Shield muck modified slurry grouting reinforcement effect evaluation method based on multi-data fusion

Abstract

The invention discloses a shield muck modified slurry grouting reinforcement effect evaluation method based on multi-data fusion, and belongs to the field of shield construction grouting. The method comprises the steps that during field monitoring, a monitoring section is selected according to tunnel geology and structure characteristics, and a sensor is installed; acquiring data of soil pressure, displacement and pore water pressure, calculating corresponding change rate and gradient, and analyzing change trend. According to the numerical simulation, a three-dimensional model is established based on geological exploration and the like to determine parameters, shield propulsion and grouting reinforcement are simulated, and stratum deformation, soil stress and slurry diffusion and permeation data are obtained. And in the data fusion link, the two types of data are subjected to standardization processing, grouting reinforcement effect grades are divided to determine a prior probability, a probability distribution model is established for monitoring indexes, and a posterior probability is calculated through a Bayesian formula to determine an evaluation result. According to the method, various data are integrated, the reinforcement effect can be comprehensively and accurately evaluated, a scientific decision basis is provided for shield tunnel construction, the construction quality and safety are improved, and waste soil resource utilization is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of grouting for shield tunneling construction, and particularly relates to a method for evaluating the grouting reinforcement effect of modified slurry of shield muck based on multi-data fusion. Background Art

[0002] In shield tunnel construction, grouting reinforcement is a key technology used to fill the gap between the tunnel and the surrounding rock, improve the strength and stability of the surrounding rock, and control ground settlement. However, there are many deficiencies in the existing methods for evaluating grouting reinforcement effects. Some evaluation methods rely only on a single type of data, such as only relying on on-site monitoring data or numerical simulation data, and it is difficult to comprehensively and accurately reflect the grouting reinforcement effect. Although on-site monitoring data can reflect the actual construction situation in real time, its data representativeness and accuracy are limited due to factors such as the layout of monitoring points and measurement errors; although numerical simulation data can simulate the reinforcement effect under different construction conditions to a certain extent, there are certain assumptions and simplifications in the model itself, which is different from the actual engineering situation.

[0003] Therefore, there is an urgent need for a method for evaluating grouting reinforcement effects that comprehensively considers various factors and can effectively fuse on-site monitoring data and numerical simulation data to improve the accuracy and reliability of the evaluation and provide a scientific basis for tunnel engineering construction. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for evaluating the grouting reinforcement effect of modified slurry of shield muck based on multi-data fusion, including:

[0005] Obtaining on-site monitoring data based on sensors;

[0006] Constructing a three-dimensional numerical model based on the tunnel structure size and the grouting reinforcement scheme of the modified slurry of shield muck, and performing simulations based on the three-dimensional numerical model to obtain numerical simulation data;

[0007] Preprocessing the on-site monitoring data and the numerical simulation data to obtain preprocessed data;

[0008] Determining the prior probability distribution of different grades of grouting reinforcement effects based on existing engineering experience;

[0009] Establishing a probability distribution model under different grades of grouting reinforcement effects as a likelihood function based on the data indexes of the preprocessed data;

[0010] Calculating the prior probability distribution and the likelihood function based on Bayes' formula to obtain the posterior probability of different grades of grouting reinforcement effects, and taking the grade with the largest posterior probability as the evaluation result of the grouting reinforcement effect.

[0011] Preferably, the process of obtaining on-site monitoring data based on sensors includes:

[0012] According to the geological conditions and structural characteristics of the tunnel, select monitoring sections around the shield muck grouting reinforcement area, and install earth pressure sensors, displacement sensors and pore water pressure sensors;

[0013] Collect data from the earth pressure sensors at different time points, calculate the ratio of the difference in earth pressure values between adjacent time points to the time interval to obtain the earth pressure change rate;

[0014] Based on the displacement values of specific monitoring points on the tunnel wall or the ground surface collected by the displacement sensors, calculate the displacement change rate; and record the total displacement of the monitoring points from the start of construction to the end of a certain stage.

[0015] Based on the pore water pressure sensors, monitor the pressure change of groundwater in the pores in real time, and calculate the ratio of the difference in the measured values of the pore water pressure sensors at different positions to the distance between the sensors.

[0016] Preferably, the process of obtaining numerical simulation data includes:

[0017] Based on a detailed tunnel engineering geological exploration report, combined with the tunnel structure size and the shield muck modified slurry grouting reinforcement plan, use numerical simulation software to establish a three-dimensional numerical model;

[0018] Based on the three-dimensional numerical model, simulate various working conditions during the shield machine propulsion process and the effects under different grouting reinforcement plans;

[0019] According to the numerical simulation process, extract the stratum deformation data, soil stress state data and slurry diffusion and penetration data of the numerical simulation.

[0020] Preferably, the process of obtaining preprocessed data includes: for each selected characteristic parameter, respectively count its minimum and maximum values in all preprocessed on-site monitoring data and numerical simulation data; select a linear normalization method to calculate all data according to the distribution characteristics and data nature of the characteristic parameters, and normalize the data to the 0-1 interval.

[0021] Preferably, the process of determining the prior probability distribution of the grouting reinforcement effect at different levels based on existing engineering experience includes: based on existing engineering experience and similar engineering cases, divide and determine that the grouting reinforcement effect is divided into four levels: excellent, good, qualified, and unqualified, and determine the prior probability distribution of the grouting reinforcement effect at different levels.

[0022] Preferably, the expression for calculating the prior probability distribution and the likelihood function based on Bayes' formula is:

[0023]

[0024] Among them, P(H i |D) is the posterior probability that the grouting reinforcement effect is at different levels, which is the sum of the probabilities of observing the monitoring data D under all possible levels of the grouting reinforcement effect. ΠP(D|H i ) is the product of the likelihood functions of multiple monitoring indicators. D is the monitoring data, and H i represents the i-th level hypothesis of the grouting reinforcement effect.

[0025] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computer program.

[0026] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program implements the method when executed by a processor.

[0027] Compared with the prior art, the present invention has the following advantages and technical effects:

[0028] By integrating on-site monitoring data and numerical simulation data and using the method of multi-data fusion, the present invention comprehensively and accurately evaluates the grouting reinforcement effect of shield muck modified slurry, thereby improving the construction quality and safety of shield tunnels, optimizing the shield muck resource utilization strategy, providing a scientific and reliable basis for engineering decision-making, and promoting the efficient, safe, and sustainable development of shield tunnel engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings that form a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0030] Figure 1 is a schematic flowchart of the method according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and combine the embodiments to detail this application.

[0032] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0033] Example 1

[0034] As Figure 1 shown, in this embodiment, a method for evaluating the grouting reinforcement effect of shield muck modified slurry based on multi-data fusion is provided, including:

[0035] Obtaining on-site monitoring data based on sensors;

[0036] Constructing a three-dimensional numerical model based on the tunnel structure size and the shield muck modified slurry grouting reinforcement scheme, and performing simulations based on the three-dimensional numerical model to obtain numerical simulation data;

[0037] Preprocessing the on-site monitoring data and the numerical simulation data to obtain preprocessed data;

[0038] Determining the prior probability distribution of the grouting reinforcement effect at different levels based on existing engineering experience;

[0039] Establishing a probability distribution model at different grouting reinforcement effect levels as a likelihood function based on the data indicators of the preprocessed data;

[0040] Calculating the prior probability distribution and the likelihood function based on Bayes' formula to obtain the posterior probability of the grouting reinforcement effect at different levels, and taking the level with the maximum posterior probability as the evaluation result of the grouting reinforcement effect.

[0041] Implementation of grouting reinforcement based on on-site monitoring:

[0042] Step 1: According to the tunnel geological conditions and structural characteristics, scientifically and reasonably select monitoring sections around the shield muck grouting reinforcement area. Install monitoring equipment such as earth pressure sensors, displacement sensors, and pore water pressure sensors on the selected monitoring sections to ensure that the sensors are firmly installed and accurately positioned, and can accurately reflect the actual situation of the monitoring area.

[0043] Step 2: During the shield tunneling and subsequent construction process, collect the data of the earth pressure sensor at different time points according to the set data collection frequency. Assume that at time t 1 the measured earth pressure is P 1 , and after a time interval Δt, at time t 2 the measured earth pressure is P 2 , then the earth pressure change rate is By calculating the earth pressure change rate, the change of formation stress can be grasped in real time.

[0044] Step 3: Use the displacement sensor to collect the displacement values of specific monitoring points on the tunnel wall or the ground surface at different times. Let the displacement of the monitoring point at the initial time t 0 be d 0, after a period of time T, the displacement becomes d at time t, then the displacement change rate is At the same time, the total displacement L of the monitoring point from the beginning of construction to the end of a certain stage is continuously recorded, and the deformation trend and stability of the tunnel and surrounding strata are evaluated based on the displacement change rate and total displacement.

[0045] Step 4: The pore water pressure sensor monitors the pressure change of groundwater in the pores in real time. Calculate the ratio of the difference between the values ​​measured by the pore water pressure sensors at different locations and the distance between the sensors to obtain the pore water pressure gradient. For example, the pore water pressure on one side of the tunnel is P 1 , the pore water pressure on the other side at a distance L is P 2 , then the pore water pressure gradient is By analyzing the dissipation of pore water pressure, the influence of grouting reinforcement on the groundwater seepage field is understood.

[0046] Implementation of grouting reinforcement based on numerical simulation

[0047] Step 1: Based on the detailed geological survey report of the tunnel project, the information such as the tunnel structure dimensions and the shield slag modified slurry grouting reinforcement plan are input into the numerical simulation software to establish a three-dimensional numerical model. The model should reflect the geological conditions and construction process of the actual project as accurately as possible.

[0048] Step 2: Combine the formation information obtained from the on-site investigation report, indoor test data (such as physical and mechanical tests of rock and soil, slurry performance tests, etc.) and engineering experience to determine the various parameters required for the numerical model. The physical and mechanical parameters of the surrounding rock include elastic modulus, Poisson's ratio, density, cohesion, internal friction angle, etc.; shield slag modified slurry parameters such as slurry density, viscosity, initial setting time, etc.; at the same time, clarify the boundary conditions and load conditions during the construction process, so that the initial state of the model is close to the actual engineering situation, and improve the accuracy of the simulation results.

[0049] Step 3: Simulate various possible working conditions during the advancement of the shield machine, such as different advancement speeds, cutterhead speeds, soil bin pressures, etc., as well as the effects of different grouting reinforcement schemes. In the simulation process, fully consider the interaction between the stratum and the structure, the diffusion law of the slurry in the soil and other complex factors.

[0050] Step 4: After the simulation reaches the predetermined monitoring time, the formation deformation data, soil stress state data and slurry diffusion and penetration data are extracted from the model output results.

[0051] The formation deformation data is obtained when the numerical simulation reaches the predetermined monitoring time T m Finally, the deformation data of the tunnel and surrounding strata are extracted from the model output results. 1 、Tunnel bottom uplift S 2, the distribution of the ground settlement trough S(x) For the ground settlement trough, the Peck formula is used for fitting analysis, where S max is the ground settlement displacement, i is the settlement trough width coefficient, and x is the horizontal distance from the tunnel centerline. By analyzing these deformation data, the stability of the stratum after grouting reinforcement can be intuitively understood.

[0052] The soil stress state data are the stress tensor data collected from the grouting reinforcement area and the surrounding soil, including the principal stresses σ 1 , σ 2 , σ 3 . Calculate the second invariant J2 of the stress deviator tensor and the octahedral shear stress τ oct to measure the shear stress level of the soil.

[0053]

[0054] By accurately tracking the diffusion front of the grout in the soil, using image processing technology or the marker element method to determine the area filled with grout at different times, and calculating the grout diffusion radius R g . By comparing with the theoretical diffusion radius R t calculated based on the theoretical seepage model, the actual diffusion efficiency of the grout is evaluated. The theoretical diffusion radius is derived based on the modified Darcy's law, considering the grout viscosity μ g , the grouting pressure P, the formation permeability coefficient k i and the grouting time t g and other factors. The formula is as follows: where R e is the equivalent radius considering the influence of the formation boundary, and R i is the grouting pipe radius.

[0055] At the same time, the saturation S r of the grout in the grouting area is statistically calculated, that is, the ratio of the grout volume to the total pore volume, to judge the grouting fullness degree. The formula is where V g is the grout volume, and V p is the pore volume.

[0056] Data fusion and effect evaluation implementation

[0057] Step 1: Standardize the on-site monitoring data and numerical simulation data using the normalization method. For each selected characteristic parameter (such as the change rate of soil pressure, the displacement change rate, the pore water pressure gradient, the formation deformation, the grout diffusion radius, the grout saturation, etc.), respectively, statistically calculate its minimum value x min and maximum value x max in all the preprocessed data. Select the linear normalization method according to the distribution characteristics and data properties of the characteristic parameters Calculate all the data and normalize it to the range of 0 - 1, so that different types of data have the same dimension and order of magnitude, facilitating subsequent fusion analysis.

[0058] Step 2: Based on existing engineering experience and similar engineering cases, divide and determine that the grouting reinforcement effect is divided into four grades: excellent, good, qualified, and unqualified. Determine the prior probability distribution P(H i ), where i = 1, 2, 3, 4 correspond to excellent, good, qualified, and unqualified respectively, and H i represents the i-th grade hypothesis of the grouting reinforcement effect, and P(H i ) represents the probability that the grouting reinforcement effect is at the i-th grade before considering the monitoring data.

[0059] Step 3: For each monitoring index (including various data indexes of on-site monitoring and numerical simulation), establish its probability distribution model under different grouting reinforcement effect grades as the likelihood function P(D|H i ). This function represents the probability of observing the monitoring data under the hypothesis that the grouting reinforcement effect is at the i-th grade.

[0060] For example, assume that the displacement change rate k d follows a normal distribution when the grouting reinforcement effect is good Then P(k d |H i ) can be calculated according to the normal distribution probability density function . Similarly, for other monitoring indexes, use a similar method to establish the probability distribution model under different grouting reinforcement effect grades and calculate the likelihood function.

[0061] Step 4: According to Bayes' formula calculate the posterior probability P(H i |D) of the grouting reinforcement effect at different grades under the monitoring data D. First, calculate the numerator part ΠP(DH i )P(H i ). For each i = 1, 2, 3, 4, ΠP(DH i ) is the product of the likelihood functions of multiple monitoring indexes. The denominator It is the sum of the probabilities of observing the monitoring data D under all possible grouting reinforcement effect grades. Finally, calculate P(H i |D), which is the posterior probability that the grouting reinforcement effect is excellent, good, qualified, and unqualified under the monitoring data. This posterior probability represents the probability that the grouting reinforcement effect is at the i-th grade after considering the monitoring data D.

[0062] Step 5: Compare the posterior probabilities of different levels. The level with the maximum posterior probability is the evaluation result of the grouting reinforcement effect. If P(H 1 |D) = 0.4, P(H 2 |D) = 0.25, P(H 3 |D) = 0.2, P(H 4 |D) = 0.15, then the evaluation of the grouting reinforcement effect is excellent.

[0063] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the method is implemented.

[0064] On the other hand, this embodiment also provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the method is implemented.

[0065] The above is only a preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the grouting reinforcement effect of shield slag modified slurry based on multi-data fusion, characterized in that: include: Acquire field monitoring data based on sensors; A three-dimensional numerical model is constructed based on the tunnel structure size and the shield slag modified slurry grouting reinforcement scheme, and a simulation is performed based on the three-dimensional numerical model to obtain numerical simulation data; Preprocessing the field monitoring data and the numerical simulation data to obtain preprocessed data; Based on the existing engineering experience, the prior probability distribution of grouting reinforcement effect at different levels is determined; Based on the data indicators of the preprocessed data, a probability distribution model under different grouting reinforcement effect levels is established as a likelihood function; The prior probability distribution and the likelihood function are calculated based on the Bayesian formula to obtain the posterior probabilities of the grouting reinforcement effect at different levels, and the level with the largest posterior probability is used as the evaluation result of the grouting reinforcement effect.

2. The method according to claim 1, characterized in that The process of acquiring on-site monitoring data based on sensors includes: According to the geological conditions and structural characteristics of the tunnel, select monitoring sections around the shield slag grouting reinforcement area and install soil pressure sensors, displacement sensors and pore water pressure sensors; The data of the earth pressure sensor are collected at different time points, and the ratio of the difference of the earth pressure values ​​at adjacent time points to the time interval is calculated to obtain the earth pressure change rate; Based on the displacement sensor, the displacement values ​​of specific monitoring points on the tunnel wall or ground surface at different times are collected to calculate the displacement change rate; and the total displacement of the monitoring point from the beginning of construction to the end of a certain stage is recorded; The pore water pressure sensor is used to monitor the pressure change of groundwater in the pores in real time, and the ratio of the difference between the measured values ​​of the pore water pressure sensors at different positions to the distance between the sensors is calculated.

3. The method according to claim 1, characterized in that The process of obtaining numerical simulation data comprises: Based on the detailed geological survey report of the tunnel engineering, combined with the tunnel structure dimensions and the shield slag modified grouting reinforcement plan, a three-dimensional numerical model was established using numerical simulation software; Based on the three-dimensional numerical model, various working conditions during the advancement of the shield machine and the effects of different grouting reinforcement schemes are simulated; According to the numerical simulation process, the numerically simulated stratum deformation data, soil stress state data and slurry diffusion and penetration data are extracted.

4. The method according to claim 1, characterized in that: The process of obtaining the preprocessed data includes: for each selected characteristic parameter, respectively counting its minimum value and maximum value in all preprocessed field monitoring data and numerical simulation data; selecting a linear normalization method according to the distribution characteristics of the characteristic parameters and the data properties to calculate all the data and normalize the data to the range of 0-1.

5. The method according to claim 1, characterized in that The process of determining the prior probability distribution of grouting reinforcement effects at different levels based on existing engineering experience includes: dividing the grouting reinforcement effects into four levels of excellent, good, qualified, and unqualified based on existing engineering experience and similar engineering cases, and determining the prior probability distribution of grouting reinforcement effects at different levels.

6. The method according to claim 1, characterized in that The expression for calculating the prior probability distribution and the likelihood function based on the Bayesian formula is: Among them, P(H i |D) is the posterior probability of the grouting reinforcement effect being at different levels, It is the sum of the probabilities of observing the monitoring data D under all possible grouting reinforcement effect levels, ΠP(D|H i ) is the product of the likelihood functions of multiple monitoring indicators, D is the monitoring data, H i Represents the i-th level hypothesis of the grouting reinforcement effect.

7. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 6 is implemented.

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