Geotechnical engineering earthquake sand liquefaction discrimination method and system

Abstract

The invention provides a geotechnical engineering earthquake sand liquefaction discrimination method and system, and relates to the technical field of geotechnical engineering, and the method comprises the steps: collecting site basic data (a site parameter table, earthquake acceleration and pore pressure time history data); establishing a preliminary numerical model and applying boundary conditions to correct the preliminary numerical model into a complete numerical model; the pore pressure ratio and the shear strain distribution are obtained through nonlinear dynamic analysis, and the liquefaction risk is comprehensively evaluated; a key soil layer observation scheme is made according to the prediction result, field verification is carried out, and a final judgment result is obtained by combining actual measurement and numerical simulation. According to the method, numerical analysis and actual measurement data are combined, the sand liquefaction risk is rapidly and accurately evaluated, and field testing and observation are guided.

Description

Technical Field

[0001] The invention relates to the technical field of geotechnical engineering, and in particular to a method and system for distinguishing liquefaction of sand in geotechnical engineering earthquakes. Background Art

[0002] In the field of geotechnical engineering, sand liquefaction is one of the most common and destructive geological phenomena in earthquake disasters. The so-called "liquefaction" refers to the fact that the pore water pressure of saturated sand increases significantly and approaches or is equal to the effective stress under the action of earthquake power or other vibrations, causing the sand to instantly change from a solid bearing medium to a liquid-like flow state and lose its bearing capacity. After liquefaction occurs, it usually causes a series of serious engineering disasters such as large deformation of the foundation, settlement or even overturning of buildings, surface sandblasting, and floating of pipelines, posing a great threat to urban infrastructure, people's livelihood facilities, and the geological environment. Therefore, how to accurately determine whether sand has a risk of liquefaction has always been an important research topic and practical difficulty in the field of geotechnical engineering.

[0003] In terms of traditional methods, engineers and researchers at home and abroad mainly use the following approaches to identify liquefaction: (1) Empirical criteria (based on specifications or empirical formulas) are mostly based on standard penetration test (SPT), cone penetration test (CPT) or shear wave velocity (Vs) as indicators, and compared with corresponding specifications or empirical formulas (such as Seed's law) to quantify the liquefaction vulnerability of soil layers. This type of method has good applicability for general sites, but in deep heterogeneous soils or special strata (such as silt, silt sand or sand and gravel interlayers, etc.), the results often do not match the actual situation on site.

[0004] (2) Shaking table test and indoor dynamic triaxial test, by applying cyclic loads to reshaped or original soil samples and observing the development of pore water pressure and deformation, the liquefaction potential under actual earthquake action can be predicted. However, indoor tests are affected by the degree of soil disturbance, sample preparation methods, test conditions and specimen size effects, which can easily lead to differences between the actual geological environment and dynamic load conditions on site; shaking table tests are difficult to be widely used in engineering projects due to their high cost and limited test sites.

[0005] (3) On-site dynamic penetration and observation methods: Some projects will deploy sensor monitoring instruments in earthquake-prone areas or major construction projects to observe the development of pore pressure, surface settlement, and changes in soil dynamic characteristics over time under the action of small and medium earthquakes or vibrations. Although this method can obtain direct on-site data, it requires a long observation period and is still not targeted at sudden strong earthquake risks.

[0006] With the continuous expansion of modern engineering construction and the increase in earthquake intensity and frequency, the limitations of traditional technologies have gradually become apparent: (1) Insufficient accuracy: Relying only on simplified criteria or a single indicator is difficult to accurately reflect the liquefaction risk under the actual complex stress path.

[0007] (2) Limited test conditions: There are large differences between laboratory and field conditions, and there are uncertainties in the quantitative evaluation process.

[0008] (3) Lack of real-time monitoring and feedback: Most methods only conduct assessments in the early stages of construction or at certain stages, and lack the ability to make real-time predictions and automatically identify earthquake processes or secondary aftershocks.

[0009] Therefore, methods based on field monitoring data (such as seismic acceleration, pore water pressure, and surface displacement) combined with numerical simulation and field test verification have gradually emerged in recent years. On the one hand, the advancement of sensors and monitoring technology has made it possible to collect high-precision seismic dynamic data in real time; on the other hand, the development of numerical analysis tools such as finite element / finite difference has provided solid theoretical and computational support for the reasonable simulation of soil dynamic response processes. However, numerical simulation or monitoring data alone are not sufficient to make reliable judgments on soil liquefaction. The results still need to be corrected and verified through drilling sampling, indoor or in-situ tests, and finally a complete and dynamically updated liquefaction discrimination method is formed. Summary of the invention

[0010] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for identifying earthquake sand liquefaction in geotechnical engineering, which can fully combine the measured data while ensuring the accuracy of numerical analysis, quickly and accurately evaluate the sand liquefaction risk of the target site under the action of an earthquake, and implement targeted on-site testing and observation of key risk soil layers.

[0011] To achieve the above object, the present invention provides the following solutions: A method for determining liquefaction of sand in geotechnical engineering earthquakes, comprising: Collecting basic on-site data of the target site; the basic on-site data includes: site parameter table, earthquake acceleration time history data and pore pressure time history data; According to the site parameter table, the soil constitutive relationship and the model grid topology structure are established to obtain a preliminary numerical model; Applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data to obtain a complete numerical model; According to the complete numerical model, nonlinear dynamic analysis is performed to obtain the pore pressure ratio variation curve with time and the shear strain distribution of sand; A comprehensive evaluation is performed based on the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; Determine the key observation soil layer plan according to the sand liquefaction prediction result; Field experiments were conducted to verify the key soil layer observation scheme, and the final sand liquefaction identification results were obtained based on the field test results and numerical simulation results.

[0012] Preferably, collecting the on-site basic data of the target site includes: Obtaining a survey report of a target site, and setting up a plurality of monitoring points at the target site; and setting up sensor equipment at each monitoring point; Conduct survey according to the survey report, collect and record the site parameter table; The target site is tracked and detected in real time using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data.

[0013] Preferably, the site parameter table includes stratum structure, porosity, water content and groundwater level.

[0014] Preferably, the target site is tracked and detected in real time by using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data, including: The sensor equipment is arranged in the monitoring area of ​​the target site and at each key soil layer depth; the sensor equipment includes: an accelerometer and a pore water pressure sensor; Real-time acquisition of original signals of seismic acceleration and pore water pressure; The original seismic acceleration signal and the original pore water pressure signal are respectively normalized, and the normalized signals are denoised to obtain the final seismic acceleration time history data and pore water pressure time history data.

[0015] Preferably, the final expressions of the seismic acceleration time history data and the pore pressure time history data are: in, Represents the original time-course signal collected by the sensor device, is the time domain signal of the earthquake acceleration time history data and the pore pressure time history data, For the Level, The value of the coefficient after denoising, , represents the sign function, which is used to preserve the sign of the original coefficient; It means taking the larger value between the value in the brackets and 0, i.e. soft interval operation; is the position wavelet basis function corresponding to scale and translation; Indicates at the same level For all coefficients sum; is the highest number of layers of wavelet decomposition, which is selected according to the spectrum range and resolution requirements of the seismic signal; is the original wavelet coefficient, which represents the signal collected by the sensor device After wavelet forward transform, At the decomposition level, The coefficients at the time positions; represents the adaptive threshold, which is used to distinguish signal from noise. , For the The standard deviation of the noise estimate at the level, For the The number of level wavelet coefficients, are the parameters for adjusting the threshold sensitivity, is the adaptive weighting coefficient used to Level, The wavelet coefficients are further weighted and modified. , is a global adjustment factor used to control the weighting range; is the attenuation coefficient, which is used to balance strong signals and weak noise.

[0016] Preferably, the complete numerical model includes: a dynamic balance equation, an effective stress-pore pressure coupling equation and a dynamic constitutive model correction equation; The formula of the dynamic balance equation is: ;in: is the soil displacement vector function; is the soil density; is the effective stress tensor; is the external load vector, including the seismic acceleration input applied at the bottom or side; is the damping matrix; t For time; The formula of the effective stress-pore pressure coupling equation is: I; among them, ( t ) is the total stress tensor changing with time, ( t ) is the time-varying quantity of the effective stress tensor, ( t ) is the pore water pressure, I is the unit matrix, and the scalar pressure u Convert to stress components of equal value in all directions; The formula of the dynamic constitutive model correction equation is: ( e e ,he p ,p,q,… );in, e e and e p are the elastic strain and plastic strain respectively; p and q are the normal stress and shear stress invariants in the Mohr-Coulomb model of soil respectively.

[0017] Preferably, a nonlinear dynamic analysis is performed according to the complete numerical model to obtain a curve of pore pressure ratio changing with time and a distribution of sand shear strain, including: Introducing all input information of the complete numerical model; the input information includes: meshing parameters, dynamic boundary conditions, soil dynamic constitutive model parameters, initial stress and pore pressure field; Set the time step length, total calculation time and convergence criterion for numerical solution; Based on the time-step integral solution method, the dynamic balance equation and the effective stress-pore pressure coupling equation are numerically iterated in each time step; Updating soil stress, pore pressure and deformation information in the modified equation of the dynamic constitutive model, and recording pore pressure ratio and shear strain; The pore pressure ratio and shear strain evolution of each grid point and key monitoring unit in the entire time domain are summarized, and a distribution map of the sand shear strain distribution is generated in space and a curve of the pore pressure ratio change over time is generated in time.

[0018] A geotechnical engineering earthquake sand liquefaction identification system, comprising: A data acquisition unit is used to acquire basic on-site data of a target site; the basic on-site data includes: a site parameter table, earthquake acceleration time history data, and pore pressure time history data; A preliminary model building unit is used to establish a soil constitutive relationship and a model grid topology structure according to the site parameter table to obtain a preliminary numerical model; A complete model building unit, used for applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data, so as to obtain a complete numerical model; A dynamic analysis unit, used for performing nonlinear dynamic analysis according to the complete numerical model to obtain a curve of pore pressure ratio changing with time and a shear strain distribution of sand; An evaluation unit, used for performing a comprehensive evaluation based on the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; A scheme determination unit, used for determining a key observation soil layer scheme according to the sand liquefaction prediction result; The result determination unit is used to perform field experimental verification according to the key observed soil layer scheme, and obtain the final sand liquefaction identification result according to the field test results and numerical simulation results.

[0019] The present invention discloses the following technical effects: The present invention provides a method and system for distinguishing sand liquefaction in geotechnical engineering earthquakes, the method comprising: collecting basic field data of a target site; the basic field data comprising: a site parameter table, earthquake acceleration time history data and pore pressure time history data; establishing a soil constitutive relationship and a model grid topological structure according to the site parameter table to obtain a preliminary numerical model; applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data to obtain a complete numerical model; performing nonlinear dynamic analysis according to the complete numerical model to obtain a pore pressure ratio variation curve over time and sand shear strain distribution; performing a comprehensive evaluation according to the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; determining a key observation soil layer scheme according to the sand liquefaction prediction result; performing field experimental verification according to the key observation soil layer scheme, and obtaining a final sand liquefaction discrimination result according to the field test results and numerical simulation results. The present invention can fully combine the measured data while ensuring the accuracy of numerical analysis, quickly and accurately assess the sand liquefaction risk of the target site under earthquake action, and implement targeted field testing and observation on key risk soil layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 A flow chart of a method provided by an embodiment of the present invention; Figure 2 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The purpose of the present invention is to provide a method and system for identifying earthquake sand liquefaction in geotechnical engineering, which can fully combine measured data while ensuring the accuracy of numerical analysis, quickly and accurately evaluate the sand liquefaction risk of the target site under the action of an earthquake, and implement targeted on-site testing and observation of key risk soil layers.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for determining liquefaction of sand in geotechnical engineering earthquakes, comprising: Step 100: Collecting basic field data of the target site; the basic field data includes: site parameter table, earthquake acceleration time history data and pore pressure time history data; Step 200: Establish soil constitutive relations and model grid topology according to the site parameter table to obtain a preliminary numerical model; Step 300: applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data to obtain a complete numerical model; Step 400: Perform nonlinear dynamic analysis based on the complete numerical model to obtain a pore pressure ratio versus time curve and sand shear strain distribution; Step 500: Perform a comprehensive evaluation based on the pore pressure ratio versus time curve and the sand shear strain distribution to obtain a sand liquefaction prediction result; Step 600: Determine a key observation soil layer plan based on the sand liquefaction prediction result; Step 700: Conduct field experiments to verify the key observed soil layer plan, and obtain the final sand liquefaction identification result based on the field test results and numerical simulation results.

[0026] Preferably, collecting the on-site basic data of the target site includes: Obtaining a survey report of a target site, and setting up a plurality of monitoring points at the target site; and setting up sensor equipment at each monitoring point; Conduct survey according to the survey report, collect and record the site parameter table; The target site is tracked and detected in real time using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data.

[0027] Specifically, step 100 of this embodiment includes: Step 101: First, a preliminary investigation report of the target site is required to determine the geological profile, soil layer distribution, and initial hydrological information of the site. After mastering this basic information, multiple monitoring points are deployed at the target site according to the conclusions and recommendations of the report. The selection of each monitoring point should comprehensively consider factors such as geological heterogeneity, terrain changes, and key structural locations to ensure that the number and location of the deployed monitoring points can fully cover the key areas of the site.

[0028] Step 102: At each monitoring point, install sensor equipment that can observe seismic motion and pore pressure changes in real time. High-precision accelerometers, pore pressure gauges, etc. can be used to continuously and stably record the earthquake acceleration time history and pore water pressure. In order to ensure the credibility of monitoring, the sensor should be calibrated and maintained regularly, such as checking the range, zero drift, and waterproof performance of the package. At the same time, since the accuracy of the survey data directly affects the accuracy of the subsequent model calculation, a trial run is required after the sensor is installed to verify the signal transmission quality and noise level.

[0029] Step 103: During the monitoring process, in combination with further on-site investigation of the site, the site parameter table is recorded and updated in detail. The site parameter table may include but is not limited to: soil layer thickness, soil layer properties (such as density, saturation, permeability, etc.), groundwater level, historical earthquake intensity records, etc. When the sensor begins to track and detect the site in real time, it can simultaneously obtain earthquake acceleration time history data and pore pressure time history data, and integrate these data with the site parameter table to form the core information of the basic data of the target site, providing high-quality input basis for subsequent numerical model establishment and parameter correction.

[0030] Preferably, the site parameter table includes stratum structure, porosity, water content and groundwater level.

[0031] Preferably, the target site is tracked and detected in real time by using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data, including: The sensor equipment is arranged in the monitoring area of ​​the target site and at each key soil layer depth; the sensor equipment includes: an accelerometer and a pore water pressure sensor; Real-time acquisition of original signals of seismic acceleration and pore water pressure; The original seismic acceleration signal and the original pore water pressure signal are respectively normalized, and the normalized signals are denoised to obtain the final seismic acceleration time history data and pore water pressure time history data.

[0032] Specifically, in this embodiment, accelerometers and pore water pressure sensors are deployed in the monitoring area of ​​the target site and at the depth of each key soil layer to form a networked monitoring system. Since the physical and mechanical properties and hydrological conditions of soil layers at different depths and locations may be significantly different, it is necessary to reasonably select the buried depth and distribution location of the sensors based on the preliminary survey results in order to fully capture the attenuation and amplification characteristics of seismic waves between different soil layers, as well as the dynamic changes of pore water pressure at different buried depths.

[0033] After obtaining stable layout and installation, start collecting seismic acceleration and pore water pressure signals in real time. Through high-precision data acquisition instruments or integrated data acquisition systems, the original electrical signals output by accelerometers and pore water pressure sensors are converted into visual time series data, and continuously recorded with special monitoring software. At this time, the original seismic acceleration signals and pore water pressure signals may contain factors such as environmental noise and instrument drift, so basic data backup and verification need to be done in the acquisition stage.

[0034] The collected original signals of seismic acceleration and pore water pressure are normalized and de-noised using digital signal processing methods such as filtering or wavelet transform. Normalization refers to mapping the data to a unified dimension according to a selected benchmark (such as peak value or interval average value) to facilitate subsequent comparative analysis of data from different monitoring points or at different depths. Noise filtering can use a variety of filtering algorithms or statistical methods to reduce high-frequency noise, low-frequency drift or burst pulse interference, thereby obtaining clear seismic acceleration time history data and pore pressure time history data, providing more accurate input for subsequent fine numerical analysis and model correction.

[0035] Preferably, the final expressions of the seismic acceleration time history data and the pore pressure time history data are: in, Represents the original time-course signal collected by the sensor device, is the time domain signal of the earthquake acceleration time history data and the pore pressure time history data, For the Level, The value of the coefficient after denoising, , represents the sign function, which is used to preserve the sign of the original coefficient; It means taking the larger value between the value in the brackets and 0, i.e. soft interval operation; is the position wavelet basis function corresponding to scale and translation; Indicates at the same level For all coefficients sum; is the highest number of layers of wavelet decomposition, which is selected according to the spectrum range and resolution requirements of the seismic signal; is the original wavelet coefficient, which represents the signal collected by the sensor device After wavelet forward transform, At the decomposition level, The coefficients at the time positions; represents the adaptive threshold, which is used to distinguish signal from noise. , For the The standard deviation of the noise estimate at the level, For the The number of level wavelet coefficients, are the parameters for adjusting the threshold sensitivity, is the adaptive weighting coefficient used to Level, The wavelet coefficients are further weighted and modified. , is a global adjustment factor used to control the weighting range; is the attenuation coefficient, which is used to balance strong signals and weak noise.

[0036] Specifically, the parameters for adjusting the threshold sensitivity are mainly used to control the sensitivity of the threshold size relative to the noise level. The general practice is to first perform a statistical analysis on the original signal collected by the sensor to determine its overall signal-to-noise ratio and the energy distribution in each frequency band (wavelet decomposition level). If the noise is relatively strong at a certain level, the sensitivity is appropriately increased to increase the threshold; if the main component of the signal is obviously dominant, the sensitivity can be appropriately reduced so that the algorithm can also retain the weaker detail components at this level. The final value is often compared through experiments: on the one hand, the consistency between the denoised waveform and the actual seismic record is observed, and on the other hand, the denoising effect is evaluated using the noise residual to balance between fidelity and noise suppression.

[0037] Furthermore, the role of the adaptive weighting coefficient is to rebalance each wavelet coefficient, retain the truly useful signal components, and attenuate the coefficients with a high proportion of noise. It is usually determined by analyzing the local energy or change characteristics of the neighborhood around the wavelet coefficient at the current position. If the local energy concentration of the wavelet coefficient at a certain point is high, it means that the coefficient reflects more of the real signal characteristics and the weighting value needs to be increased; conversely, if the noise component of the nearby frequency band accounts for a significant proportion, or the coefficient changes over time without obvious physical characteristics, the weighting value will be reduced. This process takes into account both spatial proximity and frequency domain information, and can be performed separately at different decomposition levels, and the final correction is made based on the comparison results of multiple measured data.

[0038] Furthermore, the global adjustment factor is more of a unified scale balance. When the adaptive weighted values ​​of the local coefficients at each level or frequency band have been calculated, it is used to make a scale adjustment on the whole. This is done to avoid the imbalance between levels caused by relying solely on local judgment. For example, if some high-frequency level thresholds are too strict, the high-frequency effective components will be over-suppressed, which can be compensated by slightly increasing the global adjustment factor. Usually, engineering practice will select a small number of typical seismic records or pore pressure records for multiple adjustments to obtain a relatively universal and stable global factor to adapt to the signal characteristics of different events or different measuring points.

[0039] Furthermore, the attenuation coefficient is mainly used to further balance between strong signals and weak noise, so that those signal components with large amplitudes will not be overly weakened by threshold operations or weighting processes, while ensuring sufficient suppression of noise with small amplitude but continuous existence. When determining the attenuation coefficient, each denoising result needs to be tested in the time domain and frequency domain: if it is found that the effective signal of the high-amplitude main shock segment or shock wave segment is obviously distorted after denoising, it means that the attenuation needs to be reduced; if the overall trend is good but there is still a lot of noise remaining in the low-energy part, the attenuation should be appropriately increased. In theory, the attenuation can be started from a smaller value and then gradually increased. The top layer is finally selected by global analysis and field test waveform effects, taking into account the consistency of different test points under different working conditions.

[0040] Optionally, the core equation of the preliminary numerical model in step 200 of this embodiment includes: 1. Dynamic equilibrium equation (static approximation) ▽× s +ρ g=0 2. Effective stress principle σ′=σ-u I 3. Soil constitutive relationship (taking elasticity as an example) σ′=D:ε 4. Initial pore water pressure distribution u0(z)=γ w (H water- z), when z≤H water , otherwise u0(z)=0.

[0041] Where D is the soil stiffness matrix (elastic or elastoplastic), which is determined by the material parameters; γ w is the unit weight of water, H water is the groundwater level elevation, ε represents the strain of the soil (usually the strain tensor), which is used to reflect the degree of deformation of the soil layer under the action of external force or self-weight.

[0042] 5. Grid discretization: After the soil is layered, the finite element / finite difference grid is used to discretize each soil layer, and the preliminary numerical model is established by combining material properties and initial stress field.

[0043] For example, the site parameter table lists in detail the physical and mechanical parameters of each soil layer, such as elastic modulus, Poisson's ratio, density, permeability coefficient, etc. These parameters determine the stiffness matrix D (or more generally the elastic-plastic constitutive parameters), the groundwater level elevation H water , unit weight γ w The values ​​of key values ​​such as . After the mesh is discretized, each layer of soil in the model needs to obtain the corresponding material properties from the site parameter table to ensure that the numerical model truly reflects the local soil characteristics. The site parameter table often includes the thickness of the stratum, the depth of the groundwater level and the distribution of the soil layer, which can provide key basis for the initial stress distribution, initial pore water pressure distribution, and stratification boundary position of the numerical model. In particular, the piecewise function form of the initial pore water pressure u0(z), the required groundwater level elevation H water It comes directly from the site parameter table to determine the difference between saturated soil layers and unsaturated soil layers. When performing mesh discretization, the number of mesh layers, unit type or mesh density should be determined based on the soil layer interface position and layer characteristics listed in the site parameter table. Combining the boundary depths of different soil layers provided by the site parameter table with the differences in soil properties can more reasonably divide the numerical model, so that the subsequent numerical analysis can accurately capture the mechanical response of each layer under earthquake and load.

[0044] Preferably, the complete numerical model includes: a dynamic balance equation, an effective stress-pore pressure coupling equation and a dynamic constitutive model correction equation; The formula of the dynamic balance equation is: ;in: is the soil displacement vector function; is the soil density; is the effective stress tensor; is the external load vector, including the seismic acceleration input applied at the bottom or side; is the damping matrix; t For time; The formula of the effective stress-pore pressure coupling equation is: I; among them, ( t ) is the total stress tensor changing with time, ( t ) is the time-varying quantity of the effective stress tensor, ( t ) is the pore water pressure, I is the unit matrix, and the scalar pressure u Convert to stress components of equal value in all directions; The formula of the dynamic constitutive model correction equation is: ( e e ,he p ,p,q,… );in, e e and e p are the elastic strain and plastic strain respectively; p and q are the normal stress and shear stress invariants in the Mohr-Coulomb model of soil respectively.

[0045] Specifically, step 300 in this embodiment includes: Step 301: Before applying boundary conditions to the preliminary numerical model, two key data sources need to be obtained: earthquake acceleration time history and pore pressure time history. According to the characteristics of the earthquake motion and the monitoring point information, the bottom or side power input mode and the corresponding load vector are determined and applied to the corresponding part of the model. At the same time, the initial pore water pressure distribution and boundary seepage conditions in the model are dynamically checked according to the pore pressure time history data to ensure that the dynamic change of pore pressure over time in the soil layer during the earthquake process is accurately characterized.

[0046] Step 302: After setting these dynamic and pore pressure inputs, the numerical model needs to be parameterized to coordinate the dynamic balance equation, effective stress-pore pressure coupling equation, and dynamic constitutive equation under actual site conditions. On the one hand, the matching degree of the soil damping matrix and material parameters is evaluated based on the monitoring data, and the difference between the numerical solution and the measured response is observed to revise the damping coefficient, soil elastic-plastic parameters, etc.; on the other hand, the preliminary model is compared with the measured time history by regression or iterative optimization, and the amplitude and phase characteristics of the measured acceleration and pore pressure response are gradually approached, thereby forming a "complete numerical model".

[0047] Step 303: Finally, in the complete numerical model, the dynamic constitutive relationship is updated and improved to achieve accurate characterization of the elastic strain, plastic strain, and normal stress and shear stress invariants in the Mohr-Coulomb strength criterion of the soil. In this way, the complex coupling effects of soil in the process of pore pressure growth, effective stress reduction, and plastic deformation evolution under earthquake action can be fully simulated, laying a solid numerical foundation for subsequent nonlinear dynamic analysis and sand liquefaction identification.

[0048] Preferably, a nonlinear dynamic analysis is performed according to the complete numerical model to obtain a curve of pore pressure ratio changing with time and a distribution of sand shear strain, including: Introducing all input information of the complete numerical model; the input information includes: meshing parameters, dynamic boundary conditions, soil dynamic constitutive model parameters, initial stress and pore pressure field; Set the time step length, total calculation time and convergence criterion for numerical solution; Based on the time-step integral solution method, the dynamic balance equation and the effective stress-pore pressure coupling equation are numerically iterated in each time step; Updating soil stress, pore pressure and deformation information in the modified equation of the dynamic constitutive model, and recording pore pressure ratio and shear strain; The pore pressure ratio and shear strain evolution of each grid point and key monitoring unit in the entire time domain are summarized, and a distribution map of the sand shear strain distribution is generated in space and a curve of the pore pressure ratio change over time is generated in time.

[0049] Specifically, step 400 of this embodiment includes: Step 401: Loading complete numerical model input information First, all the initial conditions and boundary conditions involved in the complete numerical model need to be loaded into the numerical calculation environment, including: meshing parameters, dynamic constitutive model parameters of each layer of soil (such as elastic modulus, plasticity parameters, Mohr-Coulomb criterion, etc.), initial effective stress field and pore water pressure distribution, and external boundary inputs such as processed earthquake acceleration time history. This step ensures that in the subsequent time step integration, all the prerequisites and constraints used in the numerical analysis are highly consistent with the real site and measured data.

[0050] Step 402: Setting the numerical solution strategy and time step control The time step length, total operation time and convergence criterion required by the numerical solver are determined in turn. The time step length will be comprehensively weighed according to the sampling frequency of the earthquake acceleration record, the mesh size of the soil layer thickness and the numerical stability requirements. The convergence criterion can be set by iterative residuals, manually set error limits or energy balance to ensure that each time step can obtain a solution with sufficient accuracy. On this basis, it is also necessary to determine whether the acceleration or deceleration time step needs to be adjusted according to the material properties of different soil layers to adapt to the differences in the dynamic response of the soil in strong or weak earthquake areas.

[0051] Step 403: Perform iterative updates in each time step The dynamic equilibrium equation and the effective stress-pore pressure coupling equation are numerically iterated using an appropriate time-step integral solution method (such as explicit central difference or implicit Newmark integral). Specifically, each time step will continuously adjust the soil stress field and pore pressure field according to the soil stress state, pore water pressure and deformation of the previous time step, combined with the seismic acceleration input and fluid-solid coupling boundary conditions of the current time step, until the convergence requirements are met. If convergence cannot be achieved within the limited number of iterations, it is necessary to check whether there are unreasonable aspects in numerical stability, material parameters or boundary settings.

[0052] Step 404: Update the constitutive model and record the pore pressure ratio and shear strain After solving the dynamic equations and coupling equations at each time step, the parameters of the dynamic constitutive model need to be modified according to the actual elastic-plastic strain increment of the soil. For example, according to the Moore-Coulomb criterion or other nonlinear models, the stress relaxation, hardening or softening effects are calculated to update the stress-strain relationship of the current soil layer. At the same time, the pore pressure ratio and shear strain values ​​of each grid unit or key monitoring point are recorded for easy summary in the final output stage. The pore pressure ratio can be calculated based on the instantaneous effective stress and pore water pressure, and the shear strain is evaluated by the deformation gradient or node displacement difference inside the unit.

[0053] Step 405: Output the full time domain pore pressure ratio curve and shear strain distribution diagram When the analysis of the entire time domain is completed, the pore pressure ratio and shear strain evolution data of each grid unit or monitoring point in all time steps are summarized. The pore pressure ratio-time curve of typical points and key soil layers can be generated in the time dimension through the numerical post-processing module, and the grids at different times can be rendered or plotted in the spatial dimension to form a cloud map of the shear strain distribution of sand. By extracting this information at different time periods, not only can the time and area of ​​liquefaction be determined, but the deformation and failure mode of the sand layer under strong earthquakes can also be intuitively seen from the shear strain distribution, thereby completing the closed loop of the entire nonlinear dynamic analysis and sand liquefaction assessment.

[0054] Optionally, step 500 of this embodiment includes: Step 501: Extract the pore pressure ratio at key locations and times View the curve of pore pressure ratio over time in the time domain, and focus on the interval where the pore pressure ratio increases rapidly and approaches 1 (you can also use the empirical threshold of predecessors to determine whether the liquefaction critical point has been reached). If the pore pressure ratio of some key grid units or monitoring points is greater than a certain critical value (usually between 0.8 and 1.0) in a certain period of time and lasts for a long time, it often means that the pore water pressure has increased significantly and the effective stress between soil particles has decreased, indicating that liquefaction or local failure may occur.

[0055] Step 502: Compare shear strain distribution to determine the failure area In addition to high pore pressure ratios, the soil will also show significant signs of plastic deformation or shear deformation after liquefaction. Therefore, it is necessary to view the distribution map of sand shear strain and its evolution over time in space, focusing on those areas where shear strain accumulates and forms large concentrated areas under strong earthquakes. If these concentrated deformation areas coincide with or are adjacent to areas with high pore pressure ratios, it often indicates that the conditions for liquefaction failure are already met or that strong soil deformation is occurring. These grid units or soil layer segments are highlighted to further confirm their failure modes (such as shear band penetration or local collapse).

[0056] Step 503: Comprehensive judgment and liquefaction classification After cross-analyzing the pore pressure ratio and shear strain results, a comprehensive prediction of the spatiotemporal distribution of sand liquefaction can be made. If the pore pressure ratio of most monitoring points is close to the critical value of liquefaction, and a large range of deformation concentration also appears in the shear strain distribution diagram, it can be determined that the soil layer is very easy to liquefy; if there are only sporadic high pore pressure ratios or shear strain concentration areas, it may be a sign of local or temporary liquefaction, and further assessment of the field risk level is required in combination with geological conditions, soil thickness, and drainage conditions. After obtaining an overall quantitative conclusion, the liquefaction risk can be described at different levels to provide guidance for subsequent reinforcement measures and engineering decisions.

[0057] Optionally, step 600 of this embodiment includes: Step 601: Identify high-risk areas for liquefaction After completing the prediction of sand liquefaction, first identify the soil layers and areas with high or potentially high liquefaction risks in the spatial scope. Focus on the depth and location of the grid units or monitoring points where the pore pressure ratio is close to the liquefaction critical value for a long time and the shear strain is concentrated in the previous analysis. Compare this information with the site investigation report to confirm the main layer position where liquefaction may occur, the thickness of the soil layer, and the relationship between the groundwater level and the soil layer structure, so as to lay the foundation for the next observation plan.

[0058] Step 602: Develop a deep and shallow monitoring point distribution strategy Based on the above identification results, the depth range of the monitoring focus is divided into several key layers, and monitoring points are appropriately added or adjusted according to site conditions and engineering requirements. For high-risk areas, such as saturated sand layers, where shear failure may occur, it is necessary to increase the density of sensor burials to obtain more detailed pore pressure change and deformation data; for layers with general risks, the original monitoring point layout can be appropriately maintained, or observations can be increased only within a limited range to save costs and resources.

[0059] Step 603: Determine a solution combining real-time observation and periodic observation After locking in the key soil layers, differentiated observation frequencies and methods are formulated for different levels of importance. For the highest risk layers, a real-time wireless monitoring system or a high-sampling rate data acquisition instrument can be used to closely observe dynamic information such as pore pressure, displacement, and seismic motion; for medium-risk layers, regular (such as daily or weekly) remote or manual inspections can be used to collect data at a lower frequency. The hierarchical observation plan improves monitoring efficiency and reliability, and accurately deploys observation resources to the most needed areas, so as to promptly grasp the signs or severity of soil liquefaction when an earthquake occurs.

[0060] Step 604: Optimize based on project purpose and site conditions Finally, the key soil layer observation plan is further optimized by comprehensively considering the specific needs of the construction stage, operation period or disaster prevention and early warning. If the project is in the early stage of construction, the monitoring focuses on grasping the dynamic changes of soil stability during the construction process and adjusting the plan at any time; if the project has entered the operation period, it is necessary to combine long-term monitoring with geological environmental changes to ensure that the strength attenuation of the soil layer and the impact of subsequent seismic activities on it are observed over a long period of time. In this way, a diversified and sustainable key soil layer observation plan is formed to achieve dynamic control of high-risk sandy soil layers for liquefaction.

[0061] Specifically, step 700 of this embodiment includes: Step 701: Field monitoring system deployment and test design First, according to the "key soil layer observation plan" determined in the early stage, the corresponding monitoring equipment (such as dynamic pore pressure gauges, accelerometers, deformation meters, etc.) are deployed at the depth of the sand layer with high risk or potential risk, and shallow or deep drilling tests are carried out. Field experiments need to design experimental plans in advance, such as how to conduct standard penetration tests, dynamic penetration tests, static penetration tests or wave velocity tests, etc., to obtain real mechanical and dynamic characteristics.

[0062] Step 702: Collect in-situ soil samples and conduct indoor testing In-situ soil samples are drilled at key observation layers to maintain the integrity of soil structure and water content as much as possible, and the quality of soil samples is ensured through on-site closed sampling or automatic samplers. After these soil samples are brought back to the laboratory, key soil parameters, including porosity, saturation, internal friction angle, cohesion, etc., are measured through triaxial tests, consolidation drainage tests, and advanced dynamic tests (such as cyclic triaxial tests), providing more accurate parameter support for numerical simulation and discriminant analysis.

[0063] Step 703: Conduct in-situ testing such as standard penetration testing or dynamic penetration testing In-situ tests such as standard penetration test (SPT) or dynamic penetration test (DPT) can be directly carried out on site, and the distribution of the corresponding number of hits or penetration with depth can be recorded. From these measured data, the compactness and liquefaction resistance of the soil layer can be preliminarily judged, as well as the consistency with the numerical simulation prediction results. If the measured number of hits is significantly different from the relative density calculated by numerical simulation, it is necessary to consider updating or correcting the parameters of the numerical model.

[0064] Step 704: Compare field test data with numerical simulation predictions With the acquisition of field test data, the actual measured pore pressure response, soil deformation trend and penetration distribution can be compared point by point or layer by layer with the pore pressure ratio and shear strain distribution in the numerical simulation. If the field measured pore pressure does approach the liquefaction threshold rapidly under the action of strong earthquake (or external loading), and the numerical simulation also predicts a similar trend at the same location and depth, it means that the model has good reliability and effectiveness; otherwise, it is necessary to trace whether the setting of parameters or boundary conditions is reasonable.

[0065] Step 705: Adjust the model and perform a second simulation If a large deviation is found during the comparison process, such as the numerically predicted pore pressure is too high or too low, or the shear strain accumulation rate is significantly different from the field measured value, further corrections need to be made to the key parameters. For example, recalibrate the settings of the elastic-plastic or Mohr-Coulomb parameters in the soil dynamic constitutive model, or make more detailed corrections to the initial stress field and groundwater distribution. Then rerun the numerical simulation and compare the revised results with the field test data again to narrow the gap.

[0066] Step 706: Comprehensive evaluation and application of liquefaction criteria After multiple comparisons and revisions, the numerical model and the field test results tend to be consistent, and the final comprehensive evaluation stage can be entered. Commonly used liquefaction discrimination criteria at home and abroad (such as Seed method, Tokimatsu method, Ishihara method, etc.) can be used for evaluation; or based on custom pore pressure ratio thresholds, deformation critical values, etc., the measured values ​​and numerical results can be compared to determine whether the soil layer has reached liquefaction or has a tendency to liquefy. At this time, the focus is not only on a single data point or test result, but also on whether the overall trend matches the engineering safety and disaster prevention needs.

[0067] Step 707: Output the final judgment result and subsequent measures Through the above verification and correction steps, a relatively objective and accurate sand liquefaction identification result can be finally formed, and corresponding engineering countermeasures can be proposed for high-risk areas. If the assessment shows that the soil layer does have a liquefaction risk, it is necessary to combine reinforcement measures (such as grouting, drainage and decompression, or increase the bearing capacity of the foundation, etc.) for risk control; if the results show that the liquefaction risk is limited, it is also necessary to maintain a certain monitoring intensity and early warning mechanism. At this point, the field test verification and numerical simulation correction have completed the closed loop, providing a decision-making basis for subsequent engineering construction and disaster prevention planning.

[0068] Corresponding to the above method, such as Figure 2 As shown, this embodiment also provides a geotechnical engineering earthquake sand liquefaction identification system, including: A data acquisition unit is used to acquire basic on-site data of a target site; the basic on-site data includes: a site parameter table, earthquake acceleration time history data, and pore pressure time history data; A preliminary model building unit is used to establish a soil constitutive relationship and a model grid topology structure according to the site parameter table to obtain a preliminary numerical model; A complete model building unit, used for applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data, so as to obtain a complete numerical model; A dynamic analysis unit, used for performing nonlinear dynamic analysis according to the complete numerical model to obtain a curve of pore pressure ratio changing with time and a shear strain distribution of sand; An evaluation unit, used for performing a comprehensive evaluation based on the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; A scheme determination unit, used for determining a key observation soil layer scheme according to the sand liquefaction prediction result; The result determination unit is used to perform field experimental verification according to the key observed soil layer scheme, and obtain the final sand liquefaction identification result according to the field test results and numerical simulation results.

[0069] The beneficial effects of the present invention are as follows: (1) The present invention applies boundary conditions such as field-measured pore pressure data and seismic acceleration data to the preliminary numerical model, and iteratively improves the parameter correction link, so that the simulation results are more in line with the actual site conditions, and the accuracy and reliability are enhanced.

[0070] (2) After obtaining the pore pressure ratio versus time curve and the shear strain distribution of sand, the present invention can simultaneously measure the coupling effect of pore water pressure accumulation and soil deformation by comprehensively evaluating them, thereby more comprehensively identifying the liquefaction occurrence time, impact range and failure mode.

[0071] (3) The present invention uses the key observation soil layer scheme to carry out field verification tests to further verify the rationality of the numerical results, and draws the final and accurate conclusion on sand liquefaction based on the comparison between the measured results and the simulation results, forming a complete "numerical analysis-experimental verification-conclusion optimization" closed loop.

[0072] (4) After clarifying the risk level and potential damage mode of sand liquefaction, the present invention can provide an intuitive basis for foundation reinforcement design, construction plan selection and post-disaster repair measures, thereby reducing the risk of project implementation and saving costs.

[0073] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0074] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying liquefaction of sand in geotechnical engineering earthquakes, characterized in that: include: Collect basic on-site data of the target site; The basic field data include: site parameter table, earthquake acceleration time history data and pore pressure time history data; According to the site parameter table, the soil constitutive relationship and the model grid topology structure are established to obtain a preliminary numerical model; Applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data to obtain a complete numerical model; According to the complete numerical model, nonlinear dynamic analysis is performed to obtain the pore pressure ratio variation curve with time and the shear strain distribution of sand; A comprehensive evaluation is performed based on the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; Determine the key observation soil layer plan according to the sand liquefaction prediction result; Field experiments were conducted to verify the key soil layer observation scheme, and the final sand liquefaction identification results were obtained based on the field test results and numerical simulation results.

2. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 1, characterized in that: Collect basic on-site data of the target site, including: Obtaining a survey report of a target site, and setting up a plurality of monitoring points at the target site; and setting up sensor equipment at each monitoring point; Conduct survey according to the survey report, collect and record the site parameter table; The target site is tracked and detected in real time using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data.

3. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 1, characterized in that: The site parameter table includes stratum structure, porosity, water content and groundwater level.

4. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 3, characterized in that: The target site is tracked and detected in real time using sensor equipment to obtain earthquake acceleration time history data and pore pressure time history data, including: The sensor equipment is arranged in the monitoring area of ​​the target site and at each key soil layer depth; the sensor equipment includes: an accelerometer and a pore water pressure sensor; Real-time acquisition of original signals of seismic acceleration and pore water pressure; The original seismic acceleration signal and the original pore water pressure signal are respectively normalized, and the normalized signals are denoised to obtain the final seismic acceleration time history data and pore water pressure time history data.

5. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 4, characterized in that: The final expressions of the earthquake acceleration time history data and pore pressure time history data are: in, Represents the original time-course signal collected by the sensor device, is the time domain signal of the earthquake acceleration time history data and the pore pressure time history data, For the Level, The value of the coefficient after denoising, , represents the sign function, which is used to preserve the sign of the original coefficient; It means taking the larger value between the value in the brackets and 0, i.e. soft interval operation; is the position wavelet basis function corresponding to scale and translation; Indicates at the same level For all coefficients sum; is the highest number of layers of wavelet decomposition, which is selected according to the spectrum range and resolution requirements of the seismic signal; is the original wavelet coefficient, which represents the signal collected by the sensor device After wavelet forward transform, At the decomposition level, The coefficients at the time positions; represents the adaptive threshold, which is used to distinguish signal from noise. , For the The standard deviation of the noise estimate at the level, For the The number of level wavelet coefficients, are the parameters for adjusting the threshold sensitivity, is the adaptive weighting coefficient used to Level, The wavelet coefficients are further weighted and modified. , is a global adjustment factor used to control the weighting range; is the attenuation coefficient, which is used to balance strong signals and weak noise.

6. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 2, characterized in that: The complete numerical model includes: a dynamic balance equation, an effective stress-pore pressure coupling equation and a dynamic constitutive model correction equation; The formula of the dynamic balance equation is: ;in: is the soil displacement vector function; is the soil density; is the effective stress tensor; is the external load vector, including the seismic acceleration input applied at the bottom or side; is the damping matrix; t For time; The formula of the effective stress-pore pressure coupling equation is: I; among them, ( t ) is the total stress tensor changing with time, ( t ) is the time-varying quantity of the effective stress tensor, ( t ) is the pore water pressure, I is the unit matrix, and the scalar pressure u Convert to stress components of equal value in all directions; The formula of the dynamic constitutive model correction equation is: ( ε e ,ε p ,p,q,… );in, ε e and ε p are the elastic strain and plastic strain respectively; p and q are the normal stress and shear stress invariants in the Mohr-Coulomb model of soil respectively.

7. The method for determining liquefaction of sand in geotechnical engineering earthquake according to claim 6, characterized in that: According to the complete numerical model, nonlinear dynamic analysis is performed to obtain the time-varying curve of pore pressure ratio and the shear strain distribution of sand, including: Introducing all input information of the complete numerical model; the input information includes: meshing parameters, dynamic boundary conditions, soil dynamic constitutive model parameters, initial stress and pore pressure field; Set the time step length, total calculation time and convergence criterion for numerical solution; Based on the time-step integral solution method, the dynamic balance equation and the effective stress-pore pressure coupling equation are numerically iterated in each time step; Updating soil stress, pore pressure and deformation information in the modified equation of the dynamic constitutive model, and recording pore pressure ratio and shear strain; The pore pressure ratio and shear strain evolution of each grid point and key monitoring unit in the entire time domain are summarized, and a distribution map of the sand shear strain distribution is generated in space and a curve of the pore pressure ratio change over time is generated in time.

8. A geotechnical engineering earthquake sand liquefaction identification system, characterized in that: include: A data collection unit, used to collect basic on-site data of the target site; The basic field data include: site parameter table, earthquake acceleration time history data and pore pressure time history data; A preliminary model building unit is used to establish a soil constitutive relationship and a model grid topology structure according to the site parameter table to obtain a preliminary numerical model; A complete model building unit, used for applying boundary conditions to the preliminary numerical model and performing parameter correction according to the earthquake acceleration time history data and the pore pressure time history data, so as to obtain a complete numerical model; A dynamic analysis unit, used for performing nonlinear dynamic analysis according to the complete numerical model to obtain a curve of pore pressure ratio changing with time and a shear strain distribution of sand; An evaluation unit, used for performing a comprehensive evaluation based on the pore pressure ratio variation curve over time and the sand shear strain distribution to obtain a sand liquefaction prediction result; A scheme determination unit, used for determining a key observation soil layer scheme according to the sand liquefaction prediction result; The result determination unit is used to perform field experimental verification according to the key observed soil layer scheme, and obtain the final sand liquefaction identification result according to the field test results and numerical simulation results.

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