KR102619016B1 - Method and apparatus for predicting soil slope failure - Google Patents

Abstract

The present invention relates to a method for an electronic device to predict slope collapse in advance. The method includes obtaining vibration data from a vibration sensor array including a plurality of vibration sensors; Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; and predicting slope collapse in advance based on the vibration data and the resistivity data. may include.

Description

Method and device for predicting soil slope collapse {METHOD AND APPARATUS FOR PREDICTING SOIL SLOPE FAILURE}

This disclosure relates to a method and device for predicting soil slope collapse in advance.

More specifically, it relates to a method and device for predicting soil slope collapse in advance using vibration data and resistivity data.

In the prior art, in order to predict slope collapse, a number of piles are installed on the soil slope, and the upper part of each pile is connected with a wire to measure the deformation of the wire in real time to predict slope collapse, or a groundwater level gauge is installed on the slope to determine the slope collapse factor. One of the methods, moisture status, was measured to predict soil slope collapse.

However, in the prior art, the expression of predicting soil slope collapse is used, but when measuring the actual deformation of the wire, the state of collapse is already measured after collapse has occurred, so human and material damage due to soil slope collapse cannot be prevented in advance. Even when measuring moisture status using a groundwater level gauge, the actual path of groundwater, etc. cannot be identified, and installing multiple groundwater level gauges on a slope is difficult to install and manage, and is too expensive, making prediction accuracy as well as economic feasibility too high. There was a problem with falling.

In reality, in order for technology to predict soil slope collapse to be realized, real-time and accurate analysis of ground conditions is required, and such analysis must enable 3D analysis of ground conditions.

Therefore, in order to predict soil slope collapse, technology for real-time and accurate analysis of ground conditions is needed, and through these technologies, technology to analyze ground conditions in 3D by considering various factors is required.

According to one embodiment, a method and apparatus for providing advance prediction of slope collapse may be provided.

More specifically, by analyzing the ground conditions using vibration data and resistivity data, a method and device can be provided that can predict slope collapse with high accuracy before soil slope collapse occurs, taking into account the stiffness and moisture status of the soil. there is.

In order to solve the problems described above, according to an embodiment of the present disclosure, a method for an electronic device to predict slope collapse in advance is provided. The method includes obtaining vibration data from a vibration sensor array including a plurality of vibration sensors; Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; and predicting slope collapse in advance based on the vibration data and the resistivity data. may include.

According to a feature of the present disclosure, the step of predicting slope collapse based on the vibration data and the resistivity data includes calculating a velocity reduction rate of elastic waves for each slope cross section based on the vibration data; Calculating a resistivity reduction rate for each slope cross section based on the resistivity data; and determining a slope collapse risk level based on the velocity reduction rate of the elastic wave and the resistivity reduction rate; may include.

According to another feature of the present disclosure, the method includes acquiring slope displacement data from a plurality of displacement sensor arrays; and determining whether the slope adjacent to the displacement sensor has collapsed or the vibration sensor has been damaged based on the slope displacement data. It may further include.

According to another feature of the present disclosure, acquiring a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data; It may further include.

According to another feature of the present disclosure, acquiring vibration data from a vibration sensor array including a plurality of vibration sensors includes generating high-frequency vibration by a striking module; Obtaining first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration; and acquiring second vibration data for analyzing a deep area from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors; may include.

According to another feature of the present disclosure, the displacement sensor array, the vibration sensor array, and the resistivity sensor array may be installed on a slope.

According to another feature of the present disclosure, the displacement sensor, vibration sensor, and resistivity sensor having the same coordinates in the displacement sensor array, the vibration sensor array, and the resistivity sensor array are included in a complex sensor structure as one set; And the complex sensor structure can be installed on a slope in an array structure.

In order to solve the problems described above, according to another embodiment of the present disclosure, an electronic device for predicting slope collapse in advance is provided. The electronic device includes a memory that stores one or more instructions; and at least one processor executing the one or more instructions, wherein the processor acquires vibration data from a vibration sensor array including a plurality of vibration sensors by executing the one or more instructions; Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; And slope collapse can be predicted in advance based on the vibration data and the resistivity data.

According to a feature of the present disclosure, by executing the one or more instructions, the processor determines the velocity reduction rate of elastic waves for each slope cross-section based on the vibration data to predict slope collapse in advance based on the vibration data and the resistivity data. calculate; Calculate the resistivity reduction rate for each slope cross section based on the resistivity data; And the slope collapse risk level can be determined based on the velocity reduction rate of the elastic wave and the resistivity reduction rate.

According to another feature of the present disclosure, the processor acquires slope displacement data from a plurality of displacement sensor arrays by executing the one or more instructions; And based on the slope displacement data, it can be determined whether the slope adjacent to the displacement sensor has collapsed or the vibration sensor has been damaged.

According to another feature of the present disclosure, the processor may acquire a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data by executing the one or more instructions.

According to another feature of the present disclosure, by executing the one or more instructions, the processor generates high-frequency vibration by a striking module to obtain vibration data from a vibration sensor array including a plurality of vibration sensors; Obtain first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration; In addition, second vibration data for analyzing a deep area can be obtained from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors.

According to another feature of the present disclosure, the displacement sensor array, the vibration sensor array, and the resistivity sensor array may be installed on a slope.

According to another feature of the present disclosure, the displacement sensor, vibration sensor, and resistivity sensor having the same coordinates in the displacement sensor array, the vibration sensor array, and the resistivity sensor array are included in a complex sensor structure as one set; And the complex sensor structure can be installed on a slope in an array structure.

Specific details of other embodiments are included in the detailed description and drawings for carrying out the invention.

1 is a diagram illustrating a process by which an electronic device predicts soil slope collapse according to one embodiment.
Figure 2 is a flowchart showing a method for an electronic device to predict slope collapse in advance, according to an embodiment.
Figure 2 is a flowchart showing a method in which an electronic device provides an experience-based matching service using artificial intelligence, according to an embodiment.
FIG. 3 is a diagram illustrating four prediction areas analyzed by an electronic device to predict slope collapse based on vibration data and resistivity data, according to an embodiment.
FIG. 4 is a flowchart specifically illustrating steps in which an electronic device predicts slope collapse based on vibration data and the resistivity data, according to an embodiment.
FIG. 5 is a flowchart specifically illustrating a process in which an electronic device calculates the velocity reduction rate of elastic waves for each slope cross section based on vibration data, according to an embodiment.
FIG. 6 is a flowchart specifically illustrating a process in which an electronic device secures data for 3D analysis of a slope by collecting speed data for each slope cross-section obtained from a vibration sensor array, according to an embodiment.
FIG. 7 is a flowchart specifically illustrating steps in which an electronic device predicts slope collapse based on vibration signal data and the resistivity data, according to an embodiment.
Figure 8 is a block diagram of an electronic device according to an embodiment.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and will be implemented in various different forms, but the present embodiments only serve to ensure that the disclosure of the present invention is complete and are within the scope of common knowledge in the technical field to which the present invention pertains. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, similar reference numbers may be used for similar components.

In this document, expressions such as “have,” “may have,” “includes,” or “may include” refer to the existence of the corresponding feature (e.g., a numerical value, function, operation, or component such as a part). , and does not rule out the existence of additional features.

In this document, expressions such as “A or B,” “at least one of A or/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, “A or B,” “at least one of A and B,” or “at least one of A or B” includes (1) at least one A, (2) at least one B, or (3) it may refer to all cases including both at least one A and at least one B.

Expressions such as “first,” “second,” “first,” or “second,” used in this document can modify various components regardless of order and/or importance, and can refer to one component. It is only used to distinguish from other components and does not limit the components. For example, a first user device and a second user device may represent different user devices regardless of order or importance. For example, the first component may be renamed as the second component without departing from the scope of rights described in this document, and similarly, the second component may also be renamed as the first component.

A component (e.g., a first component) is “(operatively or communicatively) coupled with/to” another component (e.g., a second component). When referred to as being “connected to,” it should be understood that any component may be directly connected to the other component or may be connected through another component (e.g., a third component). On the other hand, when a component (e.g., a first component) is said to be “directly connected” or “directly connected” to another component (e.g., a second component), It may be understood that no other component (e.g., a third component) exists between other components.

As used in this document, the expression “configured to” depends on the situation, for example, “suitable for,” “having the capacity to.” ," can be used interchangeably with "designed to," "adapted to," "made to," or "capable of." The term “configured (or set to)” may not necessarily mean “specifically designed to” in hardware. Instead, in some contexts, the expression “a device configured to” may mean that the device is “capable of” working with other devices or components. For example, the phrase "processor configured (or set) to perform A, B, and C" refers to a processor dedicated to performing the operations (e.g., an embedded processor), or by executing one or more software programs stored on a memory device. , may refer to a general-purpose processor (e.g., CPU or application processor) capable of performing the corresponding operations.

Terms used in this document are merely used to describe specific embodiments and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions, unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by a person of ordinary skill in the technical field described in this document. Among the terms used in this document, terms defined in general dictionaries may be interpreted to have the same or similar meaning as the meaning they have in the context of related technology, and unless clearly defined in this document, have an ideal or excessively formal meaning. It is not interpreted as In some cases, even terms defined in this document cannot be interpreted to exclude embodiments of this document.

Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may consist of one or multiple processors. At this time, one or more processors may be a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU or VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU. One or more processors control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, when one or more processors are dedicated artificial intelligence processors, the artificial intelligence dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

Predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned using a large number of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform the desired characteristics (or purpose). It means burden. This learning may be performed on the device itself that performs the artificial intelligence according to the present disclosure, or may be performed through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

An artificial intelligence model may be composed of multiple neural network layers. Each of the plurality of neural network layers has a plurality of nodes and weight values, and neural network calculation is performed through calculation between the calculation result of the previous layer and the plurality of weights. Multiple weights of multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, a plurality of weights may be updated so that loss or cost values obtained from the artificial intelligence model are reduced or minimized during the learning process. Additionally, in order to minimize the loss or cost, a plurality of weights may be updated in a direction that minimizes the gradient related to the loss or cost. Artificial neural networks may include deep neural networks (DNN), for example, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, etc., but are not limited to the examples described above.

Each feature of the various embodiments of the present invention can be partially or fully combined or combined with each other, and as can be fully understood by those skilled in the art, various technical interconnections and operations are possible, and each embodiment may be implemented independently of each other. It may be possible to conduct them together due to a related relationship.

Hereinafter, various embodiments of the present invention will be described in detail with reference to the attached drawings.

1 is a diagram illustrating a process by which an electronic device predicts soil slope collapse according to one embodiment.

In this disclosure, the term slope may mean a soil slope.

Referring to FIG. 1, the electronic device 1000 can obtain sensing data including at least vibration data and resistivity data from the sensor unit 2000 installed on the slope, and predict slope collapse in advance using the acquired sensing data. there is. The sensor unit 2000 may include a vibration sensor, a resistivity sensor, or a displacement sensor, and the sensors may form an array. Additionally, the vibration sensor, resistivity sensor, and displacement sensor can form an array by forming a complex sensor structure as one set. The sensor unit 2000 may include a storage unit for storing sensing data and a communication unit for transmitting the sensing data. Additionally, the sensor unit 2000 may include a striking module.

According to one embodiment, the electronic device 1000 may remotely control a strike module installed on or adjacent to a slope to secure sensing data for predicting slope collapse with higher accuracy. The electronic device 1000 can set or remotely control the measurement setting value of the sensor unit 2000 in order to predict slope collapse with higher accuracy.

According to one embodiment, the sensor array, for example, the displacement sensor array, the vibration sensor array, and the resistivity sensor array, may form an array in a direction perpendicular to the slope inclination and in a direction parallel to the slope inclination. Referring to FIG. 1, the sensors included in the sensor unit 2000 may form an array in a direction perpendicular to the slope inclination (first direction) and in a direction parallel to the slope inclination (second direction).

According to one embodiment, the electronic device 1000 may be implemented in various forms. For example, the electronic device 1000 described in this specification may include, but is not limited to, a PC, workstation, mobile terminal, smart phone, laptop computer, tablet PC, etc. .

According to one embodiment, the electronic device 1000 may include a pre-collapse prediction artificial intelligence model that can perform the method for pre-predicting slope collapse disclosed in the present invention. The electronic device 1000 may perform operations that model, learn, modify, or update a pre-collapse prediction artificial intelligence model.

According to one embodiment, the pre-collapse prediction artificial intelligence model may be learned and modeled to output the possibility of pre-collapse prediction according to the vibration data and resistivity data when vibration data and resistivity data are input. Additionally, the pre-collapse prediction artificial intelligence model can be learned and modeled to output a slope 3D geological map in the process of outputting the pre-collapse prediction probability.

According to one embodiment, the pre-collapse prediction artificial intelligence model may be an artificial neural network (ANN) model, which refers to a computing system inspired by biological neural networks. Examples of artificial neural network models include Deep Neural Network (DNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Bidirectional Recurrent Deep Neural Network (BRDNN). ), Deep Q-Networks, etc. In particular, the pre-collapse prediction artificial intelligence model may be a deep neural network (DNN) among artificial neural network models.

Figure 2 is a flowchart showing a method for an electronic device to predict slope collapse in advance, according to an embodiment.

In step S210, the electronic device 1000 may acquire vibration data from a vibration sensor array including a plurality of vibration sensors.

In the present disclosure, vibration data refers to data measured through a vibration sensor, and may particularly refer to measurement data for elastic waves.

Seismic waves measured on a slope can reveal properties related to soil stiffness. In particular, in the speed of elastic waves, if there are other substances (moisture, etc.) or heterogeneous layers between the medium (soil), the speed decreases, and in the section where the speed decrease occurs, the soil It can be assumed that the stiffness of is reduced. When the stiffness of the soil on a slope decreases below a certain level or percentage, the slope collapses. Therefore, the degree or rate at which soil stiffness is reduced can be estimated from the speed of the elastic wave or the change in the speed of the elastic wave, and accordingly, the risk of soil collapse can be predicted in advance.

Specifically, in relation to the stiffness of the soil, the shear modulus, Young's modulus, Poisson's ratio, etc. can be calculated from the speed of the P wave and the speed of the V wave obtained as a result of elastic wave investigation. Therefore, the change in soil stiffness and the resulting risk of soil collapse can be predicted from the rate of change in the speed of the P wave and the speed of the V wave.

Meanwhile, the speed of the P wave and the speed of the V wave may be estimated by the speed correlation with other elastic waves, such as Rayleigh waves. For example, when the Poisson's ratio is 0.25, the speed of the S-wave can be estimated from the speed of the Rayleigh wave using the correlation that the Rayleigh wave has a speed of about 90% of the speed of the S-wave.

With regard to measurement data on elastic waves, underground exploration of the surface or shallow areas (layers) is possible by using vibration data for high frequencies with a relatively short period, and using vibration data for low frequencies with a relatively long period. This makes underground exploration of deep areas (layers) possible.

According to one embodiment, the vibration data for high frequencies is measured by a vibration sensor measuring the speed of the P wave, the speed of the S wave, etc. using vibration generated using a striking module to be described later, and the vibration data for low frequencies is measured by vibration. It can be measured by a vibration sensor from vibrations generated by vehicles on roads adjacent to the location where the sensor is installed.

The vibration sensor array includes a plurality of vibration sensors. As shown in FIG. 1, the plurality of vibration sensors included in the vibration sensor array determine the slope slope, considering that soil slope collapse generally occurs in a direction horizontal to the slope slope. The array can be formed in a direction perpendicular to and parallel to the slope of the slope.

In step S220, the electronic device 1000 may obtain resistivity data from a resistivity sensor array including a plurality of resistivity sensors.

In the present disclosure, resistivity sensor data refers to data measured through a resistivity sensor, and may particularly mean electrical resistivity data for ground conditions.

As described above, it is possible to estimate whether the stiffness of the soil has been reduced or the rate at which it is reduced from the speed of the elastic wave or the change in the speed of the elastic wave, and thus the risk of soil collapse can be predicted. In reality, the rigidity of the soil decreases due to the presence or increase of moisture in the soil, causing soil collapse, typically rock slope collapse and embankment collapse. Using the characteristic that S waves cannot pass through a liquid medium, the water level of groundwater can be indirectly predicted with low accuracy, but it is not possible to directly measure or analyze the moisture status in the soil from the results of seismic waves. It is impossible.

According to one embodiment, in order to more accurately analyze the moisture state in the soil, an electrical resistivity survey method may be used as an additional cross-analysis method to the seismic wave survey method. The electrical resistivity exploration method is a method of analyzing the condition of the ground by sending an artificial electric current through the ground and measuring the potential difference due to differences in electrical characteristics (resistivity) depending on the medium. In particular, moisture in the ground has a significant impact on resistivity, and in areas where there is a lot of moisture in groundwater or soil, resistivity is relatively greatly reduced. By using this to analyze the moisture condition in the ground, the stiffness of the soil can be reduced or reduced. Collapse can be predicted.

Unlike the conventional method using a groundwater level gauge, the method using a resistivity sensor array uses data measured from the resistivity sensor array, so the 3D path of actual groundwater can be predicted with high accuracy and is easy to install and manage.

In step S230, the electronic device 1000 may predict slope collapse in advance based on the vibration data and the resistivity data.

Slope failure can be predicted in advance through a cross-analysis method using both vibration data and resistivity data. Referring to FIG. 3, data can be broadly interpreted into four prediction areas depending on vibration data and resistivity data.

More specifically, referring to FIG. 3, the x-axis represents the speed of P and S waves (m/s) and the y-axis represents the electrical resistivity value (W). The x-axis threshold (P-wave velocity threshold or S-wave velocity threshold) and y-axis threshold (electrical resistivity threshold) that distinguish the four prediction regions may be predetermined. For example, the P-wave velocity threshold may be predetermined as 1,000 m/s, the S-wave velocity threshold as 350 m/s, and the electrical resistivity threshold as 300 W.

According to one embodiment, the four prediction regions are based on vibration data and resistivity data: low speed-low resistivity region (R1), high speed-low resistivity region (R2), low speed-high resistivity region (R3), and high speed-high resistivity region (R4). Regarding the interpretation of vibration data (elastic wave velocity), if low velocities appear, it can be assumed that the soil is weakly compacted or has many pores, and if high velocities appear, it can be assumed that the soil is strongly compacted or has few pores. You can. Regarding the interpretation of resistivity data (electrical resistivity), if a low resistivity appears, it can be assumed that there is a lot of moisture in the soil or there is groundwater or a water leak area, and if a high resistivity appears, there is little moisture in the soil or there is ground water or a water leak area. It can be assumed that there are none. Therefore, at R1, the stiffness of the soil is reduced, so the soil stiffness is most likely to be weak, so the probability of slope failure is predicted to be highest, and at R4, because the soil stiffness is most likely to be strong, the probability of slope failure is predicted to be the lowest. You can.

According to one embodiment, in step S230, when vibration data and resistivity data are input, the electronic device 1000 predicts slope collapse using a pre-collapse prediction artificial intelligence model that outputs the possibility of predicting pre-collapse according to the vibration data and resistivity data. It is predictable.

FIG. 4 is a flowchart specifically illustrating steps in which an electronic device predicts slope collapse based on vibration data and the resistivity data, according to an embodiment.

In step S410, the electronic device 1000 may calculate the speed reduction rate of elastic waves for each slope cross section based on the vibration data.

The electronic device 1000 may calculate the speed reduction rate by comparing the S-wave speed or P-wave speed data at the time of installation or at a specific point in time with the S-wave speed or P-wave speed data in the measured or acquired vibration data.

More specifically, referring to FIG. 5, vibration data is converted into phase velocity-frequency data using Fourier transform (FT), and through this, S-wave velocity according to distance and depth. Data can be obtained. The reduction rate of S-wave speed can be calculated by comparing S-wave speed data continuously acquired at predetermined time intervals with S-wave speed data (original ground data) at the time of installation or at a specific point in time.

According to one embodiment, a plurality of vibration sensors may form a vibration sensor ray in a direction perpendicular to the slope inclination (first direction) and in a direction parallel to the slope inclination (second direction), as shown in FIG. 1. . The electronic device 1000 may acquire elastic wave velocity data (for example, S-wave velocity data, etc.) according to the distance and depth obtained from vibration data obtained from the vibration sensor array for each slope cross section. The electronic device 1000 may acquire elastic wave velocity data for the xy-plane, yz-plane, and zx-plane from each of the plurality of sensors. Accordingly, the electronic device 1000 collects the obtained velocity data for each slope cross-section in a direction perpendicular or horizontal to the slope inclination to perform 3D analysis of ground conditions, especially ground stiffness, or obtain a 3D geological map of the slope.

More specifically, referring to FIG. 6, the electronic device 1000 performs 3D analysis of ground conditions by collecting elastic wave velocity data for each slope cross-section, that is, each array location on the slope, from a plurality of vibration sensors included in the vibration sensor array. It can be done. The electronic device 1000 can perform 3D analysis of ground conditions by acquiring and collecting elastic wave velocity data on the xy-plane, yz-plane, and zx-plane from each of the plurality of sensors. Accordingly, in analyzing actual ground conditions, the electronic device 1000 can more accurately determine the location of the portion where rigidity is weakened, and the location of the portion where the reduction rate of elastic wave velocity is above a certain level can be more accurately identified as a 3D location. Therefore, slope collapse can be predicted with higher accuracy.

In step S420, the electronic device 1000 may calculate the resistivity reduction rate for each slope cross section based on the resistivity data.

The electronic device 1000 may calculate a resistivity reduction rate by comparing resistivity data at the time of installation or at a specific point in time with measured or acquired resistivity data.

The electronic device 1000 may acquire resistivity data according to distance and depth for each slope cross section obtained from resistivity data obtained from a resistivity sensor array. Accordingly, the electronic device 1000, like vibration data, collects resistivity data for each slope cross section in a direction perpendicular or horizontal to the slope slope, enabling 3D analysis of ground conditions, especially ground electrical resistivity, or acquisition of a 3D geology of the slope. do.

Based on the 3D analysis of underground electrical resistivity, the electronic device 1000 can more accurately identify areas in the ground where there is a lot of moisture in the soil or where there is groundwater or water leakage areas, and the location of the area where the reduction rate of electrical resistivity is above a certain level. can be identified more accurately, allowing slope collapse to be predicted with higher accuracy.

In step S430, the electronic device 1000 may determine the slope collapse risk level based on the velocity reduction rate of the elastic wave and the resistivity reduction rate.

The elastic wave velocity reduction rate and resistivity reduction rate to determine the slope collapse risk level are analyzed for each slope cross section. In reality, slope collapse is not only caused by the complex action of various factors, but also when these factors, i.e. the parts where stiffness is weakened, are not analyzed in actual 3D, but are simply analyzed in one or two dimensions for a portion of the sensing area, It is virtually impossible to predict slope collapse. For example, when conventional groundwater level gauges or seismic wave analysis are performed only in certain areas, the accuracy of identifying the path of groundwater, areas where rigidity is weakened in the ground, etc. is low or very limited depending on the environment. On the other hand, the method of calculating the rate of decrease in the speed of elastic waves and the decrease in resistivity for each slope section and determining the slope collapse risk level based on this not only enables 3D analysis of the ground condition of the soil slope, but furthermore, the degree of reduction is not only possible. Since slope collapse can be predicted by considering the rate of decline, the factors of actual slope collapse are analyzed in 3D and the changes in the 3D shape of the factors are tracked (tracking changes in stiffness and moisture state) to prevent slope collapse before it actually occurs. can provide highly accurate predictions.

According to one embodiment, the electronic device 1000 may determine the slope collapse risk level by considering the type of change in stiffness or moisture state when analyzing the ground condition of the slope. For example, even if the first case and the second case show the same elastic wave velocity reduction rate or resistivity reduction rate, the form of change in stiffness or moisture state in the first case is in the direction perpendicular to the slope slope (first direction) or in the overall form of change. While the form of change in the direction perpendicular to the slope slope (first direction) is predominant, the form of change in stiffness or moisture state in the second case is in the direction parallel to the slope slope (second direction) or in the form of overall change with the slope slope. If the change pattern in the horizontal direction (second direction) is dominant, the electronic device 1000 determines that, despite the same elastic wave velocity reduction rate or resistivity reduction rate, the second case has a higher slope collapse probability than the first case (higher slope collapse risk rating).

This is because, in terms of the safety factor in the slope collapse mechanism, changes in the direction parallel to the slope slope (i.e., slope slope direction) generally have a large impact on weakening the average shear strength of the soil. Since the electronic device 1000 can analyze changes in stiffness or moisture state in the ground state in 3D form, considering the slope collapse mechanism, even if there is a decrease in elastic wave velocity or resistivity of the same degree or rate, the safety factor is higher. Considering the type (direction) of reduction that may have an impact, it is possible to distinguish and determine cases that may have a more adverse effect on slope failure.

According to one embodiment, the electronic device 1000 acquires a 3D geological map of the slope for the form of change in stiffness and moisture state of the slope based on the velocity reduction rate of the elastic wave for each slope cross-section and the specific resistance reduction rate for each slope cross-section in step S430. step; And based on the 3D geological map of the slope, determining a case where the change in stiffness and moisture state of the slope is dominant in a direction parallel to the slope slope as a higher slope collapse risk level than when the form of change in the slope's stiffness and moisture state is dominant in a direction perpendicular to the slope slope; may include.

According to one embodiment, the electronic device 1000 acquires a slope 3D geological map of the form of change in stiffness and moisture state of the slope based on the velocity reduction rate of the elastic wave for each slope cross-section and the resistivity reduction rate for each slope cross-section in step S430. Step S430 includes the step S430, where the electronic device 1000 determines, based on the 3D geological map of the slope, that the form of change in the stiffness and moisture state of the slope is dominant in a direction parallel to the slope inclination and is dominant in the direction perpendicular to the slope incline. It may be characterized by determining a slope collapse risk level that is higher than the case.

According to one embodiment, the electronic device 1000 acquires slope displacement data from a plurality of displacement sensor arrays; And based on the slope displacement data, it can be determined whether the slope adjacent to the displacement sensor has collapsed or the vibration sensor has been damaged.

According to one embodiment, the electronic device 1000 may obtain a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data. The electronic device 1000 is capable of acquiring a 3D geological map of the ground condition using the predetermined coordinates of the sensors included in the sensing unit 2000 even when there is no slope displacement data. However, if displacement data is also used, the current slope is It is also possible to accurately determine whether it has collapsed or whether the sensor has been damaged as a result.

FIG. 7 is a flowchart specifically illustrating steps in which an electronic device acquires vibration data from a vibration sensor array including a plurality of vibration sensors according to an embodiment.

According to one embodiment, the electronic device 1000 generates high-frequency vibration by a striking module (S710) to obtain vibration data from a vibration sensor array including a plurality of vibration sensors; Obtain first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration (S720); In addition, second vibration data for analyzing a deep area can be acquired from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors (S730).

Soil slope collapse can broadly include surface collapse, internal collapse, or simultaneous surface and interior collapse. In the present disclosure, surface collapse means when the location causing the collapse is mainly on the surface, for example, when collapse occurs due to saturation due to rainwater on the soil surface, and internal collapse means when the location causing the collapse is mainly on the surface. In the case where it is mainly internal, for example, it may mean that collapse occurs due to a decrease in the rigidity of the soil interior due to groundwater, etc.

The method for predicting slope collapse described in the present invention was invented to predict collapse by measuring the state of the ground or the internal state of the stratum before collapse, not the already collapsed state. In order to more accurately predict collapse in advance, seismic analysis must be possible not only on the surface or in shallow areas (layers) (shallow failure), but also in deep areas (layers) (deep failure), depending on the cause of collapse.

The electronic device 1000 may use high frequencies with a relatively short period for underground exploration of the surface or shallow areas (layers), and use low frequencies with a relatively long period for underground exploration of deep areas (layers). .

The electronic device 1000 generates high frequencies using a strike module installed within or adjacent to the sensor unit 2000 for underground exploration of the surface or shallow area (layer) and senses the generated high frequencies again. This allows underground exploration of the surface or shallow areas (layers) to be performed. The electronic device 1000 may perform underground exploration of a deep area (layer) by sensing vibration generated by a vehicle moving adjacent to the sensor unit 2000. Accordingly, the electronic device 1000 can more accurately determine the cause of each surface collapse and internal collapse in step S230, thereby securing higher accuracy in predicting soil slope prior collapse. That is, in step S230, the electronic device 1000 can more accurately predict slope collapse in advance based on the first vibration data, the second vibration data, and the resistivity data.

According to one embodiment, the displacement sensor array, the vibration sensor array, and the resistivity sensor array may be installed on a slope.

According to one embodiment, a displacement sensor, a vibration sensor, and a resistivity sensor having the same coordinates in the displacement sensor array, the vibration sensor array, and the resistivity sensor array are included in a complex sensor structure as one set; And the complex sensor structure can be installed on a slope in an array structure. Accordingly, one complex sensor structure is installed on the slope in an array structure to acquire vibration data, resistivity data, and displacement data at the same coordinates, and analyze them to predict slope collapse more accurately using more accurately aligned data. Prediction is possible.

Figure 8 is a block diagram of an electronic device according to an embodiment.

Referring to FIG. 8, the electronic device 1000 according to one embodiment may include a processor 810 and a memory 820. However, not all illustrated components are essential components. The electronic device 1000 may be implemented with more components than the components shown, or may be implemented with fewer components. For example, the electronic device 1000 according to one embodiment may further include a user input unit, a communication unit, and a display.

The processor 810 controls the overall operation of the electronic device 1000 by executing one or more instructions in the memory 820. For example, the processor 810 can generally control the user input unit 910, communication unit 920, display 930, etc. by executing one or more instructions stored in the memory 820. Additionally, the processor 810 may perform the operations and functions of the electronic device 1000 described with reference to FIGS. 1 to 7 by executing one or more instructions stored in the memory 820.

The processor 810 may be composed of one or more processors, and the one or more processors may include general-purpose processors such as CPUs, APs, digital signal processors (DSPs), and graphics-specific processors such as GPUs and vision processing units (VPUs). Or, it may be a processor dedicated to artificial intelligence (AI), such as an NPU. According to one embodiment, when the processor 810 is implemented with a plurality of processors, a graphics-only processor, or an artificial intelligence-only processor such as an NPU, at least some of the plurality of processors, a graphics-only processor, or an artificial intelligence-only processor such as an NPU are It may be mounted on the electronic device 1000 and other electronic devices or servers connected to the electronic device 1000.

According to one embodiment, the processor 810 obtains vibration data from a vibration sensor array including a plurality of vibration sensors by executing one or more instructions; Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; And slope collapse can be predicted in advance based on the vibration data and the resistivity data.

According to another embodiment, by executing the one or more instructions, the processor 810 determines the velocity reduction rate of elastic waves for each slope cross-section based on the vibration data to predict slope collapse based on the vibration data and the resistivity data. calculate; Calculate the resistivity reduction rate for each slope cross section based on the resistivity data; And the slope collapse risk level can be determined based on the velocity reduction rate of the elastic wave and the resistivity reduction rate.

According to another embodiment, the processor 810 obtains slope displacement data from a plurality of displacement sensor arrays by executing the one or more instructions; And based on the slope displacement data, it can be determined whether the slope adjacent to the displacement sensor has collapsed or the vibration sensor has been damaged.

According to another embodiment, the processor 810 may obtain a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data by executing the one or more instructions.

According to another embodiment, by executing the one or more instructions, the processor 810 generates high-frequency vibration by the striking module to obtain vibration data from a vibration sensor array including a plurality of vibration sensors; Obtain first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration; In addition, second vibration data for analyzing a deep area can be obtained from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors.

The memory 820 may include one or more instructions for controlling the operation of the electronic device 1000.

According to one embodiment, the memory 820 is, for example, a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (e.g. For example, SD or -Only Memory), may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk, but is not limited thereto.

Although embodiments of the present invention have been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and may be modified and implemented in various ways without departing from the technical spirit of the present invention. there is. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. Various modifications and improvements made by those skilled in the art using the basic concept of the present disclosure as defined in the following claims also fall within the scope of the present disclosure.

Claims (14)

In a method for an electronic device to predict slope collapse in advance,
Obtaining vibration data from a vibration sensor array including a plurality of vibration sensors;
Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; and
Predicting slope collapse based on the vibration data and the resistivity data; Including,
The vibration sensor array and the resistivity sensor array are installed on a slope, forming an array in a first direction perpendicular to the slope inclination and a second direction parallel to the slope inclination,
The step of predicting slope collapse based on the vibration data and the resistivity data is,
Calculating a velocity reduction rate of elastic waves for each slope cross section based on the vibration data;
Calculating a resistivity reduction rate for each slope cross section based on the resistivity data;
Obtaining a slope 3D geological map of the form of change in stiffness and moisture state of the slope based on the velocity reduction rate of the elastic wave calculated for each slope cross-section and the resistivity reduction rate calculated for each slope cross-section; and
Based on the slope 3D geological map, determining that the pattern of change in stiffness and moisture state of the slope has a higher slope failure risk grade when the change in the second direction is more dominant than the first direction; Including,
In the step of predicting slope collapse in advance based on the vibration data and the resistivity data, the area for predicting slope collapse is determined to be low based on a predetermined P-wave velocity threshold or S-wave velocity threshold and an electrical resistivity threshold. The method is divided into a speed-low resistivity region, a high velocity-low resistivity region, a low velocity-high resistivity region, and a high velocity-high resistivity region. According to claim 1,
The method of claim 1, wherein the predetermined P wave velocity threshold is 1,000 m/s, the predetermined S wave velocity threshold is 350 m/s, and the predetermined electrical resistivity threshold is 300 Ω. According to claim 1,
Obtaining slope displacement data from a plurality of displacement sensor arrays; and
determining whether the slope adjacent to the displacement sensor has collapsed or the vibration sensor has been damaged based on the slope displacement data; A method further comprising: According to claim 3,
Obtaining a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data; How to further include . According to claim 1,
The step of acquiring vibration data from a vibration sensor array including a plurality of vibration sensors,
Generating high-frequency vibration by a striking module;
Obtaining first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration; and
Obtaining second vibration data for analyzing a deep area from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors; Method, including. According to claim 3,
The method wherein the displacement sensor array is installed on a slope. According to claim 6,
A displacement sensor, a vibration sensor, and a resistivity sensor having the same coordinates in the displacement sensor array, the vibration sensor array, and the resistivity sensor array are included in a complex sensor structure as one set; and
The method wherein the complex sensor structure is installed on a slope in an array structure. In the electronic device for predicting slope collapse,
A memory that stores one or more instructions; and
At least one processor executing the one or more instructions,
The processor executes the one or more instructions,
Obtaining vibration data from a vibration sensor array including a plurality of vibration sensors;
Obtaining resistivity data from a resistivity sensor array including a plurality of resistivity sensors; and
Predict slope collapse based on the vibration data and the resistivity data,
The vibration sensor array and the resistivity sensor array are installed on a slope, forming an array in a first direction perpendicular to the slope inclination and a second direction parallel to the slope inclination,
The processor executes the one or more instructions to predict slope failure based on the vibration data and the resistivity data,
Calculate the velocity reduction rate of elastic waves for each slope cross section based on the vibration data;
Calculate the resistivity reduction rate for each slope cross section based on the resistivity data;
Obtaining a 3D geological map of the slope for the form of change in stiffness and moisture state of the slope based on the velocity reduction rate of the elastic wave calculated for each slope cross-section and the resistivity reduction rate calculated for each slope cross-section; and
Based on the slope 3D geological map, determining that the pattern of change in stiffness and moisture state of the slope has a higher slope collapse risk grade when the change in the second direction is more dominant than the first direction,
The processor executes the one or more instructions to pre-predict slope collapse based on the vibration data and the resistivity data, wherein a region for slope collapse prediction is set to a predetermined P-wave velocity threshold or S-wave velocity threshold. , and divided into low speed-low resistivity region, high speed-low resistivity region, low speed-high resistivity region, and high speed-high resistivity region based on electrical resistivity threshold. According to claim 8,
The electronic device of claim 1, wherein the predetermined P wave velocity threshold is 1,000 m/s, the predetermined S wave velocity threshold is 350 m/s, and the predetermined electrical resistivity threshold is 300 Ω. According to claim 8,
The processor executes the one or more instructions,
Obtaining slope displacement data from a plurality of displacement sensor arrays; and
An electronic device that determines whether the slope adjacent to the displacement sensor collapses or the vibration sensor is damaged based on the slope displacement data. According to claim 10,
The processor executes the one or more instructions,
An electronic device that acquires a slope 3D geological map based on the vibration data, the resistivity data, and the slope displacement data. According to claim 8,
The processor executes the one or more instructions to obtain vibration data from a vibration sensor array including a plurality of vibration sensors,
Generating high-frequency vibration by the striking module;
Obtain first vibration data for analyzing the surface and shallow area of the slope from the high-frequency vibration; and
An electronic device that acquires second vibration data for analyzing a deep area from low-frequency vibration occurring in an area adjacent to the plurality of vibration sensors. According to claim 10,
An electronic device wherein the displacement sensor array is installed on a slope. According to claim 13,
A displacement sensor, a vibration sensor, and a resistivity sensor having the same coordinates in the displacement sensor array, the vibration sensor array, and the resistivity sensor array are included in a complex sensor structure as one set; and
An electronic device wherein the complex sensor structure is installed on a slope in an array structure.