tires used in the transport vehicle (e.g., model 295 / 80R22.5), when the spatial cutoff frequency of the tire envelope filter is greater than... When the corresponding wavelength is less than 0.33 meters, the elastic envelope effect of the tire will filter out most of the vertical displacement input from the road surface. The acceleration response at the axle of the transport vehicle mainly manifests as high-frequency noise rather than low-frequency coupled vibration. In order to ensure the integrity of the road excitation and cover the main energy frequency band, while avoiding invalid high-frequency calculations, the spatial cutoff frequency of the tire envelope filter is taken. The spatial cutoff frequency of the tire envelope filter and the processed driving speed are standardized to unify the dimensions, and then the road excitation energy cutoff frequency is calculated. The road surface excitation energy cutoff frequency refers to the critical frequency point where the total energy of the frequency band reconstructed signal in the road surface roughness excitation power spectral density (PSD) can be ignored; the frequency doubling extension frequency is set to 5 times the lower limit frequency, i.e. To cover higher-order modes that may cause micro-slippage at the interface; and then determine the upper frequency of the target frequency range based on five times the road excitation energy cutoff frequency and the lower limit frequency. ;

[0092] The mass of the prefabricated steel structure trestle bridge was obtained based on the mass distribution matrix. The target stiffness range is calculated based on the mass and the upper and lower limits of the target frequency range. The target stiffness range refers to the ideal range of stiffness (or local stiffness) that the prefabricated steel structure trestle must achieve or maintain in order to meet the safety requirements of the prefabricated steel structure trestle during transportation (avoiding resonance and controlling deformation within the allowable range).

[0093] Lower limit of the target stiffness range: ;

[0094] Upper limit of the target stiffness range: ;

[0095] Using the target stiffness range, a preload level (referring to the grade of tightening force applied when bolts are connected between transport supports) is found by searching a preload level in a preload database.

[0096] By measuring the forces and deformations at the contact points between standard sections of the transport support frame using sensors, a hysteresis curve is plotted. The slope of the hysteresis curve envelope is then extracted, which represents the equivalent stiffness at that preload level. Among them, equivalent stiffness refers to the macroscopic and comprehensive stiffness value exhibited by the connection interface (bolt connection surface) under a specific preload level;

[0097] By comparing the equivalent stiffness with the linear stiffness value in the linear stiffness matrix, the factor caused by micro-slip, i.e., the equivalent stiffness correction coefficient, is calculated. The equivalent stiffness correction factor refers to the mapping factor that corrects the linear stiffness values ​​in the linear stiffness matrix to the equivalent stiffness.

[0098] The equivalent stiffness correction factor is applied to the correction of the linear stiffness matrix to determine the final preload level of the connection interface. The linear stiffness matrix refers to the standard stiffness matrix calculated in finite element analysis under the assumption that the prefabricated steel structure trestle is perfectly elastic (without friction or slippage). By accurately selecting the preload, the actual stiffness of the connection interface can physically avoid the resonance zone of road excitation, effectively suppressing vibration and ensuring transportation safety.

[0099] The linear stiffness matrix includes the following steps:

[0100] The standard section of the transport support is treated as a fixed interface substructure; using the mechanics of materials and finite element theory, the theoretical stiffness is directly calculated based on the geometric parameters (dimensions) and material properties (such as the elastic modulus of steel) of the standard section of the transport support.

[0101] The theoretical stiffness is assembled into a matrix form to form the initial linear stiffness matrix.

[0102] Example scenario:

[0103] Suppose a transport vehicle is delivering prefabricated steel structure trestle bridges on a mountain road with complex road conditions. Monitoring reveals that the road surface ahead not only experiences frequent small bumps (stable excitation frequency band) but also large potholes (impact excitation frequency band). The most severe vibration frequency of the road surface (peak frequency) is calculated. At this point, the original vibration frequency of the prefabricated steel structure trestle bridge plus the inherent original vibration frequency (initial modal frequency) of the transport vehicle frame happens to coincide with this severe road surface frequency range. Driving over it would cause violent resonance, which is extremely dangerous. To avoid this danger, without changing the road surface, we can only change the stiffness of the prefabricated steel structure trestle bridge. Therefore, a safety frequency (lower limit frequency) 20% higher than the dominant frequency of the road surface is set to ensure the prefabricated steel structure trestle bridge's stability. The vibration rhythm of the structural trestle always precedes that of the road surface, never moving in sync. Simultaneously, considering that tires act as shock absorbers, filtering out excessively high-frequency vibrations (the cutoff frequency of road excitation energy), an upper limit is calculated to obtain an ideal target stiffness range. Traditional finite element models assume that tightened bolts equate to rigidity, but in reality, bolted connections experience friction and slippage. This invention introduces hysteresis curves as a benchmark, measuring the force-deformation curves of the connection interface under different preloads, extracting their slopes to obtain the true equivalent stiffness, and correcting deviations using an equivalent stiffness correction coefficient. Finally, a database is directly searched to select the preload level that ensures the actual stiffness falls precisely within the target stiffness range (avoiding resonance).

[0104] The dynamic modeling module 3 is used to receive the equivalent stiffness correction coefficient from the stiffness matching module 2. Each standard section of the transport support is defined as a fixed interface substructure. At the connection interface between the fixed interface substructures, a parallel connection model containing multiple Jenkins units is constructed (a modeling method that connects multiple independent Jenkins units side by side to work together to simulate a real connection interface). The Jenkins unit is an idealized mechanical model containing springs and sliding elements, which can accurately describe the micro-slip effect (when subjected to the load of the prefabricated steel structure trestle, the contact interface of the bolted connection of the transport support has not yet undergone overall macroscopic relative movement, but micro-slip has already occurred in a small local area on the contact surface).

[0105] Based on the final implemented connection interface preload level, the pre-set database is searched to obtain the normal contact pressure corresponding to the final implemented connection interface preload level. (The clamping force acting perpendicularly on the contact surface is provided by the bolt preload) and tangential stiffness (The resistance force generated by a unit tangential displacement before macroscopic overall slippage occurs at the connection interface), the slip critical force parameter of the Jenkins unit is calibrated based on the equivalent stiffness correction coefficient to obtain the corrected slip critical force. ;in, This refers to the coefficient of friction, the value of which is based on a material property library (such as the coefficient of friction between steel and steel); the critical slip force parameter refers to the key physical threshold that determines whether the connection interface remains stationary or slips.

[0106] By assembling the tangential stiffness matrix using tangential stiffness, and updating the tangential stiffness matrix of the interface between substructures in real time based on the corrected slip critical force, the updated tangential stiffness matrix is ​​obtained. By introducing an equivalent stiffness correction coefficient to correct the slip critical force parameter, the hysteresis characteristics (shape and area) of the connection interface are actually changed, and the tangential stiffness matrix is ​​updated in real time accordingly. This step solves the calculation error problem caused by the inability to simulate the micro-slip of the bolt interface, and can accurately reflect the stiffness softening effect and energy dissipation characteristics under a specific preload.

[0107] Dynamic deflection was collected using the suspension system of the transport vehicle. (Refers to the relative vertical displacement between sprung mass (vehicle body) and unsprung mass (wheels / axles) of the suspension system of a transport vehicle during vehicle operation) and damping force. (Refers to the resistance generated by a shock absorber (vibration damper) to suppress vibration and dissipate energy when relative motion occurs), inputting dynamic deflection and damping force. Parameter identification is performed in the hysteresis model, and the hysteresis evolution parameter A and shape control parameter are output. and smoothness parameters hysteresis variables Using hysteresis evolution parameter A and shape control parameter and smoothness parameters hysteresis variables A piecewise hysteretic nonlinear differential equation is generated and coupled as a time-varying boundary condition to the bottom interface degree of freedom of the standard section of the transport support; the specific formula is as follows:

[0108] ;

[0109] in, This refers to the first derivative of a hysteresis variable. It refers to the first derivative of dynamic deflection; the principle of this formula uses a set of smooth mathematical rules to cleverly simulate the energy conversion and shape change in the complex nonlinear physical process of elasticity-plasticity-friction-hysteresis; it simulates the complex nonlinear support and damping effect of the vehicle frame and tires on the upper transport support, and solves the problem of dynamic response prediction distortion caused by simplifying the suspension to a linear spring.

[0110] A global dynamic model is established using the updated tangent stiffness matrix and piecewise hysteretic nonlinear differential equations; the specific formula is as follows:

[0111] ;

[0112] in, , and These refer to the displacement, velocity, and acceleration vectors in the physical coordinate system, respectively. This refers to a nonlinear force vector, which includes the sliding friction force generated by the Jenkins unit and The suspension hysteresis force generated by the model This refers to the external excitation load (the sum of all external energy inputs that cause vibration, deformation, and stress in the suspension system of a transport vehicle), collected through the suspension system of the transport vehicle; this formula is the classic control equation for nonlinear multi-degree-of-freedom dynamic systems, and its principle is based on Newton's second law and d'Alembert's principle, describing the motion law of a stressed object in a dynamic environment.

[0113] The assembly sequence optimization module 4 is used to receive the final support point coordinates, main excitation bandwidth, and overall dynamic model from the road condition feature module 1 and the dynamic modeling module 3. The standard sections of the transport support frame to be installed are arranged in a full arrangement at different final support point coordinates to obtain the arrangement design scheme. Then construct a scale of ( Discrete design variable space of factorial For any arrangement design scheme in the discrete design variable space ( The arrangement design scheme is input into the overall dynamic model and modal analysis is performed. The modal shapes of the prefabricated steel structure trestle under the current arrangement design scheme are output. Based on the modal shapes, the bending stiffness components that reflect the deformation resistance of the prefabricated steel structure trestle are extracted. (Refers to the ability of prefabricated steel structure trestle bridges to resist bending deformation) and torsional stiffness components (This refers to the ability of prefabricated steel structure trestle bridges to resist torsional deformation; specifically, it refers to the overall torsional stiffness, as opposed to the tangential stiffness of the connection interface.)

[0114] The mass of the prefabricated steel structure trestle bridge is obtained by using the extracted bending stiffness and torsional stiffness components, combined with the mass distribution matrix. The first-order bending mode frequency was obtained. and first-order torsional mode frequency Among them, the first-order bending mode frequency characterizes the rate of bending vibration of the prefabricated steel structure trestle; the first-order torsional mode frequency characterizes the rate of torsional deformation of the prefabricated steel structure trestle.

[0115] To quantify the anti-resonance effect, a fitness function is constructed with the goal of maximizing modal separation. The specific algorithm formula is as follows: ; This refers to modal separation, which is defined as the ratio of the difference between the first-order bending mode frequency and the first-order torsional mode frequency to the bandwidth of the main excitation frequency band.

[0116] To find the optimal solution in the vast space of discrete design variables, a genetic algorithm is used to perform a global search of permutation design schemes in the space of discrete design variables. The specific process is as follows:

[0117] Each permutation design scheme in the discrete design variable space is encoded as a chromosome in the genetic algorithm population. During the iteration process, selection operations (preferring chromosomes with high modal separation) and crossover and mutation operations (generating new permutation design schemes) are performed to continuously evolve and produce a new generation of population. Then, a modal separation threshold is set (based on the principle that the difference between the first-order bending mode frequency and the first-order torsional mode frequency is greater than the bandwidth of the main excitation frequency band, and a safety margin of 20%-50% is added (corresponding to a modal separation threshold value of 1.2 to 1.5)). When a chromosome is found whose corresponding modal separation is greater than the modal separation threshold, the genetic algorithm terminates and outputs the current permutation design scheme as the optimal assembly sequence.

[0118] Example scenario:

[0119] Suppose there are three standard transport support sections (D1, D2, D3) to be installed. They need to be installed at three different final support point coordinates, which results in 3! (i.e., 6) different assembly sequences (such as D1-D2-D3, D1-D3-D2, etc.). A discrete design variable space containing these 6 arrangement design schemes is constructed, and the bending stiffness and torsional stiffness components of the prefabricated steel structure trestle under each arrangement design scheme are output using the overall dynamic model. The first-order bending mode frequency and the first-order torsional mode frequency are then calculated. To prevent the prefabricated steel structure trestle from collapsing on the transport vehicle... Resonance occurs within the target stiffness range of the vehicle load. Modal separation is used as the sole evaluation criterion, and a genetic algorithm is employed to perform a global search among a vast array of permutation designs, weeding out the inferior ones. The genetic algorithm treats each permutation design as a chromosome, and through continuous iteration, crossover, and mutation, selects the chromosome with the highest modal separation. Once the modal separation of a chromosome exceeds the modal separation threshold, the genetic algorithm terminates and outputs that permutation design as the optimal assembly sequence, thereby ensuring that the prefabricated steel structure trestle bridge has the best dynamic stability during actual transportation.

[0120] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A modular design system for prefabricated steel structure trestle transport support, characterized in that: The road condition feature module (1) is used to acquire road surface roughness data of the transportation route and perform time-frequency domain conversion to obtain road surface roughness time domain signal, extract the main excitation frequency band in the road surface roughness time domain signal whose energy ratio exceeds the energy ratio threshold, and determine the road surface excitation frequency characteristics in the transportation process based on the main excitation frequency band. The stiffness matching module (2) calculates the target stiffness range required to avoid resonance based on the road excitation frequency characteristics, and retrieves the matching equivalent stiffness correction coefficient from the preset database according to the target stiffness range. The equivalent stiffness correction coefficient corresponds to the preload level of the connection interface. The dynamic modeling module (3) is used to process the standard section of the transport support into a fixed interface substructure, and to update the tangent stiffness matrix of the connection interface between the substructures using the equivalent stiffness correction coefficient to characterize the micro-sliding effect, and then establish the overall dynamic model. The assembly sequence optimization module (4) is used to maximize the mode separation degree within the main excitation frequency bandwidth as the fitness function, construct the discrete design variable space of the standard section of the transport support at different final support point coordinates, and use a genetic algorithm to perform a global search on the arrangement design scheme in the discrete design variable space to obtain the optimal assembly sequence.

2. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The road condition feature module (1) extracts the main excitation frequency band in the road surface roughness time domain signal whose energy proportion exceeds the preset energy proportion threshold, including the following method steps: The wavelet packet decomposition algorithm is used to perform multi-scale decomposition of the road surface roughness time domain signal to obtain the frequency band reconstructed signal and calculate the energy ratio; Set an energy percentage threshold, then sort the energy percentages in descending order and accumulate them. When the accumulated result is greater than or equal to the energy percentage threshold, the frequency band participating in the accumulation is defined as the main excitation frequency band.

3. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The road condition feature module (1) also includes the following method steps: Obtain the mass distribution matrix and center of gravity of the prefabricated steel structure trestle to be transported, and determine the preliminary coordinates of the support points in combination with the allowable axle load of the transport vehicle. Calculate the moment of inertia tensor of the prefabricated steel structure trestle bridge in its transport posture, and correct the preliminary coordinates of the support points based on the principal axis direction of the moment of inertia tensor to obtain the final coordinates of the support points.

4. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The stiffness matching module (2) calculates the target stiffness range required to avoid resonance, including the following steps: Obtain the initial modal frequency and determine whether the initial modal frequency falls within the resonant bandwidth range of the road excitation frequency characteristics; When the initial modal frequency falls within the resonant bandwidth range of the road excitation frequency characteristic, the peak frequency of the road excitation frequency characteristic is used as the reference. The target frequency range is calculated based on the peak frequency, and the upper and lower limits of the target stiffness range are determined based on the target frequency range.

5. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The stiffness matching module (2) retrieves the equivalent stiffness correction coefficient from a preset database according to the target stiffness range, including the following steps: Based on the target stiffness range, the preload level is retrieved from the preset database, and then the hysteresis curve is obtained through the force and deformation of the connection interface. Extract the slope of the hysteresis curve envelope, which is the equivalent stiffness under the preload level. Compare the equivalent stiffness with the linear stiffness value in the linear stiffness matrix to calculate the factor caused by micro-slip, which is the equivalent stiffness correction coefficient.

6. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The dynamic modeling module (3) updates the tangent stiffness matrix of the connection interface between substructures using the equivalent stiffness correction coefficient, including the following steps: Construct a parallel connection model containing multiple Jenkins units to describe the micro-sliding behavior of the connection interface; The mapping relationship between normal contact pressure and tangential stiffness is obtained by querying the preset database according to the preload level. The equivalent stiffness correction coefficient is used to adjust the sliding critical force parameter of the Jenkins unit, thereby updating the tangential stiffness matrix in real time and obtaining the updated tangential stiffness matrix.

7. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The method steps for establishing the overall dynamic model in the dynamic modeling module (3) are as follows: Obtain the dynamic deflection and damping force of the transport vehicle's suspension system, and utilize... The hysteresis model is used for parameter identification, and piecewise hysteretic nonlinear differential equations are generated based on the parameters. An overall dynamic model is established using the updated tangent stiffness matrix and the piecewise hysteretic nonlinear differential equation.

8. The modular design system for prefabricated steel structure trestle transport support according to claim 7, characterized in that: The specific formula for the overall dynamic model is as follows: ； in, , and These refer to the displacement, velocity, and acceleration vectors in the physical coordinate system, respectively. This refers to a nonlinear force vector. This refers to external excitation load. This refers to damping force. This refers to the quality of prefabricated steel structure trestle bridges.

9. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The assembly sequence optimization module (4) constructs a discrete design variable space for the standard section of the transport support at different final support point coordinates, including the following steps: The standard sections of the transport support are arranged in a full permutation design at different final support point coordinates to obtain the permutation design scheme, and then a discrete design variable space is constructed. Calculate the mode shape for each arrangement design scheme, and extract the bending stiffness component and torsional stiffness component; The first-order bending mode frequency and the first-order torsional mode frequency are obtained based on the bending stiffness component and the torsional stiffness component, respectively.

10. The modular design system for prefabricated steel structure trestle transport support according to claim 1, characterized in that: The optimal assembly sequence in the assembly sequence optimization module (4) includes the following steps: Based on the first-order bending mode frequency and the first-order torsional mode frequency, a fitness function is constructed with the goal of maximizing the modal separation degree. Modal separation is defined as the ratio of the difference between the first-order bending mode frequency and the first-order torsional mode frequency to the bandwidth of the main excitation frequency band. A genetic algorithm is used to iteratively search in the discrete design variable space to select the arrangement design scheme with a modality separation degree greater than the modality separation degree threshold as the optimal assembly sequence.

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