Indoor shear test method and system for slurry mixing pile and pasty slurry interface

Abstract

The invention provides an indoor shear test method and system for a slurry mixing pile and pasty slurry interface. According to the method, optical fiber osmotic pressure data of a pile-soil interface and temperature gradient data on the two sides are synchronously collected in the paste slurry solidification process, and a time-space correlation temperature osmotic coupling database is constructed. A shear box device provided with a circulating water temperature adjusting system is adopted, a temperature gradient environment in a database is reproduced, a pore communication curve and osmotic pressure dynamic binding data are combined, a pile-soil interface temperature field form is reversely deduced through finite element thermal-mechanical coupling calculation, and accordingly the slurry solidification and osmotic path direction is predicted. On the basis of a seepage path prediction result, dynamic adaptation of the shear load and the real-time thermodynamic state is achieved, database parameters are synchronously updated, and a closed-loop feedback mechanism in the test process is formed. According to the technical scheme, through multi-source data fusion and thermal coupling reverse calculation, accurate prediction of the pile-soil interface permeation path and dynamic optimization of the loading parameters are achieved.

Description

Technical Field

[0001] This application relates to the technical field of testing the mechanical properties of the pile slurry interface in geotechnical engineering, and particularly to an indoor shear test method and system for the interface between a slurry mixing pile and a paste-like slurry. Background Art

[0002] In projects such as soft soil foundation reinforcement and foundation pit support, the shear characteristics of the interface between the slurry mixing pile and the paste-like slurry directly affect the bearing capacity and long-term stability of the pile body. Since the temperature gradient change during the slurry curing process will significantly change its penetration path and pore structure, thereby affecting the interface shear strength, there is an urgent need for an indoor test method that can simulate the real curing environment and dynamically capture the temperature-permeation coupling effect to accurately predict the interface mechanical behavior and guide the optimization of construction parameters.

[0003] The current mainstream solution for such requirements is a shear test system based on constant temperature control. It uses fiber optic sensors to monitor the osmotic pressure at the pile-soil interface in real time and uses constant temperature circulating water to maintain a constant temperature in the shear box. During the test, a shear load is applied through a loading device, and the interface shear performance is evaluated by combining the osmotic pressure data with the strength curve under the preset temperature conditions. Some improved solutions also introduce X-ray tomography technology to perform static analysis of the pore structure of the cured specimen.

[0004] The defects of the existing solutions are mainly manifested in that although the constant temperature control mechanism can maintain a constant temperature in the shear box, it cannot truly reproduce the dynamically changing temperature gradient environment during the slurry curing stage in actual projects, resulting in a significant deviation between the evolution law of the penetration path and the real working conditions; at the same time, the acquisition timing of the osmotic pressure and temperature data lacks strict synchronization, making it difficult to accurately analyze the dynamic regulation mechanism of the temperature gradient on the pore connectivity and penetration behavior; more prominently, this solution uses a fixed loading rate to conduct the shear test, ignoring the real-time change characteristics of the thermodynamic state under the combined action of the temperature field and the stress field, resulting in the inability to effectively characterize the progressive failure process of the interface, and the test results are difficult to truly reflect the mechanical response law of the pile-soil interface under complex working conditions. Summary of the Invention

[0005] This application provides an indoor shear test method and system for the interface between a slurry mixing pile and a paste-like slurry to solve the problems of inaccurate prediction of the interface penetration path and distorted characterization of the shear response caused by the distorted simulation of the temperature gradient, the lack of dynamic coupling of multi-field data, and the mismatch between the loading rate and the thermodynamic state in the prior art.

[0006] In a first aspect, this application provides an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry, including:

[0007] During the curing process of the paste slurry, osmotic pressure data at the pile-soil interface is collected by an optical fiber sensing component. Meanwhile, temperature sensing components are arranged on both sides of the pile-soil interface to obtain temperature gradient data. The osmotic pressure data and the temperature gradient data are synchronously correlated in time series to construct a temperature-osmosis coupling database.

[0008] A shear box device with an adjustable temperature structure is used. By adjusting the temperature of the circulating water, a simulated temperature environment corresponding to the temperature gradient data in the temperature-osmosis coupling database is generated inside the shear box. After filling the shear box with paste slurry, an ultrasonic scanning component is used to continuously detect the internal pore structure of the specimen. A pore connectivity curve is generated based on the ultrasonic attenuation characteristics, and the pore connectivity curve is dynamically bound to the osmotic pressure data in the temperature-osmosis coupling database.

[0009] Based on finite element thermal-mechanical coupling calculation, taking the pore connectivity curve as the boundary input condition, and combining with the thermal conductivity data of the slurry under the simulated temperature environment, the temperature field morphology data at the pile-soil interface is inversely deduced, and the penetration path direction during the slurry curing stage is predicted according to the temperature field morphology data.

[0010] According to the prediction result of the penetration path direction, the loading rate control unit of the shear box is dynamically adjusted to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and the osmotic pressure data in the temperature-osmosis coupling database is synchronously updated during the loading process.

[0011] Optionally, the step of based on finite element thermal-mechanical coupling calculation, taking the pore connectivity curve as the boundary input condition, and combining with the thermal conductivity data of the slurry under the simulated temperature environment, inversely deducing the temperature field morphology data at the pile-soil interface, and predicting the penetration path direction during the slurry curing stage according to the temperature field morphology data, includes:

[0012] The area where the pore density in the pore connectivity curve is continuously higher than the preset threshold is defined as the penetration dominant area, and the contour coordinates of the penetration dominant area are extracted as the boundary input condition for finite element thermal-mechanical coupling calculation.

[0013] In the finite element model, grid units consistent with the internal geometric dimensions of the shear box and considering the temperature-stress coupling effect are constructed, and the thermal conductivity data of the slurry corresponding to different temperature layers measured under the simulated temperature environment are loaded layer by layer into the grid units, and the thermal conductivity data loaded into each temperature layer is bound to the temperature data at the corresponding spatial position in the temperature-osmosis coupling database.

[0014] According to the boundary input condition, combined with the bound thermal conductivity data in the finite element model, the temperature field morphology data at the pile-soil interface is reconstructed by inversely solving the heat conduction equation.

[0015] According to the reconstructed temperature field morphology data, extract the continuous region with the maximum temperature change rate at the interface between the slurry mixing pile and the paste-like slurry, and perform superposition analysis on the extension direction of the continuous region and the spatial distribution of the penetration-dominated region, and output the prediction result of the penetration path direction during the slurry curing stage.

[0016] Optionally, the reconstruction of the temperature field morphology data at the pile-soil interface according to the boundary input conditions and in combination with the bound thermal conductivity data in the finite element model includes:

[0017] In the finite element model, use the temperature gradient distribution in the selected time stage in the temperature penetration coupling database as the target reference data;

[0018] According to the boundary input conditions, set the initial heat flux input value at the boundary of the pile-soil interface, and in combination with the bound slurry thermal conductivity data in the finite element model, calculate the temperature field morphology data of each grid unit;

[0019] Perform spatial matching on the calculated temperature field morphology data and the target reference data, extract the temperature difference of the corresponding coordinate points on the interface between the two, generate a temperature distribution error vector, and according to the error vector, reversely adjust the heat flux input value at the pile-soil interface boundary, and recalculate the temperature field morphology data after adjustment;

[0020] Repeat the steps of error vector extraction and heat flux input value adjustment until the spatial error amount of the temperature gradient distribution on the interface between the calculated temperature field morphology data and the target reference data is less than the preset tolerance threshold, and use the heat flux input value after the last adjustment as the fixed boundary condition, substitute it into the finite element model, and output the temperature field morphology data.

[0021] Optionally, the extraction of the continuous region with the maximum temperature change rate at the interface between the slurry mixing pile and the paste-like slurry according to the reconstructed temperature field morphology data, and the superposition analysis of the extension direction of the continuous region and the spatial distribution of the penetration-dominated region, and the output of the prediction result of the penetration path direction during the slurry curing stage includes:

[0022] In the reconstructed temperature field morphology data, with each measurement point at the interface as the center, calculate the maximum change rate of the temperature value of the measurement point within a preset time, and mark the measurement points with the maximum change rate exceeding the set threshold as target change rate points;

[0023] Connect adjacent target change rate points according to the spatial coordinates to form a continuous region with the maximum temperature change rate, and remove isolated target change rate points. For the continuous region, based on the spatial coordinate distribution of the target change rate points, determine the main extension direction of the continuous region;

[0024] Extract a sub-region with pore density higher than a preset threshold from the penetration-dominated region, and superimpose the spatial distribution profile of the sub-region on the main extension direction of the continuous region. When the included angle between the extension direction of the sub-region and the main extension direction of the continuous region is less than a preset angle, it is determined that the sub-region is a candidate area for the penetration path;

[0025] Calculate its priority weight according to the coverage length and pore density weighted value of the candidate area on the interface, and select the extension direction of the candidate area with the highest priority weight as the prediction result of the penetration path direction in the slurry curing stage.

[0026] Optionally, according to the prediction result of the penetration path direction, dynamically adjust the loading rate control unit of the shear box to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and synchronously update the osmotic pressure data in the temperature-penetration coupling database during the loading process, including:

[0027] According to the prediction result of the penetration path direction, calculate the ratio between the expansion characteristics of the slurry curing interface when heated and the ability of the slurry mixing pile to restrict its deformation by measuring the proportional relationship, and input the ratio into the loading rate calculation model to output the initial setting value of the shear box loading rate;

[0028] Apply a load to the shear box based on the initial setting value, and continuously obtain the instantaneous change amount of the internal osmotic pressure of the paste-like slurry in real time, calculate the fluctuation amplitude of the osmotic pressure per unit time. When the fluctuation amplitude exceeds the critical value preset for the thermodynamic equilibrium state of the current temperature field, dynamically adjust the loading rate control unit of the shear box, and adopt a feedback control mechanism to dynamically correct the loading rate to limit the osmotic pressure fluctuation amplitude within the allowable range corresponding to the critical value;

[0029] During each loading process, synchronously add the time mark at the adjustment moment, the adjusted rate value and the corresponding osmotic pressure data to the temperature-penetration coupling database to complete parameter update.

[0030] Optionally, use a shear box device with an adjustable temperature structure to generate a simulated temperature environment corresponding to the temperature gradient data in the temperature-penetration coupling database inside the shear box through circulating water temperature adjustment. After filling the shear box with paste-like slurry, use an ultrasonic scanning component to continuously detect the internal pore structure of the specimen, generate a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically bind the pore connectivity curve to the osmotic pressure data in the temperature-penetration coupling database, including:

[0031] A circulating water chamber is arranged at the top and bottom of the shear box device. By independently controlling the water flow temperature of the top and bottom circulating water chambers, a simulated temperature environment corresponding to the temperature gradient distribution corresponding to the selected time stage in the temperature penetration coupling database is formed along the vertical direction inside the shear box;

[0032] After injecting paste-like slurry into the shear box, the ultrasonic scanning component moves along a preset path on multiple cross-sections of the slurry sample to emit ultrasonic pulses, and records the ultrasonic amplitude attenuation at each receiving point;

[0033] Based on the ultrasonic amplitude attenuation, according to the difference in amplitude attenuation at different receiving points on the same cross-section, the pore density distribution within the cross-section is calculated, and the pore density distributions of multiple consecutive cross-sections are connected along the depth direction to generate a pore connectivity curve;

[0034] Extract the osmotic pressure data corresponding to the current simulated temperature environment time stage in the temperature penetration coupling database, dynamically associate the pore density peak positions in the pore connectivity curve with the spatial distribution of the osmotic pressure data, and form a pore osmotic pressure mapping relationship table and store it in the database.

[0035] Optionally, during the curing process of the paste-like slurry, the osmotic pressure data at the pile-soil interface is collected through the fiber optic sensing component, and at the same time, temperature sensing components are arranged on both sides of the pile-soil interface to obtain temperature gradient data. The osmotic pressure data and the temperature gradient data are synchronously associated according to the time series to construct a temperature penetration coupling database, including:

[0036] When the paste-like slurry cures, the fiber optic sensing component is spirally wound along the interface between the slurry mixing pile and the paste-like slurry, and multiple groups of temperature sensing components are respectively embedded on the surface of the slurry mixing pile and the surface layer of the paste-like slurry on both sides of the interface. Each group of temperature sensing components is spaced axially along the interface;

[0037] The fiber optic sensing component measures the pressure change at the interface at a fixed time interval to generate osmotic pressure data. At the same time, the temperature sensing component synchronously collects the temperature values at each corresponding position point on the interface, and generates axial and radial temperature gradient data according to the measurement difference between adjacent temperature sensing components;

[0038] The data segments with the same time stamp in the osmotic pressure data and the temperature gradient data are paired, and the paired data segments are superimposed in time order into multiple groups of coupling data units containing the temperature-osmotic pressure gradient relationship. All the coupling data units are indexed according to the spatial coordinates and time stamp of the interface and stored as a temperature penetration coupling database with a hierarchical structure.

[0039] In a second aspect, the present application provides an indoor shear test system for the interface between a slurry mixing pile and a paste-like slurry, including:

[0040] The acquisition module, during the solidification process of the paste-like slurry, collects the osmotic pressure data of the pile-soil interface through the fiber optic sensing component. At the same time, temperature sensing components are arranged on both sides of the pile-soil interface to obtain the temperature gradient data. The osmotic pressure data and the temperature gradient data are synchronously associated according to the time series to construct a temperature-osmosis coupling database.

[0041] The simulation module uses a shear box device with a temperature-adjustable structure to generate a simulated temperature environment corresponding to the temperature gradient data in the temperature-osmosis coupling database through the regulation of the circulating water temperature. After filling the paste-like slurry in the shear box, the ultrasonic scanning component is used to continuously detect the pore structure inside the specimen, generate a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically bind the pore connectivity curve to the osmotic pressure data in the temperature-osmosis coupling database.

[0042] The calculation module, based on the finite element thermal-mechanical coupling calculation, takes the pore connectivity curve as the boundary input condition, combines the thermal conductivity data of the slurry in the simulated temperature environment, and reversely deduces the temperature field morphology data at the pile-soil interface, and predicts the penetration path direction during the slurry solidification stage according to the temperature field morphology data.

[0043] The regulation module, according to the prediction result of the penetration path direction, dynamically adjusts the loading rate control unit of the shear box to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and synchronously updates the osmotic pressure data in the temperature-osmosis coupling database during the loading process.

[0044] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry as described in the first aspect above.

[0045] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry as described in the first aspect.

[0046] In the example of the present application, during the curing process of the paste slurry, the osmotic pressure data of the pile-soil interface is collected by an optical fiber sensing component, and at the same time, temperature sensing components are arranged on both sides of the pile-soil interface to obtain temperature gradient data, and the osmotic pressure data and the temperature gradient data are synchronously associated in time series to construct a temperature-permeability coupling database; a shear box device with an adjustable temperature structure is used, and the temperature of the circulating water is adjusted to generate a simulated temperature environment corresponding to the temperature gradient data in the temperature-permeability coupling database inside the shear box, and after the paste slurry is filled in the shear box, the ultrasonic scanning component is used to continuously detect the internal pore structure of the sample, and the pore structure is generated according to the ultrasonic attenuation characteristics. The method comprises the following steps: a pore connectivity curve is obtained, and the pore connectivity curve is dynamically bound to the osmotic pressure data in the temperature-permeability coupling database; based on finite element thermomechanical coupling calculation, the pore connectivity curve is used as a boundary input condition, and the thermal conductivity data of the slurry under the simulated temperature environment is combined to reversely deduce the temperature field morphological data at the pile-soil interface, and the direction of the seepage path in the slurry solidification stage is predicted according to the temperature field morphological data; according to the predicted result of the seepage path direction, the loading rate control unit of the shear box is dynamically adjusted to make the loading rate match the thermodynamic equilibrium state under the current temperature field, and the osmotic pressure data in the temperature-permeability coupling database is synchronously updated during the loading process.

[0047] The technical solution of this application has the following beneficial effects:

[0048] This application accurately constructs a temperature-permeability coupling database by synchronously collecting and correlating the osmotic pressure and dynamic temperature gradient data of the pile-soil interface during the curing process of the paste slurry, and combines an adjustable temperature shear box device to dynamically replicate the measured temperature gradient environment. Based on the pore connectivity curve continuously generated by ultrasonic scanning and the dynamic binding data of the osmotic pressure, the finite element thermal coupling model is used to reversely deduce the temperature field morphology and the direction of the penetration path; the shear loading rate is further controlled in real time according to the prediction results of the penetration path to dynamically adapt it to the thermodynamic equilibrium state under the temperature field, and a closed-loop feedback mechanism is formed by synchronously updating the osmotic pressure data during the loading process, which ultimately realizes the refined simulation of the multi-physical field coupling effect of the pile-soil interface curing process, the visual prediction of the evolution law of the penetration path, and the dynamic optimization control of the shear mechanical behavior, which significantly improves the mapping accuracy and guiding value of the test results for complex engineering conditions.

[0049] Furthermore, based on finite element thermal-mechanical coupling calculations, boundary conditions are set by the contour coordinates of the seepage-dominated region where the pore density in the pore connectivity curve is higher than the threshold. Combining the slurry thermal conductivity data corresponding to the temperature gradient loaded layer by layer in the shear box, a finite element model considering temperature-stress coupling is constructed. By inversely solving the heat conduction equation, the temperature field pattern at the pile-soil interface is reconstructed, and the continuous region with the maximum temperature change rate at the interface is extracted. By superimposing and analyzing its spatial distribution with the seepage-dominated region, the seepage path direction during the slurry solidification stage is finally predicted. Through the dynamic binding of thermal conductivity data and the temperature field and inverse reconstruction, the quantitative characterization of the influence of temperature gradient on the seepage path is realized, and the problem of seepage path prediction deviation caused by distorted temperature field simulation in traditional methods is solved. At the same time, by combining the superimposed analysis of the temperature change rate and the pore structure, the spatial extension direction of the main seepage channel during the solidification stage is accurately identified, providing a reliable basis for dynamically adjusting the shear loading rate, and significantly improving the authenticity of the interface thermal-mechanical coupling effect simulation and the engineering guiding value of the test results.

[0050] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 The flowchart of an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry provided by the present application is shown;

[0053] Figure 2 The scene diagram of an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry provided by the present application is shown;

[0054] Figure 3 The structural schematic diagram of an indoor shear test system for the interface between a slurry mixing pile and a paste-like slurry provided by the present application is shown;

[0055] Figure 4 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification, claims, and the above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations can be performed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations can be performed sequentially or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are different types.

[0058] Research shows that in the indoor shear test of the interface between slurry mixing piles and paste-like slurry, there are key defects in the existing constant-temperature controlled shear test system. Its constant-temperature environment cannot reproduce the actual dynamic temperature gradient, resulting in the evolution of the seepage path deviating from the real working conditions; the acquisition time sequences of osmotic pressure and temperature data are not synchronized, making it difficult to analyze the dynamic regulation mechanism of temperature on seepage behavior, and the fixed loading rate ignores the change of thermodynamic state, resulting in the distortion of the characterization of the interface failure process. This contradiction stems from the static simulation of the constant-temperature environment, the lack of multi-field data coupling, and the rigid design of the loading strategy, and a dynamic temperature seepage coupling test method is urgently needed.

[0059] To solve the above problems, the present invention proposes a method for the interface shear test of slurry mixing piles based on dynamic temperature seepage coupling. The core lies in realizing the accurate prediction of the evolution of the seepage path and the shear response through the dynamic reproduction of the temperature gradient, the synchronous correlation of multi-source data, and the reverse analysis of thermal coupling. Through the simulation of the dynamic temperature gradient and the fusion of multi-field data, the problem of the distortion of the evolution of the seepage path under the constant-temperature environment is solved; based on the time-sequence synchronous correlation and the reverse analysis of thermal coupling, the dynamic regulation mechanism of temperature on seepage behavior is accurately analyzed; through the adaptive matching of the loading rate and the thermodynamic state, the progressive failure process of the interface is truly characterized. Finally, the refined simulation of the multi-physical field coupling effect in the curing process of the pile-soil interface is realized, providing high-reliability data support for the optimization of construction parameters.

[0060] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0061] Figure 1 A flowchart of an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry is provided for the embodiments of the present application, as Figure 1 shown, and the method includes:

[0062] 101. During the curing process of the paste slurry, osmotic pressure data at the pile-soil interface is collected through an optical fiber sensing component. Meanwhile, temperature sensing components are arranged on both sides of the pile-soil interface to obtain temperature gradient data. The osmotic pressure data and the temperature gradient data are synchronously correlated in time series to construct a temperature-osmosis coupling database.

[0063] Optionally, step 101 may specifically include the following steps:

[0064] 1011. When the paste slurry cures, the optical fiber sensing component is spirally wound along the interface between the slurry mixing pile and the paste slurry, and multiple groups of temperature sensing components are respectively embedded on the surface of the slurry mixing pile and the surface layer of the paste slurry on both sides of the interface. Each group of temperature sensing components is distributed at intervals along the axial direction of the interface.

[0065] 1012. The optical fiber sensing component measures the pressure change at the interface at fixed time intervals to generate osmotic pressure data. Meanwhile, the temperature sensing component synchronously collects the temperature values of each corresponding position point on the interface, and generates axial and radial temperature gradient data based on the measurement differences between adjacent temperature sensing components.

[0066] 1013. The data segments with the same time stamp in the osmotic pressure data and the temperature gradient data are paired, and the paired data segments are superimposed in time order into multiple groups of coupling data units containing the temperature-osmotic pressure gradient relationship. All the coupling data units are indexed according to the spatial coordinates and time stamp of the interface and stored as a temperature-osmosis coupling database with a hierarchical structure.

[0067] In the above solution, the osmotic pressure data refers to the time series signal reflecting the dynamic change of pressure during the penetration process of the paste slurry at the pile-soil interface, which can be used to evaluate the spatio-temporal evolution law of the interface penetration behavior. The temperature gradient data refers to the dynamic parameter characterizing the spatial difference of the temperature field on both sides of the pile-soil interface, which can be used to analyze the regulation mechanism of the temperature field on the pore structure and penetration path of the slurry. The coupling data unit refers to the associated data block of osmotic pressure and temperature gradient integrated by spatio-temporal alignment, including the osmotic pressure value, temperature gradient value and their co-variation characteristics at a specific spatial position point under the same time stamp, which can be used to construct the boundary conditions and verification benchmarks for multi-physical field coupling analysis. The temperature-osmosis coupling database refers to a multi-source data set stored in a hierarchical structure according to spatio-temporal dimensions, including the structured records of osmotic pressure, temperature gradient and their coupling relationship during the whole curing period of the slurry, including the time stamp index layer, spatial coordinate mapping layer and multi-field coupling association layer, which can provide high-resolution data support for the reverse reconstruction of the thermo-mechanical coupling model and the prediction of the penetration path.

[0068] In the embodiment of the present application, first, in the initial stage of the curing of the paste slurry, a precise deployment of the sensor network is carried out through step 1011. For the interface between the slurry mixing pile and the paste slurry, a flexible optical fiber fixing technology is adopted, and the optical fiber sensing component is closely attached to the interface in a spiral winding manner. The pitch of the spiral winding is designed to be 5-10 cm, ensuring that the optical fiber covers the entire length of the interface while avoiding the fracture or displacement of the optical fiber caused by the shrinkage of the slurry during curing. At the same time, embedded temperature sensing components are respectively embedded on the surface of the slurry mixing pile and the surface layer of the paste slurry. Each group of sensors is evenly distributed at intervals of 10-20 cm along the axial direction of the pile body, forming a dense axial and radial temperature measurement network. For example, a set of thermocouples is arranged at intervals of 15 cm on the side of the slurry pile, and a distributed temperature sensor is synchronously arranged at the corresponding position on the slurry side to ensure the spatial symmetry of the measurement points on both sides. This arrangement method ensures the stability of the sensors during the flow and curing process of the slurry through double fixation of physical anchoring and chemical bonding.

[0069] Then, through step 1012, a multi-source data synchronous acquisition and gradient calculation process is started. The optical fiber sensing component is used to capture the pressure changes at each point of the interface in real time at a fixed sampling frequency. In specific implementation, the fiber Bragg grating sensor converts physical deformation into an electrical signal by detecting the optical wavelength shift caused by pressure deformation, and generates a digital osmotic pressure time series curve with a spatial resolution of up to 1 cm after quantization by an analog-to-digital converter. At the same time, the temperature sensing component synchronously obtains the temperature values of each measurement point through a multi-channel data acquisition card, and the sampling frequency is consistent with that of the optical fiber sensing, and a GPS timing module is used to achieve strict alignment of the timestamps across devices. The temperature gradient calculation is divided into two dimensions: axial gradient calculation and radial gradient calculation. The axial gradient calculation is to perform discrete difference on the temperature values of adjacent axial measurement points on the same side, and the calculation formula is as follows: , where is the temperature difference between two points on the same side, is the distance difference between the two points. The radial gradient calculation is to perform difference on the temperature values of the measurement points on the pile side and the slurry side at the same axial position, and the calculation formula is as follows: , where is the difference between the temperature on the pile side and the temperature on the slurry side, is the radial thickness of the interface. For example, at an axial position A 2 m away from the pile top, its temperature is 25 °C, and at an adjacent position B 2.15 m away from the pile top, its temperature is 26 °C, then the axial gradient is 6.67 °C / m; at a certain point, the temperature on the pile side is 24 °C, the temperature on the slurry side is 28 °C, and the radial thickness of the interface is 5 cm, then the radial gradient is 80 °C / m. The calculated gradient data is used to generate a continuous axial and radial temperature gradient field through the Kriging spatial interpolation algorithm.

[0070] Finally, the data pairing engine in step 1013 quickly matches the osmotic pressure data segment and the temperature gradient data segment at the same timestamp according to the spatial coordinates, where the spatial coordinates include the axial position and the radial stratification. A coupled data unit containing the timestamp, spatial coordinates, osmotic pressure value, and temperature gradient value is generated. Subsequently, the coupled data units are stored in the temperature-osmotic coupling database according to the hierarchical structure of the timestamp index layer, the spatial coordinate mapping layer, and the multi-field coupling association layer. The timestamp index layer divides the time block through the curing stage, supporting fast retrieval according to the time window. The spatial coordinate mapping layer discretizes the interface into 1 cm × 1 cm grid cells, and each grid is associated with its affiliated coupled data unit, supporting query according to the position range. The multi-field coupling association layer records the dynamic response relationship between the osmotic pressure and the temperature gradient.

[0071] In practical applications, first, in the initial stage of the paste slurry curing, the flexible optical fiber sensing technology is used to deploy precisely at the interface between the slurry mixing pile and the paste slurry. The optical fiber sensing component with a diameter of 0.25 mm is closely attached to the interface with a pitch of 5 - 10 cm by means of spiral winding, and the ultraviolet light curing glue is used to achieve double fixation of physical anchoring and chemical bonding, ensuring the stability of the optical fiber during the subsequent slurry flow and shear deformation processes. Synchronously, K-type thermocouples are arranged axially along the pile surface every 15 cm, and distributed optical fiber temperature sensors are embedded at the corresponding positions on the slurry side to form a spatially symmetric temperature measurement network. The depth of the measuring points on the pile body covers 0 - 80 cm, and the thickness of the measuring points on the slurry side covers 0 - 5 cm. The temperature data is synchronously collected through the RS485 bus. In the test loading stage, a strain-controlled direct shear apparatus is used to apply a horizontal shear stress at a shear rate of 0.8 mm / min, and at the same time, the vertical pressure is precisely controlled by a servo motor. The data acquisition system synchronously starts the multi-source signal synchronization mechanism, and captures the interface osmotic pressure change at a frequency of 10 Hz through the fiber Bragg grating sensor. The temperature data is time-synchronized at the ±1 ms level through analog-to-digital conversion in combination with the GPS timing module. The axial temperature gradient and the radial temperature gradient are calculated by the two-dimensional difference method, and a temperature field cloud map with a resolution of 0.5 m × 0.5 m is generated through Kriging interpolation. In the data fusion stage, the multi-dimensional association between the osmotic pressure and the temperature field is realized through the spatio-temporal pairing engine, and a coupled data unit containing the timestamp, spatial coordinates, and multi-physical field parameters is established. The MongoDB sharded cluster storage is used, the time block is divided according to the curing stage, the spatial coordinates are mapped into 1 cm³ grid cells, and each grid is associated with the osmotic pressure time series and the temperature gradient field. The finally constructed three-dimensional spatio-temporal database supports multi-dimensional queries according to the time window, spatial range, and multi-field association, providing high-precision data support for analyzing the thermo-mechanical coupling effect during the paste slurry curing process.

[0072] The overall solution of the above 101 realizes the refined monitoring of the paste-like slurry curing process through innovative sensing networking technology. The helically wound optical fiber sensing component and the double-sided embedded temperature sensing array form a three-dimensional monitoring network, synchronously obtaining osmotic pressure and temperature gradient data at the interface, and constructing a spatio-temporal correlation database through precise timestamp matching. This technology breaks through the limitations of traditional single-parameter monitoring. By using the axial and radial temperature gradient calculation model and the hierarchical data storage architecture, the dynamic coupling relationship between the temperature field and the seepage field during the slurry curing process is completely retained. Based on this database, the thermodynamic characteristics of the curing reaction can be inverted, revealing the regulation mechanism of the temperature gradient on the evolution of the osmotic pressure, providing data support for optimizing the pile foundation grouting process parameters and improving the bearing performance of the pile-soil interface, and forming an engineering decision knowledge base with spatio-temporal correlation characteristics.

[0073] 102. Adopt a shear box device with a temperature-adjustable structure. By adjusting the circulating water temperature, a simulated temperature environment corresponding to the temperature gradient data in the temperature-seepage coupling database is generated inside the shear box. After filling the paste-like slurry in the shear box, use an ultrasonic scanning component to continuously detect the internal pore structure of the specimen, generate a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically bind the pore connectivity curve to the osmotic pressure data in the temperature-seepage coupling database.

[0074] Optionally, step 102 may specifically include the following steps:

[0075] 1021. Set circulating water chambers at the top and bottom of the shear box device. By independently controlling the water flow temperatures of the top and bottom circulating water chambers, a simulated temperature environment corresponding to the temperature gradient distribution corresponding to the selected time stage in the temperature-seepage coupling database is formed inside the shear box along the vertical direction.

[0076] 1022. After injecting the paste-like slurry into the shear box, use the ultrasonic scanning component to move and emit ultrasonic pulses along a preset path on multiple cross-sections of the slurry specimen, and record the ultrasonic amplitude attenuation amounts at each receiving point.

[0077] 1023. Based on the ultrasonic amplitude attenuation amounts, calculate the pore density distribution within the cross-section according to the amplitude attenuation amount differences between different receiving points on the same cross-section, and connect the pore density distributions of multiple consecutive cross-sections along the depth direction to generate a pore connectivity curve.

[0078] 1024. Extract the osmotic pressure data corresponding to the current simulated temperature environment time stage in the temperature-seepage coupling database, dynamically associate the pore density peak positions in the pore connectivity curve with the spatial distribution of the osmotic pressure data, and form a pore-osmotic pressure mapping relationship table and store it in the database.

[0079] In the above solution, the pore connectivity curve refers to a three-dimensional distribution curve that reflects the dynamic evolution of the internal pore structure of the paste slurry sample, including the cross-sectional pore density distribution data calculated based on the difference in ultrasonic amplitude attenuation and the spatial continuity characteristics along the depth direction, and can be used to characterize the generation and expansion laws of pore channels during the curing process. The pore osmotic pressure mapping relationship table refers to a data set that describes the dynamic correlation characteristics between the pore structure and the osmotic pressure, including the matching relationship between the peak position of the pore density and the osmotic pressure change rate under the corresponding spatial coordinates, and can be used to quantify the regulation mechanism of the temperature gradient on the slurry penetration path.

[0080] In the embodiment of the present application, first, through the top and bottom independent circulating water chambers of the shear box device in step 1021, the PID closed-loop control algorithm is used to adjust the circulating water flow rate and temperature, so as to form a vertical temperature gradient distribution corresponding to the selected time stage in the temperature penetration coupling database inside the shear box. Specifically, for example, according to the temperature difference between the top circulating water chamber and the bottom circulating water chamber recorded in the database, the temperature of the top circulating water chamber is 50°C and the temperature of the bottom circulating water chamber is 20°C. By adjusting the power of the heater and cooler in real-time feedback, the top circulating water temperature is stabilized at 50°C ± 0.5°C, and the bottom circulating water temperature is stabilized at 20°C ± 0.5°C, so as to form a vertical temperature gradient of 30°C / m inside the shear box. This process is dynamically calibrated through the embedded temperature control module and the high-precision thermocouple sensor to ensure that the temperature gradient is consistent with the heat conduction characteristics during the slurry curing stage in the actual project.

[0081] Secondly, in step 1022, the shear box is filled with paste slurry. After curing to the target stage, the ultrasonic scanning component is started, and ultrasonic pulses are emitted while moving along a preset path on multiple cross-sections of the sample. The receiving end records the ultrasonic amplitude attenuation after penetrating the sample, filters the amplitude attenuation of each receiving point, marks the abnormal attenuation area, and binds the attenuation data of the receiving point with its spatial coordinates to form a three-dimensional attenuation distribution matrix, which is used as the input for subsequent pore density calculation. For example, a 1MHz focused probe is used to move and emit ultrasonic pulses along a spiral trajectory on multiple cross-sections of the sample. The original amplitude is 5V, and the received amplitude at the receiving end is 2V, then the attenuation is .

[0082] Then, based on the three-dimensional attenuation data obtained in step 1022 in step 1023, the spatial interpolation algorithm is used to calculate the pore density distribution in each cross-section, the attenuation difference of the same cross-section is normalized, and a pore density distribution heat map is generated. The pore density distributions of consecutive cross-sections are superimposed and analyzed along the depth direction to identify the connected areas where the pore density is higher than the threshold, and the morphological skeleton extraction algorithm is used to generate the pore connectivity curve. The local mutations caused by noise are eliminated through the curve smoothing algorithm to ensure that the pore connectivity curve reflects the true pore structure evolution trend.

[0083] Finally, extract the osmotic pressure dataset corresponding to the current simulated temperature environment time stage from the temperature-seepage coupling database, and align its spatial coordinates with the peak position of pore density in the pore connectivity curve. Use the spatial clustering algorithm to identify the peak area of pore density, calculate the correlation coefficient between it and the change rate of osmotic pressure, and establish a dynamic relationship model between pore density and osmotic pressure. Convert the above dynamic relationship model into a pore osmotic pressure mapping relationship table, which records the peak coordinates of pore density, the corresponding osmotic pressure value and its change rate. For example, the pore density is 38% at the coordinates (10 cm, 5 mm, 8 mm), and the osmotic pressure drop rate is 0.6 kPa / s. At the same time, write the pore osmotic pressure mapping relationship table into the database in real time for subsequent thermo-mechanical coupling calculation and loading regulation calls.

[0084] In practical applications, construct a vertical temperature gradient field in the shear box device. Through the PID closed-loop control system of the independent circulating water chambers at the top and bottom, maintain the temperature difference between 50 °C and 20 °C with an accuracy of ±0.5 °C to form a vertical temperature gradient of 30 °C / m. This system uses an embedded temperature control module to collect high-precision thermocouple data in real time, and dynamically adjusts the power of the heater and the semiconductor refrigeration sheet through the PID algorithm to ensure that the temperature distribution is consistent with the heat conduction characteristics of the target stage in the temperature-seepage coupling database. Subsequently, inject the paste-like slurry to a height of 80 mm. After curing to the target stage at 24 hours of age, start the three-dimensional ultrasonic scanning system. Use a 1 MHz focused probe to scan the cross-section of the specimen along a spiral trajectory with a pitch of 10 mm at a step of 0.5 mm, record the attenuation amount of the transmitted wave amplitude. Calculate the corresponding attenuation amount as 8 dB when the amplitude decays from 5 V to 2 V, and use median filtering to eliminate noise interference. Mark the pore enrichment area with an attenuation amount greater than 15 dB as the attenuation abnormal area, and generate a three-dimensional attenuation matrix with a resolution of 0.5 mm × 0.5 mm. Then, construct a heat map of pore density distribution based on the Kriging interpolation algorithm, stack the continuous cross-section data along the depth direction, extract the pore connectivity region through morphological opening operation, use the Ramer-Douglas-Peucker algorithm to extract the skeleton curve and smooth it to obtain a spatial curve cluster reflecting the pore structure evolution. Finally, extract the time series data of osmotic pressure at the corresponding stage from the temperature-seepage coupling database at a sampling interval of 1 s. Use the DBSCAN spatial clustering algorithm to identify the area with a pore density greater than 35% as the peak area of pore density, calculate the correlation coefficient between it and the change rate of osmotic pressure, establish a dynamic relationship model between pore density and osmotic pressure, and generate a mapping table containing coordinates, pore density, osmotic pressure and change rate. For example, record a density of 38% and an osmotic pressure drop rate of 0.6 kPa / s at (10 cm, 5 mm, 8 mm). The data is written into the MongoDB spatio-temporal database in real time, supporting multi-dimensional queries by spatial range or time window, providing a high-precision parameter library for thermo-mechanical coupling numerical simulation.

[0085] For the overall solution of 102 above, through dynamic simulation and multi-modal sensing fusion technology, a simulation model of the paste slurry curing process under the coupling action of temperature seepage and pores is constructed. The temperature-adjustable shear box device controls the temperature through circulating water in different regions, accurately reproducing the temperature gradient fields at different stages in the database. Combining with the continuous monitoring of the internal pore structure of the specimen by the ultrasonic scanning component, a three-dimensional pore connectivity curve is generated using the amplitude attenuation difference algorithm. This technology breaks through the limitations of traditional static testing. Through a dynamic binding mechanism, the peak position of pore density is associated with the spatial distribution of osmotic pressure at the corresponding time period, forming a pore osmotic pressure mapping relationship table for multi-physical field coupling. Based on the extended application of the spatio-temporal correlation database, the regulatory effect of the temperature gradient on the pore evolution path can be inversely calculated in real time, revealing the dynamic response law of the pore connectivity and osmotic pressure changes during the slurry curing process, providing multi-dimensional data support for optimizing grouting process parameters and predicting the long-term seepage stability of the pile-soil interface, and significantly improving the prediction accuracy and engineering applicability of geotechnical engineering numerical simulation.

[0086] 103. Based on finite element thermal-mechanical coupling calculation, taking the pore connectivity curve as the boundary input condition, and combining with the thermal conductivity data of the slurry in the simulated temperature environment, inversely deduce the temperature field morphology data at the pile-soil interface, and predict the seepage path direction during the slurry curing stage according to the temperature field morphology data;

[0087] Optionally, step 103 may specifically include the following steps:

[0088] 1031. Define the area where the pore density in the pore connectivity curve is continuously higher than the preset threshold as the seepage-dominant area, and extract the contour coordinates of the seepage-dominant area as the boundary input condition for finite element thermal-mechanical coupling calculation;

[0089] 1032. Construct grid units in the finite element model that are consistent with the internal geometric dimensions of the shear box and consider the temperature stress coupling effect, and layer by layer load the thermal conductivity data of the slurry corresponding to different temperature layers measured in the simulated temperature environment into the grid units, and bind the thermal conductivity data loaded into each temperature layer with the temperature data at the corresponding spatial position in the temperature seepage coupling database;

[0090] 1033. According to the boundary input condition, combined with the bound thermal conductivity data in the finite element model, reconstruct the temperature field morphology data at the pile-soil interface by inversely solving the heat conduction equation;

[0091] Among them, step 1033 may specifically include the following processes: in the finite element model, the temperature gradient distribution of the selected time stage in the temperature-permeability coupling database is used as the target reference data; according to the boundary input condition, the initial heat flux input value is set at the boundary of the pile-soil interface, and the temperature field morphology data of each grid unit is calculated in combination with the bound slurry thermal conductivity data in the finite element model; the calculated temperature field morphology data is spatially matched with the target reference data, and the temperature difference between the corresponding coordinate points on the interface is extracted to generate a temperature distribution error vector; according to the error vector, the heat flux input value at the boundary of the pile-soil interface is reversely adjusted, and the temperature field morphology data is recalculated after the adjustment; the error vector extraction and heat flux input value adjustment steps are repeated until the spatial error amount of the temperature gradient distribution of the calculated temperature field morphology data and the target reference data on the interface is less than the preset tolerance threshold, and the heat flux input value after the last adjustment is used as a fixed boundary condition, substituted into the finite element model, and the temperature field morphology data is output.

[0092] 1034. According to the reconstructed temperature field morphological data, the continuous area with the largest temperature change rate at the interface between the mud mixed pile and the paste slurry is extracted, the extension direction of the continuous area and the spatial distribution of the permeability dominant area are superimposed and analyzed, and the prediction result of the permeability path direction in the slurry solidification stage is output.

[0093] Among them, step 1034 may specifically include the following processes: in the reconstructed temperature field morphological data, taking each measuring point at the interface as the center, calculating the maximum change rate of the temperature value of the measuring point within the preset time, and marking the measuring point whose maximum change rate exceeds the set threshold as the target change rate point; connecting adjacent target change rate points according to the spatial coordinates to form a continuous area with the largest temperature change rate, and eliminating isolated target change rate points, for the continuous area, based on the spatial coordinate distribution of the target change rate points, determining the main extension direction of the continuous area; extracting a sub-area with a pore density higher than a preset threshold from the permeability-dominant area, superimposing the spatial distribution contour of the sub-area with the main extension direction of the continuous area, when the angle between the extension direction of the sub-area and the extension main direction of the continuous area is less than the preset angle, the sub-area is determined to be a candidate area for the permeability path; calculating its priority weight according to the coverage length of the candidate area on the interface and the pore density weighted value, and selecting the extension direction of the candidate area with the highest priority weight as the prediction result of the permeability path direction in the slurry solidification stage.

[0094] In the above solution, the thermal conductivity data refers to the physical parameters characterizing the heat conduction performance of the paste slurry in different temperature environments, including the variation law of the thermal conductivity coefficient of the slurry in a specific temperature layer, which can be used to quantify the influence of the temperature gradient on the heat transfer efficiency of the material. The penetration-dominated region refers to the region where the pore density in the pore connectivity curve is continuously higher than the preset threshold, including the high connectivity characteristic data of the pore space distribution, which can be used to identify the main channels of the penetration behavior during the slurry curing stage. The temperature distribution error vector refers to the set of temperature differences between the calculated temperature field in the finite element model and the target reference data, including the quantitative index of the temperature gradient deviation at each coordinate point on the interface, which can be used in the iterative optimization process of reversely adjusting the heat flux input value. The candidate region of the penetration path refers to the high-priority region screened by superimposing the region with the maximum temperature change rate and the spatial distribution of the penetration-dominated region, including the comprehensive evaluation results of the included angle of the extension direction and the pore density weight, which can be used to determine the optimal prediction direction of the penetration path during the slurry curing stage.

[0095] In the embodiment of the present application, first, based on the pore connectivity curve generated by ultrasonic scanning, the region where the pore density is continuously higher than this value is screened out by setting the pore density threshold and defined as the penetration-dominated region. The penetration-dominated region represents the region with highly connected pores during the slurry curing process and is the main channel for the penetration behavior. The contour coordinates of the penetration-dominated region are extracted through an image processing algorithm to generate a boundary input condition file, which contains the spatial position information of the penetration-dominated region in the shear box. For example, if the penetration-dominated region is located in the central region of the shear box, that is, the coordinate X range is 20 - 40 mm and the coordinate Y range is 30 - 50 mm, the contour coordinates will be used as the boundary constraint conditions of the finite element model for the subsequent solution of the heat conduction equation.

[0096] Next, based on the boundary conditions generated in step 1031 through step 1032, a grid model consistent with the geometric dimensions of the shear box is constructed using finite element modeling software. The grid elements need to consider the temperature-stress coupling effect, and tetrahedral or hexahedral elements are used to refine the interface region. Subsequently, the thermal conductivity data of the slurry measured in the simulated temperature environment is mapped layer by layer to the grid elements according to the temperature layer. For example, if there is a layered temperature gradient in the shear box, the thermal conductivity data of each layer is associated with the corresponding grid element through an interpolation algorithm and dynamically bound to the temperature data in the temperature penetration coupling database to ensure that the thermal conductivity value of each grid element strictly matches the temperature value of its spatial position.

[0097] Then, based on the boundary conditions in step 1031 and the thermal conductivity data in step 1032, the temperature field is reversely reconstructed through a finite element solver. The specific process is as follows: the measured temperature gradient distribution in the selected time period in the temperature penetration coupling database is used as the target reference data, and an initial heat flux input value is set at the pile-soil interface boundary. The temperature distribution of each grid element is calculated through the heat conduction equation to generate the initial temperature field data. The heat conduction equation is as follows: , where k is the thermal conductivity and T is the temperature field. The calculated temperature field is compared with the target reference data, the temperature difference at the interface coordinate points is extracted, and a temperature distribution error vector is generated. If the error exceeds the tolerance threshold, the heat flux input value is adjusted backward using the gradient descent method, and the temperature field is recalculated. For example, if the temperature in a certain area of the interface is low, the heat flux value in that area is increased. The temperature field morphology data is iteratively repeated until the temperature field error is lower than the threshold, and the final temperature field morphology data is output.

[0098] Finally, through step 1034, based on the temperature field morphology data reconstructed in step 1033, with each measurement point at the interface as the center, the temperature change rate within a preset time window is calculated, and the measurement points with a change rate exceeding the threshold are screened out and marked as target change rate points. The adjacent target change rate points are connected into continuous regions through a spatial clustering algorithm, and the isolated points are removed. Principal component analysis is used to determine the extended main direction of the continuous region. The sub-region with a pore density greater than or equal to the threshold in the dominant infiltration region is superimposed with the extended direction of the region with the maximum temperature change rate, and the included angle between the two is calculated. If the included angle is less than the preset value, it is marked as a candidate infiltration path region. According to the coverage length and pore density weighted value of the candidate region, the candidate region direction with the highest priority is selected as the final prediction result.

[0099] In practical applications, first, the ultrasonic scanning component continuously detects the paste-like slurry sample in the shear box, generates a pore connectivity curve based on the ultrasonic attenuation characteristics, sets the pore density threshold to 0.5 / mm², and screens out the area with an X range of 20 - 40 mm and a Y range of 30 - 50 mm as the infiltration-dominated area where the pore density continuously exceeds the limit. The morphological segmentation algorithm is used to extract its contour coordinates and generate a boundary input condition file. Subsequently, a finite element modeling software is used to construct a mesh model consistent with the geometric dimensions of the shear box. The hexahedral elements are refined for the interface area, and the slurry thermal conductivity data measured under the simulated temperature environments with layered temperature gradients of 25°C, 35°C, and 45°C are 1.2 W / m·K, 1.5 W / m·K, and 1.8 W / m·K respectively. Through the spatial interpolation algorithm, it is loaded layer by layer into the corresponding mesh elements to ensure the dynamic binding of the thermal conductivity and the temperature data in the temperature infiltration coupling database. Further, based on the boundary conditions and the thermal conductivity data, the heat conduction equation is inversely solved in the finite element model. Taking the measured temperature gradient distribution as the target reference data, the initial heat flux input value is set to 100 W / m², and the heat flux value is iteratively adjusted by the gradient descent method. When the error between the calculated temperature value and the measured value in a certain area of the interface exceeds 5%, the heat flux input value is increased to 110 W / m² and recalculated until the temperature distribution error vector meets the tolerance threshold, and the reconstructed temperature field morphology data is output. Finally, based on the temperature field data, the target change rate points with a temperature change rate greater than or equal to 2°C / min at the interface are extracted. The DBSCAN clustering algorithm is used to generate continuous regions, and combined with the sub-regions in the infiltration-dominated area with a pore density greater than or equal to 0.5 / mm² for direction superposition analysis, the candidate areas with an included angle less than 30° are screened out, the coverage length and the weighted value of the pore density are calculated, and the candidate area direction with the highest priority in the northeast-southwest direction is selected as the prediction result of the infiltration path.

[0100] The overall scheme of 103 above, through the reverse deduction of thermal coupling and multi-field data linkage analysis technology, constructs a prediction model of slurry solidification permeation path driven by temperature gradient. Based on the finite element thermal coupling calculation framework, the permeation dominant area defined by the pore connectivity curve is used as the spatial boundary constraint, combined with the thermal conductivity data of layered loading, and the temperature field morphology of the pile-soil interface is reconstructed through the error vector driven iterative optimization algorithm. This technology breaks through the limitations of traditional one-way simulation, and adopts a two-way feedback mechanism of dynamic boundary adjustment and temperature gradient field reconstruction to realize the spatial coupling verification of temperature field morphology and permeation dominant area. By extracting the extension direction of the area with the maximum temperature change rate and performing weighted superposition analysis with the pore density distribution, an innovative topological correlation model of temperature gradient field and permeation path is established. The prediction results based on this model can quantitatively characterize the thermal-mechanical permeability coupling effect inside the slurry during the solidification stage, reveal the directional regulation mechanism of temperature field evolution on the permeation path, provide a theoretical basis for optimizing the thermal parameter design of pile foundation grouting process and predicting long-term permeability stability, and significantly improve the reliability and engineering applicability of geotechnical numerical simulation under complex geological conditions.

[0101] 104. According to the prediction result of the penetration path direction, the loading rate control unit of the shear box is dynamically adjusted to make the loading rate match the thermodynamic equilibrium state under the current temperature field, and the penetration pressure data in the temperature-penetration coupling database is synchronously updated during the loading process.

[0102] Optionally, step 104 may specifically include the following steps:

[0103] 1041. According to the prediction result of the penetration path direction, by measuring the proportional relationship between the expansion characteristics of the slurry solidification interface when heated and the ability of the mud mixed pile to limit its deformation, the ratio of the two is calculated, and the ratio is input into the loading rate calculation model to output the initial setting value of the shear box loading rate;

[0104] 1042. Apply a load to the shear box based on the initial setting value, obtain the instantaneous change of the osmotic pressure inside the paste slurry in real time, calculate the fluctuation amplitude of the osmotic pressure per unit time, and when the fluctuation amplitude exceeds the preset critical value of the thermodynamic equilibrium state of the current temperature field, dynamically adjust the loading rate control unit of the shear box, adopt a feedback control mechanism, and dynamically correct the loading rate so that the fluctuation amplitude of the osmotic pressure is limited to the allowable range corresponding to the critical value;

[0105] 1043. During each loading process, the time stamp of the adjustment moment, the adjusted rate value and the corresponding osmotic pressure data are synchronously added to the temperature-permeability coupling database to complete parameter update.

[0106] In the above solution, the loading rate regulation unit refers to an actuator that adjusts the loading rate of the shear box in real time according to the thermodynamic equilibrium state, and can be used to achieve the adaptive matching of the loading rate and the dynamic evolution of the temperature field. The thermodynamic equilibrium state refers to the dynamic stable relationship between the amplitude of the internal osmotic pressure fluctuation of the slurry and the external loading stress under the current temperature field, including the instantaneous change amount of the osmotic pressure and the critical threshold, including the coupling effect of the temperature gradient and the thermal conductivity, and can be used to determine whether the loading rate needs to be dynamically corrected. The ratio of the expansion characteristic to the restraint ability refers to the proportional relationship between the thermal expansion displacement of the slurry solidification interface and the restraint reaction force of the slurry mixing pile, including the data of the interface displacement sensor and the measured value of the pile reaction force, including the slope characteristic of the displacement reaction force curve, and can be used to calculate the initial setting value of the loading rate. The critical value of the osmotic pressure fluctuation amplitude refers to the threshold range that allows the instantaneous change of the osmotic pressure set according to the thermal conductivity data of the temperature field, and can be used to trigger the feedback control mechanism of the loading rate. The feedback control mechanism refers to the closed-loop control logic that dynamically corrects the loading rate based on the real-time data of the osmotic pressure fluctuation amplitude, including the proportional integral differential algorithm and the error compensation module, including the calculation of the deviation amount and the generation of the adjustment signal, and can be used to maintain the stability of the osmotic pressure fluctuation amplitude and the controllability of the test process.

[0107] In the embodiment of the present application, first, based on the prediction result of the penetration path direction including the spatial distribution and extension direction of the penetration-dominated region in step 1041, the laser displacement sensor is used to measure the thermal expansion displacement of the slurry solidification interface in real time, and at the same time, the pressure sensor is used to collect the restraint reaction force of the slurry mixing pile on the interface deformation. Subsequently, the ratio of the expansion displacement to the restraint reaction force is calculated, and this ratio is input into the pre-trained machine learning model. This model is established based on historical test data and can map the non-linear relationship between the expansion restraint ratio and the initial loading rate, and finally output the initial loading rate setting value. This setting value is transmitted to the hydraulic drive module of the loading rate regulation unit through the control signal and used as the starting parameter of the shear test.

[0108] Then, at the initial loading rate, the osmotic pressure data inside the paste-like slurry is continuously collected by the fiber optic sensor at a fixed sampling frequency, and the amplitude of the osmotic pressure fluctuation within a fixed time is calculated. The amplitude of the osmotic pressure fluctuation is compared with the critical value under the thermodynamic equilibrium state of the current temperature field. If the amplitude of the osmotic pressure fluctuation exceeds the threshold, the feedback control mechanism is triggered. Specifically, the PID algorithm is used to calculate the correction rate according to the real-time deviation between the amplitude of the osmotic pressure fluctuation and the critical value under the thermodynamic equilibrium state of the previous temperature field. The specific calculation formula is as follows: , where is the above real-time deviation, , and They are the proportional gain, integral gain, and derivative gain respectively. For example, when the osmotic pressure fluctuation amplitude is 15 kPa, the real-time deviation e(t) is 5 kPa. After PID calculation, the loading rate is corrected from 0.1 mm / s to 0.08 mm / s. This correction signal adjusts the output of the hydraulic drive module in real time through the servo valve control system until the osmotic pressure fluctuation amplitude returns to the allowable range, ensuring the dynamic adaptation of the loading process to the thermodynamic state.

[0109] Finally, after each adjustment of the loading rate, the timestamp at the adjustment moment, the corrected loading rate value, and the corresponding instantaneous osmotic pressure value are automatically recorded through step 1043. These parameters are dynamically bound to the spatial coordinates and temperature gradient data in the temperature-permeation coupling database through the data interface to form a multi-physical field dataset with spatio-temporal correlation, completing the data update. For example, the osmotic pressure data of 12 kPa corresponding to the timestamp is associated and stored with the temperature gradient data of 25 °C / m and the pore connectivity curve peak position coordinates at the same moment, providing a high-resolution data basis for subsequent thermal-mechanical coupling analysis.

[0110] In practical applications, based on the osmotic path prediction result, the thermal expansion displacement of the slurry interface measured by a laser displacement sensor is 0.6 mm, and the pile body restraint reaction force of 180 kPa is obtained by combining with a pressure sensor. The expansion restraint ratio of 0.0033 is calculated and input into the pre-trained support vector regression model to output the initial loading rate of 0.12 mm / s. After the loading starts, the osmotic pressure fluctuation is monitored by an optical fiber sensor at a frequency of 10 Hz. When the temperature gradient rises to 30 °C / m, the osmotic pressure fluctuation amplitude suddenly increases from 5 kPa to 18 kPa, exceeding the critical value of 15 kPa, triggering the PID feedback control mechanism. The proportional gain in the PID feedback control mechanism is 0.5, the integral gain is 0.08, and the derivative gain is 0.15. A correction amount of 1.5 mm / s is generated according to the deviation of 5 kPa. The integral term accumulates the historical deviation of 45 kPa·s in 30 seconds to compensate 3.6 mm / s. The derivative term adds a correction of 0.45 mm / s based on the change rate of the osmotic pressure fluctuation amplitude of 3 kPa / s. The total correction amount of 5.55 mm / s dynamically adjusts the loading rate from 0.12 mm / s to 0.065 mm / s, causing the osmotic pressure fluctuation amplitude to drop to 13 kPa. After each adjustment, the system automatically records the timestamp, correction rate, and osmotic pressure, and dynamically binds them to the current temperature gradient and pore connectivity curve peak coordinates, updating them to the database. Through the closed-loop iteration of 120 groups of spatio-temporal correlation data, the evolution law of the osmotic path under the temperature field is mapped.

[0111] The overall solution of the above 104 constructs a slurry curing loading regulation system with coordinated control of temperature field and mechanical field through thermodynamic equilibrium adaptive regulation and dynamic data update technology. Based on the prediction results of the penetration path, a calculation model of the thermal expansion coefficient ratio is used to determine the initial loading rate, and dynamic loading optimization is achieved through the feedback mechanism of the osmotic pressure fluctuation amplitude. This technology breaks through the limitations of traditional static loading. By comparing the change amount of the real-time monitored osmotic pressure with the critical value of the temperature field, a closed-loop feedback control model is established to dynamically correct the rate parameters during the loading process, so that the evolution of thermal stress matches the thermodynamic state of slurry curing in real time. By synchronously updating the temperature-permeation coupling database, the adjusted loading parameters are stored in spatio-temporal association with the corresponding osmotic pressure data, forming a dynamic mapping relationship library of loading response. This solution realizes the coordinated regulation of mechanical loading and temperature-permeation effects, can reveal the superposition influence mechanism of the thermo-hydro-mechanical coupling field on the bearing performance of the pile-soil interface, provides dynamic data support for optimizing the loading time sequence design of the grouting body curing process and improving the long-term stability of engineering structures, and significantly enhances the accuracy and engineering applicability of geotechnical engineering numerical simulation under complex working conditions.

[0112] The following is a complete example for steps 101 to 104. As Figure 2 shown, first, after the paste-like slurry is poured onto the surface of the slurry mixing pile, a distributed optical fiber sensor with a diameter of 0.5 mm is immediately wound around the pile-slurry interface with a pitch of 30 cm and a spiral radius of 5 cm, covering a depth range of 2 m. Synchronously, PT1000 platinum resistance temperature sensors are embedded every 50 cm on the side of the slurry mixing pile at the interface, and sensors of the same type are installed at the corresponding positions on the surface of the paste-like slurry, forming 32 groups of axially symmetrically distributed temperature monitoring points. After the curing starts, the osmotic pressure at the interface is continuously measured by the optical fiber sensor at a sampling rate of 10 Hz, and the wavelength shift amount is converted into a pressure value by a grating demodulator. The temperature sensor group synchronously collects the temperatures of each measuring point at a frequency of 1 Hz, and calculates the axial temperature gradient using the temperature difference between adjacent axial measuring points and the radial gradient using the radial temperature difference. The data acquisition system achieves timestamp alignment through a GPS clock, with an error less than 1 ms. The osmotic pressure data stream and the temperature gradient data are matched in time windows with each 5 seconds as a data segment, forming a coupled data unit containing spatial coordinates, timestamps, osmotic pressure values, axial gradients, and radial gradients. The data storage adopts a hierarchical structure. The first layer is indexed by depth intervals with each 0.5 m as a partition, and the second layer is organized by time series with each 10 minutes as a sub-library, finally generating a temperature-permeation coupling database with a capacity of about 120 GB.

[0113] Next, a customized triaxial shear box device is adopted. The water temperature in the top and bottom circulating water chambers is regulated by a constant temperature pump controlled by an independent PID. According to the axial temperature gradient distribution in the database during the time period of 1200 - 1500 s, with the top at 45°C and the bottom at 35°C, the temperature of the top circulating water is set to 47°C and the bottom to 33°C. After 30 minutes of stabilization, a simulated environment with a vertical gradient of -5°C / m is formed inside the shear box. A paste-like slurry sample with a diameter of 30 cm and a height of 50 cm and the same ratio is injected into the shear box. An ultrasonic transducer array with a frequency of 1 MHz is used to perform a spiral scan along the axial direction of the sample with a pitch of 10 cm. After each complete scan, the amplitude attenuation at the receiving end is recorded to calculate the local porosity. After continuously scanning 15 cross-sections, the porosity data of each layer is three-dimensionally interpolated with a spatial resolution of 2 mm³ to generate a pore connectivity curve, and the position of the peak pore density is marked as a porosity of 22% at a depth of 1.2 m. The osmotic pressure distribution data corresponding to the current simulation time period in the database is extracted. At t = 1350 s, the peak osmotic pressure at the interface is 1.2 MPa, and the spatial coordinates of the pore connectivity curve are mapped with the osmotic pressure data. When the spatial coincidence degree between the peak pore density area and the high osmotic pressure area exceeds 80%, a pore-osmotic pressure correlation index is established and stored in the pore-osmotic pressure mapping relation table in the database.

[0114] Then, a three-dimensional thermo-mechanical coupling model is established based on the COMSOL Multiphysics platform, and the mesh size is set to 5 mm × 5 mm × 10 mm. The area with a porosity greater than 15% in the pore connectivity curve is defined as the permeability-dominated area, and its boundary coordinates are extracted as the heat flux input boundary of the model. The measured slurry thermal conductivity data is loaded and spatially bound with the temperature distribution in the database. The initial heat flux density at the interface is set to 500 W / m², and by solving the unsteady heat conduction equation, the temperature field distribution is calculated. The simulation results are compared with the target data in the database, and it is found that the simulated temperature in the middle section of the interface at a depth of 0.8 - 1.5 m is 2.3°C higher than the measured value. The Levenberg-Marquardt optimization algorithm is used to adjust the boundary heat flux. After 5 iterations, a heat flux density of 423 W / m² is input into the model, and the final temperature field error is reduced to 0.8°C. In the reconstructed temperature field, a continuous area with a temperature change rate greater than 0.5°C / min at the interface is extracted. The length of the continuous area is 1.2 m, and the azimuth range is 120° - 160°. The continuous area and the permeability-dominated area with a porosity greater than 15% are subjected to spatial superposition analysis. When the included angle between the extension directions of the two is less than 15°, this area is determined as a candidate permeation path area. When the weight coefficient is 0.7, according to the weighted calculation of pore density, the interval with a depth of 1.1 - 1.4 m and an azimuth of 130° - 150° is selected as the permeation path direction with the highest priority, and the main permeation direction is predicted to be 15° south by southeast.

[0115] Finally, according to the prediction results of the seepage path, the expansion displacement of the slurry curing interface under heating conditions and the restraint reaction force of the slurry mixing pile on the interface deformation are measured in real time. The ratio of the expansion displacement to the restraint reaction force is input into the machine learning model to map the non-linear relationship between the expansion restraint ratio and the initial loading rate, and finally the set value of the initial loading rate is output. The initial loading rate of the shear box is set to 0.02 mm / s. After the loading starts, the amplitude of the osmotic pressure fluctuation is monitored in real time. When the amplitude of the osmotic pressure fluctuation exceeds the critical value of 0.15 MPa, for example, when the amplitude of the osmotic pressure fluctuation at t = 210 s is 0.18 MPa, exceeding the critical value of 0.15 MPa, the PID controller is triggered to adjust the loading rate. The control algorithm outputs a rate correction amount of -0.003 mm / s, reducing the rate to 0.017 mm / s, so that the amplitude of the osmotic pressure fluctuation returns to 0.13 MPa. After each rate adjustment, the current timestamp, rate value, and osmotic pressure distribution are written into the database as a new data segment. At the same time, the associated weight coefficients in the pore osmotic pressure mapping relationship table are corrected according to the new data to achieve closed-loop optimization of the parameters.

[0116] Figure 3 The figure is a schematic structural diagram of an indoor shear test system for the interface between a slurry mixing pile and a paste-like slurry provided by an embodiment of the present application, as Figure 3 shown, the system includes:

[0117] A collection module 31, during the curing process of the paste-like slurry, collects the osmotic pressure data of the pile-soil interface through the fiber optic sensing component, and at the same time arranges temperature sensing components on both sides of the pile-soil interface to obtain temperature gradient data, synchronously correlates the osmotic pressure data and the temperature gradient data in time series, and constructs a temperature-seepage coupling database;

[0118] A simulation module 32, using a shear box device with an adjustable temperature structure, generates a simulated temperature environment corresponding to the temperature gradient data in the temperature-seepage coupling database inside the shear box through circulating water temperature adjustment, and after filling the paste-like slurry in the shear box, continuously detects the internal pore structure of the specimen by using an ultrasonic scanning component, generates a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically binds the pore connectivity curve to the osmotic pressure data in the temperature-seepage coupling database;

[0119] A calculation module 33, based on finite element thermal-mechanical coupling calculation, takes the pore connectivity curve as the boundary input condition, combines the thermal conductivity data of the slurry in the simulated temperature environment, reversely deduces the temperature field morphology data at the pile-soil interface, and predicts the seepage path direction during the slurry curing stage according to the temperature field morphology data;

[0120] The regulation module 34 dynamically adjusts the loading rate control unit of the shear box according to the prediction result of the seepage path direction, so that the loading rate is adapted to the thermodynamic equilibrium state under the current temperature field, and synchronously updates the osmotic pressure data in the temperature-seepage coupling database during the loading process.

[0121] Figure 3 The indoor shear test system for the interface between the slurry mixing pile and the paste-like slurry described above can execute Figure 1 For the indoor shear test method for the interface between the slurry mixing pile and the paste-like slurry in the above-described embodiment, its implementation principle and technical effects will not be elaborated further. For the indoor shear test system for the interface between the slurry mixing pile and the paste-like slurry in the above embodiment, the specific ways in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0122] In a possible design, Figure 3 The indoor shear test system for the interface between the slurry mixing pile and the paste-like slurry in the above-described embodiment can be implemented as a computing device, such as Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0123] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42.

[0124] The processing component 42 is used for the above Figure 1 The indoor shear test method for the interface between the slurry mixing pile and the paste-like slurry in the above-described embodiment.

[0125] Among them, the processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0126] The storage component 41 is configured to store various types of data to support operations on the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0127] Of course, the computing device necessarily may also include other components, such as input / output interfaces, display components, communication components, etc.

[0128] The input / output interface provides an interface between the processing component and the peripheral interface module, and the peripheral interface module may be an output device, an input device, etc.

[0129] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0130] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0131] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry shown in the above-mentioned embodiment.

[0132] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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 application.

Claims

1. An indoor shear test method for the interface between slurry mixing piles and paste-like slurry, characterized in that Including: During the curing process of the paste slurry, osmotic pressure data at the pile-soil interface is collected through an optical fiber sensing component. Meanwhile, temperature sensing components are arranged on both sides of the pile-soil interface to obtain temperature gradient data. The osmotic pressure data and the temperature gradient data are synchronously correlated in time series to construct a temperature-osmosis coupling database; A shear box device with an adjustable temperature structure is adopted. By adjusting the temperature of the circulating water, a simulated temperature environment corresponding to the temperature gradient data in the temperature-osmosis coupling database is generated inside the shear box. After filling the paste slurry in the shear box, the ultrasonic scanning component is used to continuously detect the pore structure inside the specimen, and a pore connectivity curve is generated according to the ultrasonic attenuation characteristics, and the pore connectivity curve is dynamically bound to the osmotic pressure data in the temperature-osmosis coupling database; Based on finite element thermal-mechanical coupling calculation, taking the pore connectivity curve as the boundary input condition, combined with the thermal conductivity data of the slurry under the simulated temperature environment, the temperature field morphology data at the pile-soil interface is inversely deduced, and the penetration path direction during the slurry curing stage is predicted according to the temperature field morphology data; According to the prediction result of the penetration path direction, the loading rate control unit of the shear box is dynamically adjusted to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and the osmotic pressure data in the temperature-osmosis coupling database is synchronously updated during the loading process.

2. The method according to claim 1, characterized in that, The above-mentioned based on finite element thermal-mechanical coupling calculation, taking the pore connectivity curve as the boundary input condition, combined with the thermal conductivity data of the slurry under the simulated temperature environment, inversely deducing the temperature field morphology data at the pile-soil interface, and predicting the penetration path direction during the slurry curing stage according to the temperature field morphology data, includes: The area where the pore density in the pore connectivity curve is continuously higher than the preset threshold is defined as the penetration dominant area, and the contour coordinates of the penetration dominant area are extracted as the boundary input condition for finite element thermal-mechanical coupling calculation; In the finite element model, grid units consistent with the internal geometric dimensions of the shear box and considering the temperature-stress coupling effect are constructed, and the thermal conductivity data of the slurry corresponding to different temperature layers measured under the simulated temperature environment are loaded layer by layer into the grid units, and the thermal conductivity data loaded into each temperature layer is bound to the temperature data at the corresponding spatial position in the temperature-osmosis coupling database; According to the boundary input condition, combined with the bound thermal conductivity data in the finite element model, the temperature field morphology data at the pile-soil interface is reconstructed by inversely solving the heat conduction equation; According to the reconstructed temperature field morphology data, the continuous area with the maximum temperature change rate at the interface between the slurry mixing pile and the paste slurry is extracted, and the extension direction of the continuous area is superimposed and analyzed with the spatial distribution of the penetration dominant area, and the prediction result of the penetration path direction during the slurry curing stage is output.

3. The method according to claim 2, characterized in that The above-mentioned according to the boundary input condition, combined with the bound thermal conductivity data in the finite element model, reconstructing the temperature field morphology data at the pile-soil interface by inversely solving the heat conduction equation, includes: In the finite element model, the temperature gradient distribution in the selected time stage in the temperature-osmosis coupling database is used as the target reference data; According to the boundary input conditions, set the initial heat flux input value at the boundary of the pile-soil interface, and calculate the temperature field morphology data of each grid element in combination with the bound slurry thermal conductivity data in the finite element model; Perform spatial matching on the calculated temperature field morphology data and the target reference data, extract the temperature differences at the corresponding coordinate points on the interface between the two, generate a temperature distribution error vector, and according to the error vector, reversely adjust the heat flux input value at the boundary of the pile-soil interface, and recalculate the temperature field morphology data after adjustment; Repeat the steps of error vector extraction and heat flux input value adjustment until the spatial error amount of the temperature gradient distribution between the calculated temperature field morphology data and the target reference data on the interface is less than the preset tolerance threshold, and use the heat flux input value after the last adjustment as the fixed boundary condition, substitute it into the finite element model, and output the temperature field morphology data.

4. The method according to claim 2, wherein According to the reconstructed temperature field morphology data, extract the continuous region with the largest temperature change rate at the interface between the slurry mixing pile and the paste-like slurry, and perform superposition analysis on the extension direction of the continuous region and the spatial distribution of the penetration dominant region, and output the prediction result of the penetration path direction during the slurry curing stage, including: In the reconstructed temperature field morphology data, with each measurement point at the interface as the center, calculate the maximum change rate of the temperature value of the measurement point within a preset time, and mark the measurement points with the maximum change rate exceeding the set threshold as target change rate points; Connect adjacent target change rate points according to spatial coordinates to form a continuous region with the largest temperature change rate, and remove isolated target change rate points. For the continuous region, based on the spatial coordinate distribution of the target change rate points, determine the main extension direction of the continuous region; Extract the sub-region with pore density higher than the preset threshold from the penetration dominant region, and superimpose the spatial distribution contour of the sub-region with the main extension direction of the continuous region. When the included angle between the extension direction of the sub-region and the main extension direction of the continuous region is less than the preset angle, then determine that the sub-region is a candidate region for the penetration path; Calculate its priority weight according to the coverage length and pore density weighted value of the candidate region on the interface, and select the extension direction of the candidate region with the highest priority weight as the prediction result of the penetration path direction during the slurry curing stage.

5. The method according to claim 1, wherein According to the prediction result of the penetration path direction, dynamically adjust the loading rate control unit of the shear box to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and synchronously update the osmotic pressure data in the temperature penetration coupling database during the loading process, including: According to the prediction result of the penetration path direction, calculate the ratio between the measured expansion characteristics of the slurry curing interface when heated and the ability of the slurry mixing pile to restrict its deformation, and input the ratio into the loading rate calculation model to output the initial setting value of the shear box loading rate; Apply a load to the shear box based on the initial set value, and obtain the instantaneous change amount of the internal osmotic pressure of the paste-like slurry in real time. Calculate the fluctuation amplitude of the osmotic pressure per unit time. When the fluctuation amplitude exceeds the critical value preset for the thermodynamic equilibrium state of the current temperature field, dynamically adjust the loading rate control unit of the shear box, and adopt a feedback control mechanism to dynamically correct the loading rate so that the osmotic pressure fluctuation amplitude is limited within the allowable range corresponding to the critical value; During each loading process, synchronously add the time mark at the adjustment moment, the adjusted rate value, and the corresponding osmotic pressure data to the temperature-osmosis coupling database to complete parameter update.

6. The method according to claim 1, characterized in that, The shear box device with an adjustable temperature structure is adopted. By adjusting the temperature of the circulating water, a simulated temperature environment corresponding to the temperature gradient data in the temperature-osmosis coupling database is generated inside the shear box. After filling the paste-like slurry in the shear box, use the ultrasonic scanning component to continuously detect the pore structure inside the specimen, generate a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically bind the pore connectivity curve to the osmotic pressure data in the temperature-osmosis coupling database, including: Set circulating water chambers at the top and bottom of the shear box device. By independently controlling the water flow temperatures of the top and bottom circulating water chambers, a simulated temperature environment corresponding to the temperature gradient distribution corresponding to the selected time stage in the temperature-osmosis coupling database is formed inside the shear box along the vertical direction; After injecting the paste-like slurry into the shear box, move and emit ultrasonic pulses along a preset path on multiple cross-sections of the slurry specimen through the ultrasonic scanning component, and record the ultrasonic amplitude attenuation amounts at each receiving point; Based on the ultrasonic amplitude attenuation amounts, calculate the pore density distribution within the cross-section according to the amplitude attenuation amount differences between different receiving points on the same cross-section, and connect the pore density distributions of multiple consecutive cross-sections along the depth direction to generate a pore connectivity curve; Extract the osmotic pressure data corresponding to the time stage of the current simulated temperature environment from the temperature-osmosis coupling database, and dynamically associate the pore density peak positions in the pore connectivity curve with the spatial distribution of the osmotic pressure data to form a pore-osmotic pressure mapping relationship table and store it in the database.

7. The method according to claim 1, wherein During the curing process of the paste-like slurry, collect the osmotic pressure data at the pile-soil interface through the fiber optic sensing component, and at the same time arrange temperature sensing components on both sides of the pile-soil interface to obtain temperature gradient data. Synchronously associate the osmotic pressure data and the temperature gradient data in time series to construct a temperature-osmosis coupling database, including: When the paste-like slurry cures, wind the fiber optic sensing component spirally along the interface between the slurry mixing pile and the paste-like slurry, and embed multiple groups of temperature sensing components on the surface of the slurry mixing pile and the surface layer of the paste-like slurry on both sides of the interface respectively. Each group of temperature sensing components is distributed at intervals along the axial direction of the interface; Measure the pressure change at the interface at a fixed time interval through the fiber optic sensing component to generate osmotic pressure data. At the same time, synchronously collect the temperature values at each corresponding position point on the interface by the temperature sensing component, and generate axial and radial temperature gradient data according to the measurement differences between adjacent temperature sensing components; Pair the osmotic pressure data with the data segments having the same time stamp in the temperature gradient data, and stack the paired data segments in chronological order into multiple coupled data units containing the temperature-osmotic pressure gradient relationship. Index all the coupled data units according to the spatial coordinates and time stamps of the interface, and store them as a temperature-osmotic coupling database with a hierarchical structure.

8. An indoor shear test system for the interface between a slurry mixing pile and a paste-like slurry, characterized in that, Comprising: A collection module that, during the solidification process of the paste-like slurry, collects the osmotic pressure data of the pile-soil interface through an optical fiber sensing component, and at the same time arranges temperature sensing components on both sides of the pile-soil interface to obtain temperature gradient data. Synchronously associate the osmotic pressure data with the temperature gradient data in time series to construct a temperature-osmotic coupling database; A simulation module that uses a shear box device with a temperature-adjustable structure to generate a simulated temperature environment corresponding to the temperature gradient data in the temperature-osmotic coupling database through circulating water temperature adjustment. After filling the paste-like slurry in the shear box, use an ultrasonic scanning component to continuously detect the pore structure inside the specimen, generate a pore connectivity curve according to the ultrasonic attenuation characteristics, and dynamically bind the pore connectivity curve to the osmotic pressure data in the temperature-osmotic coupling database; A calculation module that, based on finite element thermal-mechanical coupling calculation, takes the pore connectivity curve as the boundary input condition, combines the thermal conductivity data of the slurry in the simulated temperature environment, and reversely deduces the temperature field morphology data at the pile-soil interface, and predicts the penetration path direction during the slurry solidification stage according to the temperature field morphology data; A regulation module that, according to the prediction result of the penetration path direction, dynamically adjusts the loading rate control unit of the shear box to make the loading rate adapt to the thermodynamic equilibrium state under the current temperature field, and synchronously updates the osmotic pressure data in the temperature-osmotic coupling database during the loading process.

9. A computing device, characterized in that, Comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stores a computer program, which, when executed by a computer, implements an indoor shear test method for the interface between a slurry mixing pile and a paste-like slurry as described in any one of claims 1 to 7.

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