JP2999702B2 - A method for discriminating the form of landslides on cut slopes and a method for estimating critical rainfall for deep landslides on cut slopes - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for discriminating a form of a cut slope having a possibility of collapse due to rainfall and a method for estimating a critical rainfall when a deep collapse occurs.

[0002]

2. Description of the Related Art Cut slopes along railway lines may collapse due to rainfall, which hinders stable and safe transportation of trains. In order to prevent such disasters, it is necessary to identify danger points and properly implement disaster prevention measures for danger points, and at the same time, it is necessary to appropriately operate regulations that are appropriate for the actual conditions of the collapse along the railway . For this reason, engineers who maintain railway structures can accurately predict the risk of cut slope failure due to rainfall, and on-site technicians can easily evaluate the form of collapse and the risk assessment corresponding to this. We need a forecasting method that can do it. 2. Description of the Related Art Conventionally, a risk assessment regarding collapse of a cut slope in a railway is a result obtained by a discriminant analysis, and its rain resistance is indicated by a 24-hour rainfall. In other words, the evaluation of the cut slope at the danger point was performed using the grade sheet of the slope, and the soil was graded according to the soil quality, height, slope, and water collection conditions of the surface soil. The daily rainfall (24 hour rainfall) ). On the other hand, in the operation regulation for slowing down or stopping a train during rainfall, a method of empirically determining a rainfall per hour and a total rainfall from the start of rainfall from a rainfall caused by a past rainfall disaster is used.

[0003] However, in the above-described conventional method of evaluating the risk of collapse of a cut slope in a railway, the risk cannot be evaluated in accordance with the form of collapse, the risk is determined based on the appearance factor, and the characteristics of the area are determined. There was a problem in the evaluation accuracy because it was a nationwide standard that was not considered. Also,
Since the rainfall of the risk assessment and the rainfall of the driving regulation are not directly related, there is a disadvantage that the rainfall of the risk assessment and the rainfall of the driving regulation are different indices. In view of such a situation, there is a demand for the development of a method for discriminating a collapse form of a cut slope having a risk of collapse due to rainfall and a method for predicting a collapse limit rainfall with high accuracy for each collapse form.

In the past, as a method of obtaining the critical rainfall of the embankment, a method of predicting the critical rainfall of the embankment as disclosed in Japanese Patent Application Laid-Open No. 5-32043 has been developed. However, the critical rainfall of the embankment collapse is the same as the maximum rainfall per unit time within 24 hours in the area where the embankment to be predicted exists and the continuous rainfall which is the accumulated rainfall from the start of rainfall. The critical rainfall on the cut slope is greatly affected by continuous rainfall when the ground collapses deeply, and greatly depends on the hourly rainfall when the surface collapses. Therefore, in order to determine the critical rainfall on the cut slope, a method for determining the critical rainfall for each type of collapse is required, unlike the method for determining the critical rainfall for the embankment.

[0005]

According to the prior art,
There was a problem that the form of collapse of the cut slope which could collapse due to rainfall could not be determined. Another problem is that it is not possible to accurately predict the critical rainfall on the cut slope. SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and provides a method for determining the form of collapse of a cut slope that can previously determine the form of collapse of the cut slope that is likely to collapse due to rainfall. . Further, the present invention provides a method for estimating a critical depth of rainfall on a cut slope which can accurately predict a critical rainfall for a cut slope according to a mode of collapse of the cut slope.

[0006]

According to a first aspect of the present invention, there is provided a method for determining a collapse mode of a cut slope according to the first aspect of the present invention, wherein a structural condition, a soil / geological condition, and a water collecting condition are substituted into a discriminant to determine a collapse mode. It is characterized by determining whether deep collapse, superficial collapse or deep collapse, or superficial collapse will occur from the collapse form discrimination score. The method for determining the form of collapse of a cut slope according to claim 2 of the present invention is the method of claim 1, wherein the cut height is used as a structural condition and the surface soil thickness is used as a soil / geological condition. The feature is that the topography of the cut slope is used as the condition. According to a third aspect of the present invention, there is provided a method for determining a collapse mode of a cut slope according to the first or second aspect, wherein a discriminant is (collapse mode determination score) = (coefficient to cut height) × (cut area) Height) + (coefficient for surface soil thickness) x (surface soil thickness) + (coefficient for topography above cut slope) x (topography above cut slope) + (constant term) Features. In the method for determining the form of collapse of a cut slope according to claim 4 of the present invention, the method according to claim 1 or 2 or 3 determines that a deep collapse occurs when the collapse form determination score is less than -1. When the collapse type discrimination score is −1 or more and 1 or less, it is determined that deep collapse or surface collapse occurs, and when the collapse form discrimination score exceeds 1, it is determined that surface collapse occurs.

The method for predicting the critical rainfall of a cut slope in a deep slope according to claim 5 of the present invention is characterized in that the basic point for the deep collapse of the cut slope, the evaluation point of the slope of the cut slope, and the evaluation of the cut height. Point and surface soil thickness evaluation points, cut slope intrusion strength evaluation points, basement hardness evaluation points, topographical evaluation points above the cut slopes and annual average rainfall evaluation points The evaluation point is obtained, and from the comprehensive evaluation point, the time rainfall which is the maximum rainfall per unit time within 12 hours in the area where the cut slope to be predicted exists and the rainfall accumulated from the start of the rainfall. The method is characterized in that a certain continuous rainfall is obtained, and a critical collapse rainfall at a cut slope is predicted from the hourly rainfall and the continuous rainfall.

According to the method for determining the form of collapse of a cut slope according to the present invention, for a cut slope having a possibility of collapse due to rainfall, the structural conditions, soil and geological conditions, and water collection conditions of the cut slope Is used to determine the collapse type discrimination score, and the collapse type is discriminated from the collapse type discrimination score. Therefore, it is possible to quantitatively determine whether a deep collapse or a surface collapse will occur. According to the method for predicting the depth of critical rainfall of a cut slope according to the present invention, since the critical rainfall of the cut slope is predicted after determining the collapse mode, the critical rainfall corresponding to the collapse mode is calculated. Predict with high accuracy. In addition, since the collapse limit rainfall corresponding to the collapse mode can be accurately predicted, the collapse limit rainfall can be applied as an index of the rainfall of the operation regulation.

[0009]

BEST MODE FOR CARRYING OUT THE INVENTION A method of determining the form of collapse of a cut slope according to the present invention will be described. In Cut Slopes at risk of collapse by rain, the Cut height is used as a structural condition H, the surface soil thickness to be used as soil and geological conditions D _S, the Cut Slope top to be used as collecting conditions terrain Is defined as W _G , and the decay mode discrimination score serving as a discrimination reference is Z, the discriminant is given by equation (1). However, the cut height and surface soil thickness are measured in meters, and the top terrain above the cut slope is 1 for catchment terrain, 2 for equivalent inflow terrain, 3 for flat terrain, and 3 4 is assumed. Z = −0.115H−2.992D _S + 0.666W _G +4.001 (1) From these results, when Z <−1, deep collapse, −1
When ≦ Z ≦ 1, deep collapse or surface collapse, 1 <
When it is in the range of Z, it is determined that surface collapse occurs.

A method of estimating the critical rainfall of the cut slope in the deep collapse according to the present invention will be described. In the cut slope that was determined to cause deep collapse, the basic points for the deep collapse of the cut slope, the evaluation points for the slope of the cut slope, the evaluation points for the cut height, and the The evaluation point, the evaluation point of the penetration strength of the cut slope, the evaluation point of the basement hardness, the evaluation point of the topography above the cut slope and the evaluation point of the annual average rainfall are added to obtain the comprehensive evaluation point. Assuming that the hourly rainfall that is the maximum rainfall per unit time within 12 hours in the area where the cut slope exists is r, the continuous rainfall that is the rainfall accumulated since the start of rainfall is R, and the overall evaluation point is S, It becomes r ⁿ · R ^m = S. Here, the critical rainfall for deep collapse of the cut slope is n =
Assuming that 0.2 and m = 0.4, it can be obtained by equation (2). r ^0.2 · R ^0.4 = S (2) At this time, as shown in FIG. 2, when a graph is drawn with time rainfall on the vertical axis and continuous rainfall on the horizontal axis, the deep collapse limit rainfall is a nonlinear curve (deep collapse limit rainfall). Curve). When rainfall is observed, the time rainfall corresponding to the continuous rainfall is plotted on this graph. From the results, it can be predicted that if the trajectory of the plotted points reaches the deep collapse critical rainfall curve, deep collapse of the cut slope will occur. Then, when it is predicted that the cut slope that may collapse due to rainfall will collapse, the train operation in the corresponding section is restricted.

[0011]

[Table 1]

Similarly, a method for estimating the critical rainfall of the surface layer collapse on the cut slope will be described. In the cut slope that was determined to cause surface collapse, the basic points for the surface collapse of the cut slope, the evaluation points for the slope of the cut slope, the evaluation points for the soil quality of the surface soil, The total evaluation score is obtained by adding the evaluation points of the penetration strength, the rock type of the basement, the topography of the cut slope, and the evaluation point of the annual average rainfall. Assuming that the hourly rainfall that is the maximum rainfall per unit time within 12 hours in the area where the cut slope exists is r, the continuous rainfall that is the rainfall accumulated since the start of rainfall is R, and the overall evaluation point is S, It becomes r ⁿ · R ^m = S. Here, the critical rainfall of the surface layer on the cut slope is obtained by the equation (3), where n = 0.9 and m = 0.2. r ^0.9 · R ^0.2 = S (3) At this time, if a graph is drawn with time rainfall on the vertical axis and continuous rainfall on the horizontal axis as shown in FIG. 3, the surface rainfall limit rainfall is a non-linear curve (surface rainfall limit rainfall). Curve). When rainfall is observed, the time rainfall corresponding to the continuous rainfall is plotted on this graph. From the results, it can be predicted that if the locus of the plotted points reaches the surface collapse critical rainfall curve, the surface collapse of the cut slope will occur. Then, when it is predicted that the cut slope that may collapse due to rainfall will collapse, the train operation in the corresponding section is restricted.

[0013]

[Table 2]

Next, a method for predicting the form of collapse of a cut slope and
Prediction method of critical rainfall for deep slope failure of cut slope
This will be specifically described. (A) Extraction of factors related to collapse mode The geological factors and rainfall patterns are used to determine the collapse mode.
Is concerned. But the real-time limitations of the cut slope
In order to predict rainfall, rainfall patterns must be
Because it is impossible to predict,
Disintegration type is determined from a biological viewpoint. Construction of cut slope
The cutting slope β and the cutting height H are adopted as construction conditions. Soil
Conditions include slope stability as a parameter related to slope stability.
Force c, internal friction angle φ, unit weight γ, pore water pressure u_W
Is relevant. Therefore, the collapse form determination score Z is
Except for rainfall conditions related to the pattern, ε is used for other factors.
Then, Z = f (β, H, c, φ, γ, u_W, Ε)
 … (4) Among the soil conditions shown in the above equation (4),
c, φ, γ are soil classification S of cohesive soil, sandy soil, and gravel soil._E
And the penetration strength N of the cut slope_CBe represented by gap
Water pressure u _WThe increase in pore water pressure is related to the increase in pore water pressure.
The fineness fraction G and the concentration of rainwater are related
Topographical condition W on the top of cut slope_GAnd surface soil thickness
D_SLet me represent you. Therefore, the above equation (4) gives: Z = f (β, H, S_E, N_C, G, W_G, D_S) (5) Therefore, in the analysis,
To perform analysis with the seven variables shown on the right side of (5)
did.

(B) Execution of discriminant analysis on collapse mode In order to discriminate the collapse mode of the cut slope due to rainfall, a linear discriminant analysis was performed based on the above equation (5). Table 3 shows coefficients and F ₀ values of each factor obtained by the discriminant analysis.

[0016]

[Table 3]

By performing an F-value test on the result and eliminating factors that are not significant in the analysis, the following equation (6) is obtained as a significant linear discriminant function. Z = −0.115H−2.992D _S + 0.666W _G +4.001 (6) When this collapse form discrimination score Z is Z <0, deep collapse occurs, 0 <
If it is Z, it will be classified as surface collapse. The distribution of the collapse form discrimination score Z at this time is as shown in FIG. 4, and the correct answer rate in the collapse form discrimination of the cut slope is as shown in Table 4.

[0018]

[Table 4] However, if the cut slope is to be collapsed in practice, it is considered that deep collapse will occur if Z <-1; deep collapse or surface collapse will occur if -1≤Z≤1, and surface collapse will occur if 1 <Z. Is more complicated for predicting the marginal rainfall, but the probability of erroneous determination is lower. Therefore, the present invention proposes a collapse mode discrimination method in which the range of the discrimination score Z is the above range.

(C) Extraction of Factors for Predicting the Critical Rainfall of the Cut Slope In extracting the factors for predicting the critical rainfall of the cut slope, geotechnical studies on slope stability during rainfall Considering the actual theory of cut slope failure occurring along railway lines and factors that can be easily determined by field engineers, the potential for cut slope stability during rainfall S is represented by nine factors as shown in equation (7). S = f (β, H, D S, N C, S E, R h, R C, W G, R E) ... (7) where the structural condition of Cut Slope, beta is Cut glue surface gradient, H is Cut height, and as soil and geological conditions, D _S
The surface soil thickness, N _C is penetration strength of Cut Slope, S _E is soil, R _h is foundation hardness, R _C is a rock type of foundation, also,
As catchment conditions, W _G is the terrain of Cut Slope top,
As an empirical rainfall condition, _RE is an average annual rainfall.
Among the soil and geological conditions of the cut slope, the hardness R of the base
_h is classified into hard rock, soft rock, fragile rock and earth and sand, and the base rock type _RC is classified into sedimentary rock, igneous rock and metamorphic rock. In addition, the topography of the cut slope, which is the condition of water collection,
As shown in FIG. 1, the catchment terrain is classified into four types: type 1, equivalent inflow terrain is type 2, flat terrain is type 3, and non-water catchment terrain is type 4.

(D) Extraction of External Criteria Related to Cut Slope Failure Rainfall is considered as an external criterion related to cut slope failure, but in the present analysis, it has been conventionally used for railway operation regulation. In consideration of the circumstances, the product of the continuous rainfall R and the hourly rainfall r is used, and the exponent of each rainfall (constant m,
and the product of n), as耐降rain potential Cut slopes were assumed to be represented by ^{S = R m · r n ...} (8). Therefore, the equation (7) and from equation ^{(8) R m · r n} = f (β, H, D S, N C, S E, R h, R C, W G, R E) (9) Becomes The hourly rainfall was not the time when the disaster occurred, but was the maximum value within 12 hours from the occurrence. This considers the case where the peak water pressure peaks shortly after recording the maximum hourly rainfall when considering the rise in pore water pressure that affects deep-sea collapse.

(E) Prediction of critical rainfall by quantification class (a) Execution of primary analysis In order to determine the critical depth of rainfall on a cut slope due to rainfall, the right-hand side of the above equation (9) is expanded in a primary form. Then, in consideration of the above nine factors, a multivariate analysis using quantification class III was performed. At this time, the numbers m and n to be shown on the left side of the above equation (9)
Was set to 1.0 as a first-order approximation, and a multivariate analysis was performed. As a result, among the 9 factors on the right side of the equation (9), the contribution rate of soil classification S _E is low, partial correlation coefficient is 0.2
It was 5. The base rock type _RC had 80% sedimentary rock data and its partial correlation coefficient was 0.2%.
9 was relatively small. Therefore, these variables were deleted and a new analysis was performed. (B) Execution of secondary analysis Among the nine factors shown on the right side of the above equation (9), soil classification S
A multivariate analysis was again performed using quantification type III for seven factors excluding _E and the rock type _{RC of the} basement. At this time, analysis is performed by sequentially changing the numbers m and n to be shown on the left side of the above equation (9) to 1.0 at a pitch of 0.1, and drawing a contour line of the obtained multiple correlation coefficient r ₀ . It looks like 5. According to FIG. 5, the contour line peaks at m = 0.4 and n = 0.2, and the multiple correlation coefficient at this time is r ₀ = 0.83. Deep of Cut Slopes obtained by analyzing decay limit rainfall R ^m · r ^{n (m} =
0.4, n = 0.2), the relationship between the predicted value and the actually measured value is as shown in FIG. 6, and the hit rate is high.

(F) Criterion for assessing the risk of deep collapse of cut slopes Based on the weight of the factors obtained by performing the analysis using quantification class I, experience based on the actual conditions of the collapse of the cut slopes of railways Target,
Taking into account engineering considerations, we propose the criteria for evaluating the risk of deep collapse of cut slopes shown in Table 1. This criterion is in addition structural condition of the switching soil to a basic point 15.56, soil and geological conditions, collecting conditions, the sum of the corresponding evaluation points experience rainfall conditions,
Continuous rainfall and time rainfall product R ^m · r ⁿ (provided that m = 0.
4, n = 0.2). Basic point 1
5.56 is due to rain from 1975 to 1988
Using the example of the collapse of the cut slope along the railway line,
A constant term obtained as a result of multivariate analysis using quantification class I
You. Since the right side of the above equation (7) is a constant for each cut slope, it is indicated by a non-linear curve in a graph with R and r as axes. However, the marginal rainfall may be extremely small depending on the cutting conditions. In this case, the minimum value of the collapse case used in the analysis, R ^0.4 · r ^0.2 =
7.27 is the lower limit.

(G) Verification of a typical collapse case The method of discriminating the form of collapse and the evaluation method of deep collapse of a cut slope described in the previous section are applied to a case of a collapse of a cut slope generated by a railway, and its accuracy is evaluated. I tried to verify. (A) Collapse example 1 The torrential rain that hit the Tokai district on July 29, 1989
Since the hourly rainfall was recorded at 50 mm / h, the train operation was stopped according to the operation stop criteria. About 40 minutes later, when the continuous rainfall reached 160 mm and the hourly rainfall reached 65 mm / h,
A cut slope having a height of H = 14 m on the right side of the track collapsed over a length of 40 m as shown in FIG. 7 and obstructed the upper and lower lines of the track. The area near the landslide is located at the end of a hilly area composed of volcanic products, and both sides of the track have cut structures. The slope has a slight swamp topography, along which sewage and drainage works parallel to the slope are constructed. According to the sounding by the simple dynamic cone penetration test of the collapse point, 2～3.5M from the surface is deposited N _C = 10 following Rohm, 30 weathered layer of N _C = 10 to 20 is below the It was 80 cm, and at a depth below that, a base layer with N _C > 20 was confirmed. According to the above equation (4), it is determined whether the cut slope is a surface collapse pattern or a deep collapse pattern. Cut height is H
= 14m, topsoil thickness is D _S = 2~3.5m, since topographical conditions of Figure 1 showing the degree of concentration of rainfall is a W _G = 1, determination score Z = -2.9~ −7.4. Since this value is Z <-1, the cut slope is determined to be a deep collapse pattern. Since the cut slope was determined to have a deep collapse pattern, the critical rainfall R ^0.4
· R ^0.2 can calculate the decay limit rainfall by risk criteria for deep collapse of Table 1. As a result, R
^0.4 · r ^0.2 = 16.99 was obtained. Drawing this limit rainfall as a relationship between the continuous rainfall R and the hourly rainfall r results in a solid line in FIG. This collapse is caused by a continuous rainfall R of about 165 mm,
Since it is occurring when the hourly rainfall r is about 65 mm,
The predicted values are in good agreement with the rainfall observations at the time of the disaster.
FIG. 8 also shows 11 rainfall histories in which a relatively large amount of rainfall has been recorded in the past. These rainfall histories did not collapse, but they are almost in the area below the critical rainfall curve. Therefore, the estimated value sufficiently satisfies the measured value. (B) Collapse Case 2 In the Seto Inland Sea region, heavy rain in the late rainy season, which began to fall on the night of July 26, 1993, continued to fall until 18:00 on March 28, reaching a maximum annual rainfall of 34 mm / h and a continuous rainfall of 287 mm. 2
At 6:30 in the early morning of the 9th, a track maintenance worker during patrol found a crack with a height of about 0.5m on the cut slope at the upper part of the cut slope shown in Fig. 9. At 17:00 on the same day, the clod under the dog began to move, and the amount of collapsed soil reached 10 m ³ up to the track. The track at this collapsed area is constructed by cutting a ridge-shaped slope. The cut height on the left side is about 3-5 m, while that on the collapsed right cut slope is as high as 15-20 m. The basement is made of granodiorite, but the basement is very deep, and the collapse part is weathered granite secondary deposits (cliffs, Masato) at the end of the slope. According to the sounding result by the simple dynamic cone penetration tester, about the thickness of the overburden layer 2.1 m, the average of the N _C value 9.6
Met. Cut height of the glue surface H = 18.5 m, the surface layer thickness D _S = 2.1 m, because the terrain conditions are W _G = 4, discriminating value from the equation (6) is Z = -1 .75, and the collapse of the slope is determined to be a deep collapse. When the critical rainfall of the deep collapse pattern is calculated according to Table 1, R
^0.4 · r ^0.2 = 17.20 If this is drawn as the relationship between the continuous rainfall R and the hourly rainfall r, it becomes as shown by the solid curve in FIG. On the other hand, the history of rainfall at the time of the collapse shows a solid zigzag line, which clearly indicates that the collapse occurred beyond the critical rainfall curve. In FIG. 10, the major rainfall before the collapse, that is, the rainfall that did not cause the slope failure, is indicated by a broken zigzag line, but most of the rainfall history is in the area below the critical rainfall curve. From these, the estimated rainfall marginal value almost satisfies the measured value. In this way, based on the case of collapse due to rain on the cut slope,
The present invention proposes a method of determining a collapse mode by a discriminant analysis. In addition, based on the case of deep landslide on the cut slope, the critical rainfall that leads to the deep landslide by quantification class I is calculated as 0.
This proposes a method of predicting by the product of the fourth power and the hourly rainfall r to the power of 0.2. When this method of assessing the risk of deep landslides was applied to a typical case of a recent railroad cut slope failure, the predicted values agreed well with the measured values. Although the method proposed here was obtained statistically, it can be applied as one method of predicting collapse in advance, and by controlling train operation during rainfall based on this prediction, safe operation is ensured can do.

[0024]

As described above in detail, according to the method for determining the form of collapse of a cut slope according to the present invention, a collapse form discrimination score is obtained for a cut slope that is likely to collapse due to rainfall. Since the morphology is determined, it is possible to quantitatively determine whether a deep landslide or a surface landslide occurs. In addition, the determined form of collapse of the cut slope can be used for a method of predicting a critical rainfall depth of the cut slope and a method of predicting a critical rainfall amount of the surface slope of the cut slope. ADVANTAGE OF THE INVENTION According to the prediction method of the deep collapse critical rainfall of the cut slope of this invention, the collapse critical rainfall suitable for the collapse form can be predicted with high precision. In addition, the collapse limit rainfall can be applied as an index of the rainfall of the operation regulation, and the train operation management can be performed with high accuracy. According to the present invention, by obtaining the conditions of the cut slope obtained by the field survey, the form of the cut slope collapse is determined, and the cut slope deep collapse obtained by each power of the hourly rainfall and the continuous rainfall is obtained. It is possible to determine the critical rainfall of a train, regulate the operation of trains during rainfall, and implement safe operation and safety measures quickly and accurately. The practical effect is remarkable.

[Brief description of the drawings]

FIG. 1 is a diagram showing the topography of an upper part of a cut slope.

FIG. 2 is a diagram illustrating a deep collapse critical rainfall curve of a cut slope.

FIG. 3 is a diagram showing a surface collapse critical rainfall curve of a cut slope.

FIG. 4 is a diagram showing a distribution of collapse form discrimination scores.

FIG. 5 is a diagram showing contour lines of a multiple correlation coefficient in equation (9).

FIG. 6 is a diagram showing a correlation between a predicted value and a measured value of the critical rainfall for deep collapse on a cut slope.

FIG. 7 is a sectional view of a cut slope of collapse example 1.

FIG. 8 is a diagram showing a critical rainfall curve of Collapse Example 1.

FIG. 9 is a sectional view of a cut slope in Collapse Example 2.

FIG. 10 is a diagram showing a critical rainfall curve of Collapse Case 2.

──────────────────────────────────────────────────続 き Continuing from the front page (72) Inventor Masahiko Samizo 2-8-8 Hikaricho, Kokubunji-shi, Tokyo Examiner, Railway Technical Research Institute, Takayoshi Fukada (56) References JP-A-59-185224 (JP) , A) JP-A-60-61618 (JP, A) JP-A-5-323043 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) E02D 17/00 E02D 17/20 106 G01D 21/00 G01W 1/10

Claims (3)

(57) [Claims] 1. A method for determining a collapse mode when a cut slope collapses, wherein a structural condition, a soil / geological condition, and a water collecting condition are substituted into the following discriminant to obtain a collapse mode determination score. A method for determining the form of collapse of a cut slope, comprising determining whether deep collapse, surface collapse or deep collapse, or surface collapse occurs from the collapse form discrimination score. Discriminant: (collapse type discrimination score) = (coefficient for cut height) × (cut height) + (coefficient for surface soil thickness) ×
(Surface layer thickness) + (coefficient for topography above cut slope) × (topography above cut slope) + (constant term) 2. The method according to claim 1, wherein when the collapse type discrimination score is less than -1, deep collapse is determined to occur, and when the collapse form discrimination score is -1 or more and 1 or less, deep collapse or surface collapse is determined. The method for determining the form of collapse of a cut slope according to claim 1, wherein it is determined that the collapse has occurred, and when the collapse form classification score exceeds 1, it is determined that a surface layer collapse has occurred. 3. A method for predicting a critical rainfall when a deep collapse occurs according to a result of a method of determining a form of collapse of a cut slope, comprising: a basic point for the deep collapse of the cut slope; Evaluation point of surface gradient, evaluation point of cut height, evaluation point of surface soil thickness, evaluation point of penetration strength of cut slope, evaluation point of basement hardness, evaluation of topography of cut slope The total rainfall is calculated by adding the points and the annual average rainfall evaluation points, and the maximum rainfall per unit time within 12 hours in the area where the cut slope to be predicted exists from the comprehensive evaluation points And a continuous rainfall that is a cumulative rainfall from the start of rainfall, and predicting a deep collapse critical rainfall of a cut slope from the hourly rainfall and the continuous rainfall. Method for estimating the critical rainfall of the surface deep collapse.