CN114154332A - Gravel soil earthquake liquefaction evaluation method - Google Patents
Gravel soil earthquake liquefaction evaluation method Download PDFInfo
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- 239000002689 soil Substances 0.000 title claims abstract description 39
- 238000011156 evaluation Methods 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 15
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 230000000694 effects Effects 0.000 claims abstract description 6
- 230000035515 penetration Effects 0.000 claims abstract description 3
- 230000008021 deposition Effects 0.000 claims 2
- 238000005070 sampling Methods 0.000 description 9
- 125000004122 cyclic group Chemical group 0.000 description 7
- 238000013507 mapping Methods 0.000 description 5
- 230000001133 acceleration Effects 0.000 description 4
- 230000005484 gravity Effects 0.000 description 4
- 239000004576 sand Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000007476 Maximum Likelihood Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000007477 logistic regression Methods 0.000 description 2
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Abstract
The application provides a gravel soil earthquake liquefaction evaluation method, and belongs to the technical field of soil earthquake evaluation. A method for evaluating the earthquake liquefaction of sandy soil includes such steps as creating a comprehensive database for dynamic penetration test, creating a deterministic model, calculating the safety coefficient of anti-liquefaction trigger, creating the classification maps of liquefied region and non-liquefied region, and evaluating the prediction effect. The method comprehensively adopts 8 different global earthquake databases, and can accurately and conveniently carry out comprehensive evaluation on the gravel soil earthquake liquefaction in the engineering.
Description
Technical Field
The application relates to the technical field of soil earthquake assessment, in particular to a gravel soil earthquake liquefaction assessment method.
Background
The liquefaction phenomenon caused by earthquake is observed and reported all over the world, the earthquake motion is fast enough to cause that water in the soil is not discharged soon, the pore water pressure is increased, the soil body is in a fluid state, the shear strength is reduced, and the liquefaction phenomenon is generated. Because the gritty granular soil is easily compacted, the pore water pressure increases under rapid cyclic loading. Most of the prior reports and studies on liquefaction are directed only to sandy soils, however, liquefaction of gravel soils also occurs. Compared with sand, gravel soil has larger grain diameter, and the effective and economic earthquake liquefaction evaluation method is limited. At present, the existing model is only built on the basis of one example of historical earthquake.
Disclosure of Invention
In order to solve the problems, the invention provides a gravel soil earthquake liquefaction evaluation method which can effectively and accurately evaluate gravel soil earthquake liquefaction.
A method for evaluating a gravel soil seismic liquefaction, comprising:
the method comprises the following steps: a comprehensive database of dynamic sounding tests (DPT) is established by applying 8 different earthquakes all over the world. The database includes seismic magnitude, frequency of motion, outer center distance, geometry type, number of sedimentary layers, gravel soil depth, and soil properties.
Step two: and establishing a deterministic model. Based on the model, a cyclic intensity ratio (CRR) can be estimated.
The determination of the cyclic intensity ratio (CRR) involves two main steps, first estimating CSR7.5 at a given depth of the soil profile, with the estimation formula:
in the above formula, CSR is the cyclic stress ratio; a ismaxThe ground acceleration peak value caused by seismic load with gravity as a unit; g is the acceleration of gravity; sigmavIs the total overburden stress at a certain depth; sigma'vIs the effective overburden stress at a certain depth; r isdThe coefficient of reduction of the flexibility of the earth pillar above the liquefiable layer is taken into account; MSF is an order of magnitude scaling factor; kσIs a overburden correction factor.
Then, the model parameters are calculated by using a database on the basis of the formula 2 to improve the CRR 7.5.
In the above formula, N120The number of times required for hammering 30cm of sand; sigma'vIs the vertical stress of the overburden.
If the CRR is greater than CSR7.5, liquefaction occurs, otherwise non-liquefaction occurs. To solve this problem, it is necessary to compare CSR7.5 with CRR at all soil depths to determine the critical depth at which liquefaction occurs. Typically, liquefaction occurs at the surface, such as lateral displacements, oscillations, sand flow, gravel surges, etc., which are considered liquefied in the engineering case database.
Step three: calculating the safety factor of the anti-liquefaction trigger. The Safety Factor (SF) can be calculated as follows:
FS＝(CRR7.5/CSR7.5) (4)
step four: and establishing a classification map of the liquefaction area and the non-liquefaction area. Considering uncertainty existing in parameter calculation and model prediction, on the basis of maximization of a similar function, a classification diagram of a liquefaction area and a non-liquefaction area is established by using a Bayesian mapping function and logistic regression and is used for predicting whether liquefaction occurs or not.
The maximum likelihood function is a commonly used method for parameter value estimation, and parameters of the Bayesian mapping function can be estimated by using a data set. The likelihood function can be expressed as:
in the above formula, wherein WLAnd WNLWeight factors for liquefaction and non-liquefaction, respectively, whose values can be determined from engineering experience; qPIs the actual proportion of liquefaction cases in nature; qSIs the proportion of liquefaction cases in the data set; WF is a weighting factor. N is a radical ofLIs the number of liquefaction cases and NNLNumber of non-liquefaction cases.
Two weighting factors for the liquefaction site and the non-liquefaction site are introduced into the model to correct imbalances in the data in the database, so-called sampling biases, due to the tendency of investigators to collect information and perform tests at the liquefaction site, rather than at the non-liquefaction site.
Step five: and (6) evaluating the prediction effect. The influence of the deviation sampling factor on the prediction effect is researched by assuming the variation range of the deviation sampling factor and acquiring the optimal value of the deviation sampling factor.
In the technical scheme, the evaluation of the gravel soil earthquake liquefaction is based on the global 8 earthquake databases, and the gravel soil liquefaction in the engineering can be accurately and economically evaluated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic diagram of a method for evaluating a gravel soil earthquake liquefaction according to an embodiment of the present disclosure;
FIG. 2 is a deterministic model established in the examples of the present application (M ═ 7.5, σ'v＝1)；
FIG. 3 is a relationship between liquefaction probability and safety factor calculated in the application example.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Examples
The embodiment of the application provides a gravel soil earthquake liquefaction evaluation method, as shown in fig. 1, the method mainly comprises 5 steps, specifically:
the method comprises the following steps: a comprehensive database of dynamic sounding tests (DPT) is established by applying 8 different earthquakes all over the world. The database includes seismic magnitude, frequency of motion, outer center distance, geometry type, number of sedimentary layers, gravel soil depth, and soil properties.
Table 1 is a global comprehensive database of dynamic sounding tests (DPTs) established for 8 different earthquakes, in which earthquake magnitude, movement frequency, outer-center distance, geometric type, number of sedimentary layers, gravel soil depth and soil properties are summarized when earthquakes occur in different regions.
TABLE 1 comprehensive database of Dynamic Penetration Tests (DPT) established for 8 different earthquakes worldwide
Step two: and establishing a deterministic model. Based on the model, a cyclic intensity ratio (CRR) can be estimated.
The determination of the cyclic intensity ratio (CRR) involves two main steps, first estimating CSR7.5 at a given depth of the soil profile, with the estimation formula:
in the above formula, CSR is the cyclic stress ratio; a ismaxThe ground acceleration peak value caused by seismic load with gravity as a unit; g is the acceleration of gravity; sigmavIs the total overburden stress at a certain depth; sigma'vIs the effective overburden stress at a certain depth; r isdThe coefficient of reduction of the flexibility of the earth pillar above the liquefiable layer is taken into account; kσIs a overburden correction factor.
Then, the model parameters are calculated by using a database on the basis of the formula 2 to improve the CRR 7.5.
If the CRR is greater than CSR7.5, liquefaction occurs, otherwise non-liquefaction occurs. To solve this problem, it is necessary to compare CSR7.5 with CRR at all soil depths to determine the critical depth at which liquefaction occurs. Typically, liquefaction occurs at the surface, such as lateral displacements, oscillations, sand flow, gravel surges, etc., which are considered liquefied in the engineering case database.
Fig. 2 shows the deterministic model established in this study, and it can be seen that the deterministic model proposed in this example can predict the CRR value of the gravel soil well.
Step three: calculating the safety factor of the anti-liquefaction trigger. The Safety Factor (SF) can be calculated as follows:
FS＝(CRR7.5/CSR7.5) (3)
step four: and establishing a classification map of the liquefaction area and the non-liquefaction area. Considering uncertainty existing in parameter calculation and model prediction, on the basis of maximization of a similar function, a classification diagram of a liquefaction area and a non-liquefaction area is established by using a Bayesian mapping function and logistic regression and is used for predicting whether liquefaction occurs or not.
The maximum likelihood function is a commonly used method for parameter value estimation, and parameters of the Bayesian mapping function can be estimated by using a data set. The likelihood function can be expressed as:
in the above formula, wherein WLAnd WNLWeight factors for liquefaction and non-liquefaction, respectively, whose values can be determined from engineering experience; qPIs the actual proportion of liquefaction cases in nature; qSIs the proportion of liquefaction cases in the data set; WF is a weighting factor. N is a radical ofLIs the number of liquefaction cases and NNLNumber of non-liquefaction cases.
Two weighting factors for the liquefaction site and the non-liquefaction site are introduced into the model to correct imbalances in the data in the database, so-called sampling biases, due to the tendency of investigators to collect information and perform tests at the liquefaction site, rather than at the non-liquefaction site.
Fig. 3 is a relationship between liquefaction probability and safety factor in a bayesian mapping function model based on different sampling deviations calculated in the application example. It can be seen that the model with a weight factor of 1 has the highest probability of liquefaction for the same safety factor value. The maximum difference in safety factor values is between 0.5 and 1, a difference of about 25% being observed between the highest line (weight factor 1) and the lowest line (weight factor 3); furthermore, it was found from the different curves that the safety factor value decreases by 10% to 5% for every 0.5 value increase of the weighting factor. When the safety factor is greater than 1, the gap gradually and uniformly decreases until the maximum safety factor value 6 is reached. Furthermore, the liquefaction probabilities of the different weight factor values gradually coincide with an increase in the safety factor value (safety factor greater than 5.0).
Step five: and (6) evaluating the prediction effect. The influence of the deviation sampling factor on the prediction effect is researched by assuming the variation range of the deviation sampling factor and acquiring the optimal value of the deviation sampling factor.
Claims (3)
1. A method for evaluating the seismic liquefaction of a gravel soil, the method comprising:
the method comprises the following steps: establishing a comprehensive database of dynamic penetration tests;
step two: establishing a deterministic model;
step three: calculating a safety factor for resisting liquefaction triggering;
step four: establishing a classification chart of a liquefaction area and a non-liquefaction area;
step five: and (6) evaluating the prediction effect.
2. The method of claim 1, wherein the evaluation of the liquefaction of the gravel soil is performed by a computer,
the comprehensive database of the dynamic sounding test established in the first step is established based on 8 different earthquakes all over the world, and comprises earthquake magnitude, movement frequency, outer center distance, geometric type, deposition layer number, gravel soil depth and soil property.
3. The method of claim 1, wherein the evaluation of the liquefaction of the gravel soil is performed by a computer,
the comprehensive database of the dynamic sounding test established in the first step is established based on 8 different earthquakes all over the world, and comprises earthquake magnitude, movement frequency, outer center distance, geometric type, deposition layer number, gravel soil depth and soil property.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005156273A (en) * | 2003-11-25 | 2005-06-16 | Michiyo Sugai | Earthquake motion predicting method and its evaluation method |
| CN102879551A (en) * | 2012-10-24 | 2013-01-16 | 中国地震局工程力学研究所 | Gravel soil mechanical property evaluation method |
| CN106408211A (en) * | 2016-10-26 | 2017-02-15 | 中国水利水电科学研究院 | Deep saturated sand earthquake-induced liquefaction judgment method |
| CN112508124A (en) * | 2020-12-22 | 2021-03-16 | 三峡大学 | Gravel soil earthquake liquefaction discrimination method based on Bayesian network |
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- 2021-12-06 CN CN202111476385.5A patent/CN114154332A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005156273A (en) * | 2003-11-25 | 2005-06-16 | Michiyo Sugai | Earthquake motion predicting method and its evaluation method |
| CN102879551A (en) * | 2012-10-24 | 2013-01-16 | 中国地震局工程力学研究所 | Gravel soil mechanical property evaluation method |
| CN106408211A (en) * | 2016-10-26 | 2017-02-15 | 中国水利水电科学研究院 | Deep saturated sand earthquake-induced liquefaction judgment method |
| CN112508124A (en) * | 2020-12-22 | 2021-03-16 | 三峡大学 | Gravel soil earthquake liquefaction discrimination method based on Bayesian network |
Non-Patent Citations (1)
| Title |
|---|
| PIRHADI, N (PIRHADI, NIMA); HU, JL (HU, JILEI); FANG, Y (FANG, YU); JAIRI, I (JAIRI, IDRISS); WAN, XS (WAN, XUSHENG); LU, JG (LU, : "Seismic gravelly soil liquefaction assessment based on dynamic penetration test using expanded case history dataset", 《BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT》, 21 September 2021 (2021-09-21) * |
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