A design method and experimental system of a bridge deck panel scale model in a plateau environment

Abstract

The application provides a bridge deck panel scale model design method and an experimental system under a plateau environment, wherein the design method comprises the following steps: based on the similarity principle, physical parameters affecting the structure performance of a bridge deck panel prototype, and plateau environment parameters, a multi-factor coupling similarity criterion is constructed; based on the multi-factor coupling similarity criterion and the plateau environment parameters, a dynamic stress similarity ratio model is determined; based on the dynamic stress similarity ratio model and the cross-sectional shape and size of the bridge deck panel prototype structure, the bridge deck panel prototype structure is designed in scale to obtain an initial scale model; based on a preset numerical simulation strategy and a preset plateau environment simulation strategy, the initial scale model is subjected to simulation experiment, simulation verification, and parameter optimization of the dynamic stress ratio similarity model to design a target scale model. The scheme provided by the application can efficiently and accurately perform experimental simulation of the bridge deck panel under the plateau environment and reduce the experimental simulation cost.

Description

Technical Field

[0001] This invention relates to the field of material scaling design technology, and in particular to a method and experimental system for designing a scaled-down model of a bridge deck in a high-altitude environment. Background Technology

[0002] Current methods for scaled-down testing of bridges in high-altitude areas have significant shortcomings: traditional studies often focus on reducing geometric dimensions but fail to adequately consider the synergistic effect of the extreme high-altitude environment and structural stress. Existing methods lack scientific definitions for the scaling ratio of environmental parameters (such as UV intensity, temperature gradient, and oxygen concentration), leading to significant deviations between models and actual working conditions. Furthermore, the correlation models between mechanical parameters and size effects in theoretical analysis are incomplete, making it difficult to accurately reflect the true response of structures under complex loads. These problems result in insufficient reliability of scaled-down experimental results, hindering the optimized design and performance verification of high-altitude bridge decks.

[0003] Therefore, it is urgent to establish a systematic high-precision scaled design theory to solve the problem of constructing similarity relationships between multiple environmental and mechanical factors, and to provide more scientific experimental support for plateau engineering. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a design method and experimental system for a scaled-down model of a bridge deck in a high-altitude environment, so as to accurately reproduce the fatigue performance of the bridge in real-world environments, and provide highly reliable experimental support for the optimized design of high-altitude engineering equipment.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for designing a scaled-down model of a bridge deck in a high-altitude environment, comprising:

[0006] Based on the principle of similarity, physical parameters affecting the performance of the bridge deck prototype structure, and plateau environmental parameters, a multi-factor coupled similarity criterion is constructed.

[0007] Based on the multi-factor coupling similarity criterion and the plateau environment parameters, a dynamic stress similarity ratio model is determined between the bridge deck prototype structure and its corresponding scaled-down model under material performance degradation conditions. The dynamic stress similarity ratio model is an integrated model of freeze-thaw damage and thermo-optical-chemical coupling effects under extreme plateau conditions to ensure the equivalence of fatigue performance between the bridge deck prototype structure and the scaled-down model under extreme plateau conditions. The degradation of the material performance of the bridge deck prototype structure is affected by the plateau environment parameters and is represented by a preset material performance degradation model.

[0008] Based on the dynamic stress similarity ratio model and the cross-sectional shape and size of the bridge deck prototype structure, the bridge deck prototype structure is scaled down to obtain an initial scaled model corresponding to the bridge deck prototype structure.

[0009] The initial scaled model is simulated and verified based on a preset numerical simulation strategy and a preset plateau environment simulation strategy. The parameters in the dynamic stress similarity ratio model are then optimized based on the simulation results or the simulation verification results in order to design the target scaled model.

[0010] In one embodiment, based on the principle of similarity and the physical parameters affecting the performance of the bridge deck prototype structure, a multi-factor coupled similarity criterion is constructed, including:

[0011] The physical parameters and the plateau environment parameters are homogenized in dimension based on the pre-set Buckingham π theorem to obtain the corresponding dimensionless π number group.

[0012] Based on the dimensionless π number group, multiple parameter similarity ratios are determined between the prototype structure of the bridge deck and its corresponding scaled model parameters. These multiple parameter similarity ratios include geometric similarity ratio, stress similarity ratio, load similarity ratio, temperature gradient similarity ratio, elastic modulus similarity ratio, and freeze-thaw cycle similarity ratio. Furthermore, these multiple parameter similarity ratios constitute the multi-factor coupled similarity criterion.

[0013] In one embodiment, based on the multi-factor coupling similarity criterion and the plateau environment parameters, the dynamic stress similarity ratio model of the bridge deck prototype structure and its corresponding scaled-down model under material performance degradation is determined. The dynamic stress similarity ratio model includes:

[0014] Based on the elastic modulus similarity ratio and the geometric similarity ratio, a mechanical similarity factor is determined, which reflects the stress response relationship between the scaled-down model and the bridge deck prototype structure under static load.

[0015] Based on the geometric similarity ratio and the preset material performance degradation model, a material performance degradation correction factor is determined. The material performance degradation correction factor represents unifying the rate of decay of material strength or durability with time and environmental load to the same time scale, so as to ensure that the scaled model and the bridge deck prototype structure have similar dynamic performance degradation.

[0016] Based on the temperature gradient similarity ratio and the preset attenuation factor, an environmental coupling correction factor is determined. The environmental coupling correction factor represents the synergistic accelerating effect of temperature change, ultraviolet intensity and oxygen concentration in plateau environmental parameters on the attenuation of material performance.

[0017] The dynamic stress similarity ratio model is determined based on the stress similarity ratio, the mechanical similarity factor, the material property degradation correction factor, and the environmental coupling correction factor.

[0018] In one embodiment, determining the material performance degradation correction factor based on the geometric similarity ratio and the preset material performance degradation model includes:

[0019] In the scaled-down model, the time parameters and number of freeze-thaw cycles of the preset material property degradation model are scaled down according to the geometric similarity ratio;

[0020] Based on the results of the scaled-down adjustment, the material performance degradation term in the preset material performance degradation model is corrected to obtain the material performance degradation correction factor.

[0021] In one embodiment, the preset material performance degradation model is expressed as:

[0022] ;

[0023] in, This indicates the time t and the number of freeze-thaw cycles. The material property degradation coefficient under action; Indicates the initial performance coefficient of the material; Indicates the degradation of material properties; Indicates the oxidation rate; The constant represents the freeze-thaw damage rate; time t is used to characterize the aging degradation of material properties.

[0024] In one embodiment, the dynamic stress similarity ratio model is expressed as:

[0025] ;

[0026] in, This represents the dynamic stress similarity ratio model; This indicates the similarity ratio of the elastic modulus; This represents the geometric similarity ratio; This represents the mechanical similarity factor; Indicates the oxidation rate; This represents the freeze-thaw damage rate constant; The number of freeze-thaw cycles is represented by t; t represents time, used to characterize the aging degradation of material properties. The material's performance degradation correction factor is represented by ΔT; ΔT represents the temperature gradient of the high-altitude environment; I UV Indicates the intensity of ultraviolet radiation in a high-altitude environment; Indicates the oxygen concentration in a high-altitude environment; This represents the environmental coupling correction factor.

[0027] In one embodiment, the multi-factor coupling similarity criterion further includes boundary condition similarity, which includes consistency of external excitations borne by the structural surface, consistency of load application sequence, consistency of constraint conditions, and consistency of initial conditions.

[0028] In one embodiment, the bridge deck scaled-down model design method for high-altitude environments further includes:

[0029] Based on the preset scaling strategy, the dynamic stress similarity ratio model, and the cross-sectional dimensions and shape of the composite layer bridge deck structure in the bridge deck prototype structure, the composite layer bridge deck structure in the bridge deck prototype structure is scaled down; the preset scaling strategy includes a stepped thickness reduction strategy and a nano-coating compensation strategy.

[0030] In one embodiment, simulation experiments and simulation verifications are performed on the initial scaled model based on a preset numerical simulation strategy and a preset plateau environment simulation strategy, including:

[0031] Finite element models of the bridge deck prototype structure and the initial scaled model are established based on preset simulation analysis software. Multi-physics field coupling analysis of preset simulation temperature gradient, preset simulation ultraviolet intensity, preset simulation oxygen content and preset simulation dynamic load is integrated. By comparing the stress distribution cloud maps of the finite element models corresponding to the bridge deck prototype structure and the initial scaled model, the simulation stress distribution deviation between the bridge deck prototype structure and the initial scaled model is determined.

[0032] A partial full-scale model of the bridge deck prototype structure and a corresponding scaled-down model specimen were constructed. Fatigue loading experiments were conducted on the partial full-scale model and the scaled-down model specimen in a low-temperature, low-pressure corrosion environment simulation chamber. The fatigue crack propagation length of the partial full-scale model and the scaled-down model specimen were monitored by a preset strain gauge array in the simulation chamber to determine the simulated stress deviation between the partial full-scale model and the scaled-down model specimen.

[0033] An embodiment of the present invention also provides an experimental system for a scaled-down bridge deck model in a high-altitude environment. The scaled-down bridge deck model is designed using the scaled-down bridge deck model design method for high-altitude environments described in the above embodiments. The experimental system includes:

[0034] The simulation chamber includes a foldable experimental enclosure and sensor components, the sensor components being housed within the foldable experimental enclosure. The simulation chamber provides a testing environment and simulates a high-altitude environment for the scaled-down model, based on the experimental conditions required for fatigue loading experiments. The sensor components are used to monitor real-time status data within the simulation chamber.

[0035] The central control module is communicatively connected to the simulation chamber and the host computer. The central control module stores a multi-altitude environmental parameter library and a dynamic parameter conversion model. The dynamic parameter conversion model determines the target altitude and the target working condition corresponding to the target altitude from the multi-altitude environmental parameter library according to the fatigue loading test requirements, and dynamically scales the target working condition to obtain the experimental working condition required for the fatigue loading test.

[0036] The above-described solution of the present invention has at least the following beneficial effects:

[0037] 1. A nonlinear dynamic stress similarity ratio model is established by using a multi-factor coupling similarity criterion and plateau environmental parameters. The temperature gradient, oxygen concentration, ultraviolet intensity and other parameters in the plateau environment are deeply coupled with the performance degradation law of bridge deck materials to ensure that the stress distribution and fatigue crack propagation characteristics of the scaled model remain dynamically consistent with the prototype under extreme working conditions such as ultraviolet radiation, low temperature and low pressure.

[0038] 2. By adopting a stepped bridge deck composite layer scaling strategy and a nano-coating compensation strategy, while ensuring the corrosion resistance threshold, fatigue damage equivalence can be accurately matched, breaking through the bottleneck of the difficulty in coordinating corrosion resistance and load-bearing performance in traditional scaling models.

[0039] 3. The initial scaled model was verified by combining multi-physics field coupled numerical simulation with plateau environment simulation experiment. This achieved dual verification of the structural response under complex boundary conditions and dynamic loads, and significantly improved the prediction accuracy of the scaled model in the special plateau environment.

[0040] 4. By constructing an experimental system and integrating a multi-altitude environmental database and a dynamic parameter conversion model into the system, the system can accurately reproduce environmental conditions under different altitude gradients, effectively reducing the difficulty of experimental parameter calibration and providing highly reliable experimental support for the optimized design of plateau engineering equipment.

[0041] It should be understood that the implementation of any embodiment of the present invention does not mean that it will simultaneously possess or achieve multiple or all of the above-mentioned beneficial effects. Attached Figure Description

[0042] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0043] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0044] Figure 1 This is a flowchart of a bridge deck scale model design method in a high-altitude environment provided by an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of a stepped thickness reduction of a composite bridge deck provided in an optional embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of a composite bridge panel coated with a nano-coating according to an optional embodiment of the present invention;

[0047] Figure 4 This is a prototype structure stress distribution cloud diagram provided in an optional embodiment of the present invention;

[0048] Figure 5 This is a scaled-down model stress distribution cloud diagram provided in an optional embodiment of the present invention;

[0049] Figure 6 This is a stress analysis diagram of a key part of the weld toe of the node top plate in a prototype structure provided by an optional embodiment of the present invention;

[0050] Figure 7 This is a stress analysis diagram of a key part of the weld toe on the top plate of a node in a scaled-down model provided in an optional embodiment of the present invention;

[0051] Figure 8 This is a graph showing the relationship between fatigue crack propagation length and cycle number for a partial full-scale model and a corresponding scaled-down model specimen provided in an optional embodiment of the present invention.

[0052] Figure 9 This is a diagram showing the relationship between crack propagation length and stress intensity factor ΔK1 of a partial full-scale model and a corresponding scaled-down model specimen provided in an optional embodiment of the present invention.

[0053] Figure 10 This is a flowchart of a scaled-down model design provided in an optional embodiment of the present invention;

[0054] Figure 11 This is a three-dimensional structural diagram of the foldable experimental box in the experimental system provided by an embodiment of the present invention;

[0055] Figure 12This is a schematic diagram of the foldable experimental box provided in an optional embodiment of the present invention after folding;

[0056] Figure 13 This is a schematic diagram of the unfolded folding experimental box provided in an optional embodiment of the present invention;

[0057] Figure 14 This is a schematic block diagram of a computing device provided in an embodiment of the present invention.

[0058] Explanation of reference numerals: A, Nano-coating; B, Stainless steel layer; C, Weathering steel layer;

[0059] 1. Foldable experimental chamber; 11. First telescopic part; 12. Second telescopic part; 13. Connecting end; 14. Observation window; 15. Loading slot; 16. End of chamber; 17. Tail of chamber; 2. Liquid nitrogen storage; 3. Oxygen generator; 4. Turbulence generator; 5. Loading device; 6. External equipment support frame; 7. Rubber skin. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] It should be understood that the terms "comprising / including," "consisting of," or any other variations are intended to cover non-exclusive inclusion, such that a product, apparatus, process, or method that comprises a list of elements includes not only those elements but may also include, where necessary, other elements not expressly listed, or elements inherent to such a product, apparatus, process, or method. Without further limitation, an element defined by the phrases "comprising / including," "consisting of," does not exclude the presence of additional identical elements in the product, apparatus, process, or method that includes said element.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0063] like Figure 1 As shown, an embodiment of the present invention proposes a method for designing a scaled-down model of a bridge deck in a high-altitude environment, which may include:

[0064] Step 11: Based on the similarity principle, physical parameters affecting the performance of the bridge deck prototype structure, and plateau environmental parameters, construct a multi-factor coupled similarity criterion;

[0065] Step 12: Based on the multi-factor coupling similarity criterion and plateau environmental parameters, determine the dynamic stress similarity ratio model of the bridge deck prototype structure and its corresponding scaled model under the condition of material performance degradation; the dynamic stress similarity ratio model is an integrated model of freeze-thaw damage and thermo-optical-chemical coupling effect under the extreme plateau environment to ensure the equivalence of fatigue performance between the bridge deck prototype structure and the scaled model under the extreme plateau environment; the degradation of the material performance of the bridge deck prototype structure is affected by the plateau environmental parameters and is represented by a preset material performance degradation model;

[0066] Step 13: Based on the dynamic stress similarity ratio model and the cross-sectional shape and size of the bridge deck prototype structure, the bridge deck prototype structure is scaled down to obtain an initial scaled model corresponding to the bridge deck prototype structure.

[0067] Step 14: Based on the preset numerical simulation strategy and the preset plateau environment simulation strategy, conduct simulation experiments and simulation verification on the initial scaled model, and optimize the parameters in the dynamic stress similarity ratio model based on the simulation results or simulation verification results in order to design the target scaled model.

[0068] In this embodiment, based on the principle of similarity, a multi-factor coupled similarity criterion is constructed to accurately identify and quantify all key physical parameters and plateau environment parameters affecting the performance of the bridge deck. Simultaneously, dimensional analysis and other methods can be used to ensure the comparability of the behavior of the scaled model and the prototype bridge deck structure on these physical parameters, thereby guaranteeing the accuracy of subsequent design of the scaled model and experiments based on it. Due to the unique characteristics of the plateau environment—its extreme and variable conditions such as significant temperature gradients, high-intensity ultraviolet radiation, low oxygen concentration, and frequent freeze-thaw cycles—these factors have a nonlinear impact on material properties (e.g., elastic modulus and oxidation rate). Based on the multi-factor coupled similarity criterion, a nonlinear relationship is derived between the physical parameters of the prototype structure and the plateau environment parameters to establish a dynamic stress similarity ratio model that considers the degradation of the prototype structure's material properties. This model simultaneously couples multiple factors such as the plateau environment, external loads, and size effects, thereby realistically and accurately representing the impact of the plateau environment on the material properties of the original bridge deck prototype structure. This ensures the dynamic consistency of the scaled model in simulating the aging process of the prototype structure, thus guaranteeing the accuracy of fatigue loading experiments conducted on the scaled model.

[0069] Furthermore, based on the determined dynamic stress similarity ratio model and the cross-sectional shape and size of the bridge deck running structure, the prototype structure is designed on a scale. During the scale design process, the size of the model and other design parameters should follow the dynamic stress similarity ratio model so that the scaled model can accurately simulate the complex response of the prototype structure in the plateau environment.

[0070] Furthermore, to ensure that the dynamic response of the scaled model in the extreme environment of the plateau is highly consistent with that of the prototype structure, the feasibility of the designed original scaled model is verified by pre-set numerical simulation and plateau environment simulation experiments. On the one hand, this can achieve dual verification of the structural response under complex environment and dynamic load. On the other hand, it can also adjust the parameters in the dynamic stress similarity ratio model in a timely manner based on the verification results. During the dual verification, the stress deviation between the prototype structure and the original scaled model can be compared. If the stress deviation of both verifications is within the set threshold range, the current original scaled model is considered to meet the design requirements, and the original scaled model is determined as the target scaled model for subsequent fatigue loading experiments. Otherwise, the above steps 11 to 13 are repeated to optimize the parameters in the dynamic stress similarity ratio model. Based on the optimized dynamic stress similarity ratio model, the original scaled model is designed and dual verified in sequence until the target scaled model that meets the requirements is obtained.

[0071] Based on a dynamic stress similarity model design that couples plateau environmental parameters and physical parameters, a scaled-down model corresponding to the bridge deck prototype structure is designed to accurately reproduce the fatigue performance of the bridge under real-world environmental dimensions. This solves the existing problem of constructing similarity relationships involving multiple environmental and mechanical factors, providing more scientific and reliable experimental support for plateau engineering. Simultaneously, by accurately simulating complex behaviors under real plateau conditions on a smaller-scale scaled-down model, the need for expensive, time-consuming, and logistically complex full-scale field experiments is reduced. This provides a more reliable and economical durability assessment method for critical infrastructure projects in high-altitude areas, thereby improving infrastructure resilience while effectively reducing long-term operational risks and maintenance costs.

[0072] In an optional embodiment of the present invention, step 11 above may include:

[0073] Step 111: Based on the preset Buckingham π theorem, the physical parameters and plateau environmental parameters are homogenized in dimension to obtain the corresponding dimensionless π number group.

[0074] Step 112: Based on the dimensionless π number group, determine multiple parameter similarity ratios between the bridge deck prototype structure and its corresponding scaled-down model. These multiple parameter similarity ratios include geometric similarity ratio, stress similarity ratio, load similarity ratio, temperature gradient similarity ratio, elastic modulus similarity ratio, and freeze-thaw cycle similarity ratio. These multiple parameter similarity ratios constitute a multi-factor coupled similarity criterion.

[0075] In this embodiment, the key physical parameters and plateau environment parameters are first homogenized in order to make the bridge deck prototype structure and the scaled-down model comparable in multiple parameters.

[0076] Here, physical parameters may include the geometric dimensions of the bridge deck prototype structure (such as length L, thickness d1, cross-sectional dimensions d2, etc.), material parameters (such as elastic modulus E, coefficient of thermal expansion α, density ρ, yield strength σy, and material oxidation rate k(T), etc.), and mechanical parameters (such as stress σ, load P, strain ε, etc.). Plateau environmental parameters may include temperature gradient ΔT, ultraviolet radiation intensity I, etc. UV Oxygen concentration W O2 And the number of freeze-thaw cycles N in high-altitude environments f ;

[0077] Here, dimensional analysis can be performed by pre-setting Buckingham's π theorem to obtain multiple dimensionless π number groups, each of which represents the physical similarity between the scaled model and the prototype structure.

[0078] Through dimensional homogenization, the resulting multiple dimensionless π number groups can be represented as:

[0079] This indicates stress-load geometric similarity to ensure that the proportional relationship between stress and load under the influence of geometric dimensions in the scaled-down model remains consistent with the prototype structure.

[0080] This indicates material stiffness matching to ensure that the ratio of the product of the material's elastic modulus and geometry to the load remains consistent between the model and the prototype.

[0081] This indicates that the thermal gradient is similar to the coupling of thermal expansion, capturing the complex interaction between temperature change, structural size, and material thermal expansion coefficient;

[0082] The similarity of environmental factors (such as oxygen concentration or corrosion rate) ensures that environmental degradation processes such as oxidation and corrosion are appropriately simulated in the scaled-down model; where D is the oxygen diffusion coefficient (dimension L). 2 T -1 );

[0083] This represents the similarity between the number of freeze-thaw cycles and material damage, ensuring accurate reproduction of cumulative damage from freeze-thaw cycles in cold, high-altitude environments; where B is the material damage coefficient (dimension L). 2 T -1 );

[0084] This indicates the coupling between ultraviolet intensity and material performance degradation, taking into account the photoaging effect caused by high-intensity ultraviolet radiation in plateau regions.

[0085] Furthermore, based on multiple dimensionless π number groups, several parameter similarity ratios between the scaled-down model and the prototype structure can be defined, as follows (where lowercase p represents the parameters of the original structure, and m represents the parameters of the scaled-down model):

[0086] Geometric similarity ratio λ L =L p / L m This similarity ratio represents the reduction ratio of all linear dimensions of the scaled model relative to the prototype, in order to maintain an accurate reproduction of the geometry;

[0087] Stress similarity ratio λ σ =σ p / σ m =1, this similarity ratio means that the internal stress distribution of the scaled model under appropriately scaled loads must be completely consistent with the prototype structure;

[0088] Temperature gradient similarity ratio λ ΔT =ΔT p / ΔT m =1 / λ L This similarity ratio takes into account the consistency of the coefficient of thermal expansion, which means that in a smaller scaled-down model, a larger temperature gradient needs to be applied to produce an equivalent thermal strain.

[0089] freeze-thaw cycle similarity ratio This similarity ratio specifies how the number of freeze-thaw cycles in the scaled-down model is adjusted to simulate the equivalent damage accumulation effect in the prototype structure, where λ B λ represents a parameter indicating the material's resistance to freeze-thaw damage, reflecting the relative difference in resistance between the prototype structure material and the scaled-down model material. B >1 indicates that the prototype structure material is more durable (deteriorates more slowly), so the scaled-down model needs more iterations. Increase); λ B <1 indicates that the prototype material is more fragile (damages faster), so the scaled-down model requires fewer cycles. Decrease); λ B =1 indicates that the materials have completely identical freeze-thaw properties, so the material item does not affect scaling;

[0090] Load similarity ratio λP =P p / P m =λ L 2 This indicates that the load applied to the model should be scaled proportionally to the square of the geometric dimensions, which is consistent with the scaling law of area and ensures accurate simulation of pressure or distributed loads.

[0091] In this embodiment, the stress similarity ratio (λ) σ Setting the stress scaling factor to 1 requires that the scaled-down model have the same stress distribution as the prototype structure, so as to ensure that the stress state and fatigue performance of the scaled-down model are directly comparable to those of the prototype structure, without the need for a complex stress scaling factor.

[0092] In an optional embodiment of the present invention, step 12 above may include:

[0093] Step 121: Based on the elastic modulus similarity ratio and geometric similarity ratio, determine the mechanical similarity factor. The mechanical similarity factor reflects the stress response relationship between the scaled-down model and the bridge deck prototype structure under static load, which constitutes the basic mechanical scaling basis. This mechanical similarity factor can be expressed as: ;in, Indicates the similarity ratio of elastic modulus;

[0094] Step 122: Based on the geometric similarity ratio and the preset material performance degradation model, determine the material performance degradation correction factor. The material performance degradation correction factor means unifying the degradation rate of material strength or durability with time and environmental load to the same time scale to ensure that the scaled model and the bridge deck prototype structure have similar dynamic performance degradation.

[0095] Specifically, this may include:

[0096] Step 1221: In the scaled-down model, the time parameters and number of freeze-thaw cycles of the preset material performance degradation model are scaled down according to the geometric similarity ratio to ensure that the scaled-down model can accurately reflect the long-term aging process of the prototype structure.

[0097] Step 1222: Based on the scaled-down results, the material performance degradation term in the preset material performance degradation model is corrected to obtain the material performance degradation correction factor.

[0098] Here, the material performance degradation term is modified to unify the degradation rate of the prototype structure and the model under the same "aging" time scale. By dividing the number of freeze-thaw cycles by the square of the geometric similarity ratio λL², it is ensured that the cumulative damage and performance degradation of the material in the fatigue loading test are dynamically consistent with the performance of the prototype under the same service time or number of cycles. This is crucial for accurately predicting the long-term durability of the prototype structure from accelerated test results.

[0099] Here, the pre-defined material property degradation model is expressed as:

[0100] ;

[0101] in, This indicates the time t and the number of freeze-thaw cycles. The material property degradation coefficient under action; Indicates the initial performance coefficient of the material; Indicates the degradation of material properties; Indicates the oxidation rate; The freeze-thaw damage rate constant is represented (which can be calibrated through freeze-thaw cycle experiments); time t is used to characterize the aging degradation of material properties.

[0102] Due to the time similarity ratio λ t =λ L 2 Therefore, the material property degradation term can be corrected to the following material property degradation correction factor: This correction dynamically adjusts the aging rate of the scaled-down model to ensure that its performance degradation is consistent with the prototype structure under the same time or number of cycles, so that accelerated experiments can truly reflect the changes in material properties under actual service conditions.

[0103] Step 123: Based on the temperature gradient similarity ratio and the preset attenuation factor, determine the environmental coupling correction factor. The environmental coupling correction factor represents the synergistic accelerating effect of temperature change, ultraviolet intensity and oxygen concentration in the plateau environment on the attenuation of material performance.

[0104] In this embodiment, an environmental coupling factor is first introduced based on the temperature gradient similarity ratio and the oxygen content similarity ratio to incorporate environmental changes such as temperature, oxygen concentration, and ultraviolet radiation into the scaled-down model. This quantifies the additional impact of these environmental factors on material performance degradation, thereby ensuring the accuracy of fatigue loading experiments. Preferably, the environmental coupling factor can be expressed as... Where ΔTm and ΔTp represent the temperature gradients of the scaled-down model and the prototype structure, respectively, and the temperature gradient ratio is... It reflects the relative difference in temperature fluctuation between the scaled-down model and the prototype structure. Due to the huge diurnal temperature difference in the plateau region, significant thermal stress will be caused, which will accelerate fatigue damage. The temperature gradient ratio can effectively capture the influence of thermal stress. and The oxygen concentrations for the prototype structure and the scaled-down model are respectively. The influence of the low-oxygen environment at high altitude on the oxidation and corrosion rate of materials was considered in order to simulate specific corrosion effects; This represents the ultraviolet intensity of the scaled model, and the ultraviolet intensity term... The accelerated effect of high-intensity ultraviolet radiation on the photoaging process of materials (such as coatings) in plateau regions was quantified; γ and β both represent preset attenuation factors, and both can be empirical parameters calibrated through accelerated corrosion experiments, reflecting the specific contribution and interaction strength of different environmental factors to the aging rate of materials, so that the scaled model can accurately simulate the response of real materials.

[0105] Furthermore, this can be achieved through multiplication terms. (That is, the environmental coupling correction factor) The environmental coupling factor is corrected to quantify the additional impact of environmental differences on material aging, ensuring that fatigue loading experiments can reproduce the complex effects of multi-factor coupling in real high-altitude environments. When the actual ambient temperature or ultraviolet intensity is greater than the target environment, f>0, resulting in a stress similarity ratio λ. σ A decrease indicates that the performance degradation of the scaled-down model is faster than that of the prototype structure. If the environmental conditions are the same, then f=0, and the environmental coupling correction factor degenerates to 1. In other words, only the inherent degradation effect of the material is considered (the degradation follows the material performance degradation model).

[0106] Step 124: Determine the dynamic stress similarity model based on the stress similarity ratio, mechanical similarity factor, material property attenuation correction factor, and environmental coupling correction factor.

[0107] Here, the dynamic stress similarity ratio model can be expressed as:

[0108] ;

[0109] in, This represents the dynamic stress similarity ratio. With λ σ Maintain consistency; Indicates the similarity ratio of elastic modulus; Represents the geometric similarity ratio; Represents the mechanical similarity factor; Indicates the oxidation rate; This represents the freeze-thaw damage rate constant; The number of freeze-thaw cycles is represented by t; t represents time, used to characterize the aging degradation of material properties. Indicates the material performance degradation correction factor; ΔT represents the temperature gradient of the high-altitude environment; I UV Indicates the intensity of ultraviolet radiation in a high-altitude environment; Indicates the oxygen concentration in a high-altitude environment; This represents the environmental coupling correction factor.

[0110] By coupling mechanical similarity factors, material performance degradation correction factors, and environmental coupling correction factors into a dynamic stress similarity ratio model, the traditional static scaled design is transformed into a dynamic scaled design (dynamic degradation of material properties and dynamic environmental coupling correction factors). This captures the degradation mechanism of external environmental changes on material properties and their impact, ensuring the equivalence of the fatigue performance of the scaled model with the prototype structure in the extreme environment of high altitude. This improves the accuracy and reliability of predicting the long-term performance of bridge deck structures under actual service conditions.

[0111] In an optional embodiment of the present invention, the multi-factor coupling similarity criterion may further include the dynamic similarity principle. When designing a scaled model, considering dynamic problems such as earthquakes and frequency vibrations, and because the similarity relationship (modal frequency ratio, modal stiffness ratio, modal mass ratio) between the scaled model and the prototype structure is the core theoretical basis for ensuring the consistency of the dynamic response between the scaled model and the prototype structure, the design of the scaled model should also follow the dynamic similarity principle. Its derivation process is based on Newton's second law, dimensional analysis, and the similarity principle, as detailed below:

[0112] Modal frequency ratio: ;

[0113] Modal stiffness ratio: ;

[0114] Modal mass ratio: ;

[0115] (1) As we know from the above, the geometric scaling ratio is λ. L =L p / L m Stiffness scaling ratio Mass scale ratio The frequency ratio is then derived as follows: Modal frequency ratio: ;

[0116] (2) Stiffness is defined as the force required per unit displacement, which is related to the material's elastic modulus E and geometric dimensions. Modal stiffness ratio: For the beam structure inside the bridge, bending stiffness In which the moment of inertia Therefore If the materials are the same ,but The scaling relationship of the stiffness matrix is ​​determined by geometric similarity, and the overall stiffness scaling ratio is λ. L .

[0117] (3) Mass is defined as M = ρ·V, where volume V ∝ L 3 If the material density is the same (ρ) p =ρ m), Then the mass scaling ratio and modal mass ratio are: Inertial force: The acceleration of the scaled-down model needs to be calculated according to... Scaling to maintain similar dynamics.

[0118] In an optional embodiment of the present invention, the multi-factor coupling similarity criterion may further include boundary condition similarity, which includes the consistency of external excitations borne by the structural surface, the consistency of load application sequence, the consistency of constraint conditions, and the consistency of initial conditions.

[0119] In this embodiment, when designing the scaled-down model, the similarity of boundary conditions should also be followed to ensure that the behavior of the scaled-down model is equivalent to that of the prototype structure.

[0120] Here, the consistency of external excitation is the core to ensure that the dynamic response of the scaled model is consistent with that of the prototype structure. Preferably, the low-pressure wind field at high altitude can be simulated using virtual boundary medium technology, combined with the dynamic excitation effect of freeze-thaw cycles, directly acting on the boundary layer of the scaled model (which is also the boundary layer of the bridge deck prototype structure, such as the bridge deck support area), to reproduce the wind-induced vibration and frost heave effect on the surface of the prototype structure under real conditions. The scaled adjustment of external excitation parameters (amplitude, frequency) can be linked through boundary contact stiffness optimization to avoid deviations caused by boundary stress concentration or local damage accumulation due to excitation distortion.

[0121] The consistency of the loading sequence directly affects the fatigue damage evolution of the boundary region. During the scaled-down model experiment, the loading sequence of the fatigue load spectrum of the prototype structure is strictly followed. Through bidirectional verification of multi-level loading experiments and numerical simulation, it is ensured that the crack propagation path of the boundary layer (such as the composite interface) of the scaled-down model is consistent with that of the prototype structure.

[0122] Constraint consistency requires that the scaled model and the prototype structure be strictly consistent in terms of constraint type (such as fixed hinge, sliding support), contact stiffness (interface adhesion force), and load application sequence (fatigue load spectrum) to avoid stress distribution distortion caused by differences in boundary constraints.

[0123] Initial condition consistency requires that the scaled-down model and the prototype be strictly consistent in initial damage state (such as microcrack distribution and residual stress field) and manufacturing process parameters, so as to avoid fatigue crack propagation and life prediction deviation due to differences in initial defects.

[0124] In an optional embodiment of the present invention, it may further include:

[0125] Step 21: Based on the preset scaling strategy, dynamic stress similarity ratio model, and the cross-sectional dimensions and shape of the composite layer bridge deck structure in the bridge deck prototype structure, scale down the composite layer bridge deck structure in the bridge deck prototype structure; the preset scaling strategy includes a stepped thickness reduction strategy and a nano-coating compensation strategy.

[0126] This embodiment focuses specifically on the prototype structure of a composite bridge deck in a high-altitude environment (e.g., the prototype structure could be composed of 316L stainless steel and Q420qNH weathering steel). When designing the scaled-down structure corresponding to the prototype composite bridge deck, the scaled-down composite layer must consider both corrosion resistance (stainless steel layer) and fatigue resistance (weathering steel layer). Taking a 3mm thick stainless steel layer and a 16mm thick weathering steel layer in the prototype composite bridge deck as an example, the above process is explained as follows:

[0127] The main design logic is as follows:

[0128] Functional layering principle: Since the core function of the stainless steel layer (3mm) is corrosion protection, the minimum effective corrosion protection thickness (≥1mm) must be guaranteed after scaling down to avoid corrosion protection failure due to excessive thinning; the core function of the weathering steel layer (16mm) is load bearing and fatigue resistance. After scaling down, it must meet the dynamic similarity (stiffness, mass ratio) while retaining the fatigue crack propagation characteristics.

[0129] Stepped thickness reduction strategy: setting the geometric scaling ratio λ L According to the size requirements of the scaled-down model (e.g., λ) L =4), the thickness of the weathering steel is reduced proportionally to the thickness of the weathering steel layer m=16 / λ. L =4mm, the stainless steel layer is cut in a stepped manner (e.g.) Figure 2 As shown in the figure, while considering the optimization of the stepped boundary, the stress gradient of the bridge deck is determined by the finite element analysis model to determine the stepped transition area and avoid local stress concentration caused by abrupt changes in thickness.

[0130] For high-stress areas of the stainless steel layer (such as the mid-span of the bridge deck): the thickness should be kept at 1mm (minimum functional thickness) to prioritize ensuring its minimum functional corrosion protection thickness and ensure the corrosion resistance of the structure in the most vulnerable parts.

[0131] For low-stress areas of the stainless steel layer (such as the ends): scaled down to 0.75 mm (λ). L =4), supplemented by a nano-coating to compensate for the corrosion resistance performance, in order to make up for the possible decrease in corrosion resistance due to the reduction in thickness, such as Figure 3 As shown.

[0132] For corrosion-resistant nano-coatings, biomimetic nanocomposite coatings (such as ZnO / TiO2 nanoparticles + epoxy resin) are used to enhance the corrosion resistance of thin layers. The corrosion rate can be reduced by an order of magnitude, which can effectively compensate for the degradation of the corrosion resistance of the structure caused by the change in the thickness of the stainless steel layer.

[0133] Considering the fatigue equivalence of weathering steel, fatigue damage of the prototype structure is simulated through pre-strain treatment. The crack propagation rate of the weathering steel in the scaled-down model must satisfy the same relationship as that in the prototype. =λ L 2 By employing a stepped thickness reduction strategy and a nano-coating compensation strategy, the challenge of synergistic optimization of corrosion resistance and fatigue performance in a scaled-down model of a high-altitude composite bridge deck was solved. It should be noted that the aforementioned pre-defined scaling strategy is not limited to bridge decks; it can be applied to low-temperature simulation scenarios for composite materials and corrosion-resistant scenarios related to steel structures, such as composite panel materials, steel columns, and steel beams.

[0134] In an optional embodiment of the present invention, step 14 above may include:

[0135] Step 141: Based on the preset simulation analysis software, establish the finite element model of the bridge deck prototype structure and the initial scaled model, and integrate the multi-physics field coupling analysis of preset simulation temperature gradient, preset simulation ultraviolet intensity, preset simulation oxygen content and preset simulation dynamic load. By comparing the stress distribution cloud map of the finite element model corresponding to the bridge deck prototype structure and the finite element model corresponding to the initial scaled model, determine the simulation stress distribution deviation between the bridge deck prototype structure and the initial scaled model.

[0136] Step 142: Construct a full-scale model of the bridge deck prototype structure and a scaled-down model specimen corresponding to the full-scale model. Conduct fatigue loading experiments on the full-scale model and the scaled-down model specimen in a low-temperature, low-pressure corrosion environment simulation chamber. Monitor the fatigue crack propagation length of the full-scale model and the scaled-down model specimen using a preset strain gauge array in the simulation chamber to determine the simulated stress deviation between the full-scale model and the scaled-down model specimen.

[0137] When conducting simulation experiments using a pre-defined numerical simulation strategy, finite element models of the prototype structure and the scaled-down model can be established using ABAQUS simulation software; with a geometric similarity ratio λ... L Taking λ as an example, the numerical simulation integrates the diurnal temperature range of the plateau ΔT=60°C, the ultraviolet accelerated corrosion model (IUV=200W / m²), and the λ-based model. P =λ L ²Dynamic load spectrum with scaling amplitude.

[0138] like Figure 4 and Figure 5The figures show the stress distribution cloud maps of the full-scale and scaled-down models of the prototype structure under vehicle load simulation. As can be seen from the figures, the maximum stress values ​​and local stress distributions of the two models show a high degree of consistency. Figure 6 and Figure 7 The figures show the stress analysis results of the full-scale and scaled-down models of the prototype structure at their respective node positions (key parts of the weld toe on the top plate of the node) under vehicle load. As can be seen from the figures, stress is concentrated at the node positions. However, the full-scale and scaled-down models of the prototype structure have a high degree of consistency in stress distribution, and the difference between their maximum stresses does not exceed 15%. In engineering, the consistency between the two can be considered to be good.

[0139] Combining the stress distribution cloud map and the stress analysis diagram of the key parts of the weld toe on the top plate of the node, it is confirmed that the stress distribution consistency between the scaled model and the full-scale model corresponding to the prototype structure reaches more than 90%. At this point, the scaled model can be confirmed as having passed verification. Otherwise, based on the simulation data, the environmental coupling factor f(ΔT, I) is inverted. UV W O2 Furthermore, the dynamic stress similarity model was optimized. Through theoretical analysis and simulation, the consistency between the mechanical behavior of the scaled model and the prototype structure in the extreme environment of the plateau can be preliminarily confirmed, providing guidance and parameter optimization basis for subsequent physical experiments.

[0140] In the actual physical simulation experiment, a full-scale model of the bridge deck prototype structure (1:1 composite interface) and a corresponding scaled-down model specimen (such as a scaled-down CT specimen) were first constructed. Fatigue loading experiments were carried out in a low-temperature, low-pressure corrosion environment simulation chamber (-40°C, oxygen concentration 15%) and at room temperature (20°C). The stability of the full-scale model and the corresponding scaled-down model specimen was monitored using strain gauge arrays and digital image correlation (DIC) technology. The deviation between the dynamic response of the scaled-down model specimen and the theoretical prediction was analyzed. Simultaneously, data on the evolution of crack propagation length on the full-scale model and the corresponding scaled-down model specimen with the number of cycles were collected, and the results were obtained as follows: Figure 8 The diagram shows the relationships between various parameters, where SW-1 represents the curve corresponding to a full-scale model specimen at low temperature, SW-2 represents the curve corresponding to a scaled-down model specimen at low temperature, DW-1 represents the curve corresponding to a full-scale model specimen at room temperature (20°C), and DW-2 represents the curve corresponding to a scaled-down model specimen at room temperature (20°C). Furthermore, based on fatigue data (crack propagation length) at different temperatures, the correlation between crack propagation length and stress intensity factor ΔK1 at different temperatures can be inverted (e.g.,...). Figure 9As shown in the figure, the low-temperature, low-pressure corrosive environment significantly improves the fatigue performance of the structure. By incorporating fatigue data from actual physical experiments at different temperatures into the finite element model, a more accurate simulation of the overall model's fatigue condition can be achieved, while also verifying the accuracy of the numerical simulation results. By acquiring data on material performance degradation and fatigue crack propagation under real-world conditions, and correcting and improving the environmental coupling factor and coefficients corresponding to the similarity ratio in the dynamic stress similarity model of the scaled-down model, a precise match between theory and actual working conditions can be ensured.

[0141] like Figure 10 As shown, the design method for a scaled-down bridge deck model in a high-altitude environment provided by the above embodiments of the present invention is implemented as follows:

[0142] First, plateau environmental parameters are coupled with geometric and dynamic similarity criteria. The design is carried out through composite layer dynamic scaling strategy and dynamic analysis, and integrated with dynamic stress similarity ratio model. Among them, environmental parameter coupling requires first considering freeze-thaw attenuation and oxidation rate material properties and then dynamically correcting environmental parameters. Then, environmental coupling parameters such as temperature, ultraviolet radiation, and oxygen concentration are considered. Finally, similarity ratio superposition correction is performed to obtain dynamic stress similarity ratio model.

[0143] Furthermore, a finite element full-scale related model was established based on the dynamic stress similarity ratio model, and numerical simulation was conducted to verify the correlation coefficient between the bridge deck prototype structure and the initial scaled model. At the same time, low-temperature fatigue tensile tests were conducted on the local full-scale model parts of the bridge deck prototype structure and the scaled model specimens corresponding to the local full-scale model parts to determine the low-temperature fatigue properties of the materials and the difference in fatigue performance of the full-scale panel.

[0144] Furthermore, based on the bidirectional verification of key parameters through numerical simulation and local experiments, if the deviation exceeds the limit, the similarity ratio coefficient is corrected and the dynamic stress similarity ratio model is iteratively optimized. Based on the corrected dynamic stress similarity ratio model, a target scaled-down model is designed; otherwise, the scaled-down model experiment is initiated.

[0145] like Figure 11 to Figure 13As shown, an embodiment of the present invention also provides an experimental system for a scaled-down model of a bridge deck in a high-altitude environment. This scaled-down model is designed using the aforementioned method for designing a scaled-down model of a bridge deck in a high-altitude environment. The experimental system includes a simulation chamber and a central control module. The simulation chamber includes a foldable experimental enclosure 1 and sensor components housed within the foldable enclosure 1. The simulation chamber provides a location for fatigue loading experiments and simulates a high-altitude environment for the scaled-down model, based on the experimental conditions required for the fatigue loading experiment. The sensor components monitor real-time status data within the simulation chamber. The central control module communicates with the simulation chamber and a host computer. The central control module stores a multi-altitude environmental parameter library and a dynamic parameter conversion model. The dynamic parameter conversion model determines the target altitude and corresponding target conditions from the multi-altitude environmental parameter library based on the fatigue loading experiment requirements, and dynamically scales the target conditions to obtain the experimental conditions required for the fatigue loading experiment.

[0146] Preferably, the foldable experimental box 1 is equipped with a platform module for placing scaled-down model specimens. Each platform module can be embedded with an RFID chip (to store platform module ID, functional parameters and historical data). The central control module can automatically identify the combination status of the platform modules through near-field communication and call the pre-stored assembly logic (such as the loading unit preferentially connecting to the cross-section area).

[0147] The foldable experimental chamber 1 is equipped with multiple external devices, such as a liquid nitrogen storage device 2, an oxygen generator 3, a turbulence generator 4, and a loading device 5. These external devices are detachably connected to the outside of the foldable experimental chamber 1 via an external device support frame 6. Here, the specific structure of the external device support frame 6 is not limited, as long as it matches the structural design of the foldable experimental chamber 1 and can withstand the weight and size of different devices.

[0148] The liquid nitrogen storage device 2, oxygen generator 3, turbulence generator 4, and loading device 5 are all connected to the foldable experimental chamber 1. The liquid nitrogen storage device 2 delivers liquid nitrogen into the foldable experimental chamber 1 to ensure the stability of the low-temperature environment during the experiment. The oxygen generator 3 delivers prepared oxygen into the foldable experimental chamber 1 to simulate high-oxygen or low-oxygen conditions in a plateau environment. The turbulence generator 4 generates turbulence to simulate airflow disturbances in the natural environment, thereby reducing the possibility of boundary layer separation, reducing drag, and improving experimental accuracy. The loading device 5 can apply loads to the model structure specimen from the outside of the foldable experimental chamber 1. Since larger model structure specimens may occupy a large amount of internal space, applying loads externally is more in line with assembly convenience and allows for the application of larger tonnage and more effective excitation loads. Preferably, the liquid nitrogen storage device 2, oxygen generator 3, turbulence generator 4, and loading device 5 can all be connected to the central control module through their respective control units. During the experiment, the sensor components installed inside the chamber monitor the environment and status parameters inside the chamber in real time and feed them back to the central control module so as to adjust the intensity and frequency of the movement of each device in a timely manner, thereby ensuring the accuracy of the experimental simulation.

[0149] Here, the foldable experimental chamber 1 adopts a pull-out frame (unfolded dimensions 6m × 3m × 2.5m, folded volume reduced to 1 / 3), enabling rapid deployment. The pull-out frame includes multiple foldable telescopic sections (for rapid assembly). To ensure the airtightness of the foldable experimental chamber 1, after the multiple foldable telescopic sections are connected and locked, a sealing and pressurizing device is installed inside the connecting sealing strip between adjacent foldable telescopic sections. Internal pressure is provided to stabilize the airtightness. When the foldable experimental chamber 1 is unfolded, each stepped telescopic section is equipped with an airtight connecting strip, ensuring that the entire device is isolated from external gases after unfolding. A foldable rubber skin 7 is installed on the outer layer of the unfolded chamber. This rubber skin 7 has strong mobility and waterproof sealing capabilities, preventing the chamber structure from being exposed to the outside and also preventing gas exchange between the inner and outer layers.

[0150] Here, the number of folding telescopic sections can be set according to actual needs. In one feasible example, such as... Figure 13As shown, two folding telescopic parts are provided, namely, a first telescopic part 11 and a second telescopic part 12. The end 16 and the tail 17 of the folding experimental box 1 are telescopically connected to the first telescopic part 11 and the second telescopic part 12, respectively. The connecting end 13 of the folding experimental box 1 is located between the first telescopic part 11 and the second telescopic part 12 and is tightly connected to both. A sliding mechanism is provided inside the first telescopic part 11 and the second telescopic part 12. The design allows the box to pressurize the airtight connecting strip at the connection point through a sealing and pressurizing device (such as an electromagnetic connection part) after telescopic expansion, while simultaneously tightening the sliding mechanism to achieve rapid assembly and airtightness.

[0151] Preferably, the housing is provided with an observation window 14 and a loading slot 15. The observation window 14 can be located on the second telescopic part 12. Outside the observation window 14, a visual positioning system (integrated into a DIC camera) can detect the docking deviation of the platform modules in real time, and the internal loading position can be finely adjusted in a timely manner via a servo motor. The loading device 5 applies load to the model structure specimen through the loading slot 15; preferably, a flexible sealing layer is provided at the bottom of the loading slot 15 to prevent gas leakage during loading.

[0152] Here, the multi-altitude environmental parameter library covers key parameters such as extreme temperatures (3000m, 4500m, 5500m, etc.), low air pressure, and high-intensity ultraviolet radiation at different altitude gradients, supporting continuous plateau environment simulation from temperate plains to high-altitude mountains. Here, interpolation algorithms can dynamically generate target curves for arbitrary altitudes, providing a precise benchmark for fatigue loading experiments on scaled models. During fatigue loading experiments, the dynamic parameter conversion model extracts relevant parameters from the multi-altitude environmental parameter library according to experimental requirements, ensuring that the simulated environmental parameters match the real plateau environment.

[0153] The dynamic parameter transformation model serves as a bridge, combining environmental parameters from a multi-altitude environmental parameter database with the geometric, material, and mechanical properties of a scaled-down model to determine the specific values ​​of environmental parameters in experiments. Here, the dynamic parameter transformation model can be based on analyzing the actual parameter characteristics of the prototype environment's geographical location, and then using the scaling ratio to construct a dynamic scaling relationship for physical quantities. This achieves cross-scale equivalent mapping of complex environmental parameters such as temperature and humidity, ensuring the consistency of the mechanical and chemical behavior between the scaled-down model and the prototype structural environment.

[0154] During the experiment, considering the experimental system's ability to self-adjust at multiple altitudes and its rapid assembly capabilities, the structure of the experimental chamber within the system was designed. A folding module was incorporated, reducing its transport volume by one-third. Simultaneously, an external equipment support frame 6 was installed, allowing large external equipment to be detachably mounted on the exterior of the folding experimental chamber 1. The folding experimental chamber 1, by considering sealing and mobility, allows for rapid assembly. After fixing the rear of the chamber, the end is stretched, and the first telescopic part 11 and the second telescopic part 12 of the folding experimental chamber 1 are pulled out and unfolded. Simultaneously, after full unfolding, the built-in magnetic suction device applies pressure to the sealing strip. To ensure the sealing of the connection points, a folding rubber skin 7 is installed on the exterior of the chamber, effectively wrapping the external structure and isolating it from the external environment.

[0155] The specific process of fatigue loading experiment using the experimental system designed above is as follows:

[0156] Step 31: Determine the target altitude and experimental conditions according to the experimental requirements.

[0157] Step 32: The dynamic parameter conversion model extracts environmental parameters for that altitude, such as temperature, air pressure, and oxygen concentration, from a multi-altitude environmental parameter database.

[0158] Step 33: The dynamic parameter conversion model combines the geometric, material, and mechanical properties of the scaled-down model to dynamically scale the extracted environmental parameters to obtain the environmental parameters required for the experiment.

[0159] Step 34: The multifunctional environmental chamber adjusts the environmental parameters such as temperature, air pressure, and oxygen concentration inside the chamber according to the environmental parameters sent by the dynamic parameter conversion model, so as to provide the corresponding experimental environment for the scaled-down model.

[0160] Step 35: During the experiment, the multifunctional environmental chamber monitors the environmental parameters inside the chamber in real time and feeds the data back to the dynamic parameter conversion model.

[0161] Step 36: The dynamic parameter conversion model determines whether environmental parameters need to be adjusted based on real-time feedback data, and sends the adjusted parameters to the multi-functional environmental chamber to achieve closed-loop control of environmental parameters.

[0162] The modular design and intelligent control of the experimental system can significantly improve experimental efficiency and convenience, providing highly reliable experimental support for the optimized design of plateau engineering equipment. At the same time, the simulation chamber, multi-altitude environmental parameter library and dynamic parameter conversion model in the experimental system work together to provide accurate and dynamic plateau environment simulation for the scaled-down model, ensuring the accuracy and reliability of the experiment.

[0163] Figure 14A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown, the computing device being used to perform... Figure 1 or Figure 10 At least one operation in. For example... Figure 14 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for device operation. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0164] Multiple components in the electronic device are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, optical disk, etc.; and a communication unit 409, such as a network interface card, modem, wireless transceiver, etc. The communication unit 409 allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0165] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the methods of the various embodiments of this disclosure. For example, in some embodiments, the methods of the various embodiments of this disclosure may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods of the various embodiments of this disclosure described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the methods of the various embodiments of this disclosure by any other suitable means (e.g., by means of firmware).

[0166] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0167] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0168] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0170] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0171] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0172] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0173] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for designing a scaled-down model of a bridge deck in a high-altitude environment, characterized in that, include: Based on the principle of similarity, physical parameters affecting the performance of the bridge deck prototype structure, and plateau environmental parameters, a multi-factor coupled similarity criterion is constructed. The multi-factor coupling similarity criterion consists of multiple parameter similarity ratios between the bridge deck prototype structure and its corresponding scaled-down model. These multiple parameter similarity ratios include geometric similarity ratio, stress similarity ratio, load similarity ratio, temperature gradient similarity ratio, elastic modulus similarity ratio, and freeze-thaw cycle similarity ratio. Based on the multi-factor coupling similarity criterion and the plateau environment parameters, a dynamic stress similarity ratio model is determined between the bridge deck prototype structure and its corresponding scaled-down model under material performance degradation conditions. The dynamic stress similarity ratio model is an integrated model of freeze-thaw damage and thermo-optical-chemical coupling effects under extreme plateau conditions to ensure the equivalence of fatigue performance between the bridge deck prototype structure and the scaled-down model under extreme plateau conditions. The degradation of the material performance of the bridge deck prototype structure is affected by the plateau environment parameters and is represented by a preset material performance degradation model. Based on the dynamic stress similarity ratio model and the cross-sectional shape and size of the bridge deck prototype structure, the bridge deck prototype structure is scaled down to obtain an initial scaled model corresponding to the bridge deck prototype structure. The initial scaled model is simulated and verified based on a preset numerical simulation strategy and a preset plateau environment simulation strategy. The parameters in the dynamic stress similarity ratio model are optimized based on the simulation results or simulation verification results in order to design the target scaled model. Specifically, based on the multi-factor coupling similarity criterion and the plateau environment parameters, the dynamic stress similarity ratio model between the bridge deck prototype structure and its corresponding scaled-down model under material performance degradation conditions is determined, including: Based on the elastic modulus similarity ratio and the geometric similarity ratio, a mechanical similarity factor is determined, which reflects the stress response relationship between the scaled-down model and the bridge deck prototype structure under static load. Based on the geometric similarity ratio and the preset material performance degradation model, a material performance degradation correction factor is determined. The material performance degradation correction factor represents unifying the rate of decay of material strength or durability with time and environmental load to the same time scale, so as to ensure that the scaled model and the bridge deck prototype structure have similar dynamic performance degradation. Based on the temperature gradient similarity ratio and the preset attenuation factor, an environmental coupling correction factor is determined. The environmental coupling correction factor represents the synergistic accelerating effect of temperature change, ultraviolet intensity and oxygen concentration in plateau environmental parameters on the attenuation of material performance. The dynamic stress similarity ratio model is determined based on the stress similarity ratio, the mechanical similarity factor, the material property degradation correction factor, and the environmental coupling correction factor.

2. The method for designing a scaled-down model of a bridge deck in a plateau environment according to claim 1, characterized in that, Based on the principle of similarity, physical parameters affecting the performance of the bridge deck prototype structure, and plateau environmental parameters, a multi-factor coupled similarity criterion is constructed, including: The physical parameters and the plateau environment parameters are homogenized in dimension based on the pre-set Buckingham π theorem to obtain the corresponding dimensionless π number group. Based on the dimensionless π number group, the similarity ratios of several parameters between the prototype structure of the bridge deck and its corresponding scaled-down model are determined.

3. The method for designing a scaled-down model of a bridge deck in a high-altitude environment according to claim 1, characterized in that, Based on the geometric similarity ratio and the preset material performance degradation model, the material performance degradation correction factor is determined, including: In the scaled-down model, the time parameters and number of freeze-thaw cycles of the preset material property degradation model are scaled down according to the geometric similarity ratio; Based on the results of the scaled-down adjustment, the material performance degradation term in the preset material performance degradation model is corrected to obtain the material performance degradation correction factor.

4. The method for designing a scaled-down model of a bridge deck in a high-altitude environment according to claim 1, characterized in that, The preset material performance degradation model is expressed as follows: ； in, This indicates the time t and the number of freeze-thaw cycles. The material property degradation coefficient under action; Indicates the initial performance coefficient of the material; Indicates the degradation of material properties; Indicates the oxidation rate; The constant represents the freeze-thaw damage rate; time t is used to characterize the aging degradation of material properties.

5. The method for designing a scaled-down model of a bridge deck in a plateau environment according to claim 1, characterized in that, The dynamic stress similarity ratio model is expressed as follows: ； in, This represents the dynamic stress similarity ratio model; This indicates the similarity ratio of the elastic modulus; This represents the geometric similarity ratio; This represents the mechanical similarity factor; Indicates the oxidation rate; This represents the freeze-thaw damage rate constant; The number of freeze-thaw cycles is represented by t; t represents time, used to characterize the aging degradation of material properties. The material's performance degradation correction factor is represented by ΔT; ΔT represents the temperature gradient of the high-altitude environment; I UV Indicates the intensity of ultraviolet radiation in a high-altitude environment; Indicates the oxygen concentration in a high-altitude environment; This represents the environmental coupling correction factor.

6. The method for designing a scaled-down model of a bridge deck in a plateau environment according to claim 1, characterized in that, The multi-factor coupling similarity criterion also includes boundary condition similarity, which includes the consistency of external excitations borne by the structural surface, the consistency of load application sequence, the consistency of constraint conditions, and the consistency of initial conditions.

7. The method for designing a scaled-down model of a bridge deck in a plateau environment according to claim 1, characterized in that, Also includes: Based on the preset scaling strategy, the dynamic stress similarity ratio model, and the cross-sectional dimensions and shape of the composite layer bridge deck structure in the bridge deck prototype structure, the composite layer bridge deck structure in the bridge deck prototype structure is scaled down; the preset scaling strategy includes a stepped thickness reduction strategy and a nano-coating compensation strategy.

8. The method for designing a scaled-down model of a bridge deck in a plateau environment according to claim 1, characterized in that, The initial scaled model was simulated and verified based on a preset numerical simulation strategy and a preset plateau environment simulation strategy, including: Finite element models of the bridge deck prototype structure and the initial scaled model are established based on preset simulation analysis software. Multi-physics field coupling analysis of preset simulation temperature gradient, preset simulation ultraviolet intensity, preset simulation oxygen content and preset simulation dynamic load is integrated. By comparing the stress distribution cloud maps of the finite element models corresponding to the bridge deck prototype structure and the initial scaled model, the simulation stress distribution deviation between the bridge deck prototype structure and the initial scaled model is determined. A partial full-scale model of the bridge deck prototype structure and a corresponding scaled-down model specimen were constructed. Fatigue loading experiments were conducted on the partial full-scale model and the scaled-down model specimen in a low-temperature, low-pressure corrosion environment simulation chamber. The fatigue crack propagation length of the partial full-scale model and the scaled-down model specimen were monitored by a preset strain gauge array in the simulation chamber to determine the simulated stress deviation between the partial full-scale model and the scaled-down model specimen.

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