KR101009657B1 - Probabilistic stability analysis method of slopes using terrestrial lidar - Google Patents

Abstract

PURPOSE: A statistical rock slope stability analysis method is provided to improve the reliability of the interpretation result by excluding subjective error using quantitive conversion technique in JRC(Joint Roughness Coefficient) determination and statistical parameter. CONSTITUTION: A geometric condition of the surface and profile of the discontinuity surface are calculated from point group data about the terrestrial LiDAR(Light Detection And Raging)(S20). The quantitive JRC is calculated using the discontinuity surface profile(S40). The JCS(Joint Compressive Strength) is calculated using the SHV(Schmidt Hammer Value) about the discontinuity surface(S50). The PDF(Probability Density Function) and CDF(Cumulative Density Function) are obtained using JCS and JRC as the random variable. The damage probability is calculated from the probability value of the unsafe region boundary(S60).

Description

Probabilistic Stability Analysis Method of Slopes Using Terrestrial LiDAR}

The present invention relates to a stochastic rock slope stable analysis method using ground lidar, and more particularly, to solve the sufficient data securing problem pointed out as a limitation of the conventional stochastic stability analysis and to improve the reliability of the analysis results. A stochastic rock slope stability analysis using LiDAR.

Recently, unexpected disasters such as localized torrential rain occur frequently due to extreme weather and abnormal weather. As a result, civil engineering structures collapse and the resulting damage to life is increasing, especially the damage caused by slope collapse is increasing.

Particularly, in case of rock slope, unlike soil slope, it has various and complicated forms of destruction depending on the weathering degree of the rock and the characteristics of discontinuity. It is important to analyze structural defects through slope stability analysis because it results in concentration of stress in the direction, which reduces slope stability.

Geotechnical problems, including slope stability analysis, include uncertainties as they target natural objects, and these uncertainties reduce the reliability of the process and results. Therefore, by considering such uncertainties in the analytical model, efforts are being made to secure the rationality of the analytical model itself and to promote efficient design and economic analysis.

For example, probabilistic analysis is known to be effective as a method to consider uncertainty in geotechnical problems, and the trend of replacing existing deterministic methods with probabilistic methods in order to increase the reliability of analysis results.

Probabilistic stable analysis, however, exposes limitations that cannot be used in practice because the interpretation process is difficult and a sufficient amount of experimental data is required. The difficulty of the analysis is somewhat overcome by the development of a commercial solution that emphasizes the user's convenience, but the problem of securing enough experimental data still acts as an obstacle to probabilistic stability analysis.

The present invention has been made in view of the above points, and it is possible to quickly extract a large amount of geometrical conditions and profiles of discontinuous surfaces using ground lidar to solve the problem of securing a sufficient number of experimental data. To provide a stochastic rock slope stability analysis method using a ground lidar.

It is another object of the present invention to provide a stochastic rock slope stability analysis method using terrestrial lidar that can increase reliability and utility by applying data obtained using terrestrial lidar to a parameter for quantitative calculation.

The stochastic rock slope stability analysis method using the ground lidar according to the embodiment of the present invention is based on the geometric conditions and the discontinuity of slopes, heights, orientations, and slopes from point group data on the slope surface measured using the ground lidar. Calculate the profile, measure the Schmidt Hammer Valve (SHV) on the discontinuous surface to indirectly obtain the wall strength of the joint surface, calculate the JCS (Joint Compressive Strength) using the measured SHV, and profile the discontinuous surface. Probabilistic Density Function (PDF) and Cumulative Density Function by calculating quantitative Joint Roughness Coefficient (JRC) and Monte Carlo simulation using the calculated JCS and JRC as random variables (CDF; Cumulative Density Function) and estimating the probability of failure from the probability value of the unstable area boundary. Lose.

In the process of calculating the quantitative JRC using the profile of the discontinuous surface, it is preferable to exclude subjective error using the empirical formula of the Z ₂ and Ai parameters.

According to the stochastic rock slope stability analysis method using the ground lidar according to the embodiment of the present invention, the geometric conditions such as slope extension, height, azimuth, slope, and the like from point group data on the slope surface measured using the ground lidar Since the profile of the discontinuities is calculated, there is no hassle that the investigator has to move directly to the station to measure various geometrical information in order to obtain various geometrical information during the existing visual survey. In addition, the use of the ground lidar does not need to move to the top of the slope to measure the slope height or the direction of the discontinuous surface, and has the advantage that the necessary data can be easily obtained through the indoor analysis process from the point cloud data obtained remotely. Furthermore, the larger the slope, the greater the likelihood of falling rocks or further collapse, the greater the effectiveness of this contactless telemetry.

In addition, since a profile gauge is used to measure JRC, there are many limitations on the access conditions and the number of surveys for obtaining data at a desired location. However, in the case of the present invention, since the ground lidar is used, it is possible to extract a sufficient amount of JRC at a desired location in the indoor analysis process, and it is possible to safely perform the investigation work as compared to the direct measurement work.

According to the stochastic rock slope stability analysis method using the ground lidar according to the embodiment of the present invention, it is possible to exclude subjective errors by introducing quantitative conversion techniques and statistical parameters when determining JRC, thereby improving reliability of the analysis results. Can be secured

1 is a block diagram conceptually illustrating a process of performing a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
2 is a block diagram conceptually illustrating a process of acquiring point group data by scanning using a ground lidar in the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.
3 is a block diagram conceptually illustrating a process of generating a DSM using point cloud data in a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
4 conceptually illustrates a state in which overlapping, flipping, and twisting occur in a process of generating a DSM using point cloud data in a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention. The image to represent.
5 is a photograph of a rock slope in which a stable analysis is performed by applying a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
FIG. 6 is an image illustrating a state in which a DSM is generated and a profile is generated using the point cloud data acquired by scanning the rock reflecting surface of FIG. 5.
7 is a model diagram for calculating statistical parameters of a profile in the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.
FIG. 8 is a diagram illustrating a result of calculating a profile and JRC of a rock slope of FIG. 5 by applying a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
FIG. 9 is a table illustrating JRC calculations for rock slopes of FIG. 5 by applying a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
FIG. 10 is a histogram showing a result of calculating JRC by applying a Z ₂ parameter to a rock slope of FIG. 5 by applying a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
FIG. 11 is a histogram showing a result of calculating JRC by applying an Ai parameter to a rock slope of FIG. 5 by applying a stochastic rock slope stability analysis method using a ground lidar according to an embodiment of the present invention.
12 is a graph showing a relationship for converting SHV into JCS in the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.
FIG. 13 is a table showing data converted from JV to SHV measured on the rock slope of FIG. 5 by applying the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.
14 is a histogram showing the results of converting the SHV measured on the rock slope of FIG. 5 into JCS by applying the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.
15 is a conceptual diagram schematically illustrating a process of performing Monte Carlo simulation in the stochastic rock slope stability analysis method using the ground lidar according to the embodiment of the present invention.
FIG. 16 is an image illustrating a result of modeling by converting an analysis section CAD drawing file of a rock reflection surface of FIG. 5 into dxf. FIG.
17 is a result of calculating the probability density function by performing Monte Carlo simulation using the Z ₂ parameter on the rock slope of FIG. 5 by applying the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention. Histogram indicating.
FIG. 18 illustrates the results of calculating the probability density function by performing Monte Carlo simulation using the Ai parameter on the rock slope of FIG. 5 by applying the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention. It is a histogram.
19 is a histogram showing the result of calculating the cumulative density function using the probability density functions of FIGS. 17 and 18 in the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention.

Next, a preferred embodiment of the stochastic rock slope stability analysis method using the ground lidar according to the present invention will be described in detail with reference to the accompanying drawings.

First, the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention is a process of acquiring point cloud data on the slope surface using Terrestrial LiDAR as shown in FIG. S10).

Terrestrial LiDAR is a remote sensing technology that can obtain a distance or other information on the surface of the object by irradiating light to the object and receiving light reflected from the surface of the object again.

The process of obtaining the point group data on the slope surface using the ground lidar as shown in Figure 2, the control point (CP) installation (S12), scanning (scanning) (S14), merging (S18) Proceed in order.

In order to increase the resolution of the point cloud data to be obtained above, LiDAR (Light Detection And Raging) should be approached to the slope, in which case the occlusion area that is obscured by irregular surface curvature of the rock slope is Is generated. Therefore, in order to compensate for this, the point cloud data is acquired from various locations and then aligned. A control point (CP) is installed before scanning in the field for merging.

The control point CP selects a position suitable for scanning and is installed such that at least four or more control points exist at an overlapping portion with adjacent scanning positions.

The scanning process S14 consists of sequentially performing positioning scans, fine scans, and surface scans for each position.

The positioning scan is a process of identifying each control point CP by automatically recognizing a control point CP installed at a site by a high reflectance and comparing it with a coordinate list previously input.

The fine scan is a process of acquiring a precise center coordinate of the control point CP from an intensity intensity image obtained by intensively irradiating thousands of laser pulses for each control point CP. This fine scan process is based on probability theory and applies the assumption that the measured values of the center coordinates of a control point (CP) obtained by random sampling follow a normal distribution around a true value.

The surface scan is a process of acquiring point cloud data of a full surface according to a predetermined resolution.

As described above, the scanning process is repeatedly performed for each position, and transformation matrix for integrating the scanning data for each position into a single data system according to the coordinate values and coordinate system of the control point CP input in advance for the acquired point group data. matrix) Perform configuration work.

The process of alignment (S18) is a process of merging the final point group data for each position into one point group data by a unified coordinate system (global / local coordinate).

Next, using the merged point group data as described above proceeds to the process (S20) of calculating the geometric profile and the profile of the discontinuous surface, such as slope extension, height, orientation, slope.

For example, the point group data merged as described above is a discrete data and does not include information between points. Therefore, DSM is a continuous data type that can extract information for any position or direction. Convert point cloud data to (Digital Surface Model).

In order to obtain the final DSM, as shown in FIG. 3, a four-step data processing process of filtering, tessellation, fix abnormal face, and fill hole is performed.

The filtering step is a step of removing noise caused by ghost phenomenon when noise other than an object such as an investigator or an electric wire included in the point cloud data and the incident angle of the laser are large and cause total reflection. .

In addition, the filtering step removes the redundancy of the overlapping portion generated in the merging process for the point group of the various positions.

The tessellation step is a step of constructing a triangulated irregular network (TIN) using point cloud data arranged through a filtering step.

If the redundant data is not removed in the filtering step, a problem arises in that a triangular network having an irregular and excessively sharp shape is formed in the tessellation step.

The fix abnormal face step is not a single surface but a redundant, non-manifold, and crossing step in the TIN generation process due to the nature of the point cloud data distributed in three-dimensional space. This step is to remove the generated surface (see Fig. 4).

The fill hole step is a step of appropriately filling a hole generated in the tessellation step distributed in the TIN progressed to the fix up normal face step.

In the fill hole step, nonlinear interpolation in the form of a quadratic function considering linear interpolation and curvature around the hole is used.

Through the four steps of data processing such as filtering, tessellation, fix abnormal face, and fill hole, DSM is obtained and using this, JRC (Joint Roughness) In order to calculate Coefficient, a process of extracting a profile of the discontinuous surface is performed.

For example, dozens (eg, 30, 50, 80, etc.) cross sections are set from the starting point to the end point of the slope, and the remaining portions other than those corresponding to the active plane are selected from the profiles extracted by the set cross sections. In addition, the profile of the discontinuity surface is extracted to remove the abnormal shape and to quantitatively calculate JRC in the future.

FIG. 6 shows a state in which the DSM profile is performed based on the point cloud data acquired using the terrestrial lidar for the region shown in FIG. 5.

As described above, when point cloud data is acquired using the ground lidar and the DSM profile is performed, it is possible to acquire a profile for a large amount of discontinuous surfaces in a short time.

In step S40, a quantitative Joint Roughness Coefficient (JCC) is calculated using the extracted profile of the discontinuous surface.

JRC (Joint Roughness Coefficient) is a value indicating roughness when jointed.

Figure 7 shows an example of statistical parameters for quantifying JRC in one of the extracted profiles of discontinuities. In FIG. 7, the x axis represents a distance along measurement line from a reference point, and the y axis represents a surface height around the reference line.

In general, the method of obtaining JRC includes comparison selection in standard profile, method using proposed chart, simple slope test on joint specimens, inversion from Barton's empirical equation by shear test.

Recently, in order to overcome the high probability of obtaining different results for each investigator according to individual subjectivity and proficiency in the process of obtaining JRC, the method of using statistical parameters, the fractal dimension, is used to quantify the JRC. Methods, methods using spectrum analysis, and the like are used.

For example, in the process of calculating the quantitative JRC using the profile of the discontinuous surface (S40), it is preferable to exclude the subjective error using the empirical formula of the Z ₂ and Ai parameters.

In the above, Z ₂ and Ai are representative parameters among statistical parameters, which are the most used parameters for JRC quantification.

Z ₂ is represented by Equation 1 below as a standard deviation of a root mean square (RMS) or a small slope value of a first derivative (micro gradient) (see FIG. 7).

Ai is represented by the following Equation 2 as an average value of the micro average i angle or the micro element roughness angle (see FIG. 7).

In the above, the Z ₂ parameter is evaluated to have the best correlation with JRC, and is known to have the best correlation with the friction angle in the joint without the volume expansion caused by shearing.

The quantification of JRC is performed by substituting Z ₂ and Ai obtained as described above for the corresponding profile into the following equations (3) and (4).

FIG. 8 shows an example in which JRC quantification is performed using Z ₂ and Ai parameters for some of the profiles shown in FIG. 6.

Fig. 9 shows the chart of JRC quantization using Z ₂ and Ai parameters, and Figs. 10 and 11 show histograms prepared for grasping the distribution characteristics of parameters for the quantified JRC values.

As confirmed from FIG. 10, the average of 5.40 and the standard deviation of 1.45 were analyzed for JRC based on the Z ₂ parameter. As confirmed from FIG. 11, in the case of JRC based on the Ai parameter, the average was 6.30 and the standard deviation was 1.93.

The results of the analysis show that the mean and standard deviation of JRC by Ai parameter are slightly higher than JRC by Z ₂ parameter, but both histograms are applied to the stochastic rock slope stability analysis because the overall distribution pattern follows the normal distribution. It is possible to.

And the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention, as shown in Figure 1, to measure the SHV (Schmidt Hammer Valve) for the discontinuous surface to indirectly obtain the wall strength of the joint surface It includes a process (S30).

The SH (Schmidt Hammer) used to measure the SHV is a non-destructive device for measuring the elastic modulus or strength of the sample by hitting rock or concrete with a certain energy and measuring the degree of rebound.

For example, the SHV is measured by sand paper to remove foreign substances according to the method suggested by the Suggested method (revised version, 2008) of the International Society for Rock Mechanics (ISRM), and then Twenty SHVs are obtained per set, separated by a diameter, and a total of five sets are performed, and the angle of attack is carried out while keeping the angle perpendicular to the surface where possible.

Then, a process (S50) of calculating a joint compression strength (JCS) using the SHV measured through the above process is performed.

In the above, JCS (Joint Compressive Strength) is a value indicating compressive strength, and a method of obtaining JCS using SHV is proposed by ISRM, and a JCS estimation method using Schmidt hammer (SH) in the field is illustrated in FIG. 12. A graph as shown or an empirical formula such as the following Equation 5 is used.

In Equation 5, γ represents the unit weight of the rock, and the unit of JCS is expressed in MPa (megapascal).

In addition, there is a high probability that the large-scale JCS of the site will be lower than that of the JCS (cold compressive strength) for the test specimen in the room, and the empirical formula shown in Equation 6 below gives the scale effect in consideration of the scale effect. use.

In Equation 5, JCS ₀ and L ₀ represent values on a 100 mm laboratory scale, and JCS _n and L _n represent values for rock in a field experiment.

FIG. 13 is a table showing data obtained by JCS conversion using Equation 5 for 100 SHV values measured in the field shown in FIG. 5, and FIG. 14 shows these data as histograms.

As can be seen from FIG. 14, since the JCS value follows a normal distribution, it is possible to apply the stochastic rock slope stability analysis.

And the stochastic rock slope stability analysis method using the ground lidar according to an embodiment of the present invention Monte Carlo simulation using the JCS and JRC calculated through the above process as a probability variable as shown in FIG. Probability density function (PDF) and cumulative density function (CDF) are calculated by Carlo simulation, and the probability of failure is calculated from the probability value of the unstable area boundary (S60).

Monte Carlo simulation refers to a procedure used for probabilistic simulation of an analytical model in a situation in which uncertainty is taken into account. It is a simulation that generates a right link fruit by random sampling according to a probability distribution assumed in an analytical model.

The Monte Carlo simulation is known to be the most perfect among stochastic analysis techniques because it provides accurate information about the random variables used in calculating the state function, that is, probability distribution, mean, and standard deviation.

In an embodiment of the present invention, the Monte Carlo simulation is applied to calculate the probability of failure of the rock slope through iterative calculation.

In this case, the probability of failure means the possibility of destruction of the rock slope, and the portion having a safety factor of 1 or less occupies the whole.

In the case where the probability of failure is 10% or more, the rock slope is determined to be unstable.

In addition, a reliability index (RI) is calculated and used to supplement the accuracy of the failure probability.

The confidence index (RI) is obtained by using Equation 7 below, and means how many times the standard deviation is a distance between the reference safety factor and the safety factor average. In general, the standard safety factor is calculated by setting 1.0.

In Equation 7, β represents a confidence index (RI), μ represents a safety factor average, and σ represents a standard deviation of the safety factor.

If the confidence index (RI) obtained using Equation (7) is a negative value, the safety factor average has a value smaller than the reference safety factor (= 1.0), which means an unstable state (low reliability). On the contrary, if the confidence index (RI) is a positive value, it can be considered that the average safety factor calculated is larger than the reference safety factor, indicating a stable state (high reliability), but spaced three times longer than the standard deviation. If so, it is desirable to evaluate it as safe (reliable).

In addition, the calculation of the probability of failure of the rock slope using the Monte Carlo simulation is performed through the following six steps, as shown in FIG. 15.

For example, the Monte Carlo simulation is a first step in determining a state function of an analytical model. The Monte Carlo simulation finds a mean characteristic, a standard deviation σ, and the like by identifying probability characteristics of variables considered as random variables among input variables used in the model. The second step of calculating, the third step of generating a random valve from each variable, the fourth step of obtaining a state function value (safety factor (FS)) for the randomly generated values, and the calculated result The fifth step of obtaining the probability distribution function and dividing it by the total number of repetitions is to obtain the cumulative density function (CDF) from the probability density function (PDF) and the probability of failure (Pf) ) Is a sixth step of calculating (see FIG. 15).

In the sixth step, the unstable area boundary means a portion where the safety factor FS is less than or equal to the reference safety factor (generally 1.0).

In the second step, each random variable (for example, mean, standard deviation (STD), and position (dist. Function), etc., which are parameter values of JRC and JCS) is infinite in accordance with mathematical distribution characteristics. -Set the upper and lower limits to prevent the divergence indefinitely. For example, the upper and lower limit values are set to ± 3σ (three times the standard deviation) as a basis, and the lower limit values are set to have a value of 0 (zero) or more.

In the third step, the cumulative distribution function of each random variable is used and a random number generator is used. For example, in the third step, random values are generated by using Latin Hypercube Sampling (HLS).

In the LHS method, each St (here, t = 1, ..., K) is divided into N equal probability intervals having a probability of 1 / N to make the entire S into N ^K cells. Among them, one point is extracted from N different rooms, and the extracted N points are extracted to enter one of each section of St.

In the sixth step, the cumulative density function (CDF) is obtained by integrating the probability density function (PDF).

In the graph of the probability density function (PDF), the x axis represents a factor of safety (FS), and the y axis represents a frequency.

In the graph of the cumulative density function (CDF), the x-axis represents the factor of safety (FS), and the y-axis represents the cumulative probability.

In the graph of the cumulative density function CDF, when the cumulative probability value is obtained when the safety factor FS is a reference safety factor (for example, 1.0), the cumulative probability value is the failure probability Pf.

Fig. 16 shows the results of modeling the analysis cross-section CAD drawing file of the corresponding rock slope into dxf, and then reading it from the analysis program to perform Monte Carlo simulation.

17 to 19 show the results of Monte Carlo simulations using the JRC calculated using the Z ₂ parameter of FIG. 10 and the Ai parameter of FIG. 11 as parameters.

That is, in FIG. 17, the frequency distribution of each section of the Factor of Safety (FS), which is 10,000 times calculated using the LHS method using the Z ₂ parameter of FIG. FIG. 18 shows a probability density function (PDF) for each frequency distribution of the factor of safety (FS), which is 10,000 times calculated using a random variable extracted by the LHS method using the Ai parameter of FIG. 10. In the histogram.

19 shows a histogram of a cumulative density function (CDF) calculated using the probability density function (PDF) of FIGS. 17 and 18.

Analyzing the histogram of FIG. 17, the distribution of the safety factor (FS) has a lognormal distribution of mean 1.09 and standard deviation (STD) 0.17.

In FIG. 17, the failure probability Pf is the ratio of the dark (left) portion of the graph having the safety factor FS of 1 or less to the total number of repetitions, and the failure probability Pf of FIG. The cumulative probability value at that time. The calculated probability of failure (Pf) is 0.292, which is 29.2%.

  Therefore, since the probability of failure Pf exceeds 10%, the rock slope can be judged to be unstable (highly likely to be destroyed).

However, when the confidence index (RI) is calculated according to Equation 7, the safety factor mean (μ) is 1.09, and the standard deviation (σ) of the safety factor is 0.17. Therefore, RI (β) = (1.09-1) /0.17≒ Calculated as 0.53.

Therefore, the reliability index (RI = 0.53) is available for the value to exceed the trust criteria of the standard deviation of the probability density function (σ = 0.17) 3.11 times the level is then more than three times that of (PDF), for the case of using the Z ₂ Parameter It can be confirmed that the reliability of the fracture probability (Pf) on the rock slope is high.

Analyzing the histogram of FIG. 18, the distribution of the safety factor (FS) has a lognormal distribution of mean 1.22 and standard deviation (STD) 0.25.

In FIG. 18, the failure probability Pf is the ratio of the dark (left) portion of the graph having the safety factor FS of 1 or less to the total number of repetitions, and the failure probability Pf of FIG. This is the cumulative probability value at the time. The calculated probability of failure is 0.197, which is 19.7%.

Therefore, the probability of failure (Pf) was slightly lower than that of the Z ₂ parameter, but exceeded 10%, so the rock slope is considered unstable (highly likely to be destroyed). Can be.

When the confidence index (RI) is calculated by Equation 7, the safety factor mean (μ) is 1.22, and the standard deviation (σ) of the safety factor is 0.25, so that RI (β) = (1.22-1) /0.25 = Calculated as 0.88.

Therefore, the confidence index (RI = 0.88) is 3.25 times the standard deviation (σ = 0.25), which is more than three times the value, and exceeds the confidence criterion of the probability density function (PDF), even when the Ai parameter is used. It can be confirmed that the reliability of the failure probability (Pf) on the slope is high.

According to the stochastic rock slope stability analysis method using the ground lidar according to the embodiment of the present invention as described above, the possibility of collapse of the rock slope is examined by examining the probability of failure (Pf) and the confidence index (RI) together. It is possible to predict and interpret more accurately.

In the above, a preferred embodiment of the stochastic rock slope stability analysis method using the ground lidar according to the present invention has been described, but the present invention is not limited thereto, and various modifications are made within the scope of the claims and the specification and the accompanying drawings. It is possible to implement, and this also belongs to the scope of the present invention.

Claims (7)

From the point cloud data on the surface of the slope measured using the ground lidar, the geometrical conditions of the slope extension, height, orientation, and slope and the profile of the discontinuities are calculated.
Measure Schmidt Hammer Valve (SHV) on the discontinuous surface to indirectly obtain the wall strength of the joint surface,
Using the measured SHV to calculate Joint Compressive Strength (JCS),
Quantitative joint roughness coefficient (JCC) is calculated using the profile of the discontinuous surface,
Monte Carlo simulation is performed using the calculated JCS and JRC as random variables to obtain the probability density function (PDF) and cumulative density function (CDF), and to calculate the probability of failure from the probability value of the unstable area boundary. Stochastic rock slope stability analysis method. The method according to claim 1,
The process of obtaining point cloud data on the surface of the slope using the above ground lidar proceeds in the order of control point (CP) installation, scanning, and merging.
The control point is installed so that at least four or more overlap in the adjacent scanning position,
The scanning is sequentially performed positioning scan, fine scan and surface scan for each position,
The merging is a probability using terrestrial lidar that integrates the acquired point group data into a unified data system according to the coordinate values and coordinate system of the control point (CP) input in advance and constructs a transformation matrix to form a single point group data. Theoretical rock slope stability analysis method. The method according to claim 2,
The process of calculating the profile of the discontinuity surface using the point cloud data goes through the four-step data processing process of filtering, tessellation, fix-up normal face, and peel hole,
In the filtering step, the noise other than the object included in the point group data, the noise generated by the ghost phenomenon when the incident angle of the laser is large, and the overlapping data generated in the merging process for the point group at various positions are removed. ,
In the tessellation step, a Triangulated Irregular Network (TIN) is formed by using the point cloud data arranged through the filtering step.
In the fix up normal face step, the surface of the point group data distributed in the three-dimensional space is removed, not a single surface in the process of generating a TIN, but overlapping, flipping, and twisting,
In the fill-hole step, a ground lidar filling the holes generated in the tessellation step distributed to the TIN proceeding to the fix up normal face step by using a linear interpolation method and a nonlinear interpolation method in the form of a quadratic function considering the curvature of the hole periphery. Stochastic rock slope stability analysis method. The method according to claim 3,
In the process of calculating the quantitative JRC using the profile of the discontinuities, Z ₂ (Root Mean Square) of the first derivative (micro gradient) or standard deviation with respect to the small gradient value) and Ai (microscopic mean) Empirical formula of the parameter (micro average i angle) or average value of micro element roughness angle

And

, Where x represents the distance from the reference point to the measurement line, and y represents the height from the baseline to the surface.

And

Stochastic rock slope stability analysis using ground lidar. The method according to claim 4,
In the process of calculating the JCS (unit: MPa) using the measured SHV,

Probabilistic rock slope stability analysis using terrestrial lidar using (where γ represents the unit weight of rock). The method according to claim 1,
The confidence index (β), which represents how many times the distance between the reference safety factor and the safety factor average (μ) is the standard deviation of the safety factor (σ)

A stochastic rock slope stability analysis method using ground lidar, which is calculated by using and uses the calculated confidence index to determine the reliability of the failure probability. The method according to claim 1,
The Monte Carlo simulation is a first step of determining a state function of an analytical model, a second step of calculating a mean, a standard deviation, and the like by identifying probability characteristics of variables considered as random variables among input variables used in the model. A third step of generating random values from the variables, a fourth step of obtaining a state function value (safety factor) for the randomly generated values, and a probability density obtained by obtaining a frequency distribution of the calculated result and dividing it by the total number of iterations Probabilistic rock formation using terrestrial lidar consisting of the fifth step of acquiring the function (PDF), the sixth step of calculating the cumulative density function (CDF) from the probability density function (PDF) and calculating the probability of failure from the probability value of the unstable area boundary Slope stability analysis method.

Images