A method and test device for simulating deep-water rock seepage deformation

Abstract

The present invention discloses a method for simulating deep-water rock seepage deformation and a test device. The test device includes a boosting mechanism, a pressure vessel and a data acquisition system that cooperate with each other, and is used to simulate the deep-water rock seepage deformation, thereby obtaining water pressure, temperature and strain data with continuity and time series. The method of use includes fitting the obtained water pressure, temperature and strain data with continuity and time series using multiple time series models respectively, and the fitted data is used through a constructed test data prediction model to realize the analysis of the deformation law of the rock mass under the long-term action of water pressure and water temperature changes.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering, and in particular to a method and a test device for simulating deep-water rock seepage deformation. Background Art

[0002] Arch dam impoundment causes the fractured rock mass along its banks to become saturated and pressurized. This high water pressure cycle causes deformation in the fractured rock mass, leading to arch dam valley contraction. Excessive valley contraction poses a serious threat to the dam's safe operation. Research has shown that most arch dams in my country experience persistent valley contraction, with rock expansion along the banks of the river valley after impoundment being one of the primary drivers. Therefore, uncovering the mechanism of rock deformation under high water pressure has become a critical issue that needs to be addressed.

[0003] Current research on rock deformation under high water pressure typically examines rock failure processes under varying confining pressures. While high confining pressures are common, the water pressure fluctuations caused by water level fluctuations in high dam reservoirs in real-world projects are small but frequent. Large-scale rock servo testing machines can provide high confining pressures, which can lead to significant deviations in data measurements. Using other types of data acquisition systems can lead to asynchronous data collection, further complicating data processing. Furthermore, large-scale rock servo testing machines are expensive, making them unavailable to most researchers. Finally, indoor testing struggles to simulate long-term (monthly or annual) water pressure fluctuations and capture the corresponding rock deformation patterns.

[0004] Based on this, the existing test equipment and methods still have great limitations in studying deep-water rock seepage and deformation problems. The core problems are measurement errors of large equipment, high research costs and difficulty in conducting annual long-term tests. Summary of the Invention

[0005] In order to solve the current technical problems, the main purpose of the present invention is to provide a method and test device for simulating deep-water rock seepage deformation. By establishing a prediction model and combining experiments and models to analyze the deformation law of rock mass under long-term action, the data measurement error is reduced while reducing the cost of such research.

[0006] The technical solution adopted by the present invention is: a method for simulating deep-water rock seepage deformation, comprising:

[0007] S10, obtaining water pressure, temperature, and strain data of the test core to obtain a time series data set of each data;

[0008] S20, fitting the data set with multiple time series models;

[0009] S30. The fitted data is processed by the test data prediction model and the experimental results are output. The test data prediction model is defined as follows:

[0010] ;

[0011] in: The experimental data after weighted calculation for model fitting prediction is: For the time series model The fitting results are The test data at the time, For the time series model The coefficient of determination of the fitted data, is the corresponding timing model;

[0012] S40. The more accurate test data obtained by fitting is brought into the established water pressure-strain relationship for inversion to obtain the pore water pressure value in the test. The mathematical model of the water pressure-strain relationship is as follows:

[0013] ;

[0014] in: is the rock strain data, is the temperature strain, is the water pressure strain, is the coefficient of thermal expansion, is the original length, is the current temperature, is the initial temperature, is the total stress, is the interaction coefficient with water, is the pore water pressure, represents the rock bulk elastic modulus, represents the bulk elastic modulus of the rock skeleton, represents the initial porosity, represents the pore pressure difference, Indicates the current porosity.

[0015] In S1, also included:

[0016] S11: Homogenize the test cores of the same group to make them consistent in size, diameter and height;

[0017] S12: After cleaning the surface of the test core, strain gauges are attached to the top, bottom, and sides.

[0018] S13: placing the temperature sensor, the pressure sensor, and the test core with the strain gauge attached into the pressure vessel, and setting the data acquisition frequency of the dynamic data acquisition system at the same time;

[0019] S14: Injecting water of a corresponding temperature into the pressurizing mechanism according to the established test conditions, then starting the pressurizing mechanism to adjust the internal pressure of the pressure vessel, and starting the data acquisition system to record the internal pressure and temperature of the pressure vessel, as well as the strain data of the test core. After the test is completed, remove the test core, and drain the water from the pressurizing mechanism and the pressure vessel.

[0020] S15: Repeat S12-S14 to analyze the data and mechanism of deformation of the test core under deep water pressure, and obtain a time series data set of water pressure, temperature and strain data.

[0021] In S13, the strain gauge uses a full-bridge strain gauge.

[0022] A test device for simulating deep-water rock seepage deformation, used to implement the method for simulating deep-water rock seepage deformation, the test device comprising:

[0023] A pressure vessel, comprising a test tank for placing a test core, a detachable top cover provided on the top of the test tank, and a pressure boost valve and a pressure relief valve installed on the top cover;

[0024] A data acquisition system, the data acquisition system comprising a dynamic data acquisition system, and a temperature sensor, a pressure sensor, and a plurality of strain gauges provided on the test core, each of which is connected to the dynamic data acquisition system and disposed in the test tank;

[0025] The boosting mechanism is connected to the boosting valve of the pressure vessel and is used to inject pressurized water of different temperatures into the test tank.

[0026] The boosting mechanism comprises a water tank and a boosting pump which are connected to each other. The boosting pump has a pressure regulating valve block. The water outlet of the boosting pump is connected to the boosting valve through a high-pressure water pipe.

[0027] The pressure relief valve is connected to the water tank through a pressure relief pipe.

[0028] The thickness of the pressure vessel is determined by the following formula:

[0029] ;

[0030] Where, is the thickness of the pressure vessel, mm; is the maximum pressure of the pressure vessel, MPa; is the internal diameter of the pressure vessel; Allowable stress of the material used to make the pressure vessel; is the welding coefficient, which is taken as 1.0.

[0031] The top cover is also equipped with a pressure measuring device.

[0032] The top cover is connected to the test tank by bolts, and a silicone rubber plate is provided between the test tank and the top cover; three assembly holes with two ends through are opened on the top cover, and the boost valve, pressure relief valve and pressure measuring device are assembled on the assembly holes one by one.

[0033] The temperature sensor, pressure sensor and multiple strain gauges are all connected to the dynamic data acquisition system through transmission lines. The top cover is also provided with a threading hole for the transmission line to pass through, and an anti-seepage treatment is performed between the transmission line and the threading hole.

[0034] The present invention has the following beneficial effects:

[0035] The present invention utilizes the set test device to simulate the seepage deformation of deep-water rocks, and then obtains water pressure, temperature and strain data with continuity and time series. Then, based on the obtained water pressure, temperature and strain data with continuity and time series, a test data prediction model is constructed to realize the analysis of the deformation law of rock mass under the long-term action of water pressure and water temperature changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 It is a schematic diagram of the overall structure of the test device of the present invention.

[0038] Figure 2 Schematic diagram of the structure of the top cover in the test device of the present invention.

[0039] Figure 3 The figure shows the result of fitting the experimental data through LSTM, BP and BiLSTM time series models.

[0040] Figure 4 This is the result diagram of the experimental data fitted with the experimental data prediction model.

[0041] Figure identification: 11. Water tank, 12. Booster pump, 13. Pressure regulating valve block, 21. Test tank, 22. Top cover, 23. Booster valve, 24. Pressure relief valve, 25. Pressure measuring device, 26. Assembly hole, 27. Threading hole, 31. Dynamic data acquisition system, 32. Temperature sensor, 33. Pressure sensor, 34. Strain gauge, 35. Transmission line, 40. Test core, 50. High-pressure water pipe, 60. Pressure relief pipe. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] This embodiment discloses a test device and a method for using the device to simulate deep-water rock seepage deformation, and uses a method combining experiments and models to analyze the deformation law of rock mass under long-term effects.

[0044] Under water pressures of hundreds of meters, rock specimens have a high elastic modulus and remain in an elastic state. However, due to the presence of pores or cracks within the rock, and the constant fluctuation of reservoir water levels depending on the reservoir's operating conditions, the long-term effects of deep-water pressure cause high-pressure water to continuously penetrate through cracks on the rock surface into the pores, damaging the rock's pore channels and causing surface deformation. Currently, sensors used to measure rock deformation include strain gauges and strain gauges. Concrete tests can use strain gauges, while rock core tests can use strain gauges attached to the rock core surface. Strain gauges are categorized by connection method: full-bridge, half-bridge, and quarter-bridge, with corresponding accuracy and anti-interference performance decreasing. Since the entire test is performed underwater, full-bridge strain gauges are used. Rock deformation is also affected by temperature and environmental parameters. Therefore, measuring rock core strain also requires measuring the stress and temperature parameters at the rock core location. Therefore, the regulation of stress and temperature parameters must be fully considered when designing the test apparatus and conducting the test.

[0045] like Figure 1-2The test device for simulating deep-water rock seepage deformation shown in the figure can measure water pressure and temperature data with time series and rock strain data in the test of simulating deep-water rock seepage deformation, and the obtained test data are all continuous. The specific structure includes: a boosting mechanism, the boosting mechanism includes a water tank 11 and a boosting pump 12 connected to each other, the boosting pump 12 has a pressure regulating valve block 13; a pressure vessel, the pressure vessel includes a test tank 21 for placing a test core 40, the top of the test tank 21 is provided with a detachable top cover 22, the top cover 22 is equipped with a boosting valve 23 and a pressure relief valve 24, the water outlet end of the boosting pump 12 is connected to the boosting valve 23 through a high-pressure water pipe 50, and the pressure relief valve The door 24 is connected to the water tank 11 through the pressure relief pipe 60; the data acquisition system includes a dynamic data acquisition system 31, and a temperature sensor 32, a pressure sensor 33 and a plurality of strain gauges 34 provided on the test core 40, which are respectively connected to the dynamic data acquisition system 31 and placed in the test tank 21; the boosting mechanism is used to adjust and control the pressure inside the pressure vessel during the experiment; the data acquisition system is used to collect temperature and pressure data of the environment in which the test core 40 is located during the experiment, as well as deformation data of the test core 40 under temperature and pressure data; during the experiment, the pressure vessel is used to test the test core 40, and it cooperates with the boosting mechanism to form different test environments.

[0046] During the specific implementation process, the boosting mechanism provides stable high water pressure to the pressure vessel. The pressure vessel is pressurized by the internal water pressure of the pressure relief valve 24 through the pressure relief pipe 60. The high-pressure water inside the pressure vessel penetrates into the test core 40 through the surface cracks of the test core 40, inducing slight deformation of the surface of the test core 40. The deformation is monitored by the strain gauge 34 attached to the surface of the test core 40 and reflected in the form of strain data. The pressure data and temperature data of the ambient water in which the test core 40 is located are detected by the pressure sensor 33 and the temperature sensor 32 respectively. The surface strain data of the test core 40 and the ambient water pressure data and temperature data of the test core 40 are collected simultaneously by the dynamic data acquisition system 31. Subsequently, the LSTM, BP and BiLSTM time series models are used to fit and predict the test data respectively. Finally, the deformation mode and deformation mechanism of the test core 40 under the conditions of long-term deep-water pressure changes are analyzed through the stress, strain and temperature data processed by the model.

[0047] The thickness of the above pressure vessel is determined by the following formula:

[0048] ;

[0049] Where, is the thickness of the pressure vessel, mm; is the maximum pressure of the pressure vessel, MPa; is the internal diameter of the pressure vessel; Allowable stress of the material used to make the pressure vessel; is the welding coefficient, which is taken as 1.0.

[0050] As a preferred solution of the above embodiment, the top cover 22 is also equipped with a pressure measuring device 25. During the experiment, the pressure measuring device 25 can read the internal pressure of the pressure vessel in real time so that the boosting mechanism can maintain the internal pressure of the pressure vessel stable at a predetermined working condition.

[0051] As a preferred solution of the above embodiment, the top cover 22 is connected to the test tank 21 by bolts, and a silicone rubber plate is provided between the test tank 21 and the top cover 22 to ensure the airtightness between the top cover 22 and the test tank 21. A plurality of assembly holes 26 with through ends are provided on the top cover 22, and the boost valve 23, the pressure relief valve 24 and the pressure measuring device 25 are assembled one by one on the plurality of assembly holes 26.

[0052] As a preferred solution of the above embodiment, the temperature sensor 32, the pressure sensor 33 and the multiple strain gauges 34 are all connected to the dynamic data acquisition system 31 through the transmission line 35 to ensure the stability of data transmission. Specifically, a threading hole 27 for the transmission line 35 to pass through is also provided on the top cover 22, and an anti-seepage treatment is performed between the transmission line 35 and the threading hole 27.

[0053] Based on the above-mentioned test device for simulating deep-water rock seepage deformation, the test method includes the following steps:

[0054] S1: Homogenize the test cores 40 of the same group to make them consistent in size, diameter, and height to reduce external factors that may affect the deformation of the test cores 40. Specifically, the test cores 40 may be natural cores. Since the shapes and sizes of natural cores vary during the mining process, the sizes of the test cores of the same group must be consistent during the test. A rock cutter and grinder may be used to homogenize the natural cores to ensure that the diameters of the test cores of the same group are highly consistent. Concrete test blocks may also be used as the test cores 40, but a uniform mold must be used when preparing the concrete.

[0055] S2: After cleaning the surface of the test core 40, strain gauges 34 are attached to the top, bottom, and circumferential sides to measure the axial, radial, and circumferential deformation of the test core 40 under high water pressure changes. Strain gauges can be divided into full-bridge, half-bridge, and quarter-bridge connections according to different connection methods, with corresponding accuracy and anti-interference performance from high to low. Considering that the entire test is underwater and requires high accuracy, full-bridge strain gauges are used as strain gauges 34;

[0056] S3: Place the temperature sensor 32 and the pressure sensor 33, as well as the test core 40 with the strain gauge 34 attached, into the pressure vessel, set the data acquisition frequency of the dynamic data acquisition system 31, and check the connection status of the test equipment;

[0057] S4: According to the established test conditions, water of a corresponding temperature is injected into the water tank 11, and then the pressurizing mechanism is activated to adjust the internal pressure of the pressure vessel, and the data acquisition system is activated to begin recording the internal pressure and temperature of the pressure vessel, as well as the strain data of the test core 40. After the test is completed, the test core 40 is removed, and the water in the pressurizing mechanism and the pressure vessel is drained. During this experiment, the dynamic data acquisition system 31 obtains continuous and time-series water pressure and temperature data, as well as strain data of the test core 40;

[0058] S5: Repeat S2-4 to analyze the data and mechanism of deformation of the core under deepwater pressure. The purpose of repeatedly obtaining data is to compare the test. Generally speaking, the test is full of errors. Multiple tests can reduce the errors and achieve more accurate test conclusions. Specifically, since the above test process is the same, but the loading conditions are inconsistent, it is necessary to repeat the experiment to obtain data to reduce the test error.

[0059] S6: The obtained series of test data of the test core 40 are respectively subjected to regression fitting using the time series models LSTM, BP and BiLSTM. The fitted data are processed by the test data prediction model and then the experimental results are output, thereby analyzing the deformation law of the test core 40 under the change of water pressure.

[0060] Specifically, the LSTM described above is a special type of recurrent neural network (RNN) that controls the flow of information through three main gates: input gate, forget gate, and output gate. It excels at processing time series and sequence data, and is particularly effective in modeling the context of long sequences. LSTM can effectively avoid the vanishing or exploding gradient problems encountered by traditional RNN models during training. The model function at any time is expressed as follows:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ; (1)

[0067] In formula (1), express Controls whether information flows into cell units at all times; The flag controls whether the information in the cell unit at the previous moment is accumulated into the cell unit at the current moment; Control the current Whether the information in the cell at this moment flows into the current hidden state middle; Represents a long-term memory unit, representing the memory of the neuron state, which enables the LSTM unit to save, read, reset and update long-distance historical information; Indicates the current memory unit; 、 、 、 The weight matrix of the forget gate, input gate, output gate, and computational unit state; 、 、 、 Represent the bias terms of the forget gate, input gate, output gate and computing unit state respectively; and There are two activation functions respectively.

[0068] LSTM, through its unique gating structure, maintains good stability when processing time series data and can capture long-term dependency features. LSTM is widely used in various time series prediction tasks.

[0069] The above-mentioned BP neural network is a feedforward artificial neural network. Its basic structure consists of an input layer, a hidden layer, and an output layer. It optimizes the network weights through the back-propagation algorithm to minimize the error between the output and the target. It focuses on the forward propagation and back-propagation processes of sequence data, which respectively calculate the predicted value and use the error to correct the prediction model parameters.

[0070] Forward pass:

[0071] Assume a single-layer RNN network model where the input is time series data , the output is the predicted value , and use the activation function ,include or .

[0072] Weights from input layer to hidden layer:

[0073] ;(1)

[0074] Weights from hidden layer to output layer:

[0075] ;(2)

[0076] Where, is the current time step The hidden layer state of is the weight matrix input to the hidden layer; is the input of the current time step; is the bias term of the hidden layer; is the output of the model (i.e., the predicted value); is the weight matrix from the hidden layer to the output layer; is the bias term of the output layer.

[0077] Backward Propagation:

[0078] Output layer gradient:

[0079] ;(3)

[0080] Hidden layer gradient:

[0081] ;(4)

[0082] Gradient of the input layer:

[0083] ;(5)

[0084] Parameter update:

[0085] ;

[0086] ;

[0087] ;

[0088] ; (7)

[0089] Where, is the output layer error; is the error of the hidden layer; is the activation function in the current hidden state The derivative at ; Represents the current time step input; Represents the learning rate.

[0090] The BP time series model optimizes neural network weights through the backpropagation algorithm, effectively fitting nonlinear patterns in time series and making it suitable for a wide range of forecasting tasks. However, due to its limitations in modeling long-term dependencies, it often requires integration with other network structures (such as LSTM) to fully leverage its advantages in complex time series problems. In practical applications, the BP network is often used as a basic forecasting tool in conjunction with other models.

[0091] BiLSTM is a recurrent neural network (RNN) based on the LSTM architecture, designed for processing and predicting time series data. Unlike traditional unidirectional LSTMs, BiLSTMs learn information simultaneously in both directions (forward and backward) of time series data, enabling them to capture more contextual information. This is particularly effective for data with long-term dependencies.

[0092] BiLSTM data passes through two LSTM networks simultaneously, one is a forward (from front to back) LSTM, the other is a backward (from back to front) LSTM, and finally their outputs are combined. It is an extension of the standard LSTM, which outputs the states of the forward LSTM and the backward LSTM at the same time, and finally their results are spliced ​​or weighted merged. Assume that the forward output is , the reverse output is , the final hidden state is usually formed by concatenating the outputs of the forward and backward LSTMs:

[0093] ;(6)

[0094] Where, Indicates the forward LSTM at time step Output; Represents the reverse LSTM at time step Output.

[0095] The BiLSTM time series model, by combining forward and backward learning capabilities, demonstrates significant advantages in many practical time series forecasting and classification tasks. By simultaneously considering both past and future information, BiLSTM can provide more accurate forecasts, particularly in scenarios with complex temporal dependencies.

[0096] Three models were selected to simulate and predict the above series of data obtained from the experiment, and the model determination coefficient R2 was used as the weight to perform weighted average on the fitted prediction data to obtain the experimental data prediction model:

[0097] ;(7)

[0098] Where, Represents the experimental data after model fitting prediction weighted calculation; Representation Model Fitting results Test data at the moment; Representation Model The coefficient of determination of the fitted data, It is a time series model LSTM, BP, or BiLSTM.

[0099] Calculated by formula (7) That is, the test data prediction model outputs the test results. The method is also applicable to model processing of water pressure, temperature, and strain data. According to the corresponding relationship between the three, the deformation law of rock under water pressure changes can be analyzed.

[0100] Finally, the accuracy of the test data was further improved based on the coefficient of determination in the model evaluation index. The specific method is as follows: the stress data obtained from the test was selected as a sample, and the LSTM, BP and BiLSTM time series models were used for fitting and prediction respectively. The fitting results Figure 3 As shown in Table 1:

[0101] Table 1 is the model evaluation table

[0102]

[0103] Depend on Figure 3 It can be seen that the gap between the model prediction data and the true value is small, and the prediction results are good. From the model evaluation table 1, it can be seen that the overall prediction accuracy of the model is high and the feasibility is strong.

[0104] Depend on Figure 3 , Table 1 and the experimental data calculated by formula (7) are the results of fitting and prediction by the experimental data prediction model. Figure 4 As shown, the corresponding temperature and strain data can be calculated by the same logic, and long-term predictions can be made to obtain the deformation mode and deformation mechanism of the core under water pressure.

[0105] The effects of temperature and water pressure on rock deformation are particularly important for fitting and predicting the experimental results.

[0106] 1. Effect of temperature on rock deformation:

[0107] Temperature changes have a significant impact on the mechanical properties of rocks. Rocks exhibit different deformation behaviors at different temperatures, mainly including the following:

[0108] ① Thermal expansion: When the temperature rises, the minerals in the rock expand. This expansion can increase the rock's volume, causing internal stress and even the formation of cracks. Generally, rising temperatures cause the rock's elastic modulus to decrease, increasing its ability to deform.

[0109] ② Degradation and softening: When the temperature reaches a certain level, the mineral composition of the rock may degrade or soften. For example, at high temperatures, certain minerals (such as quartz) may undergo changes in their crystal structure, causing the rock to become more deformable.

[0110] Common rocks will only degrade and soften when the temperature reaches above 800℃. For this test and the corresponding actual project, the ambient temperature of the rock is 30-50℃. At this temperature, the rock will not degrade or soften. Therefore, the effect of temperature on rock deformation only includes thermal expansion. The thermal expansion formula is

[0111] ;(8)

[0112] Where, is the change in rock length, is the coefficient of thermal expansion, is the original length, is the current temperature, is the initial temperature.

[0113] The change in rock length can be converted into strain change according to the strain formula principle:

[0114] ;(9)

[0115] Where, is the temperature strain component.

[0116] 2. Effect of water pressure on rock deformation:

[0117] Water pressure also has a significant effect on rock deformation, especially when the rock is in a groundwater-saturated environment. The effects of water pressure on rock deformation are mainly reflected in the following aspects:

[0118] ① Pore water pressure: When the pores of a rock are filled with water, the water pressure exerts additional stress on the rock, especially when the rock is in a compressed state. The water pressure pushes the water in the rock pores, causing the rock to expand and deform.

[0119] ② The influence of water pressure on pores: The presence of water in rock pores can change the rock's microstructure. For example, water can penetrate into microcracks in the rock, causing them to expand further, thereby reducing the rock's strength and making it more susceptible to deformation.

[0120] ③ Lubricating effect of water: Water acts as a lubricant in the cracks of rocks, making the cracks easier to expand and thus promoting the plastic deformation of rocks.

[0121] The effect of pore water pressure on rock strength and deformation can be described by the effective stress principle of water pressure:

[0122] ;(10)

[0123] Where, is the effective stress, is the total stress, is the interaction coefficient with water, is the pore water pressure.

[0124] The influence of water pressure on pores and the lubrication of water will cause changes in rock physical parameters, directly affecting the rock bulk modulus:

[0125] ;(11)

[0126] Where, represents the rock bulk elastic modulus, represents the bulk elastic modulus of the rock skeleton, represents the initial porosity, represents the pore pressure difference, Indicates the current porosity.

[0127] The mathematical formula for the effect of water pressure change on rock strain is:

[0128] ;(12)

[0129] Where, is the water pressure strain component.

[0130] The total strain formula of rock under water pressure and temperature changes is:

[0131] ;(13)

[0132] In this formula, 、 、 、 It is the essential parameter of rock material and can be obtained by experience. is the rock porosity, which can be measured by instruments. 、 、 、 Can be measured in an experiment, that is, the independent variable.

[0133] Since the actual pore water pressure of rock cannot be measured during the test, we can first invert the test data to calculate the actual pore water pressure change curve of the rock during the test, and then extend this curve according to the regularity. Finally, based on the pore water pressure, temperature, and external water pressure, we can calculate the change pattern of the rock under the influence of long-term water pressure cycle.

[0134] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for simulating deepwater rock seepage deformation, characterized in that: include: S10, obtaining water pressure, temperature and strain data of the test core (40) to obtain a time series data set of each data; S20, fitting the data set with multiple time series models; S30. The fitted data is processed by the test data prediction model and the experimental results are output. The test data fitting model is defined as follows: ； in: The experimental data after weighted calculation for model fitting prediction is: For the time series model The fitting results are The test data at the time, For the time series model The coefficient of determination of the fitted data, is the corresponding timing model; S40. The test data obtained by fitting is brought into the established water pressure-strain relationship for inversion to obtain the pore water pressure value in the test. The mathematical model of the water pressure-strain relationship is as follows: ； in: is the rock strain data, is the temperature strain, is the water pressure strain, is the coefficient of thermal expansion, is the original length, is the current temperature, is the initial temperature, is the total stress, is the interaction coefficient with water, is the pore water pressure, represents the rock bulk elastic modulus, represents the bulk elastic modulus of the rock skeleton, represents the initial porosity, represents the pore pressure difference, Indicates the current porosity.

2. The method for simulating deep-water rock seepage deformation according to claim 1, characterized in that: The time series models include LSTM models, BP models and BiLSTM models.

3. The method for simulating deep-water rock seepage deformation according to claim 1, characterized in that: In S1, also included: S11: homogenize the test cores (40) of the same group to make them consistent in size, diameter and height; S12: After cleaning the surface of the test core (40), strain gauges (34) are attached to the top, bottom, and circumference of the core; S13: placing the temperature sensor (32) and the pressure sensor (33), as well as the test rock core (40) with the strain gauge (34) attached thereto, into the pressure vessel, and simultaneously setting the data acquisition frequency of the dynamic data acquisition system (31); S14: according to the established working conditions of the test, water of a temperature corresponding to the initial working conditions is injected into the pressurizing mechanism, and then the pressurizing mechanism is started to adjust the internal pressure of the pressure vessel, and the data acquisition system is started to record the pressure and temperature changes inside the pressure vessel, as well as the strain data of the test core (40). After the test is completed, the test core (40) is taken out, and the water in the pressurizing mechanism and the pressure vessel is discharged; S15: Repeat S12-S14 to analyze the data and mechanism of deformation of the test core (40) under deep water pressure, and obtain a time series data set of water pressure, temperature and strain data.

4. The method for simulating deep-water rock seepage deformation according to claim 3, characterized in that: In S13, the strain gauge (34) adopts a full-bridge strain gauge.

5. A test device for simulating deep-water rock seepage deformation, characterized in that: For implementing the method for simulating deepwater rock seepage deformation as described in any one of claims 1 to 3, the test device comprises: A pressure vessel, the pressure vessel comprising a test tank (21) for placing a test core (40), a detachable top cover (22) being provided on the top of the test tank (21), and a pressure boosting valve (23) and a pressure relief valve (24) being mounted on the top cover (22); A data acquisition system, the data acquisition system comprising a dynamic data acquisition system (31), a temperature sensor (32), a pressure sensor (33), and a plurality of strain gauges (34) provided on the test core (40), each connected to the dynamic data acquisition system (31) and disposed in the test tank (21); A pressure boosting mechanism is connected to the pressure boosting valve (23) of the pressure vessel and is used to inject pressurized water into the test tank (21).

6. The test device for simulating deep-water rock seepage deformation according to claim 5, characterized in that: The boosting mechanism comprises a water tank (11) and a boosting pump (12) connected to each other. The boosting pump (12) has a pressure regulating valve block (13). The water outlet of the boosting pump (12) is connected to the boosting valve (23) via a high-pressure water pipe (50).

7. The test device for simulating deep-water rock seepage deformation according to claim 6, characterized in that: The pressure relief valve (24) is connected to the water tank (11) via a pressure relief pipe (60).

8. The test device for simulating deep-water rock seepage deformation according to claim 5, characterized in that: The thickness of the pressure vessel is determined by the following formula: ； Where, is the thickness of the pressure vessel, mm; is the maximum pressure of the pressure vessel, MPa; is the internal diameter of the pressure vessel; Allowable stress of the material used to make the pressure vessel; is the welding coefficient, which is taken as 1.

0.

9. The test device for simulating deep-water rock seepage deformation according to claim 5, characterized in that: The top cover (22) is also equipped with a pressure measuring device (25); the top cover (22) is connected to the test tank (21) by bolts, and a silicone rubber plate is provided between the test tank (21) and the top cover (22); three assembly holes (26) with two ends through are opened on the top cover (22), and the boost valve (23), the pressure relief valve (24) and the pressure measuring device (25) are assembled one by one on the assembly holes (26).

10. The test device for simulating deep-water rock seepage deformation according to claim 5, characterized in that: The temperature sensor (32), the pressure sensor (33) and the plurality of strain gauges (34) are connected to the dynamic data acquisition system (31) via transmission lines (35). A threading hole (27) for the transmission line (35) to pass through is also provided on the top cover (22), and an anti-seepage treatment is performed between the transmission line (35) and the threading hole (27).

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