Simulation test system and method for waterlogged subgrade based on repeated seepage under load action

Abstract

This invention relates to the field of flooded roadbed simulation technology, and particularly provides a simulation test system and method for flooded roadbeds under repeated seepage based on load. The system includes a dynamic water pressure gradient generation subsystem, a stress-seepage coupling response chamber subsystem, and a seepage instability early warning network subsystem. The method involves: a programmable pressure wavefront forming a spatiotemporally alternating water pressure gradient field on a permeable substrate; importing the water pressure gradient field into a three-dimensional independent loading frame; applying an asymmetric cyclic stress wave to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the sample's internal pores in real time; inputting the local strain energy density distribution and the pore dynamic water pressure pulsation intensity into a chaotic feature extractor, outputting a structural degradation energy entropy value; and triggering a three-stage early warning when the structural degradation energy entropy value exceeds the phase transition threshold. This invention provides controllable experimental conditions and quantitative evaluation methods for studying the progressive failure mechanism of flooded roadbeds under repeated seepage-load coupling.

Description

Technical Field

[0001] This invention relates to the field of flooded roadbed simulation technology, and in particular to a flooded roadbed simulation test system and method based on repeated seepage under load. Background Technology

[0002] In the construction of transportation infrastructure such as highways and railways, the long-term stability and deformation resistance of the roadbed are crucial. Especially in areas with heavy rainfall, high groundwater levels, or seasonal waterlogging, the mechanical properties of the roadbed under vehicle dynamic loads and repeated seepage can significantly degrade, leading to settlement, frost heave, softening, and other defects that severely impact road service life and traffic safety. Therefore, studying the deformation characteristics of flooded roadbeds under load-seepage coupling is of great significance for optimizing roadbed design and improving durability. Currently, research on flooded roadbeds mainly relies on numerical simulation and indoor model tests. Numerical simulation is limited by the accuracy of constitutive models and cannot accurately reflect complex water-mechanical coupling effects; while traditional indoor tests (such as conventional consolidation tests and triaxial tests) can only simulate single working conditions (such as constant seepage or static load) and cannot reproduce the actual environment of dynamic traffic loads plus repeated seepage. Therefore, there is an urgent need for a test device that can accurately simulate the coupling effects of long-term dynamic loads and repeated seepage to reveal the progressive damage mechanism of the roadbed soil.

[0003] Prior art 1, Chinese Patent Application No. 202210575620.2, discloses a model test device for simulating the stability of a flooded roadbed, including a roadbed system, a waterway and a water circulation system. The roadbed system is equipped with a roadbed system model box and a roadbed slope as a flooded roadbed. The roadbed system model box is used to stack the roadbed slope. The upper part of the cross-section of the roadbed slope is set as a trapezoid so that the left and right sides of the roadbed slope form two slopes to be tested. The waterway and water circulation system is equipped with a left circulation waterway system and a right circulation waterway system. The left circulation waterway system and the right circulation waterway system are clamped and set on both sides of the roadbed slope. The left circulation waterway system and the right circulation waterway system are equipped with flowing water. All or part of the two slopes to be tested are clamped and immersed in the flowing water of the left circulation waterway system and the right circulation waterway system. While a test device that allows water to flow and wet the two slopes under test, enabling bilateral immersion, regulating water level changes, and simulating water flow, would improve related research in this field, bilateral immersion using a circulating waterway system can only simulate constant or slowly changing water levels. It cannot generate a dynamic, spatiotemporally alternating water pressure gradient field, making it difficult to simulate unsteady seepage conditions caused by real rainfall, tides, or groundwater fluctuations.

[0004] Current technologies suffer from simulation distortion and lack of protection due to static immersion, unidirectional loading, and passive observation. Therefore, this invention provides a simulation test system and method for immersion subgrade based on repeated seepage under load. Summary of the Invention

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In one aspect, the present invention provides a simulation test system for immersion subgrade based on repeated seepage under load, comprising:

[0007] The seepage instability early warning network subsystem is configured to synchronously acquire the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample through an embedded flexible sensing unit array; input the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity into a chaotic feature extractor, and output the structural deterioration energy entropy value.

[0008] In one optional implementation, the seepage instability early warning network subsystem includes:

[0009] The multi-source data spatiotemporal registration component is configured to output raw data from an embedded flexible sensing unit, acquire local strain energy density distribution, differential measure of elastic-plastic deformation energy inside the sample; capture pore hydrodynamic pressure pulsation intensity, statistical deviation of pressure fluctuation in the fluid within the oscillating pores; and generate a spatiotemporal synchronous coordinate mapping field to align the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity with the spatial coordinates and timestamps inside the sample.

[0010] The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the spatiotemporal synchronous coordinate mapping field into the strain concentration tensor extractor, outputting the principal strain trajectory manifold, which describes the differential geometry of the strain energy accumulation path; input the pore hydrodynamic pressure fluctuation intensity in the spatiotemporal synchronous coordinate mapping field into the fluctuation chaos degree resolver, generating a fluctuation singular attractor, which characterizes the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the fluctuation singular attractor are associated through a conformal mapping transformer;

[0011] The entropy value generation and phase transition criterion component is configured as a strain-percolation coupled phase space input Lyapunov exponential integrator, which calculates the exponential divergence rate of the strain energy dissipation path and the orbital contraction intensity of the pulsating attractor along the coupled phase space, and outputs the structural deterioration entropy value.

[0012] In one alternative implementation, the curvature invariant of the master strain trajectory manifold is mapped to the attractor space of the pulsating singular attractor; the fractal dimension of the pulsating singular attractor is projected onto the manifold surface of the master strain trajectory manifold to form a strain-percolation coupled phase space.

[0013] In one optional implementation, the chaotic feature deep fusion component includes:

[0014] The manifold attractor feature decoupling module is configured to take into account the main strain trajectory manifold and the pulsating singular attractor;

[0015] The principal strain trajectory manifold contains Gaussian curvature invariants: describing the surface torsion intensity of the strain energy accumulation region; and carries geodesic deflection angles: characterizing the abrupt changes in the strain energy transfer path.

[0016] The pulsating singular attractor contains a fractal dimension scale: quantifying the spatial filling degree of the pressure fluctuation trajectory; and carries a trajectory contraction parameter: reflecting the rate at which the pulsation returns to equilibrium.

[0017] The bidirectional conformal mapping module is configured to input Gaussian curvature invariants into a curvature-attractor modulator and output constrained attractor boundaries; and to input fractal dimension scaling into a dimension-manifold projector to generate scale-distorted manifolds.

[0018] The coupled phase space synthesis module is configured to import the constrained attractor boundary and the scale-distorted manifold into the conformal tensor synthesizer to perform a two-field coupling operation. The constrained attractor boundary serves as the outer envelope of the phase space, and the scale-distorted manifold serves as the inner basis of the phase space, forming a strain-percolation coupled phase space.

[0019] In one alternative implementation, the strain-percolation coupled phase space of the coupled phase space synthesis module inherits and enhances the original features: the strain energy dissipation path is distributed along the folded ridges of the scale-distorted manifold, and the pulsating chaotic trajectory is constrained by the compressive boundary of the constrained attractor boundary.

[0020] In one optional implementation, the coupled phase space synthesis module includes:

[0021] The dual-field basis preprocessing submodule is configured with input constraint attractor boundaries and input scale twisted manifolds;

[0022] The bounded attractor boundary includes: carrying a boundary curvature modulation factor, derived from the compression / expansion effect of Gaussian curvature on the attractor space; and possessing a phase constraint strength describing the degree to which the pulsating orbit is restricted;

[0023] The scale-distorted manifold contains dimension-projected deformations: reflecting the transformation of the manifold surface by the fractal scale into wrinkles / ridges, and contains geodesic reconstruction parameters: recording the geometric distortion characteristics of the strain path;

[0024] The conformal tensor coupling operation submodule is configured to input the boundary curvature modulation factor into the connection torsion generator and output a non-uniform boundary homology field; and to import the dimension projection deformation of the scaled twisted manifold into the deformation connection adapter to generate an affine scale connection.

[0025] The dual-field differential homeomorphic module is configured to couple the non-uniform boundary homology field and the affine scaling connection through a Chern-Simons integrator. The torsion fiber bundle of the non-uniform boundary homology field and the affine connection of the affine scaling connection are integrated in curvature form. The integration path reconstructs the parameter orientation along the geodesic of the scaling twisted manifold, forming a strain-percolation coupled phase space.

[0026] In one optional implementation, the two-field differential homeomorphism submodule includes:

[0027] The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory by a path density modulator for path curvature invariants, and to form an adaptive step size sequence by a variable step size generator for parameterized rate fields; the torsion fiber bundle is decoupled from the main constraint gauge field and the extended degree of freedom spinor field by a constraint field structure separator.

[0028] The affine scaling connection conversion unit is configured such that the contraction type Christofel symbol is converted into an energy contraction differential 1-form by an energy operator converter, and the expansion type covariant derivative is converted into an energy diffusion differential 2-form by an energy operator converter; the principal constraint gauge field and the energy contraction differential 1-form are converted into a constraint invariance 3-form by a principal channel curvature synthesizer, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted into a deformation covariance 3-form by a deformation channel curvature synthesizer;

[0029] The directional integral execution unit is configured to generate constrained topological invariants by passing through the main channel feature class integral kernel with an adaptive step size sequence along the density-weighted integral trajectory in the constrained invariant 3-form; and to generate deformation feature classes by passing through the deformation channel feature class integral kernel with the same path parameters in the deformation covariant 3-form. The constrained topological invariants and deformation feature classes are then combined by a global invariant fusion unit to form a strain-percolation coupled phase space.

[0030] In one optional implementation, the entropy generation and phase transition criterion component includes:

[0031] The coupled phase space structure analysis module is configured to generate a strain energy transfer tangent bundle by passing the strain-permeation coupled phase space through the phase space trajectory tangent bundle generator; at the same time, the pulsating attractor orbital space is separated; the strain energy transfer tangent bundle is input into the divergence rate feature extractor to calculate the asymptotic separation intensity of adjacent trajectories in the tangent bundle and output the maximum divergence rate feature value.

[0032] The orbital contraction intensity capture module is configured as a generator for importing the orbital space of the pulsating attractor into a regression equilibrium trend field, constructing a vector field describing the orbital regression trend towards the attractor center; the vector field generates the orbital contraction density integral through the density field integral kernel;

[0033] The energy entropy value synthesis module is configured as a tensor shrinking operator that inputs the maximum divergence rate eigenvalue and the orbital contraction density integral, and performs the shrinking operation of the eigenvalue-density product to generate the structural deterioration energy entropy value.

[0034] In one optional implementation, the coupled phase space structure resolution module includes:

[0035] The tangent bundle structure preprocessing submodule is configured as a strain energy transfer tangent bundle input tangent bundle trajectory parameterizer to generate parameterized trajectory bundles; the parameterized trajectory bundles are imported into a local scaling field generator to establish a trajectory spacing scaling reference at each point of the tangent bundle;

[0036] The progressive separation tensor evolution submodule is configured to advance along the tangent bundle energy transfer direction. The local scaling field passes through the conformal evolution operator and outputs the trajectory spacing deformation field. The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor.

[0037] The extreme value feature extraction submodule is configured as an asymptotically separated tensor input extreme value feature extractor, which calculates the maximum eigenvalue of the tensor and outputs the maximum divergence rate feature value.

[0038] Another aspect of the present invention provides a method for simulating the immersion of a roadbed under repeated seepage under load based on the aforementioned load-based repeated seepage simulation test system, comprising the following steps:

[0039] A programmable pressure wave is generated in a sealed cavity by a multi-axis linkage piston array. The wavefront of the programmable pressure wave forms a spatiotemporally alternating water pressure gradient field on the water-permeable medium substrate.

[0040] The hydraulic pressure gradient field is introduced into the triaxial independent loading frame; the triaxial independent loading frame applies asymmetric cyclic stress waves to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the sample in real time.

[0041] The local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample are synchronously acquired by an embedded flexible sensing unit array; the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity are input into a chaotic feature extractor, and the structural degradation energy entropy value is output; when the structural degradation energy entropy value exceeds the phase transition threshold, a three-stage early warning is triggered.

[0042] This invention achieves full-parameter simulation and active protection of flooded roadbeds under complex loads and seepage coupling through the synergistic action of multiple modules. Specifically, the dynamic water pressure gradient generation subsystem constructs a spatiotemporally alternating water pressure gradient field using programmable pressure waves, accurately reproducing unsteady seepage boundary conditions caused by natural rainfall and tides, overcoming the limitations of traditional static water pressure loading. The stress-seepage coupling response cavity subsystem couples the hydraulic gradient field with a triaxial mechanical stress field, simulating the bidirectional coupling effect of seepage and stress through a piezoelectric lattice array, while simultaneously using hydraulic impedance spectroscopy to monitor and reflect the evolution of the seepage path inside the sample in real time. The seepage instability early warning network subsystem quantifies the structural degradation energy entropy value through a chaotic feature extractor, establishing a multi-level intervention mechanism based on phase transition thresholds: spectral modulation changes seepage excitation characteristics, stress wave intervention adjusts the load transmission path, and microcapsules target and repair local damage, forming a closed-loop control system of "excitation-response-regulation". Attached Figure Description

[0043] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a block diagram of the immersion subgrade simulation test system based on repeated seepage under load provided in Embodiment 1 of the present invention;

[0045] Figure 2 This is a block diagram of the dynamic water pressure gradient generation subsystem provided in Embodiment 2 of the present invention;

[0046] Figure 3 This is a block diagram of the stress-seepage coupling response cavity subsystem provided in Embodiment 3 of the present invention;

[0047] Figure 4 This is a block diagram of the seepage instability early warning network subsystem provided in Embodiment 4 of the present invention;

[0048] Figure 5 This is a flowchart of the simulated test method for immersion subgrade based on repeated seepage under load provided in Embodiment 5 of the present invention;

[0049] Figure 6 A block diagram of the electronic device provided by the present invention;

[0050] Figure 7 A block diagram of a computer-readable storage medium provided for this invention. Detailed Implementation

[0051] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0052] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0053] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0054] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0055] Example 1:

[0056] like Figure 1 As shown, this embodiment of the invention provides a simulated test system for immersion roadbed based on repeated seepage under load, comprising:

[0057] The dynamic water pressure gradient generation subsystem is configured to generate programmable pressure waves in a closed cavity through a multi-axis linkage piston array. The wavefront of the programmable pressure waves forms a spatiotemporally alternating water pressure gradient field on the permeable medium substrate.

[0058] The stress-seepage coupled response cavity subsystem is configured to introduce the hydraulic pressure gradient field into the triaxial independent loading frame; the triaxial independent loading frame applies an asymmetric cyclic stress wave to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the sample in real time.

[0059] The seepage instability early warning network subsystem is configured to synchronously acquire the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample through an embedded flexible sensing unit array; input the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity into a chaotic feature extractor, and output the structural deterioration energy entropy value; when the structural deterioration energy entropy value exceeds the phase transition threshold, a three-stage early warning is triggered.

[0060] The three-stage early warning system includes sending a pressure wave spectrum modulation command to the dynamic water pressure gradient generation subsystem (changing the frequency composition of the water pressure gradient field); injecting a stress wave standing wave intervention signal into the stress seepage coupling response cavity subsystem (generating a reverse damping wave in the asymmetric cyclic stress wave); and initiating the targeted release of microcapsule self-healing agents (based on the strain concentration area located by the embedded flexible sensing unit array).

[0061] In the above embodiments, this embodiment achieves full-parameter simulation and active protection of flooded roadbeds under complex load and seepage coupling through the synergistic effect of multiple modules. Specifically, the dynamic water pressure gradient generation subsystem constructs a spatiotemporally alternating water pressure gradient field through programmable pressure waves, accurately reproducing the unsteady seepage boundary conditions caused by natural rainfall, tides, etc., breaking through the limitations of traditional static water pressure loading. The stress-seepage coupling response cavity subsystem couples the hydraulic gradient field with the triaxial mechanical stress field, realizes the simulation of seepage-stress bidirectional coupling through a piezoelectric lattice array, and simultaneously uses hydraulic impedance spectroscopy to monitor and reflect the evolution of the seepage path inside the sample in real time. The seepage instability early warning network subsystem quantifies the structural deterioration energy entropy value through a chaotic feature extractor and establishes a multi-level intervention mechanism based on the phase transition threshold: spectrum modulation changes the seepage excitation characteristics, stress wave intervention adjusts the load transmission path, and microcapsules target and repair local damage, forming a closed-loop control system of "excitation-response-regulation".

[0062] In summary, the linkage of the three subsystems in this embodiment realizes a full-chain experimental capability from environmental simulation (water pressure field), coupled loading (stress-seepage field) to intelligent control (early warning-intervention), providing controllable experimental conditions and quantitative evaluation methods for studying the progressive failure mechanism of flooded roadbeds under repeated seepage-load coupling.

[0063] Example 2:

[0064] like Figure 2 As shown, based on Example 1, the dynamic water pressure gradient generation subsystem provided in this embodiment of the invention includes:

[0065] The phase offset motion module is configured such that each piston of the multi-axis linkage piston array reciprocates according to an independent preset displacement-time function, and the motion trajectory of the piston group of the multi-axis linkage piston array forms a coherent motion matrix; the piston group pushes the fluid to generate pressure pulsation, and the phase difference of the coherent motion matrix causes the pressure waves generated by adjacent pistons to coherently superimpose during propagation, resulting in interference pressure waves;

[0066] The medium substrate reconstruction module is configured to allow interfering pressure waves to contact the surface of a permeable medium substrate, and the substrate pore structure selectively attenuates and refracts the pressure waves.

[0067] The spatiotemporal alternation forming module is configured to generate a dynamic osmotic pressure difference inside the substrate by pressure waves that are selectively attenuated and refracted. The dynamic osmotic pressure difference generates a pressure gradient that reverses direction with the piston movement cycle, and the phase difference of the piston group causes the pressure gradient to migrate in a spiral shape in three-dimensional space.

[0068] In the above embodiments, the dynamic water pressure gradient generation subsystem of this embodiment achieves complex fluid dynamics control through multi-module collaboration. Its overall technical significance is as follows: In the dimension of precise wavefield control, the phase offset motion module achieves active programming control of the pressure wavefield through the coherent motion matrix of a multi-axis linked piston array; each piston moves according to a preset displacement-time function, forming a pressure wave emission source array with controllable phase difference; this overcomes the limitations of traditional single-point pulsating sources, enabling precise interference superposition of pressure waves during propagation, providing a controllable wavefield foundation for subsequent modules. In the dimension of medium-wavefield coupling, the medium substrate reconstruction module utilizes the porous structure characteristics of the permeable medium to transform the interfering pressure wave into a spatially selective attenuation and refraction mode; it couples the artificially generated coherent wavefield with the natural medium structural characteristics, forming a medium-dependent pressure wave modulation effect, providing a physical carrier for the formation of dynamic osmotic pressure difference. In the dimension of spatiotemporal dynamic field construction, the spatiotemporal alternation generation module ultimately transforms the modulated pressure wavefield into a dynamic osmotic pressure difference with three-dimensional spatiotemporal characteristics. The periodic reversal and spiral migration characteristics of the pressure gradient enable the unsteady three-dimensional distribution of the fluid driving force; the dynamic field construction method breaks through the limitations of the traditional steady-state pressure gradient and forms a fluid driving mechanism with spatiotemporal evolution characteristics.

[0069] In summary, this embodiment establishes a fluid dynamic drive system with three-dimensional spatiotemporal programming capabilities through a technical chain of artificially controllable coherent pressure wave field generation, medium coupling modulation, and dynamic permeation field construction. While maintaining the precision of mechanical drive, it obtains complex pressure gradient fields that are difficult to achieve using traditional methods through wave field interference and medium coupling.

[0070] Example 3:

[0071] like Figure 3As shown, based on Embodiment 1, the stress-seepage coupled response cavity subsystem provided in this embodiment of the invention includes:

[0072] The water pressure gradient field to stress conversion module is configured to trigger the loading frame motion with the spiral water pressure field generated by water pressure. The spatial curvature parameter of the spiral water pressure field drives the universal joint axis of the three-way independent loading frame to perform focus tracking. The direction reversal osmotic pressure difference modulates the piezoelectric excitation, and the phase reversal signal is input to the piezoelectric lattice control unit to generate the polarization direction reversal command.

[0073] The asymmetric cyclic stress wave synthesis module is configured to generate piezoelectric strain gradients in lattice cells based on the focal coordinates of focal tracking; a polarization direction inversion command applies a reverse driving voltage to adjacent lattice cells; the piezoelectric strain gradient and the reverse driving voltage couple to form a strain wave phase shear.

[0074] The seepage mechanics coupled response module is configured to induce the sample to generate crack opening and closing resonances by strain wave phase shear. The oscillation of the crack opening and closing resonances forces the pore fluid to exhibit: inertial escape flow: the fluid accelerates away from the oscillating crack zone, and viscous lock-in flow: the fluid is trapped in the micropores due to shear resistance.

[0075] The transient hydraulic impedance spectrum reconstruction module is configured to generate asymmetric charge accumulation on the lattice surface due to the dual-mode impedance effect (inertial escape flow / viscous lock-in flow). The spatiotemporal distribution of the asymmetric charge accumulation is acquired in real time, and the impedance phase divergence field is output through a chaotic phase trajectory decomposer. The singular value feature points of the impedance phase divergence field are extracted to construct an impedance kernel density cloud map. The kernel density peak of the impedance kernel density cloud map corresponds to the seepage instability sensitive region.

[0076] In the above embodiments, the stress-seepage coupling response cavity system of this embodiment achieves precise control and dynamic monitoring of hydraulic-mechanical-electrical multi-field coupling through the synergistic action of multiple modules. Its core significance can be broken down as follows: precise loading of the mechanical field; the hydraulic pressure gradient field to stress conversion module converts fluid pressure into a three-dimensional dynamic mechanical load; the spatial curvature parameter of the spiral hydraulic pressure field realizes the focus tracking control of the three-dimensional independent loading frame, solving the limitation of fixed direction in traditional loading systems; the combination of direction-reversed osmotic pressure difference and phase modulation of piezoelectric excitation realizes real-time programmable control of the loading path; and fine synthesis of strain waves; the asymmetric cyclic stress wave synthesis module utilizes the polarization direction reversal characteristic of the piezoelectric lattice to generate strain waves with phase shear characteristics through reverse driving voltage coupling of adjacent lattice units; the strain waves can accurately match the dynamic response requirements of the internal structure of the sample, providing customized mechanical input for crack control. Dynamic control of seepage state: The seepage mechanics coupled response module induces fracture resonance through strain wave phase shearing, generating a dual-mode fluid motion of inertial escape flow and viscous lock-in flow. The controlled seepage mode realizes active zonal control of pore fluid motion, providing an experimental basis for studying seepage mechanisms under different flow regimes. Intelligent identification of instability precursors: The transient hydraulic impedance spectrum reconstruction module transforms the dual-mode impedance effect into a quantifiable impedance phase divergence field through spatiotemporal analysis of asymmetric charge accumulation. Singular value features extracted using chaotic phase trajectory decomposition technology can accurately calibrate the seepage instability sensitive zone, providing multi-dimensional criteria for system stability assessment.

[0077] In summary, this embodiment constructs a complete closed-loop research system of hydraulic excitation-mechanical loading-seepage response-instability early warning, which is particularly suitable for research scenarios that require precise control of seepage-stress interaction, such as multi-field coupling tests of soil and rock masses and energy reservoir stimulation assessment. The cascading effect of each module realizes full-scale observation from macroscopic mechanical loading to microscopic fluid motion, and its impedance kernel density cloud map output provides a new quantitative analysis tool for engineering safety early warning.

[0078] Example 4:

[0079] like Figure 4 As shown, based on Embodiment 1, the seepage instability early warning network subsystem provided in this embodiment of the invention includes:

[0080] The multi-source data spatiotemporal registration component is configured to output raw data from an embedded flexible sensing unit, acquire local strain energy density distribution, differential measure of elastic-plastic deformation energy inside the sample; capture pore hydrodynamic pressure pulsation intensity, statistical deviation of pressure fluctuation in the fluid within the oscillating pores; and generate a spatiotemporal synchronous coordinate mapping field to align the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity with the spatial coordinates and timestamps inside the sample.

[0081] The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the spatiotemporal synchronous coordinate mapping field into the strain concentration tensor extractor, outputting the principal strain trajectory manifold, which describes the differential geometry of the strain energy accumulation path; input the pore hydrodynamic pressure fluctuation intensity in the spatiotemporal synchronous coordinate mapping field into the fluctuation chaos degree resolver, generating a fluctuation singular attractor, which characterizes the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the fluctuation singular attractor are associated through a conformal mapping transformer;

[0082] The curvature invariant of the principal strain trajectory manifold is mapped to the attractor space of the pulsating singular attractor; the fractal dimension of the pulsating singular attractor is projected onto the manifold surface of the principal strain trajectory manifold to form a strain-percolation coupled phase space.

[0083] The entropy value generation and phase transition criterion component is configured as a strain-percolation coupled phase space input Lyapunov exponential integrator, which calculates the exponential divergence rate of the strain energy dissipation path and the orbital contraction intensity of the pulsating attractor along the coupled phase space, and outputs the structural deterioration entropy value.

[0084] In the above embodiments, the multi-source data spatiotemporal registration component of the seepage instability early warning network subsystem of this embodiment constructs the basic framework for physical quantity observation. Through embedded sensing units, it achieves the synchronous capture of two key parameters inside the sample: one is the differential measure of the material's structural deformation energy (local strain energy density distribution), and the other is the statistical characteristics of pore fluid pressure fluctuations (dynamic water pressure pulsation intensity). The establishment of a spatiotemporal synchronous coordinate mapping field ensures strict alignment of the two types of heterogeneous data in a four-dimensional spatiotemporal coordinate system. The chaotic feature deep fusion component realizes the coupled analysis of multiple physics fields: the strain concentration tensor extractor analyzes the material deformation characteristics from the perspective of continuous medium mechanics, generating the main strain trajectory manifold describing the strain energy accumulation path; the pulsating chaos degree analyzer, based on nonlinear dynamics theory, transforms pressure fluctuations into strange attractors with fractal characteristics; through the correlation established by the conformal mapping transformer, the coupled expression of the solid deformation field and the fluid pressure field is mathematically realized. The entropy generation and phase transition criterion component quantitatively characterizes the dynamic stability of the coupled system through a Lyapunov exponential integrator: it analyzes both the divergence characteristics of the strain energy dissipation path (reflecting the trend of structural damage evolution) and the contraction characteristics of the pressure pulsation attractor (characterizing fluid motion stability). The final output structural degradation entropy value is essentially a quantitative indicator of the system's phase transition threshold.

[0085] In summary, this embodiment realizes cross-scale analysis from the microscale (differential metric) to the macroscopic behavior (phase transition criterion), providing a quantitative early warning index based on nonlinear dynamics theory for identifying precursors of seepage instability; the structural degradation energy entropy value output by the system can be regarded as a topological invariant of the coupling effect between the internal strain energy of the material and the chaotic characteristics of seepage pressure, and its numerical evolution directly reflects the critical state of the system tending towards instability.

[0086] Example 5:

[0087] Based on Example 4, the chaotic feature deep fusion component provided in this embodiment of the invention includes:

[0088] The manifold attractor feature decoupling module is configured to take into account the main strain trajectory manifold and the pulsating singular attractor;

[0089] The principal strain trajectory manifold contains Gaussian curvature invariants: describing the surface torsion intensity of the strain energy accumulation region; and carries geodesic deflection angles: characterizing the abrupt changes in the strain energy transfer path.

[0090] The pulsating singular attractor contains a fractal dimension scale: quantifying the spatial filling degree of the pressure fluctuation trajectory; and carries a trajectory contraction parameter: reflecting the rate at which the pulsation returns to equilibrium.

[0091] The bidirectional conformal mapping module is configured to input Gaussian curvature invariants into a curvature-attractor modulator and output constrained attractor boundaries; and to input fractal dimension scaling into a dimension-manifold projector to generate scale-distorted manifolds.

[0092] High curvature region (Gaussian curvature invariant > 0) → compressible attractor boundary;

[0093] Negative curvature region (Gaussian curvature invariant < 0) → expanding attractor boundary;

[0094] High-dimensional regions (fractal dimension scale ↑) → produce concave wrinkles on the manifold surface; low-dimensional regions (fractal dimension scale ↓) → form raised ridges on the manifold surface;

[0095] The coupled phase space synthesis module is configured to import the constrained attractor boundary and the scale-distorted manifold into the conformal tensor synthesizer to perform a two-field coupling operation. The constrained attractor boundary serves as the outer envelope of the phase space, and the scale-distorted manifold serves as the inner basis of the phase space, forming a strain-percolation coupled phase space.

[0096] The strain-percolation coupled phase space inherits and enhances the original features: the strain energy dissipation path is distributed along the folded ridges of the scale-distorted manifold, and the pulsating chaotic trajectory is constrained by the compressive boundary of the constrained attractor boundary.

[0097] In the above embodiments, the manifold attractor feature decoupling module of this embodiment realizes independent feature extraction of the strain field and the pulsating field: the local torsional intensity of the strain energy accumulation region is quantified by the Gaussian curvature invariant of the main strain trajectory manifold, and the geodesic deflection angle captures the abrupt change characteristics of the energy transfer path; at the same time, the fractal dimension scaling of the pulsating singular attractor is used to accurately describe the spatial expansion of the pressure fluctuation, and the orbital contraction parameter objectively records the dynamic rate of the pulsating return to equilibrium state. The bidirectional conformal mapping module establishes a feature interaction mechanism between the two physical fields: the curvature-attractor modulator dynamically adjusts the attractor boundary morphology (compression / expansion) according to the curvature characteristics of the strain field, and the dimension-manifold projector accurately reconstructs the geometric structure of the manifold surface (concave folds / convex ridges) according to the fractal characteristics of the pulsating field, forming a parameterized mapping relationship between the two physical fields. The coupled phase space synthesis module integrates the modulated attractor boundary (outer envelope) and the reconstructed manifold (internal basis) into a unified topological structure through conformal tensor operations. This strain-percolation coupled phase space has the following core characteristics: the strain energy dissipation path strictly follows the geometric characteristics of the scale-distorted manifold; the evolution range of the pulsating chaotic orbit is strictly limited by the constrained attractor boundary; all quantitative characteristics of the original physical field (curvature invariant, geodesic deflection angle, fractal dimension scaling, orbit contraction parameter) are inherited and enhanced in the coupled phase space.

[0098] In summary, this embodiment achieves deep coupling between the strain field and the pulsating field at the geometric feature level, and transforms the characteristic parameters of the two physical fields into a computable topological structure in a unified phase space through mathematical mapping relationships.

[0099] Example 6:

[0100] Based on Embodiment 5, the coupled phase space synthesis module provided in this embodiment of the invention includes:

[0101] The dual-field basis preprocessing submodule is configured with input constraint attractor boundaries and input scale twisted manifolds;

[0102] The bounded attractor boundary includes: carrying a boundary curvature modulation factor, derived from the compression / expansion effect of Gaussian curvature on the attractor space; and possessing a phase constraint strength describing the degree to which the pulsating orbit is restricted;

[0103] The scale-distorted manifold contains dimension-projected deformations: reflecting the transformation of the manifold surface by the fractal scale into wrinkles / ridges, and contains geodesic reconstruction parameters: recording the geometric distortion characteristics of the strain path;

[0104] The conformal tensor coupling operation submodule is configured to input the boundary curvature modulation factor into the connection torsion generator and output a non-uniform boundary homology field; and to import the dimension projection deformation of the scaled twisted manifold into the deformation connection adapter to generate an affine scale connection.

[0105] In the compression region, the boundary curvature modulation factor > 0 generates positive torsion fiber bundles; in the expansion region, the boundary curvature modulation factor < 0 induces a negative torsion spinor field; in the concave fold region (dimensional projection deformation increases), a contractile Christofel sign is generated, and in the convex ridge region (dimensional projection deformation decreases), an expansion covariant derivative is formed.

[0106] The dual-field differential homeomorphic module is configured to couple the non-uniform boundary homology field and the affine scaling connection through a Chern-Simons integrator. The torsion fiber bundle of the non-uniform boundary homology field and the affine connection of the affine scaling connection are integrated in curvature form. The integration path reconstructs the parameter orientation along the geodesic of the scaling twisted manifold, forming a strain-percolation coupled phase space.

[0107] In the above embodiments, the dual-field basis preprocessing submodule performs structured analysis on the input data. The constrained attractor boundary carries the boundary curvature modulation factor (reflecting the compression / expansion effect of Gaussian curvature on the attractor space) and phase constraint strength (quantifying the degree of restriction on the pulsating trajectory); the scale-distorted manifold contains dimension projection deformation variables (describing the fractal scale's modification of the surface's wrinkles / ridges) and geodesic reconstruction parameters (recording the geometric distortion characteristics of the strain path); ensuring that the core feature parameters of the two physical fields are accurately extracted, providing structured input for subsequent coupling operations. The conformal tensor coupling operation submodule realizes the dynamic interaction of feature parameters. The boundary curvature modulation factor is input into the network torsion generator to generate a non-uniform boundary homology field, the characteristics of which are determined by the curvature modulation factor. The dimension projection deformation variables are input into the deformation connection adapter to generate an affine scale connection, the geometric characteristics of which are determined by the fractal scale; this operation establishes a dynamic mapping between boundary constraints and manifold geometry, enabling the feature parameters of the two physical fields to modulate each other. The dual-field differential homeomorphic submodule performs the final coupling through a Chern-Simons integrator. The non-uniform boundary homology field (torsion fiber bundle) and the affine scaling connection (affine connection) are integrated in curvature form. The integration path is oriented along the geodesic reconstruction parameters to ensure that the coupling process conforms to the geometric characteristics of the strain path. The resulting strain-percolation coupled phase space has the following characteristics: the boundary constraints (compression / expansion) and the manifold geometry (folds / ridges) form a unified structure under the differential homeomorphic mapping; the distortion characteristics of the strain path (geodesic reconstruction parameters) directly affect the integration path of the coupling operation, so that the phase space inherits the dynamic characteristics of the original physical field.

[0108] In summary, this embodiment achieves strict coupling between the constrained attractor boundary and the scale-distorted manifold at the differential geometry level, forming a strain-percolation coupled phase space with dynamic constraint characteristics.

[0109] Example 7:

[0110] Based on Example 6, the dual-field differential homeomorphism submodule provided in this embodiment of the invention includes:

[0111] The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory by a path density modulator for path curvature invariants, and to form an adaptive step size sequence by a variable step size generator for parameterized rate fields. The torsion fiber bundle is decoupled by a constraint field structure separator from the main constraint gauge field (originating from the phase constraint characteristics of the positive torsion region) and the extended degree of freedom spinor field (the orbit release characteristics of the negative torsion region).

[0112] The affine scaling connection conversion unit is configured such that the contraction type Christofel symbol is converted into an energy contraction differential 1-form by an energy operator converter, and the expansion type covariant derivative is converted into an energy diffusion differential 2-form by an energy operator converter; the principal constraint gauge field and the energy contraction differential 1-form are converted into a constraint invariance 3-form by a principal channel curvature synthesizer, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted into a deformation covariance 3-form by a deformation channel curvature synthesizer;

[0113] The directional integral execution unit is configured to generate constrained topological invariants by passing through the main channel feature class integral kernel with an adaptive step size sequence along the density-weighted integral trajectory in the constrained invariant 3-form; and to generate deformation feature classes by passing through the deformation channel feature class integral kernel with the same path parameters in the deformation covariant 3-form. The constrained topological invariants and deformation feature classes are then combined by a global invariant fusion unit to form a strain-percolation coupled phase space.

[0114] In the above embodiments, the overall significance of the dual-field differential homeomorphism submodule lies in constructing a structured differential homeomorphism classification system. Through the synergistic effect of the three units, it achieves coupled calculation of the constraint gauge field and the deformation degree-of-freedom field, ultimately generating a strain-percolation coupled phase space.

[0115] In this embodiment, the geodesic reconstruction parameter processing unit is responsible for path parameterization and torsion field decomposition: the path curvature invariant is density modulated to generate a density-weighted integral trajectory (e.g., defining geodesics on the manifold). and assign weight functions This allows the integration path to adapt to changes in curvature. The parameterized rate field, through a variable step-size generator, forms an adaptive step-size sequence (e.g., adjusting the step size based on local curvature during numerical integration). To ensure computational accuracy); the torsion fiber bundle is decomposed into: principal constraint gauge field (originating from the positive torsion region, possessing phase constraint characteristics, for example: satisfying (closed form); extended degree-of-freedom spinor field (originating from the negative torsion region, possessing orbital release characteristics, for example: allowing gauge transformation) (degrees of freedom). The role of the affine scale connection transformation unit is responsible for the construction of differential forms and curvature synthesis: contracted Christofel notation (such as...) After transformation by the energy operator, an energy-contracted differential 1-form is generated (e.g., ( After mapping, it becomes a closed form. Extended covariant derivatives (such as...) After transformation by the energy operator, the energy diffusion differential in 2-form is generated (e.g.: (Description of local deformation). Principal constraint gauge field and Composition constraint invariance 3-form (e.g.: satisfy (Maintaining gauge invariance); extended degree-of-freedom spinor field and Synthetic deformation covariance 3-form (e.g.: Allowing non-zero exterior derivatives (Describing deformable structures). The role of the directional integral execution unit, which is responsible for calculating topological invariants and constructing the phase space: constraint invariance 3-form. Calculate along the density-weighted integral trajectory to generate constrained topological invariants (e.g.: () is an integer representing a certain topological feature of the manifold. Deformation covariance 3-form Calculate along the same path to generate deformation feature classes (e.g.: (Gauge degrees of freedom describing local deformation). Constrained topological invariants. N With deformation feature class C Fusion, forming a strain-permeation coupled phase space (e.g., phase space) This framework describes the possible states of a manifold under different constraints and deformation conditions. Through three steps—geodesic reconstruction, differential form synthesis, and directional integration—it achieves the separation of the constraint field and the deformation field (maintaining gauge constraints in the positive torsion region and allowing deformation degrees of freedom in the negative torsion region); coupled computation of differential forms (1-form contraction, 2-form diffusion, 3-form synthesis); and fusion of topological invariants and deformation characteristics (ultimately generating a strain-percolation coupled phase space to describe the constrained deformation structure of the manifold). This framework is applicable to gauge field theory, geometric manifold analysis, topological quantum computing, and other fields, and can handle the classification problem of differential homeomorphisms with nonholonomic constraints.

[0116] Example 8:

[0117] Based on Example 4, the energy entropy value generation and phase transition criterion component provided in this embodiment of the invention includes:

[0118] The coupled phase space structure analysis module is configured to generate a strain energy transfer tangent bundle (a set of tangent spaces for strain energy dissipation paths) by passing the strain-percolation coupled phase space through the phase space trajectory tangent bundle generator; at the same time, the pulsating attractor orbital space (a subset of phase space characterizing fluid pulsating orbits) is separated; the strain energy transfer tangent bundle is input to the divergence rate feature extractor, which calculates the asymptotic separation intensity of adjacent trajectories in the tangent bundle and outputs the maximum divergence rate feature value (measuring the chaotic expansion intensity of the strain energy dissipation path).

[0119] The orbital contraction intensity capture module is configured as a generator for importing the orbital space of the pulsating attractor into a regression equilibrium trend field, constructing a vector field describing the orbital regression trend towards the attractor center; the vector field generates the orbital contraction density integral through the density field integral kernel;

[0120] The energy entropy value synthesis module is configured as a tensor shrinking operator that inputs the maximum divergence rate eigenvalue and the orbital contraction density integral, and performs the shrinking operation of the eigenvalue-density product to generate the structural deterioration energy entropy value.

[0121] In the above embodiments, the overall significance of the energy entropy value generation and phase transition criterion components in this embodiment is to achieve a quantitative characterization of the phase transition behavior of complex dynamic systems through the collaborative operation of the three modules.

[0122] Example 9:

[0123] Based on Embodiment 8, the coupled phase space structure analysis module provided in this embodiment of the invention includes:

[0124] The tangent bundle structure preprocessing submodule is configured as a strain energy transfer tangent bundle input tangent bundle trajectory parameterizer to generate parameterized trajectory bundles (including the initial spacing and orientation angle of adjacent trajectories); the parameterized trajectory bundles are imported into the local scaling field generator to establish a trajectory spacing scaling reference at each point of the tangent bundle (defining the initial relative positional relationship of adjacent trajectories).

[0125] The progressive separation tensor evolution submodule is configured to advance along the tangent bundle energy transfer direction. The local scaling field passes through the conformal evolution operator and outputs the trajectory spacing deformation field (the real-time rate of change of the spacing between adjacent trajectories). The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor (quantizing the expansion / contraction intensity of the trajectory spacing).

[0126] The extreme feature extraction submodule is configured as an asymptotically separating tensor input extreme feature extractor, which calculates the maximum eigenvalue of the tensor and outputs the maximum divergence rate feature value (characterizing the most intense trajectory separation intensity in the tangent bundle).

[0127] In the above embodiments, the coupled phase space structure analysis module of this embodiment achieves a quantitative characterization of trajectory separation characteristics in strain energy transfer tangential bundles through the collaborative operation of three sub-modules. This module first transforms the strain energy transfer tangential bundle into a parameterized trajectory bundle containing initial spacing and orientation angle information using a tangential bundle trajectory parameterizer. Then, a scaling reference for the trajectory spacing is established using a local scaling field generator. During the asymptotic separation tensor evolution stage, the system advances along the energy transfer direction, using a conformal evolution operator to transform the local scaling field into a trajectory spacing deformation field. Then, an asymptotic separation tensor that quantifies the trajectory spacing change is generated using a separation tensor synthesizer. Finally, the maximum eigenvalue of the separation tensor is calculated using an extreme value feature extractor, outputting the maximum divergence rate feature value characterizing the strongest trajectory separation intensity in the tangential bundle. The entire process realizes a complete computational link from the original tangential bundle data to key chaotic feature values, providing a basic quantitative index for subsequent phase transition criteria.

[0128] Example 10:

[0129] like Figure 5 As shown, based on Examples 1-9, the immersion subgrade simulation test method based on repeated seepage under load provided by the embodiments of the present invention includes the following steps.

[0130] Step S100: A programmable pressure wave is generated in the sealed cavity through a multi-axis linkage piston array. The wavefront of the programmable pressure wave forms a spatiotemporally alternating water pressure gradient field on the water-permeable medium substrate.

[0131] Step S200: The hydraulic pressure gradient field is introduced into the triaxial independent loading frame; the triaxial independent loading frame applies an asymmetric cyclic stress wave to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the sample in real time.

[0132] Step S300: Synchronously acquire the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample through an embedded flexible sensing unit array; input the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity into the chaotic feature extractor and output the structural degradation energy entropy value; when the structural degradation energy entropy value exceeds the phase transition threshold, trigger a three-stage early warning.

[0133] In the above embodiments, this embodiment achieves full-parameter dynamic simulation and active control of flooded roadbeds under complex environments through the synergistic coupling of multiple technical features. Its comprehensive technical effects are reflected in: dynamic seepage-stress coupling loading; constructing a spatiotemporally alternating water pressure gradient field through programmable pressure waves; applying asymmetric cyclic stress waves through a three-dimensional independent loading frame; accurately simulating the interaction between seepage and mechanical loads in actual working conditions; and achieving precise reproduction of hydraulic-mechanical coupling excitation. Real-time multi-physics monitoring: capturing transient hydraulic impedance spectra using piezoelectric lattice arrays; simultaneously acquiring strain energy density and dynamic water pressure pulsation intensity through embedded flexible sensing units; achieving multi-dimensional in-situ observation of seepage path evolution, local stress concentration, and dynamic response of pore water pressure. Intelligent early warning and active control: calculating structural degradation energy entropy based on a chaotic feature extractor; triggering multi-modal intervention strategies when the phase transition threshold is exceeded, including pressure wave spectrum modulation, stress wave standing wave damping intervention, and microcapsule targeted repair, forming a closed-loop control system of "monitoring-evaluation-control" to effectively suppress the unstable development of seepage.

[0134] In summary, this embodiment organically integrates dynamic seepage excitation, multi-directional stress loading, real-time field monitoring, and intelligent control mechanisms, providing a controllable, measurable, and adjustable experimental means for studying the progressive failure mechanism and protective measures of flooded roadbeds under repeated seepage-load coupling.

[0135] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0136] Electronic devices may include a central processing unit / microprocessor / main control chip, etc.; a storage medium coupled to the central processing unit / microprocessor / main control chip, etc., and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by a processor.

[0137] The central processing unit / microprocessor / main control chip, etc., may include, but are not limited to, one or more processors or microprocessors.

[0138] Storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0139] In addition, the electronic device may include (but is not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (e.g., keyboard, mouse, speaker, etc.).

[0140] The central processing unit / microprocessor / main control chip, etc., can communicate with external devices via I / O bus through wired or wireless networks (not shown).

[0141] The storage medium may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when run by a central processing unit / microprocessor / main control chip, etc.

[0142] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0143] Figure 7 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0144] like Figure 7 As shown, instructions, such as computer-readable instructions, are stored on a non-transitory computer-readable storage medium. When the computer-readable instructions are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0145] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0149] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulated test system for immersion subgrade under repeated seepage based on load, characterized in that, Include: The dynamic water pressure gradient generation subsystem is configured to generate programmable pressure waves in a closed cavity through a multi-axis linkage piston array. The wavefront of the programmable pressure waves forms a spatiotemporally alternating water pressure gradient field on the permeable medium substrate. The stress-seepage coupled response cavity system is configured to introduce the hydraulic pressure gradient field into a three-dimensional independent loading frame; The three-dimensional independent loading frame applies asymmetric cyclic stress waves to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the sample's internal pores in real time. The seepage instability early warning network subsystem is configured to synchronously acquire the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample through an embedded flexible sensing unit array; input the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity into a chaotic feature extractor, and output the structural deterioration energy entropy value; when the structural deterioration energy entropy value exceeds the phase transition threshold, a three-stage early warning is triggered. The seepage instability early warning network subsystem includes: The multi-source data spatiotemporal registration component is configured to output raw data from an embedded flexible sensing unit, acquire local strain energy density distribution, differential measure of elastic-plastic deformation energy inside the sample; capture pore hydrodynamic pressure pulsation intensity, statistical deviation of pressure fluctuation in the fluid within the oscillating pores; and generate a spatiotemporal synchronous coordinate mapping field to align the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity with the spatial coordinates and timestamps inside the sample. The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the spatiotemporal synchronous coordinate mapping field into the strain concentration tensor extractor, outputting the principal strain trajectory manifold, which describes the differential geometry of the strain energy accumulation path; input the pore hydrodynamic pressure fluctuation intensity in the spatiotemporal synchronous coordinate mapping field into the fluctuation chaos degree resolver, generating a fluctuation singular attractor, which characterizes the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the fluctuation singular attractor are associated through a conformal mapping transformer; The entropy value generation and phase transition criterion component is configured as a strain-percolation coupled phase space input Lyapunov exponential integrator, which calculates the exponential divergence rate of the strain energy dissipation path and the orbital contraction intensity of the pulsating attractor along the coupled phase space, and outputs the structural deterioration entropy value. Map the curvature invariant of the principal strain trajectory manifold to the attractor space of the pulsating singular attractor; The fractal dimension of the pulsating singular attractor is projected onto the manifold surface of the principal strain trajectory manifold to form a strain-percolation coupled phase space.

2. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 1, characterized in that, The chaotic feature deep fusion component includes: The manifold attractor feature decoupling module is configured to take into account the main strain trajectory manifold and the pulsating singular attractor; The principal strain trajectory manifold contains a Gaussian curvature invariant: describing the surface torsion intensity of the strain energy accumulation region; Carrying geodesic deflection angle: characterizing the abrupt change in the strain energy transfer path; The pulsating singular attractor contains a fractal dimension scale: quantifying the spatial filling degree of the pressure fluctuation trajectory; and carries a trajectory contraction parameter: reflecting the rate at which the pulsation returns to equilibrium. The bidirectional conformal mapping module is configured to input Gaussian curvature invariants into a curvature-attractor modulator and output constrained attractor boundaries; and to input fractal dimension scaling into a dimension-manifold projector to generate scale-distorted manifolds. The coupled phase space synthesis module is configured to import the constrained attractor boundary and the scale-distorted manifold into the conformal tensor synthesizer to perform a two-field coupling operation. The constrained attractor boundary serves as the outer envelope of the phase space, and the scale-distorted manifold serves as the inner basis of the phase space, forming a strain-percolation coupled phase space.

3. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 1, characterized in that, The strain-percolation coupled phase space of the coupled phase space synthesis module inherits and enhances the original features: The strain energy dissipation path is distributed along the folded ridges of the scale-distorted manifold, and the pulsating chaotic trajectory is constrained by the compressive boundary of the constrained attractor boundary.

4. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 2, characterized in that, Coupled phase space synthesis module, including: The dual-field basis preprocessing submodule is configured to input a constraint-type attractor boundary and an input scale-distorted manifold; The bounded attractor boundary includes: a boundary curvature modulation factor, derived from the compression / expansion effect of Gaussian curvature on the attractor space; and a phase constraint strength describing the degree to which the pulsating orbit is restricted. The scale-distorted manifold contains dimension-projected deformations: reflecting the transformation of the manifold surface by the fractal scale into wrinkles / ridges, and contains geodesic reconstruction parameters: recording the geometric distortion characteristics of the strain path; The conformal tensor coupling operation submodule is configured to input the boundary curvature modulation factor into the connection torsion generator and output a non-uniform boundary homology field. The dimension projection deformation of the scale-distorted manifold is imported into the deformation connection adapter to generate an affine scale connection; The dual-field differential homeomorphic module is configured to couple the non-uniform boundary homology field and the affine scaling connection through a Chern-Simons integrator. The torsion fiber bundle of the non-uniform boundary homology field and the affine connection of the affine scaling connection are integrated in curvature form. The integration path reconstructs the parameter orientation along the geodesic of the scaling twisted manifold, forming a strain-percolation coupled phase space.

5. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 4, characterized in that, The two-field differential homeomorphism submodule includes: The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory by a path density modulator for path curvature invariants, and to form an adaptive step size sequence by a variable step size generator for parameterized rate fields; the torsion fiber bundle is decoupled from the main constraint gauge field and the extended degree of freedom spinor field by a constraint field structure separator. The affine scaling connection conversion unit is configured such that the contraction type Christofel symbol is converted into an energy contraction differential 1-form by an energy operator converter, and the expansion type covariant derivative is converted into an energy diffusion differential 2-form by an energy operator converter; the principal constraint gauge field and the energy contraction differential 1-form are converted into a constraint invariance 3-form by a principal channel curvature synthesizer, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted into a deformation covariance 3-form by a deformation channel curvature synthesizer; The directional integral execution unit is configured to generate constrained topological invariants by passing through the main channel feature class integral kernel with an adaptive step size sequence along the density-weighted integral trajectory in the constrained invariant 3-form; and to generate deformation feature classes by passing through the deformation channel feature class integral kernel with the same path parameters in the deformation covariant 3-form. The constrained topological invariants and deformation feature classes are then combined by a global invariant fusion unit to form a strain-percolation coupled phase space.

6. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 1, characterized in that, The component for generating entropy values ​​and determining phase transition criteria includes: The coupled phase space structure analysis module is configured to form a strain energy transfer tangent by the phase space trajectory tangent generator in the strain-permeation coupled phase space; at the same time, the pulsating attractor orbital space is separated. The strain energy transfer tangent bundle input divergence rate feature extractor calculates the asymptotic separation intensity of adjacent trajectories in the tangent bundle and outputs the maximum divergence rate feature value. The orbit contraction intensity capture module is configured as a generator for importing regression equilibrium trend field into the orbital space of the pulsating attractor, and constructs a vector field describing the orbit's regression trend towards the attractor center. The vector field generates the orbital contraction density integral through the density field integral kernel; The energy entropy value synthesis module is configured as a tensor shrinking operator that inputs the maximum divergence rate eigenvalue and the orbital contraction density integral, and performs the shrinking operation of the eigenvalue-density product to generate the structural deterioration energy entropy value.

7. The immersion subgrade simulation test system based on repeated seepage under load as described in claim 6, characterized in that, Coupled phase space structure analysis module, including: The tangent bundle structure preprocessing submodule is configured as a strain energy transfer tangent bundle input tangent bundle trajectory parameterizer to generate parameterized trajectory bundles; the parameterized trajectory bundles are imported into a local scaling field generator to establish a trajectory spacing scaling reference at each point of the tangent bundle; The progressive separation tensor evolution submodule is configured to advance along the tangent bundle energy transfer direction. The local scaling field passes through the conformal evolution operator and outputs the trajectory spacing deformation field. The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor. The extreme value feature extraction submodule is configured as an asymptotically separated tensor input extreme value feature extractor, which calculates the maximum eigenvalue of the tensor and outputs the maximum divergence rate feature value.

8. A method for simulating flooded roadbeds under repeated seepage based on load, employing the flooded roadbed simulation test system based on repeated seepage under load as described in any one of claims 1 to 7, characterized in that, Includes the following steps: A programmable pressure wave is generated in a sealed cavity by a multi-axis linkage piston array. The wavefront of the programmable pressure wave forms a spatiotemporally alternating water pressure gradient field on the water-permeable medium substrate. The hydraulic pressure gradient field is introduced into the triaxial independent loading frame; the triaxial independent loading frame applies asymmetric cyclic stress waves to the roadbed sample through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the sample in real time. The local strain energy density distribution and pore hydrodynamic pressure pulsation intensity of the sample are synchronously acquired by an embedded flexible sensing unit array; the local strain energy density distribution and pore hydrodynamic pressure pulsation intensity are input into a chaotic feature extractor, and the structural degradation energy entropy value is output; when the structural degradation energy entropy value exceeds the phase transition threshold, a three-stage early warning is triggered.

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