Repeated seepage immersed roadbed simulation test system and method based on load effect

Abstract

The invention relates to the technical field of immersed roadbed simulation, in particular to an immersed roadbed simulation test system and method for repeated seepage based on load action, and the system comprises a dynamic water pressure gradient generation subsystem, a stress seepage coupling response cavity subsystem and a seepage stability loss early warning network subsystem. The method comprises the following steps: forming a time-space alternating water pressure gradient field on a permeable medium substrate by a wave front of programmable pressure waves; introducing the water pressure gradient field into a three-way independent loading frame; asymmetrical cyclic stress waves are applied to the roadbed sample through the piezoelectric lattice array, and meanwhile the transient hydraulic impedance spectrum of pores in the sample is captured in real time; inputting the local strain energy density distribution and the pore dynamic water pressure pulsation intensity into a chaotic feature extractor, and outputting a structure degradation energy entropy value; and when the structure degradation energy entropy value breaks through the phase change threshold value, three-stage early warning is triggered. Controllable experimental conditions and quantitative evaluation means are provided for studying the progressive failure mechanism of the immersed roadbed under the action of repeated seepage-load coupling.

Description

Technical Field

[0001] The present invention relates to the technical field of submerged roadbed simulation, and in particular to a submerged roadbed simulation test system and method based on repeated seepage under load action. Background Art

[0002] The long-term stability and deformation resistance of roadbeds are crucial in the construction of transportation infrastructure, such as highways and railways. Particularly in areas with high rainfall, high groundwater levels, or seasonal waterlogging, the mechanical properties of roadbeds can significantly degrade under the effects of vehicle dynamic loads and repeated seepage, leading to problems such as settlement, slurrying, and softening, seriously impacting road service life and driving safety. Therefore, studying the deformation behavior of submerged roadbeds under load-seepage coupling is crucial for optimizing roadbed design and improving durability. Currently, research on submerged roadbeds relies primarily on numerical simulations and laboratory model tests. Numerical simulations, limited by the accuracy of constitutive models, fail to accurately reflect the complex hydraulic-mechanical coupling. Traditional laboratory tests (such as conventional consolidation tests and triaxial tests) can only simulate a single operating condition (such as constant seepage or static load) and cannot replicate the real-world environment of dynamic traffic loads combined with repeated seepage. Therefore, a test device that can accurately simulate the long-term coupling of dynamic loads and repeated seepage is urgently needed to reveal the progressive damage mechanisms of subgrade soils.

[0003] Prior art 1, Chinese patent, application number 202210575620.2 discloses a model test device for simulating the stability of a submerged roadbed, including a roadbed system, a waterway and a water circulation system. The roadbed system is provided with a roadbed system model box and a roadbed slope as a submerged roadbed. The roadbed system model box is used to pile up the roadbed slope, and the upper part of the roadbed slope cross-section is set to a trapezoid so that the left and right sides of the roadbed slope form two slope surfaces to be tested; the waterway and water circulation system are provided with a left circulating water channel system and a right circulating water channel system, and the left circulating water channel system and the right circulating water channel system are clamped on both sides of the roadbed slope; the left circulating water channel system and the right circulating water channel system are provided with flowing water, and all or part of the two slope surfaces to be tested are clamped when immersed in the flowing water of the left circulating water channel system and the right circulating water channel system. Although the device that allows flowing water to form flow infiltration on the two slopes being tested, which can be immersed on both sides, adjust water level changes, and simulate water flow, will improve the relevant research in this field; however, the use of a circulating waterway system for bilateral immersion can only simulate a constant or slowly changing water level, and cannot generate a dynamic, time-space alternating water pressure gradient field, making it difficult to simulate the non-steady-state seepage conditions caused by real rainfall, tides or groundwater fluctuations.

[0004] The existing technology has problems with simulation distortion and protection loss caused by static immersion, unidirectional loading, and passive observation. Therefore, the present invention provides a submerged roadbed simulation test system and method based on repeated seepage under load. Summary of the Invention

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In one aspect of the present invention, a submerged roadbed simulation test system based on repeated seepage under load is provided, comprising: The seepage loss and instability early warning network subsystem is configured to synchronously collect the local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample through an embedded flexible sensor unit array; input the local strain energy density distribution and pore dynamic water pressure pulsation intensity into the chaotic feature extractor, and output the structural degradation energy entropy value.

[0006] In an optional implementation, the seepage loss and instability early warning network subsystem includes: The multi-source data spatiotemporal registration component is configured to output raw data from embedded flexible sensing units, collecting local strain energy density distribution, a differential measure of elastic-plastic deformation energy within the specimen; capturing pore dynamic water pressure pulsation intensity, the statistical dispersion of fluid pressure fluctuations within the oscillating pores; and generating a spatiotemporal synchronized coordinate mapping field to align the local strain energy density distribution and pore dynamic water pressure pulsation intensity according to the spatial coordinates within the specimen and the timestamp. The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the time-space synchronous coordinate mapping field into the strain concentration tensor extractor, output the principal strain trajectory manifold, and describe the differential geometric structure of the strain energy accumulation path; import the pore water pressure pulsation intensity in the time-space synchronous coordinate mapping field into the pulsation chaos degree analyzer to generate the pulsation strange attractor, which represents the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the pulsation strange attractor are associated through the conformal mapping transformer; The energy entropy value generation and phase change criterion component is configured to input the Lyapunov exponent integrator in the strain-percolation coupled phase space, calculate the exponential divergence rate of the strain energy dissipation path and the orbital contraction strength of the pulsating attractor along the coupled phase space, and output the structural degradation energy entropy value.

[0007] In an optional embodiment, the curvature invariant of the principal strain trajectory manifold is mapped to the attractor space of the pulsating strange attractor; the fractal dimension of the pulsating strange attractor is projected onto the manifold surface of the principal strain trajectory manifold to form a strain-percolation coupled phase space.

[0008] In an optional implementation, the chaotic feature deep fusion component includes: A manifold attractor feature decoupling module is configured to input the principal strain trajectory manifold and the pulsating strange attractor; The principal strain trajectory manifold contains Gaussian curvature invariants: describing the surface distortion strength of the strain energy accumulation area; and carries geodesic deflection angles: characterizing the mutation characteristics of the strain energy transfer path; The pulsation strange attractor includes a fractal dimension scale that quantifies the spatial filling degree of the pressure fluctuation trajectory; and an orbital contraction parameter that reflects the rate at which the pulsation returns to equilibrium. a bidirectional conformal mapping module configured to input a Gaussian curvature invariant into a curvature-attractor modulator to output a constrained attractor boundary; and to input a fractal dimension scale into a dimension-manifold projector to generate a scale-distorted manifold; 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 dual-field coupling operations, with the constrained attractor boundary as the external envelope of the phase space and the scale-distorted manifold as the internal basis of the phase space to form a strain-percolation coupled phase space.

[0009] In an optional embodiment, the strain-percolation coupled phase space of the coupled phase space synthesis module inherits and enhances the original characteristics: the strain energy dissipation path is distributed along the wrinkle ridges of the scale-distorted manifold, and the pulsating chaotic orbit is constrained by the compression boundary of the constrained attractor boundary.

[0010] In an optional embodiment, the coupled phase space synthesis module includes: A dual-field basis preprocessing submodule configured to input a constrained attractor boundary and an input scale-distorted manifold; The constraint attractor boundary includes: carrying a boundary curvature modulation factor, which comes from the compression / expansion effect of Gaussian curvature on the attractor space; and having a phase constraint strength that describes the degree to which the pulsation orbit is restricted; The scale-distorted manifold contains dimensionally projected deformation variables, which reflect the fractal scaling of the manifold surface’s folds / ridges, and contains geodesic reconstruction parameters, which record the geometric distortion characteristics of the strain path. The conformal tensor coupling operator module is configured to input the boundary curvature modulation factor into the connection torsion generator and output the inhomogeneous boundary coherence field; import the dimension projection deformation variable of the scale-distorted manifold into the deformation connection adapter to generate an affine scaling connection; The dual-field differential homeomorphism submodule is configured to couple the inhomogeneous boundary homology field with the affine scaling connection through the Chern-Simons integrator, and integrate the torsion fiber bundle of the inhomogeneous boundary homology field with the affine scaling connection in a curvature form. The integration path is oriented along the geodesic reconstruction parameters of the scaling-distorted manifold to form the strain-percolation coupled phase space.

[0011] In an optional embodiment, the dual-field diffeomorphism submodule includes: The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory through a path curvature invariant via a path density modulator, and to form an adaptive step sequence through a variable step size generator for the parameterized rate field; the torsion fiber bundle is decoupled into the main constraint gauge field and the extended degree of freedom spinor field via a constraint field structure separator; The affine scaling connection conversion unit is configured to convert the contraction Christoffel symbol through an energy operator converter to generate an energy contraction differential 1-form, and the expansion covariant derivative through an energy operator converter to form an energy diffusion differential 2-form; the master constraint gauge field and the energy contraction differential 1-form are converted through a master channel curvature synthesizer to generate a constraint invariance 3-form, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted through a deformation channel curvature synthesizer to generate a deformation covariance 3-form; The directed integration execution unit is configured to constrain the invariance 3-form along the density-weighted integration trajectory, and generate the constrained topological invariant through the main channel feature class integration kernel with an adaptive step sequence; the deformation covariance 3-form generates the deformation feature class along the same path parameters through the deformation channel feature class integration kernel; the constrained topological invariant and the deformation feature class are combined with the global invariant fuser to form the strain-percolation coupling phase space.

[0012] In an optional embodiment, the energy entropy value generation and phase change criterion component includes: The coupled phase space structure analysis module is configured to generate a strain energy transfer tangent bundle through the phase space trajectory tangent bundle generator for the strain-percolation coupled phase space. Simultaneously, the pulsating attractor orbit space is separated. The strain energy transfer tangent bundle is input into the divergence feature extractor, which calculates the asymptotic separation strength of adjacent trajectories in the tangent bundle and outputs the maximum divergence eigenvalue. The orbit contraction intensity capture module is configured as a generator of the regression equilibrium trend field imported into the pulsating attractor orbit space, constructing a vector field describing the trend of the orbit returning to the attractor center; the vector field generates the orbit contraction density integral through the density field integral kernel; The energy entropy value synthesis module is configured as a tensor reduction operator for the maximum divergence rate eigenvalue and the orbital contraction density integral input, performs the eigenvalue-density product reduction operation, and generates the structural degradation energy entropy value.

[0013] In an optional embodiment, the coupled phase space structure analysis module includes: The tangent bundle structure preprocessing submodule is configured to input the strain energy transfer tangent bundle into the tangent bundle trajectory parameterizer to generate a parameterized trajectory bundle; the parameterized trajectory bundle is imported into the local scaling field generator to establish a trajectory spacing scaling benchmark 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 the trajectory spacing deformation field is output. The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor; The extreme value feature extraction submodule is configured to progressively separate the tensor input extreme value feature extractor, calculate the maximum eigenvalue of the tensor, and output the maximum divergence rate eigenvalue.

[0014] Another aspect of the present invention provides a method for simulating a submerged roadbed under load and repeated seepage, of the submerged roadbed under load and repeated seepage simulation test system, comprising the following steps: A programmable pressure wave is generated in a closed cavity through a multi-axis linkage piston array, and the wavefront of the programmable pressure wave forms a time-space alternating water pressure gradient field on the permeable medium base; The water pressure gradient field is introduced into a three-dimensional independent loading frame. The three-dimensional independent loading frame applies asymmetric cyclic stress waves to the roadbed specimen through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the specimen in real time. The local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample are synchronously collected through an embedded flexible sensing unit array; the local strain energy density distribution and pore dynamic water pressure pulsation intensity are input into the chaotic feature extractor, and the structural degradation energy entropy value is output; when the structural degradation energy entropy value exceeds the phase change threshold, a three-stage warning is triggered.

[0015] Through the synergistic action of multiple modules, this invention achieves full-parameter simulation and active protection for submerged roadbeds under the complex coupling of loads and seepage. Specifically, the dynamic water pressure gradient generation subsystem constructs a spatiotemporally alternating water pressure gradient field using programmable pressure waves, accurately reproducing the unsteady-state seepage boundary conditions caused by natural rainfall, tides, and other factors, thus overcoming the limitations of traditional static water pressure loading. The stress-seepage coupling response cavity subsystem couples the hydraulic gradient field with the three-dimensional mechanical stress field, simulating the bidirectional seepage-stress coupling effect through a piezoelectric lattice array. It also utilizes the hydraulic impedance spectrum to monitor the evolution of the seepage path within the specimen in real time. The seepage instability warning network subsystem quantifies the structural degradation energy entropy through a chaotic feature extractor and establishes a multi-level intervention mechanism based on phase transition thresholds: spectrum modulation changes the seepage excitation characteristics, stress wave intervention adjusts the load transfer path, and microcapsules target and repair local damage, forming a closed-loop "excitation-response-regulation" control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a block diagram of a submerged roadbed simulation test system based on repeated seepage under load provided in Example 1 of the present invention; Figure 2 This is a block diagram of the dynamic water pressure gradient generating subsystem provided in Example 2 of the present invention; Figure 3 This is a block diagram of the stress-permeability coupling response cavity subsystem provided in Example 3 of the present invention; Figure 4 This is a block diagram of the seepage loss and instability early warning network subsystem provided in Example 4 of the present invention; Figure 5 This is a flow chart of the submerged roadbed simulation test method based on repeated seepage under load provided in Example 5 of the present invention; Figure 6 A block diagram of the electronic device provided by the present invention; Figure 7 A block diagram of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] In the following, 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 the technical features being referred to. Thus, a feature identified with "first," "second," etc., may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0019] In the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integrated one; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. In addition, unless otherwise clearly specified and limited, the term "coupling" should be understood in a broad sense. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components, or it can be understood as the electrical connection between different components in a circuit structure through a physical line that can transmit electrical signals, such as printed circuit board (PCB) copper foil or wire, so as 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 an airless / non-contact manner, such as electrical connection between two components using capacitive coupling to transmit electrical signals.

[0020] In an embodiment of the present invention, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to changes in the orientation of the components in the drawings.

[0021] Example 1: like Figure 1As shown, an embodiment of the present invention provides a submerged roadbed simulation test system based on repeated seepage under load, comprising: A dynamic water pressure gradient generating subsystem is configured to generate a programmable pressure wave in a closed cavity through a multi-axis linkage piston array, wherein the wavefront of the programmable pressure wave forms a temporally and spatially alternating water pressure gradient field on a permeable medium substrate; The stress-seepage coupled response cavity subsystem is configured to introduce the hydraulic gradient field into a three-dimensional independent loading frame. The three-dimensional independent loading frame applies asymmetric cyclic stress waves to the roadbed specimen through the piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the specimen's internal pores in real time. The seepage loss and instability early warning network subsystem is configured to synchronously collect the local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample through an embedded flexible sensor unit array. The local strain energy density distribution and pore dynamic water pressure pulsation intensity are input into the chaotic feature extractor, which outputs the structural degradation energy entropy value. When the structural degradation energy entropy value exceeds the phase change threshold, a three-stage early warning is triggered. Among them, the three-stage warning includes sending pressure wave spectrum modulation instructions to the dynamic water pressure gradient generation subsystem (changing the frequency composition of the water pressure gradient field); injecting stress wave standing wave intervention signals into the stress seepage coupling response cavity subsystem (generating reverse damping waves in asymmetric cyclic stress waves); and initiating the targeted release of microcapsule self-healing agents (according to the strain concentration area located by the embedded flexible sensing unit array).

[0022] In the aforementioned embodiment, this embodiment utilizes the synergistic effect of multiple modules to achieve full-parameter simulation and active protection for submerged roadbeds under the complex coupling of loads and seepage. Specifically, the dynamic hydraulic gradient generation subsystem uses programmable pressure waves to construct a spatiotemporally alternating hydraulic gradient field, accurately reproducing the unsteady seepage boundary conditions caused by natural rainfall, tides, and other factors, thus overcoming the limitations of traditional static hydraulic loading. The stress-seepage coupling response cavity subsystem couples the hydraulic gradient field with a three-dimensional mechanical stress field, simulating the bidirectional seepage-stress coupling effect through a piezoelectric lattice array. It also utilizes hydraulic impedance spectroscopy to monitor the evolution of the seepage path within the specimen in real time. The seepage instability warning network subsystem quantifies the structural degradation energy entropy through a chaotic feature extractor and establishes a multi-stage intervention mechanism based on phase transition thresholds: spectrum modulation modulates the seepage excitation characteristics, stress wave intervention adjusts the load transfer path, and microcapsules target and repair local damage, forming a closed-loop "excitation-response-control" control system.

[0023] In summary, the linkage of the three subsystems in this embodiment realizes the full chain of testing capabilities from environmental simulation (water pressure field), coupled loading (stress-seepage field), to intelligent regulation (early warning-intervention), providing controllable experimental conditions and quantitative evaluation methods for studying the progressive failure mechanism of submerged roadbed under repeated seepage-load coupling.

[0024] Example 2: like Figure 2 As shown, based on Example 1, the dynamic water pressure gradient generating subsystem provided by the embodiment of the present invention includes: The phase-shift motion module is configured such that each piston in a multi-axis linkage piston array reciprocates according to an independently preset displacement-time function. The motion trajectories of the piston group in the multi-axis linkage piston array form a coherent motion matrix. The piston group pushes the fluid to generate pressure pulsations. The phase difference in the coherent motion matrix causes the pressure waves generated by adjacent pistons to coherently superimpose during propagation, generating an interference pressure wave. The medium substrate reconstruction module is configured so that interference pressure waves contact the surface of the permeable medium substrate, and the substrate pore structure selectively attenuates and refracts the pressure waves; The time-space alternation formation module is configured to form a dynamic osmotic pressure difference inside the substrate through pressure waves modulated by selective attenuation and refraction. The dynamic osmotic pressure difference produces a pressure gradient with reversed direction along with the piston movement cycle. The phase difference of the piston group causes the pressure gradient to migrate in a spiral shape in three-dimensional space.

[0025] In the aforementioned embodiment, the dynamic water pressure gradient generation subsystem of this embodiment achieves complex fluid dynamics control through the collaborative efforts of multiple modules. Its overall technical significance lies in the following: In the dimension of precise wavefield control, the phase-shift motion module achieves active programmable 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 an array of pressure wave emission sources with controllable phase differences. 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-base reconstruction module utilizes the pore structure characteristics of the permeable medium to convert interfering pressure waves into spatially selective attenuation and refraction patterns. The artificially generated coherent wavefield is coupled with the structural characteristics of the natural medium to form a medium-dependent pressure wave modulation effect, providing a physical carrier for the formation of dynamic osmotic pressure differences. In the dimension of spatiotemporal dynamic field construction, the spatiotemporal alternation formation module ultimately converts 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 realize the non-steady-state 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.

[0026] In summary, this embodiment establishes a fluid dynamic drive system with three-dimensional spatiotemporal programming capabilities through a technological chain combining artificially controllable coherent pressure wave field generation, medium coupling modulation, and dynamic permeability field construction. While maintaining mechanical drive accuracy, complex pressure gradient fields, difficult to achieve with traditional methods, are achieved through wavefield interferometry and medium coupling.

[0027] Example 3: like Figure 3 As shown, based on Example 1, the stress-seepage coupling response cavity subsystem provided by the embodiment of the present invention includes: The hydraulic gradient field to stress conversion module is configured so that the spiral hydraulic pressure field generated by the water pressure triggers the movement of the loading frame. The spatial curvature parameters of the spiral hydraulic pressure field drive the universal joint axis of the three-axis independent loading frame to perform focus tracking. The direction reversal osmotic pressure difference modulates the piezoelectric excitation, and the phase reversal signal is input into the piezoelectric lattice control unit to generate a polarization direction inversion instruction. The asymmetric cyclic stress wave synthesis module is configured to generate a piezoelectric strain gradient in the lattice unit according to the focal coordinates of the focus tracking; the polarization direction inversion instruction causes the adjacent lattice unit to be loaded with a reverse driving voltage; the piezoelectric strain gradient and the reverse driving voltage are coupled to form a strain wave phase shear; The seepage mechanics coupled response module is configured to induce the specimen to produce crack opening and closing resonance due to strain wave phase shear. The oscillation of the crack opening and closing resonance forces the pore fluid to exhibit the following: inertial escape flow: the fluid accelerates to escape from the oscillating crack area; viscous locking flow: the fluid is retained in the micropores due to shear resistance; The transient hydraulic impedance spectrum reconstruction module is configured to generate asymmetric charge accumulation on the lattice surface due to the dual-mode resistance effect (inertial escape flow / viscous locking flow). The spatiotemporal distribution of the asymmetric charge accumulation is collected in real time, and the impedance phase divergence field is output through the 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 sensitive area of ​​seepage instability.

[0028] In the above-mentioned embodiment, the stress-seepage coupled response chamber subsystem of this embodiment achieves precise control and dynamic monitoring of hydraulic, mechanical, and electrical multi-field coupling through the synergistic action of multiple modules. Its core significance can be decomposed into: precise loading of the mechanical field, the water pressure gradient field to stress conversion module converts fluid pressure into a three-dimensional dynamic mechanical load, and realizes focus tracking control of the three-dimensional independent loading frame through the spatial curvature parameters of the spiral water pressure field, which overcomes the limitation of the fixed direction of traditional loading systems; the combination of the direction-reversed osmotic pressure difference and the phase modulation of the piezoelectric excitation realizes real-time programmable control of the loading path. The precise synthesis of strain waves, the asymmetric cyclic stress wave synthesis module, utilizes the polarization direction inversion characteristics of the piezoelectric lattice to generate strain waves with phase shear characteristics through the reverse driving voltage coupling of adjacent lattice units; the strain waves can accurately match the dynamic response requirements of the internal structure of the specimen, providing customized mechanical input for crack control. Dynamically control the seepage state. The seepage mechanics coupling response module induces fracture resonance through strain wave phase shear, generating dual-mode fluid motion of inertial escape flow and viscous locking flow. The controlled seepage mode realizes active zoning control of pore fluid motion, providing an experimental basis for studying seepage mechanisms under different flow regimes. Intelligently identify instability precursors. The transient hydraulic impedance spectrum reconstruction module converts the dual-mode resistance 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 sensitive areas of seepage instability, providing multi-dimensional criteria for system stability assessment.

[0029] In summary, this embodiment constructs a complete closed-loop research system of hydraulic excitation-mechanical loading-seepage response-instability warning, which is particularly suitable for research scenarios that require precise control of seepage-stress interaction, such as multi-field coupling experiments on rock and soil and energy reservoir reconstruction evaluation. 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 warning.

[0030] Example 4: like Figure 4 As shown, based on Example 1, the seepage loss and instability early warning network subsystem provided by the embodiment of the present invention includes: The multi-source data spatiotemporal registration component is configured to output raw data from embedded flexible sensing units, collecting local strain energy density distribution, a differential measure of elastic-plastic deformation energy within the specimen; capturing pore dynamic water pressure pulsation intensity, the statistical dispersion of fluid pressure fluctuations within the oscillating pores; and generating a spatiotemporal synchronized coordinate mapping field to align the local strain energy density distribution and pore dynamic water pressure pulsation intensity according to the spatial coordinates within the specimen and the timestamp. The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the time-space synchronous coordinate mapping field into the strain concentration tensor extractor, output the principal strain trajectory manifold, and describe the differential geometric structure of the strain energy accumulation path; import the pore water pressure pulsation intensity in the time-space synchronous coordinate mapping field into the pulsation chaos degree analyzer to generate the pulsation strange attractor, which represents the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the pulsation strange attractor are associated through the conformal mapping transformer; The curvature invariant of the principal strain trajectory manifold is mapped to the attractor space of the pulsating strange attractor; the fractal dimension of the pulsating strange attractor is projected onto the manifold surface of the principal strain trajectory manifold to form the strain-percolation coupled phase space; The energy entropy value generation and phase change criterion component is configured to input the Lyapunov exponent integrator in the strain-percolation coupled phase space, calculate the exponential divergence rate of the strain energy dissipation path and the orbital contraction strength of the pulsating attractor along the coupled phase space, and output the structural degradation energy entropy value.

[0031] In the above-mentioned embodiment, the multi-source data spatiotemporal registration component of the seepage instability warning network subsystem of this embodiment establishes a basic framework for physical quantity observation. Through embedded sensing units, it achieves the simultaneous capture of two key parameters within the specimen: the differential measure of the material structure's deformation energy (local strain energy density distribution), and the statistical characteristics of pore fluid pressure fluctuations (hydrodynamic pressure pulsation intensity). The establishment of a spatiotemporal synchronous coordinate mapping field ensures the strict alignment of the two types of heterogeneous data in the four-dimensional spatiotemporal coordinate system. The chaotic feature deep fusion component implements coupled analysis of multiple physical fields: the strain concentration tensor extractor analyzes material deformation characteristics from the perspective of continuum mechanics, generating principal strain trajectory manifolds that describe the strain energy accumulation path. The pulsation chaos degree analyzer, based on nonlinear dynamics theory, converts pressure fluctuations into fractal-like strange attractors. 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 energy entropy value generation and phase change criterion component quantitatively characterizes the dynamic stability characteristics of the coupled system through the Lyapunov exponent integrator: it analyzes the divergence characteristics of the strain energy dissipation path (reflecting the evolution trend of structural damage) and evaluates the contraction characteristics of the pressure pulsation attractor (characterizing the stability of fluid motion). The final output of the structural degradation energy entropy value is essentially a quantitative indicator of the system's phase change threshold.

[0032] In summary, this embodiment achieves cross-scale analysis from the microscopic scale (differential measurement) to the macroscopic behavior (phase transition criterion), providing a quantitative early warning indicator based on nonlinear dynamics theory for the identification of 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 the seepage pressure, and its numerical evolution directly reflects the critical state of the system tending towards instability.

[0033] Example 5: Based on Example 4, the chaotic feature deep fusion component provided in this embodiment of the present invention includes: A manifold attractor feature decoupling module is configured to input the principal strain trajectory manifold and the pulsating strange attractor; The principal strain trajectory manifold contains Gaussian curvature invariants: describing the surface distortion strength of the strain energy accumulation area; and carries geodesic deflection angles: characterizing the mutation characteristics of the strain energy transfer path; The pulsation strange attractor includes a fractal dimension scale that quantifies the spatial filling degree of the pressure fluctuation trajectory; and an orbital contraction parameter that reflects the rate at which the pulsation returns to equilibrium. a bidirectional conformal mapping module configured to input a Gaussian curvature invariant into a curvature-attractor modulator to output a constrained attractor boundary; and to input a fractal dimension scale into a dimension-manifold projector to generate a scale-distorted manifold; High curvature region (Gaussian curvature invariant > 0) → compressed attractor boundary; Negative curvature region (Gaussian curvature invariant < 0) → expansion attractor boundary; High-dimensional region (fractal dimension scale ↑) → produces concave folds on the manifold surface; low-dimensional region (fractal dimension scale ↓) → forms convex ridges on the manifold surface; 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 dual-field coupling operations, with the constrained attractor boundary as the outer envelope of the phase space and the scale-distorted manifold as the inner basis of the phase space to form a strain-percolation coupled phase space; The strain-percolation coupled phase space inherits and strengthens the original features: the strain energy dissipation paths are distributed along the wrinkle ridges of the scale-distorted manifold, and the pulsating chaotic orbits are constrained by the compression boundaries of the constrained attractor boundaries.

[0034] In the aforementioned embodiment, the manifold attractor feature decoupling module of this embodiment achieves independent feature extraction of the strain field and the pulsation field: the Gaussian curvature invariant of the principal strain trajectory manifold is used to quantify the local distortion intensity of the strain energy accumulation zone, and the geodesic deflection angle captures the sudden change characteristics of the energy transfer path. Simultaneously, the fractal dimension scaling of the pulsation strange attractor is used to accurately describe the spatial extension of the pressure fluctuation, and the orbital contraction parameter objectively records the kinetic rate of the pulsation returning to equilibrium. 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) based on the curvature characteristics of the strain field, while the dimension-manifold projector accurately reconstructs the manifold surface geometry (concave wrinkles / convex ridges) based on the fractal characteristics of the pulsation field, forming a parameterized mapping relationship between the two physical fields. The coupled phase space synthesis module integrates the modulated attractor boundary (external envelope) and the reconstructed manifold (internal basis) into a unified topological structure through conformal tensor operations. The strain-percolation coupled phase space has the following core characteristics: the strain energy dissipation path strictly follows the geometric characteristic distribution of the scale-distorted manifold; the evolution range of the pulsating chaotic orbit is strictly limited by the constrained attractor boundary; all the quantitative characteristics of the original physical field (curvature invariant, geodesic deflection angle, fractal dimension scale, orbit contraction parameter) are inherited and enhanced in the coupled phase space.

[0035] In summary, this embodiment achieves deep coupling of the strain field and the pulsation 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 a mathematical mapping relationship.

[0036] Example 6: Based on Example 5, the coupled phase space synthesis module provided in this embodiment of the present invention includes: A dual-field basis preprocessing submodule configured to input a constrained attractor boundary and an input scale-distorted manifold; The constraint attractor boundary includes: carrying a boundary curvature modulation factor, which comes from the compression / expansion effect of Gaussian curvature on the attractor space; and having a phase constraint strength that describes the degree to which the pulsation orbit is restricted; The scale-distorted manifold contains dimensionally projected deformation variables, which reflect the fractal scaling of the manifold surface’s folds / ridges, and contains geodesic reconstruction parameters, which record the geometric distortion characteristics of the strain path. The conformal tensor coupling operator module is configured to input the boundary curvature modulation factor into the connection torsion generator and output the inhomogeneous boundary coherence field; import the dimension projection deformation variable of the scale-distorted manifold into the deformation connection adapter to generate an affine scaling connection; In the compression region, the boundary curvature modulation factor > 0 generates a positive torsion fiber bundle; in the expansion region, the boundary curvature modulation factor < 0 induces a negative torsion spinor field; the concave fold region (the dimension projection deformation variable increases) produces a contraction-type Christoffel symbol, and the convex ridge region (the dimension projection deformation variable decreases) forms an expansion-type covariant derivative; The dual-field differential homeomorphism submodule is configured to couple the inhomogeneous boundary homology field with the affine scaling connection through the Chern-Simons integrator, and integrate the torsion fiber bundle of the inhomogeneous boundary homology field with the affine scaling connection in a curvature form. The integration path is oriented along the geodesic reconstruction parameters of the scaling-distorted manifold to form the strain-percolation coupled phase space.

[0037] In the aforementioned embodiment, the dual-field basis preprocessing submodule of this embodiment performs a structured analysis of the input data. The constrained attractor boundary carries a boundary curvature modulation factor (reflecting the compression / expansion effect of Gaussian curvature on the attractor space) and a phase constraint strength (quantifying the degree of confinement of the pulsation trajectory). The scale-distorted manifold contains a dimensional projected deformation variable (describing the surface wrinkle / ridge transformation caused by fractal scaling) and a geodesic reconstruction parameter (recording the geometric distortion characteristics of the strain path). This ensures that the core characteristic parameters of the two physical fields are accurately extracted, providing structured input for subsequent coupling operations. The conformal tensor coupling operator submodule implements dynamic interaction of characteristic parameters. The boundary curvature modulation factor is input into the network torsion generator to generate an inhomogeneous boundary coherence field, whose characteristics are determined by the curvature modulation factor. The dimensional projected deformation variable is input into the deformation connection adapter to generate an affine scaling connection, whose geometric characteristics are determined by fractal scaling. This operation establishes a dynamic mapping between the boundary constraint and the manifold geometry, allowing the characteristic parameters of the two physical fields to modulate each other. The dual-field diffeomorphism submodule performs the final coupling via a Chern-Simons integrator. The inhomogeneous boundary coherence field (torsion fiber bundle) is integrated with the affine scaling connection (affine connection) in a curvature-form integration. The integration path is oriented along the geodesic reconstruction parameter 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 diffeomorphism mapping. The distortion characteristics of the strain path (geodesic reconstruction parameter) directly affect the integration path of the coupling operation, so that the phase space inherits the dynamic characteristics of the original physical field.

[0038] In summary, this embodiment achieves strict coupling between the constraint-type attractor boundary and the scale-distorted manifold at the differential geometry level, forming a strain-percolation coupled phase space with dynamic constraint characteristics.

[0039] Example 7: Based on Example 6, the dual-field diffeomorphic submodule provided in this embodiment of the present invention includes: The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory from the path curvature invariant through a path density modulator, and to form an adaptive step sequence from the parameterized rate field through a variable step size generator; the torsion fiber bundle is decoupled into the main constraint gauge field (derived from the phase constraint characteristics of the positive torsion region) and the extended degree of freedom spinor field (orbital release characteristics of the negative torsion region) through a constraint field structure separator; The affine scaling connection conversion unit is configured to convert the contraction Christoffel symbol through an energy operator converter to generate an energy contraction differential 1-form, and the expansion covariant derivative through an energy operator converter to form an energy diffusion differential 2-form; the master constraint gauge field and the energy contraction differential 1-form are converted through a master channel curvature synthesizer to generate a constraint invariance 3-form, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted through a deformation channel curvature synthesizer to generate a deformation covariance 3-form; The directed integration execution unit is configured to constrain the invariance 3-form along the density-weighted integration trajectory, and generate the constrained topological invariant through the main channel feature class integration kernel with an adaptive step sequence; the deformation covariance 3-form generates the deformation feature class along the same path parameters through the deformation channel feature class integration kernel; the constrained topological invariant and the deformation feature class are combined with the global invariant fuser to form the strain-percolation coupling phase space.

[0040] In the above-mentioned embodiment, the overall significance of the dual-field diffeomorphism submodule of this embodiment is to construct a structured diffeomorphism classification system. Through the synergistic effect of the three units, the coupled calculation of the constrained gauge field and the deformation degree of freedom field is realized, and the strain-percolation coupled phase space is ultimately generated. The overall significance of the dual-field diffeomorphism submodule of this embodiment is to construct a structured diffeomorphism classification system. Through the synergistic effect of the three units, the coupled calculation of the constrained gauge field and the deformation degree of freedom field is realized, and the strain-percolation coupled phase space is ultimately generated.

[0041] The function of the geodesic reconstruction parameter processing unit in this embodiment is to parameterize the path and decompose the torsion field: the path curvature invariant is density modulated to generate a density-weighted integral trajectory (for example, defining a geodesic on a manifold). , and assign a weight function , so that the integration path can adapt to the curvature change). The parameterized rate field forms an adaptive step sequence through a variable step generator (for example, in numerical integration, the step size is adjusted according to the local curvature). , ensuring the accuracy of calculation); the torsion fiber bundle is decomposed into: the main constraint gauge field (derived from the positive torsion region, with phase constraint characteristics, such as: satisfying closed form); expanded degree of freedom spinor field (derived from the negative torsion region, with orbital release properties, such as: allowing gauge transformation degrees of freedom). The role of the affine scaling connection transformation unit is responsible for the construction of differential forms and curvature synthesis: contraction Christoffel symbols (such as ) is transformed by the energy operator to generate the energy contraction differential 1-form (for example: ( ) becomes a closed form after mapping . Extended covariant derivatives (such as ) is transformed by the energy operator to generate the energy diffusion differential 2-form (for example: describes the local deformation). Master constraint gauge field and Synthesize constrained invariants 3-forms (e.g.: satisfy , maintain gauge invariance); expand the degree of freedom spinor field and Synthetic deformation covariance 3-forms (e.g.: Allow non-zero exterior differentials , describing deformable structures). The role of the directed integration execution unit, which is responsible for the calculation of topological invariants and the construction of phase space: Constrained invariance 3-form Compute along the density-weighted integral trajectory to generate constrained topological invariants (e.g.: ) is an integer representing some topological feature of the manifold. Deformation covariance 3-form Calculate along the same path to generate deformation feature classes (for example: The gauge degrees of freedom describe local deformations). Constrained topological invariants N Deformation feature class C Fusion, forming a strain-percolation coupled phase space (e.g. phase space ) describes the possible states of a manifold under different constraints and deformation conditions. Through three steps: geodesic reconstruction, differential form synthesis, and directional integration, the constraint field and deformation field are separated (gauge constraints are maintained in the positive torsion region, while deformation degrees of freedom are allowed in the negative torsion region); coupled differential form calculations are performed (1-form contraction, 2-form diffusion, and 3-form synthesis); and topological invariants and deformation characteristics are integrated, ultimately generating a strain-percolation coupled phase space that describes the constrained deformation structure of the manifold. This framework is applicable to fields such as gauge field theory, geometric manifold analysis, and topological quantum computing, and can handle the classification of diffeomorphisms with nonholonomic constraints.

[0042] Example 8: Based on Example 4, the energy entropy value generation and phase change criterion component provided in this embodiment of the present invention includes: The coupled phase space structure analysis module is configured to generate a strain energy transfer tangent bundle (a tangent space collection of strain energy dissipation paths) through the strain-percolation coupled phase space through a phase space trajectory tangent bundle generator. Simultaneously, it separates the pulsation attractor orbit space (a phase space subset representing the fluid pulsation trajectory). The strain energy transfer tangent bundle is input into a divergence feature extractor, which calculates the asymptotic separation strength of adjacent trajectories in the tangent bundle and outputs the maximum divergence eigenvalue (a measure of the chaotic expansion strength of the strain energy dissipation path). The orbit contraction intensity capture module is configured as a generator of the regression equilibrium trend field imported into the pulsating attractor orbit space, constructing a vector field describing the trend of the orbit returning to the attractor center; the vector field generates the orbit contraction density integral through the density field integral kernel; The energy entropy value synthesis module is configured as a tensor reduction operator for the maximum divergence rate eigenvalue and the orbital contraction density integral input, performs the eigenvalue-density product reduction operation, and generates the structural degradation energy entropy value.

[0043] In the above embodiment, the overall significance of the energy entropy value generation and phase change criterion component of this embodiment is to achieve quantitative characterization of the phase change behavior of complex dynamic systems through the coordinated operation of three modules.

[0044] Example 9: Based on Example 8, the coupled phase space structure analysis module provided by the embodiment of the present invention includes: The tangent bundle structure preprocessing submodule is configured to input the strain energy transfer tangent bundle into the tangent bundle trajectory parameterizer to generate a parameterized trajectory bundle (including the initial spacing and orientation angles of adjacent trajectories). The parameterized trajectory bundle is then fed into the local scaling field generator to establish a trajectory spacing scaling benchmark at each point in the tangent bundle (defining the initial relative position relationship between adjacent trajectories). 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 track spacing deformation field (the real-time rate of change of the spacing between adjacent tracks). The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor (which quantifies the expansion / contraction strength of the track spacing). The extreme value feature extraction submodule is configured to progressively separate the tensor input extreme value feature extractor, calculate the maximum eigenvalue of the tensor, and output the maximum divergence eigenvalue (characterizing the most intense trajectory separation intensity in the cut bundle).

[0045] In the above-mentioned embodiment, the coupled phase space structure analysis module of this embodiment achieves a quantitative characterization of the trajectory separation characteristics in the strain energy transfer tangent bundle through the collaborative operation of three submodules. This module first converts the strain energy transfer tangent bundle into a parameterized trajectory bundle containing initial spacing and angular information through the tangent bundle trajectory parameterizer, and then establishes a scaling benchmark for the trajectory spacing through the 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 convert the local scaling field into a trajectory spacing deformation field, and then generates an asymptotic separation tensor that quantifies the trajectory spacing change through the separation tensor synthesizer. Finally, the maximum eigenvalue of the separation tensor is calculated through the extreme value feature extractor, and the maximum divergence rate eigenvalue representing the strongest trajectory separation strength in the tangent bundle is output. The entire process realizes a complete computational chain from the original tangent bundle data to the key chaotic eigenvalue, providing a basic quantitative indicator for subsequent phase transition judgment.

[0046] Example 10: like Figure 5 As shown, based on Examples 1 to 9, the embodiment of the present invention provides a method for simulating a submerged roadbed under repeated seepage under load, comprising the following steps: Step S100: generating a programmable pressure wave in a closed cavity through a multi-axis linkage piston array, wherein the wavefront of the programmable pressure wave forms a temporally and spatially alternating water pressure gradient field on a permeable medium substrate; Step S200: importing the water pressure gradient field into a three-dimensional independent loading frame; the three-dimensional independent loading frame applies an asymmetric cyclic stress wave to the roadbed sample through the piezoelectric lattice array, and simultaneously captures the transient hydraulic impedance spectrum of the pores inside the sample in real time; Step S300: Synchronously collect the local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample through the embedded flexible sensing unit array; input the local strain energy density distribution and pore dynamic water 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 change threshold, trigger a three-stage warning.

[0047] In the aforementioned embodiment, this embodiment achieves full-parameter dynamic simulation and active control of submerged roadbeds in complex environments through the synergistic coupling of multi-step technical features. Its comprehensive technical benefits include: dynamic seepage-stress coupled loading, which uses programmable pressure waves to construct a spatiotemporally alternating water pressure gradient field. Combined with a three-dimensional independent loading framework to apply asymmetric cyclic stress waves, this method accurately simulates the interaction between seepage and mechanical loads in actual working conditions, achieving precise reproduction of hydraulic-mechanical coupled excitation. Multi-physics real-time monitoring: A piezoelectric lattice array is used to capture transient hydraulic impedance spectra, while embedded flexible sensing units simultaneously collect strain energy density and dynamic water pressure pulsation intensity. This enables multi-dimensional in situ observation of seepage path evolution, local stress concentration, and the dynamic response of pore water pressure. Intelligent early warning and active control: A chaotic feature extractor is used to calculate the structural degradation entropy value. When a phase transition threshold is exceeded, a multimodal intervention strategy is triggered, including pressure wave spectrum modulation, stress wave standing wave damping intervention, and microcapsule targeted repair. This forms a closed-loop "monitoring-assessment-control" control system, effectively curbing the steady development of seepage.

[0048] In summary, this embodiment organically integrates dynamic seepage excitation, multi-directional stress loading, real-time field quantity monitoring, and intelligent control mechanisms, providing a controllable, measurable, and adjustable experimental method for studying the progressive failure mechanism and protective measures of submerged roadbeds under repeated seepage-load coupling.

[0049] Figure 6 A block diagram is shown of an exemplary electronic device suitable for implementing embodiments of the present invention.

[0050] The electronic device 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 each method of an embodiment of the present invention when executed by the processor.

[0051] The central processing unit / microprocessor / main control chip etc. may include but is not limited to, for example, one or more processors or microprocessors etc.

[0052] The storage medium may include, but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0053] In addition, the electronic device may also include (but not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.).

[0054] The central processing unit / microprocessor / main control chip etc. can communicate with external devices via an I / O bus via a wired or wireless network (not shown).

[0055] The storage medium may also store at least one computer-executable instruction for executing the various functions and / or method steps in the embodiments described in this technology when executed by a central processing unit / microprocessor / main control chip, etc.

[0056] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0057] Figure 7 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0058] like Figure 7 As shown, a non-transitory computer-readable storage medium stores instructions, such as computer-readable instructions. When the computer-readable instructions are executed by a processor, the various methods described above can be executed. Non-transitory computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. 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.

[0059] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0060] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0061] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0062] 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 the present invention, or the portion 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 various embodiments of the method of the present invention via a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A submerged roadbed simulation test system based on repeated seepage under load, characterized in that: Include: The seepage loss and instability early warning network subsystem is configured to synchronously collect the local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample through an embedded flexible sensor unit array; input the local strain energy density distribution and pore dynamic water pressure pulsation intensity into the chaotic feature extractor, and output the structural degradation energy entropy value.

2. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 1, characterized in that: Seepage and instability early warning network subsystem, including: The multi-source data spatiotemporal registration component is configured to output raw data from embedded flexible sensing units, collecting local strain energy density distribution, a differential measure of elastic-plastic deformation energy within the specimen; capturing pore dynamic water pressure pulsation intensity, the statistical dispersion of fluid pressure fluctuations within the oscillating pores; and generating a spatiotemporal synchronized coordinate mapping field to align the local strain energy density distribution and pore dynamic water pressure pulsation intensity according to the spatial coordinates within the specimen and the timestamp. The chaotic feature deep fusion component is configured to input the local strain energy density distribution in the time-space synchronous coordinate mapping field into the strain concentration tensor extractor, output the principal strain trajectory manifold, and describe the differential geometric structure of the strain energy accumulation path; import the pore water pressure pulsation intensity in the time-space synchronous coordinate mapping field into the pulsation chaos degree analyzer to generate the pulsation strange attractor, which represents the fractal dimension of the pressure fluctuation phase space trajectory; the principal strain trajectory manifold and the pulsation strange attractor are associated through the conformal mapping transformer; The energy entropy value generation and phase change criterion component is configured to input the Lyapunov exponent integrator in the strain-percolation coupled phase space, calculate the exponential divergence rate of the strain energy dissipation path and the orbital contraction strength of the pulsating attractor along the coupled phase space, and output the structural degradation energy entropy value.

3. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 2, characterized in that: The curvature invariant of the principal strain trajectory manifold is mapped to the attractor space of the pulsating strange attractor; the fractal dimension of the pulsating strange attractor is projected onto the manifold surface of the principal strain trajectory manifold to form the strain-percolation coupled phase space.

4. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 2, characterized in that: Chaos feature deep fusion components, including: A manifold attractor feature decoupling module is configured to input the principal strain trajectory manifold and the pulsating strange attractor; The principal strain locus manifold contains Gaussian curvature invariants: describing the surface distortion strength in the strain energy accumulation area; Carrying geodesic deflection angle: characterizes the mutation characteristics of the strain energy transfer path; The pulsation strange attractor includes a fractal dimension scale that quantifies the spatial filling degree of the pressure fluctuation trajectory; and an orbital contraction parameter that reflects the rate at which the pulsation returns to equilibrium. a bidirectional conformal mapping module configured to input a Gaussian curvature invariant into a curvature-attractor modulator to output a constrained attractor boundary; and to input a fractal dimension scale into a dimension-manifold projector to generate a scale-distorted manifold; 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 dual-field coupling operations, with the constrained attractor boundary as the external envelope of the phase space and the scale-distorted manifold as the internal basis of the phase space to form a strain-percolation coupled phase space.

5. The submerged roadbed simulation test system based on repeated seepage under load as claimed 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 paths are distributed along the wrinkle ridges of the scale-distorted manifold, and the pulsating chaotic orbits are constrained by the compression boundaries of the constrained attractor.

6. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 1, characterized in that: Coupled phase space synthesis module, including: A dual-field basis preprocessing submodule configured to input a constrained attractor boundary and an input scale-distorted manifold; The constraint attractor boundary includes: carrying a boundary curvature modulation factor, which comes from the compression / expansion effect of Gaussian curvature on the attractor space; and having a phase constraint strength that describes the degree to which the pulsation orbit is restricted; The scale-distorted manifold contains dimensionally projected deformation variables, which reflect the fractal scaling of the manifold surface’s folds / ridges, and contains geodesic reconstruction parameters, which record the geometric distortion characteristics of the strain path. A conformal tensor coupling operator module is configured to input a boundary curvature modulation factor into a connection torsion generator and output an inhomogeneous boundary coherence field; Import the dimension-projected deformation variables of the scale-distorted manifold into the deformation connection adapter to generate an affine scaling connection; The dual-field differential homeomorphism submodule is configured to couple the inhomogeneous boundary homology field with the affine scaling connection through the Chern-Simons integrator, and integrate the torsion fiber bundle of the inhomogeneous boundary homology field with the affine scaling connection in a curvature form. The integration path is oriented along the geodesic reconstruction parameters of the scaling-distorted manifold to form the strain-percolation coupled phase space.

7. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 1, characterized in that: The dual-field diffeomorphism submodule includes: The geodesic reconstruction parameter processing unit is configured to generate a density-weighted integral trajectory through a path curvature invariant via a path density modulator, and to form an adaptive step sequence through a variable step size generator for the parameterized rate field; the torsion fiber bundle is decoupled into the main constraint gauge field and the extended degree of freedom spinor field via a constraint field structure separator; The affine scaling connection conversion unit is configured to convert the contraction Christoffel symbol through an energy operator converter to generate an energy contraction differential 1-form, and the expansion covariant derivative through an energy operator converter to form an energy diffusion differential 2-form; the master constraint gauge field and the energy contraction differential 1-form are converted through a master channel curvature synthesizer to generate a constraint invariance 3-form, and the expansion degree of freedom spinor field and the energy diffusion differential 2-form are converted through a deformation channel curvature synthesizer to generate a deformation covariance 3-form; The directed integration execution unit is configured to constrain the invariance 3-form along the density-weighted integration trajectory, and generate the constrained topological invariant through the main channel feature class integration kernel with an adaptive step sequence; the deformation covariance 3-form generates the deformation feature class along the same path parameters through the deformation channel feature class integration kernel; the constrained topological invariant and the deformation feature class are combined with the global invariant fuser to form the strain-percolation coupling phase space.

8. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 2, characterized in that: Energy entropy value generation and phase change criterion components, including: The coupled phase space structure analysis module is configured to form a strain energy transfer tangent bundle through the phase space trajectory tangent bundle generator of the strain-percolation coupled phase space; at the same time, the pulsating attractor orbit space is separated; The strain energy transfer tangent bundle is input into the divergence feature extractor, which calculates the asymptotic separation strength of adjacent trajectories in the tangent bundle and outputs the maximum divergence feature value; The track contraction intensity capture module is configured to import the regression equilibrium trend field generator of the pulsating attractor track space, and construct a vector field describing the trend of the track to return to 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 reduction operator for the maximum divergence rate eigenvalue and the orbital contraction density integral input, performs the eigenvalue-density product reduction operation, and generates the structural degradation energy entropy value.

9. The submerged roadbed simulation test system based on repeated seepage under load as claimed in claim 8, characterized in that: Coupled phase space structure analysis module, including: The tangent bundle structure preprocessing submodule is configured to input the strain energy transfer tangent bundle into the tangent bundle trajectory parameterizer to generate a parameterized trajectory bundle; the parameterized trajectory bundle is imported into the local scaling field generator to establish a trajectory spacing scaling benchmark 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 the trajectory spacing deformation field is output. The deformation field is input into the separation tensor synthesizer to generate the progressive separation tensor; The extreme value feature extraction submodule is configured to progressively separate the tensor input extreme value feature extractor, calculate the maximum eigenvalue of the tensor, and output the maximum divergence rate eigenvalue.

10. A method for simulating a submerged roadbed under load and repeated seepage according to any one of claims 1 to 9, characterized in that: The following steps are involved: A programmable pressure wave is generated in a closed cavity through a multi-axis linkage piston array, and the wavefront of the programmable pressure wave forms a time-space alternating water pressure gradient field on the permeable medium base; The water pressure gradient field is introduced into a three-dimensional independent loading frame. The three-dimensional independent loading frame applies asymmetric cyclic stress waves to the roadbed specimen through a piezoelectric lattice array, while simultaneously capturing the transient hydraulic impedance spectrum of the pores inside the specimen in real time. The local strain energy density distribution and pore dynamic water pressure pulsation intensity of the sample are synchronously collected through an embedded flexible sensing unit array; the local strain energy density distribution and pore dynamic water pressure pulsation intensity are input into the chaotic feature extractor, and the structural degradation energy entropy value is output; when the structural degradation energy entropy value exceeds the phase change threshold, a three-stage warning is triggered.

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