JP2595412B2 - Prediction method of railroad embankment critical rainfall and train operation management system using the method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting a critical rainfall amount at which a railway embankment will collapse and a train operation management system using the method.

[0002]

2. Description of the Related Art Embankments along railway lines may collapse due to prolonged rainy season or torrential rain caused by typhoons, which hinders safe and stable transportation of trains. In order to prevent such disasters, it is, of course, necessary to identify danger points and appropriately implement disaster prevention measures in these areas.
Appropriate operation regulations that match the characteristics of the line section are required. For this reason, a railway maintenance person needs a risk evaluation method that can accurately predict the risk of collapse of the railway embankment due to rainfall and that can be easily utilized by on-site engineers.

Conventionally, the evaluation of the risk of collapse of a railway embankment is based on the result obtained by discriminant analysis, and the rain resistance is shown as a 24-hour rainfall. The regulation uses a method of empirically determining the amount of rainfall per hour and the total amount of rainfall from the start of rainfall from the amount of rainfall caused by past rainfall disasters. In other words, as the evaluation of the degree of danger of the slope at the danger point, the slope is graded according to the surface soil, height, and water collection conditions using a scoring table, and daily rainfall (24-hour rainfall) is used as rain resistance. I try to evaluate.

[0004]

However, in the above-mentioned conventional risk evaluation method for collapse of railway embankment,
The scoring of the slope is determined based on the appearance factors, and is based on a uniform nationwide standard that does not consider the characteristics of the area, and there was a problem with the accuracy of the evaluation results. In addition, there was a problem that the graded result of the slope was not directly associated with the above-mentioned rainfall for operation regulation, and the rainfall for the risk evaluation and the rainfall for operation regulation were different indices.

[0005] In view of such circumstances, there has been a demand for the development of a highly accurate method for evaluating the collapse critical rainfall of railway embankments. The present invention, in order to solve the above problems, can be directly applied to the collapse rainfall of the embankment obtained as a result of the risk assessment to the operation management rainfall used in the railway, high accuracy, safe operation of the train It is an object of the present invention to provide a method of predicting a collapse rainfall of a railway embankment that can be performed and a train operation management system using the method.

[0006]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides a method for predicting a collapse limit rainfall of a railway embankment, comprising: a structure and soil condition of an embankment;
The sum of the respective evaluation points of the water collection and infiltration conditions and the empirical rainfall conditions is obtained, and the total evaluation points are added to the basic points to obtain an overall evaluation point. The time rainfall which is the maximum rainfall per unit time within 24 hours is obtained, the continuous rainfall which is the rainfall accumulated from the start of rainfall in the area is obtained, and the continuous rainfall and the time rainfall are calculated based on the comprehensive evaluation point. It estimates the critical rainfall of the railway embankment obtained by each power of.

In a train operation management system,
Embankment structure / soil condition, foundation structure / soil condition,
There are means for calculating the sum of the respective evaluation points of the infiltration condition and the empirical rainfall condition, means for adding the totaled evaluation points to the basic points to obtain a comprehensive evaluation point, and a railway embankment to be predicted. A means for calculating a time rainfall which is a maximum rainfall per unit time within 24 hours of the area; a means for calculating a continuous rainfall which is a rainfall accumulated from the start of rainfall in the area; and Means for estimating the collapse limit rainfall of the railway embankment obtained by each power of the continuous rainfall and the hourly rainfall, and means for determining whether or not the collapse embankment of the railway embankment is reached, and as a result, When it reaches, the operation of the train in the section of the railway embankment is regulated.

[0008]

According to the present invention, as described above, the structure of the embankment
The total score of each of soil condition, basement structure / soil condition, water collection / infiltration condition and empirical rainfall condition is obtained, and the total score is added to the evaluation basic point to obtain an overall evaluation score. By substituting the evaluation points into the following equation, the critical rainfall of the embankment is estimated. (Time rainfall) ⁿ × (Total rainfall) ^m = Σ (Score of embankment condition) Here, n = m = 0.3, and the collapse limit rainfall R · r of railway embankment is Rr = (Time rainfall) × (Continuous rainfall) = [Σ (Score of embankment condition)] ^{1 / 0.3 Based on} this railroad embankment's limit collapse rainfall, it is determined whether or not the railway embankment's collapse limit rainfall is reached. , The operation of the train in the section of the railway embankment is regulated.

[0009]

Embodiments of the present invention will be described below in detail with reference to the drawings. FIG. 1 is a diagram showing an embankment risk evaluation criterion and a collapse limit rainfall of the embankment showing an embodiment of the present invention. FIG. 1 is a schematic overall configuration diagram of a prediction of a collapse critical rainfall of a railway embankment and a train operation control system using the prediction according to an embodiment of the present invention.

Hereinafter, a description will be given of a risk evaluation criterion for embankment, a prediction of embankment critical rainfall, and a train operation regulation system using the same. (A) Extraction of factors related to embankment collapse The potential for embankment stability during rainfall is generally calculated from the theory of slope stability, as follows: S = f (β, H, c, φ, γ, u _W , ε) It is well known that (1) is represented. Here, as the structural conditions of the embankment, β is the embankment gradient, H is the embankment height, and as the soil condition, c is the adhesive force of the embankment, φ is the internal friction angle, γ is the unit volume weight, and u _W is the pore water pressure. And ε is another factor.

The above equation (1) is for the collapse in the embankment. Considering the base collapse involving the basement, the base thickness D, the groundwater level h _{B of the} base, the soil strength q _{B of} the surface ground,
The base inclination angle θ _{B and the} like are related as ε. Further, when actually think the entity of degradation by rain in the railway embankment, said ε of the formula (1) of the water collecting-penetration condition of rain, the line top of topographical catchment state W _G, line direction The vertical form T _{L of the} embankment represented by the drop slope of the embankment and the transverse form T _H of the embankment such as a one-piece section embankment are factors that influence the collapse.

Further, immediately after the start of operation as a railway embankment,
Insufficiently compacted portions of the blade may be damaged by rainfall erosion, etc., or may be damaged in a small scale such that they do not collapse during a long period of service. However, since they are slightly repair in the range of routine maintenance is performed, the greater the experienced rainfall R _E which embankment is subjected in service, high probability of receiving a small repair, embankment against fatal collapse It is considered that the rain resistance is increased. It should be noted that, for this experience rainfall R _E will be described later.

[0013] Thus, said epsilon of formula (1) of, ε = g (D, h B, q B, θ B, W G, T L, T H, R E) is represented by ... (2) . The above equations (1) and (2) are factors that affect the embankment collapse due to rainfall,
It shows environmental conditions such as soil conditions and catchment topography, and empirical rainfall conditions. However, when trying to make a practical evaluation at a railway site based on these, it is necessary for the factors to be used to be obtained by simple tests and site surveys.

The soil conditions c, φ, and γ of the embankment are represented by the soil classification S _E of cohesive soil, sandy soil and gravel soil, and the penetration strength N _C of the surface layer of the embankment by a simple penetration test. I made it. Factors related to the rise in the pore water pressure u _W of the embankment can be broadly classified by soil classification.
The permeability coefficient k obtained from the particle size characteristics proposed by Azen, Creager and the like is used. In the embankment structural conditions, the embankment gradient β is almost constant in railway embankment.
That is, since the average value of the data used here is 1: 1.4 and the standard deviation is 1: 0.2, this factor is omitted.

Since almost no base collapse involving the surface ground under the embankment has occurred, it is not necessary to consider D, h _B , q _B and θ _B which are the structural and soil conditions of the base as factors.
Since the rise in water level in the fill are considered closely related to groundwater level, as factors related surface layers, geology S _B (alluvium, etc.) of the base inclination angle theta _B and surface ground to be represented by.

[0016] In addition, W _G related to the catchment-penetration of rainfall to be applied to fill in the above formula (2), T _L, by T _H is on-site investigation, also, experience rainfall R _E is the statistical amount of rainfall Is more easily sought. Therefore, the above equations (1) and (2)
From, S is, S = f (H, S E, N C, k, S B, θ B, W G, T L, T H, R E) ... in becoming represented that by (3) That is, the items used in this analysis are represented by the ten variables shown on the right side of the above equation (3). (B) Extraction of external standards related to embankment collapse Rainfall is considered as an external standard related to embankment collapse.
There are many studies on the concept of effective rainfall and precedent rainfall on what kind of rainfall concept should be applied.

On the other hand, conventionally, the evaluation using the daily rainfall is performed in the slope scoring table. As for the rainfall used for driving regulation, a combination of continuous rainfall and hourly rainfall based on past disaster records in a certain line section is applied. In this analysis, the critical rainfall based on the product of the hourly rainfall r and the continuous rainfall R is used as an external reference in consideration of the background that has been conventionally used for the operation regulation, but the power of each rainfall (constant m,
to be the product value of n), as耐降rain potential embankment was assumed to be represented by ^{S = R m · r n ...} (4). When m = 1 and n = 1, it becomes an external standard used for the conventional driving regulation, and m = 1 and n =
In the case of 0, it corresponds to the daily continuous rainfall shown in the above-mentioned slope scoring table.

[0018] Therefore, the above equations (3) from equation ^{(4), R m · r} n = f (H, S E, N C, k, S B, θ B, W G, T L, T H , R _E ) (5). The hourly rainfall shall not be the value at the time of disaster,
The maximum value was set within 4 hours. This is because not only collapse due to slope erosion but also collapse due to rise in pore water pressure in the embankment was considered. In other words, after recording the maximum hourly rainfall at the time of one rainfall, there was a case that collapsed several hours later,
This also takes into account the fact that the measurement results of rainfall and pore water pressure in the embankment on the existing embankment show a similar upward trend.

If the embankment evaluation point does not coincide with the existing railway rain gauge installation point or weather observation point, the rainfall there is calculated as the rainfall (r and R) at the embankment evaluation point shown in the above equation (5). Cannot be used as the value of In such a case, by AMEDAS Interpolation methods by the present inventors to estimate the amount of rainfall per hour r _D embankment evaluation point from the time rainfall r _0i of history of rainfall observation point was estimated rainfall of the data. In other words, rainfall observation location number of the history of the p, when the distance between the D ₁ At the same disaster areas,

[0020]

(Equation 1)

When constants N and p are determined so as to minimize the residual difference between r _D and r _Oi , N = 0.96 and p = 3
Becomes (C) External Criteria and Statistics of Items (a) Rainfall Conditions at Collapse FIG. 2 shows the frequency distribution of continuous rainfall R and hourly rainfall r until collapse. The continuous rainfall is the cumulative rainfall from the start of the rainfall to the time of the occurrence of a disaster, and when rainfall is interrupted for 12 hours or more on the way, the continuous rainfall is the accumulated value after the interruption. (B) Embankment structure / soil condition (H, S _E , N _C ) Among the embankment structure / soil conditions, the frequency distribution of the embankment height H is shown in FIG. 5 m. FIG. 4 shows the relationship between the continuous rainfall R, the hourly rainfall r, and the embankment height H. The continuous rainfall R and the hourly rainfall r represent average values for the range of the embankment height H shown on the horizontal axis of FIG. 4, but when the embankment height H increases, the continuous rainfall R increases.
And the hourly rainfall r tend to be small, which seems to substantially match the tendency in the geodynamic stability calculation.

The soil S _E of the embankment was classified by a particle size test of a sample taken from a depth of 0.5 m to 1.0 m on the surface of the slope. According to this, 70% was sandy soil, 22% gravel soil and 8% clayey soil. Conventionally, the embankment strength of railways was often determined by a Swedish sounding tester, etc., but the embankment strength was determined by a simple penetrating tester for slope inspection, which enables simpler and more rapid surveys. . According to the survey, more than 70% of collapse cases are less than 3m in collapse thickness, and there are many cases where the embankment surface layer collapses. Therefore embankment strength N _C was be an average of up to 3m deep from the surface layer. The frequency distribution of the embankment strength at the collapse site was as shown in FIG. The average value of the embankment strength of the collapsed embankment was N _C = 5.4. Also,
Embankments with N _C <6 make up about 70% of the total. In addition,
The correlation formula between the Swedish sounding test and the simple penetration test, which have been conventionally used, is given by the present inventors for each soil type. (C) Structural and soil conditions of the basement (S _E , θ _B ) Of the collapsed embankment, the embankment on the so-called sloping ground where the base inclination angle θ _B is 10 degrees or more occupies more than half of the whole, and Has an effect on the stability of the embankment.

There are many geological classification methods for the surface ground S _E on which the embankment is constructed, but as a method that can be classified as simply as possible, alluvial sedimentation and flooding are performed because the softness of the ground and the groundwater level can be distinguished. It was divided into three layers, lamination and bedrock. The frequencies are 23%, 15% and 24%, respectively. (D) Water collection and infiltration conditions (W _G , k, T _L , T _H ) The concentration of water on the embankment greatly contributes to the collapse.
Albeit it has been said always about the past of the collapse of the case, but the collapse side as the water collecting topographical conditions W _G is the water collecting terrain, the other side of the collapse were classified into three catchment terrain, and Hishu water terrain.
Approximately 20% of the collapse cases have catchment topography.

The vertical profile T _L of the embankment includes a single slope, a drop slope point, a cut embankment boundary, and the like. According to FIG. 5A, the collapse of the cut boundary where the concentration of rainwater is expected accounts for 30% or more. Moreover, the transverse mode T _H in FIG. 5 (b), the
Shows the frequency of pure embankment, single embankment (including single section embankment), and belly embankment. When one kind of one-sided embankment is evaluated according to one-sided embankment, the number of collapse cases is larger than that of pure embankment, which indicates that it is structurally vulnerable to collapse. (E) Empirical rainfall condition (R _E ) Analysis data is distributed almost uniformly from relatively new embankments less than 5 years after embankment construction to old ones more than 90 years old.
On the other hand, as shown in FIG. 6A, when comparing the relationship between the aging y and the continuous rainfall R at the time of collapse and the hourly rainfall r for each region, the small embankment over time collapses with a small amount of rainfall. In addition, as shown in FIG. 6B, it is clear that the embankment in the area where the annual average rainfall _Ry is small at the weather station near the collapse point also collapses with small rainfall. In other words, the higher the probability to receive a small-scale damage, such as not lead to disaster at the location annual average rainfall R _y is large, since the construction, etc. of the rich and glue surface engineering of drainage facilities, the average annual rainfall R _y It is considered that the rainfall resistance may be higher than the embankment of the same condition in a small area. This can be seen from FIG. 7 which shows the damage rate P obtained by dividing the number of damages of embankment including the disaster caused by rainfall that did not lead to disaster by the track length for each region, and the relationship between this and the annual average rainfall _Ry . Can be estimated.

As described above, since the elapsed years y of the embankment and the average rainfall _Ry are considered to be related to each other in the rainfall resistance, attention was paid to the total rainfall that the embankment received from the time of its construction until its collapse. the concept of experience rainfall R _E also was decided to introduce. (D) Prediction of critical rainfall by quantification type I (a) Execution of analysis In order to determine the critical rainfall of embankment collapse due to rainfall, the above-mentioned (C) is obtained by linearly expanding the right side of the above equation (5).
In consideration of (b) to (e), a multivariate analysis was performed on the category as shown in FIG.

At this time, analysis is performed by sequentially changing the powers m and n shown on the left side of the above equation (5) to 1.2 at a pitch of 0.1 to complement the obtained multiple correlation coefficient r _O. Drawing contour lines results in FIG. According to the figure, the contour line is 0.2 <
This shows that there is a place where r _O is high in the region where m, n <0.4, and the peak position is when m = n = 0.3. Multiple correlation coefficient at that time is r _O = 0.87.
FIG. 8 shows the partial correlation coefficient and weight of the item for each category corresponding to r _O.

The relationship predicted and observed values for the limit rainfall ^{R m · r n (m =} n = 0.3) is like in Figure 10, predictive value is high. (B) I mean rank from greatly contributes item most limiting rainfall from the partial correlation of each item was obtained in the analysis summary the above analysis _{results, R E> W G> θ} B> N C> T L >H>k> S _E > S
_B> is a T _H.

The ranking of each item in the range obtained from the category weights shown in FIG. 8 shows that the embankment strength N _C , the longitudinal profile T _L of the embankment and the embankment are compared with those obtained from the partial correlation coefficient. The rank is different for the height H. This is because the former range indicates the width of the absolute value of the discrete weight in each item, whereas the latter partial correlation coefficient is different for each category in the item. , A value corresponding to the correlation coefficient with respect to is given, and it is considered that there is a slight difference between the two.

[0029] In accordance with experience rainfall R _E increases, limit rainfall tends to be greater, the risk of embankment collapse tends to be low. Among the embankment conditions, the embankment height H shows almost the same tendency as that in FIG. 4, and as the embankment height H increases, the critical rainfall tends to decrease. Regarding the embankment strength N _C , the marginal rainfall tends to increase as the strength increases, except for items with low frequency N _C > 8. Regarding the embankment soil S _E, it was said that the surface layer of the newly built embankment of sandy soil was likely to be eroded and collapsed by concentrated torrential rain. However, in this analysis, the weight of the cohesive embankment was negative, The risk was higher for cohesive embankment than for embankment. This is considered to indicate that the discharge of pore water pressure is suppressed more than that of sandy or gravel soil. On the other hand, it is shown that the permeability is most dangerous when k is on the order of 10 ⁻³ cm / s.

Among the structural and soil conditions of the basement, the base inclination angle θ _B indicates that the danger increases when the inclination angle θ _B is 10 degrees or more, which has been empirically described. Matches. For surface soil geology S _B, the case of alluvium soil has become a result of a high degree of risk. Among the catchment-osmotic conditions, for the water collecting terrain W _G, danger degree of water collecting terrain of collapse side is very high, if not the water collecting terrain has become a safe side. Embankment crossing form _TH
As for, when the conditions were such as single-section and one-sided embankment, the risk became high. On the other hand, with respect to the longitudinal form _TL of the embankment, unlike the consensus on the safe side, the result was safe under special conditions such as a cut boundary. The embankment under such conditions can be said to be a result of the fact that measures such as drainage measures have been taken in advance. (E) Criterion evaluation criteria for embankment (a) Criterion evaluation criteria for railway embankment In addition to the evaluations described in (D) and (b), the railway embankment Considering empirical and engineering considerations based on the actual situation, we propose the evaluation criteria for the risk of embankment shown in FIG.

This evaluation criterion is obtained by adding 13.14 points, which are the basic points, to the sum of the corresponding evaluation points of the embankment structure / soil condition, basement structure / soil condition, water collection / penetration condition, and empirical rainfall condition. is intended to be obtained a continuous rainfall and time rainfall product values R ^m · r ⁿ (provided that m = n = 0.3). For each embankment, the right side of the above equation (5) is a constant, so that the critical rainfall is represented by a hyperbola with R and r as variables, as shown in FIG.

However, depending on the conditions of the embankment, the critical rainfall may become very small. In this case, the minimum rainfall of the collapse case used in the analysis is R · r = 635 mm.
² / h (R ^0.3 r ^0.3 = 6.93) is the lower limit. At this time, the method of risk assessment for rainfall on railway embankment is shown in Fig. 1.
It looks like 3. First, the embankment of interest is surveyed, and the items shown in the risk evaluation criteria of FIG. 11 are evaluated [step (1)]. Generally embankment strength N
Except for _C , sufficient data can be obtained by reconnaissance alone. The embankment strength N _C performs sounding by cone penetration test if necessary [Step (2)]. The evaluation data are adapted to the 11 [Step (3)], to perform the calculation of R ^m · r ⁿ [Step (4)].

The critical rainfall R · r is equal to the above-mentioned lower limit of 635 mm.
Check if it is ² / h or more [Step (5)]
Determine the critical rainfall for rainfall disasters. Furthermore, it is determined whether or not it is necessary to evaluate the risk over time for the rainfall that is actually falling [Step (7)]. [Step (8)], and if there is no time-dependent rainfall data at the embankment point, a supplementary rainfall is calculated from the nearby AMeDAS and rain gauge from the above equation (6) [Step (9)] . The hourly rainfall r at this time is, as defined in (B), the maximum hourly rainfall within the past 24 hours from the falling current time. The maximum value within 24 hours will be adopted as the hourly rainfall r. On the other hand, when it is difficult to grasp the rainfall amount at the just point of the embankment in a timely manner, the data of the nearby rainfall gauge is substituted into the above equation (6) for complementation. In this way, the critical rainfall R · r is compared with the actual rainfall [Step (1)
0)], and the risk evaluation over time [step (11)] becomes possible. (F) Verification of typical collapse cases The risk evaluation criteria shown in the preceding section were applied to embankment collapse cases, and verification of the accuracy was attempted. (A) Collapse example 1 Maximum hourly rainfall r = 41.5 mm / h, continuous rainfall R at the end of the rainy season in the middle and northwestern part of Mie Prefecture in July 1988
= H due to torrential rainfall reaching 389.7 mm
The right slope of the 6.6 m double track embankment collapsed over a length of about 50 m from the shoulder including the construction base. The collapsed sectional view is as shown in FIG.

The collapse site is located in an alluvial lowland, and is a back marsh of a natural embankment, and the surrounding area is used as a paddy field. The thickness of the soft layer is 3 to 4 m. There is a 1m high waist sill at the bottom of the slope, but there is no rooting and the thickness is 30cm
It is on the chestnut stone. Although the lower part of the slope is covered with a flat block, it is considered that only the effect of preventing surface erosion can be exerted. On the other hand, on the left side of the slope opposite to the collapsed land, as shown in Fig. 15, in addition to the structure of the right side slope, a 3.5m precast grid frame (1.5m steel pipe pile) is constructed above the flat plate block. It is thought that he had escaped collapse.

The soil obtained by the grain size test on the track at a 1.2% downward gradient toward the end point is (SM),
The permeability k is of the order of 10 ^-3 cm / s. In addition, the average embankment strength up to a depth of 3 m of the slope was N _C = 5.0. FIG. 16 shows the result of applying the proposed evaluation criteria to the right side of the track where the embankment collapsed due to the lack of reinforcement work on the slope under such circumstances.
The obtained critical rainfall is R · r = 7870 mm ² / h, and the relationship between the critical rainfall curve and the rainfall at the time of disaster is shown in FIG.
It looks like 7. This collapse is caused by a continuous rainfall R of about 350 mm.
Since it occurred at the time of the disaster, the predicted value agrees well with the rainfall observation value at the time of this disaster.

The figure also shows ten past rainfall histories. In two of them, the embankment under similar conditions in the vicinity has collapsed, and the measured rainfall history exceeds the estimated critical rainfall curve and is in the danger zone. Also,
The remaining cases, which did not collapse, are almost in the area below the marginal rainfall curve. Therefore, the estimated value sufficiently satisfies the measured value. (B) Collapse Case 2 Heavy rain caused by typhoon No. 17 which hit the Shikoku region in August 1989 caused slope disasters at various places along the railway along the Seto Inland Sea, including debris flow. Among these disasters, we will apply it to the case where a single-line embankment collapsed.

This embankment is a pure embankment on a sloping ground along the hillside composed of alternating layers of sandstone and shale of the Izumi Group. The track has a downward slope of 12.5% toward the end point, and the starting point corresponds to the cut boundary of the cut section in the longitudinal direction. The embankment material is sandy soil, and the embankment strength is as shown in Fig. 18 at the right shoulder of the embankment, which is the collapse side, and the embankment strength N _C = about 2.3 m from the surface layer, which is very loose. is there. FIG. 16 shows the result of applying the proposed evaluation criteria under these conditions.
Shown in

The obtained critical rainfall is R · r = 3851 mm
² / h, and the estimated critical rainfall curve and the actual rainfall measured at the time of disaster are in good agreement as shown in FIG. In addition,
The figure also shows six past typical rainfall histories.
Two of these cases exceeded the critical rainfall curve and entered the collapse danger zone.
The record of Typhoon No. 20 in 1979 was cut off along the railroads nationwide, and the embankment was damaged by many disasters. Several disasters occurred near the embankment. . The remaining rainfall history, which did not cause collapse, is almost on the safe side of the critical rainfall curve, and the estimated critical rainfall almost satisfies the measured value.

As described above, based on the past collapse cases due to the rainfall on the railway embankment, a method of predicting the critical rainfall that will lead to the collapse by quantification type I by the product value of the continuous rainfall R and the hourly rainfall r has been proposed. Things. When this method for evaluating the risk of embankment was applied to a typical recent case of railway embankment collapse, the predicted value and the measured value agreed well. The evaluation criteria proposed here are statistically calculated based on past collapse cases of general embankments that have not taken measures such as slope work, but are applied as one method of predicting collapse in advance. It is possible to ensure safe operation by restricting train operation based on the prediction.

In this way, it is possible to predict the collapse rainfall due to the rainfall on the railway embankment. Then, the critical rainfall is compared with the actual rainfall, and when approaching the critical rainfall, the train is slowed down, and when the rainfall is reached, the operation of the train at the embankment point of interest is stopped. In the following, the train operation regulation system using the risk evaluation criteria in the railway embankment,
This will be described with reference to FIG.

As described above, the train operation management center 1
Data input device 8 to embankment risk prediction device 2 installed in
From the following, (1) embankment height H, soil S _E , embankment strength N _C as the embankment structure / soil condition, (2) surface ground geology S _B as the base structure / soil condition, base inclination angle θ _B , (3) permeability of the water collecting-osmotic conditions k, collecting terrain W _G, vertical form T _L, transverse form T _H, enter the experience rainfall R _E as (4) experience rainfall conditions, each evaluation Find the sum of points. In FIG. 1, reference numeral 3 denotes a CPU (central processing unit) for performing overall information processing and general control of the embankment risk prediction device 2, 4 denotes an input interface for receiving rainfall data from each line section, and 5 denotes external rainfall information. An interface for receiving data and transmitting and receiving data to and from the other embankment risk prediction devices 11 and 12. A RAM 6 for temporarily storing data and a program for performing data processing and control of the embankment risk prediction device 2 are stored. A memory having a built-in ROM, 7 is a display device (CRT) for data of the embankment risk prediction, 9 is an external storage device, 10 is an output interface for transmitting output data of the embankment risk prediction device to the outside, and 13 is an output. A communication device that is connected to the interface 10 and issues a command to execute a train operation to each line section. The communication device 20 receives rainfall information from the external weather information center 50. Te is an external rainfall information processing apparatus for performing the process. This external rainfall information processing device 20
In, specifically, a complementary rainfall is calculated based on external rainfall information, and the rainfall data is transmitted to the embankment risk estimation devices 2, 11, and 12. Reference numeral 21 denotes a transmission line (for example, a LAN) connecting the devices.

Next, the embankment risk prediction device 2 has the basic point 1
3.14 is input, and the basic points are added to the totaled evaluation points to obtain an overall evaluation point. On the other hand, in the railway rainfall data collection system 30 installed in the area where the railway embankment to be predicted exists, the actual rainfall is measured by the rain gauges 31 installed in various parts of the area, and the measurement data is obtained. Is transmitted by the transmitting device 32, the transmitted measurement data is received by the receiving device 33, and is managed as rainfall data by the rainfall data management device 35, and the rainfall data is train operation management via the communication line 42. It is sent to the embankment risk estimation device 2 installed in the center 1. In the rainfall data management device 35, 36 is a central processing unit (CPU), 37 is a display device (CRT),
38 is a data input device, 39 is an input interface,
40 is an output interface, 41 is RAM and ROM
And the like.

As described above, when rainfall data is supplemented by the AMeDAS interpolation method, the rainfall information of the external weather information center 50 (for example, Japan Meteorological Association) is transmitted to the external rainfall information processing device via the communication line 51. Collect to 20,
It can be replaced with an actual measurement value of the rainfall data. On the basis of these rainfall data, the embankment risk prediction device 2 obtains the hourly rainfall, which is the maximum rainfall per unit time within 24 hours.

Further, a continuous rainfall, which is a rainfall accumulated from the start of rainfall in the area, is obtained. Therefore, each of power of the time rainfall and the continuous rainfall on the basis of the overall score [R ^m (continuous rain) × r ⁿ (time rainfall)] railway embankment obtained by disintegrating limit rainfall R · r = (Time Rainfall) ×
(Continuous rainfall) = [Σ (score of embankment condition)] ^{1 / 0.3} is estimated.

From the collapse rainfall R · r of this railway embankment,
As shown in FIG. 12, the degree of danger can be predicted, and as shown in FIG. 20, the operation management of the train in the line section where the railway embankment exists is performed based on it. For example, when approaching the critical rainfall curve a, the train slow-down information is displayed on the display device 7 for the data of the embankment risk prediction, an alarm device (not shown) is operated, and the slow-down of the train in the line section is performed by the communication device. In the case where an instruction is made via the line 13 or the threshold rain curve a is exceeded, measures are taken to stop the operation of the trains in the line section. In FIG. 20, a dotted line b is a critical rainfall information curve set for train management. When the line reaches this line, train operation is stopped, and safe train operation management can be performed. Line c indicates the conventional train operation regulation.

It should be noted that the present invention is not limited to the above embodiment, but various modifications are possible based on the spirit of the present invention, and these are not excluded from the scope of the present invention.

[0047]

As described above in detail, according to the present invention, by obtaining the embankment conditions obtained in the field survey, the collapse limit rainfall obtained by each power of the hourly rainfall and the continuous rainfall can be obtained. And improve the accuracy of the prediction of the risk of collapse of railway embankments, and based on this critical rainfall, ensure that train operation is regulated and that safe operation and associated safety measures can be taken promptly and accurately. The effect is significant.

[Brief description of the drawings]

FIG. 1 is an overall configuration diagram of a prediction of a collapse rainfall of a railway embankment and a train operation management system using the prediction according to an embodiment of the present invention.

FIG. 2 is a diagram showing a frequency distribution of continuous rainfall and hourly rainfall.

FIG. 3 is a diagram showing a frequency distribution of embankment height and embankment strength.

FIG. 4 is a diagram showing a relationship between continuous rainfall, hourly rainfall, and embankment height.

FIG. 5 is a diagram showing a frequency distribution of a vertical form and a transverse form of an embankment.

FIG. 6 is a diagram showing a relationship between continuous rainfall, hourly rainfall, aging, and annual average rainfall.

FIG. 7 is a diagram showing the relationship between the annual average rainfall and the embankment damage rate.

FIG. 8 is a diagram showing partial correlation coefficients and weights.

FIG. 9 is a diagram showing contour lines of a multiple correlation coefficient.

FIG. 10 is a diagram showing a correlation between an observed value and a predicted value of a critical rainfall.

FIG. 11 is a diagram showing a risk evaluation standard for railway embankment.

FIG. 12 is a diagram showing a degree of risk based on a critical rainfall curve.

FIG. 13 is a flowchart for evaluating the risk of railway embankment.

FIG. 14 is an embankment cross-sectional view of Collapse Example 1.

FIG. 15 is a diagram showing an embankment cross-sectional shape and embankment strength.

FIG. 16 is a diagram showing an application example of a risk evaluation standard for embankment.

FIG. 17 is a diagram showing a critical rainfall curve of Collapse Example 1.

FIG. 18 is a diagram illustrating a cross-sectional view of embankment example 2 and embankment strength.

FIG. 19 is a diagram showing a critical rainfall curve of Collapse Case 2.

FIG. 20 is a diagram showing a critical rainfall curve and train operation management.

[Explanation of symbols]

1 Train Operation Management Center 2 Embankment Risk Prediction Device 3,36 CPU (Central Processing Unit) 4,39 Input Interface 5 Interface 6,41 Memory 7,37 Display Device (CRT) 8,38 Data Input Device 9 External Memory 10, Reference Signs List 40 Output interface 11, 12 Other embankment risk prediction device 13 Communication device 20 External rainfall information processing device 21 Transmission line 30 Railway rainfall data collection system 31 Rain gauge 32 Transmitting device 33 Receiving device 35 Rainfall data management device 42, 51 Communication line 50 external weather information center H embankment height S _E soil N _C embankment strength S _B surface ground geology theta _B foundation inclination k permeability W _G collecting terrain T _L vertical form T _H cross form R _E experience rainfall

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Katsuya Okada, 2-8-8 Hikaricho, Kokubunji-shi, Tokyo 38 Within the Railway Technical Research Institute (72) Tatsuo Noguchi 2--8 Hikaricho, Kokubunji, Tokyo 38 National Railway Technical Research Institute (56) References Japanese Patent Publication No. 3-50969 (JP, B2) Japanese Patent Publication No. 3-22953 (JP, B2)

Claims (6)

(57) [Claims] 1. The sum of the evaluation points of (a) the structure / soil condition of the embankment, the structure / soil condition of the base, the water collection / infiltration condition and the empirical rainfall condition is calculated, and (b) the sum is calculated to the basic point. (C) determine the hourly rainfall, which is the maximum rainfall per unit time within 24 hours in the area where the railway embankment to be predicted exists, and (c) calculate the total rainfall.
A continuous rainfall, which is the amount of rainfall accumulated from the start of rainfall in the area, is obtained, and (e) a collapse critical rainfall of the railway embankment obtained by each power of the continuous rainfall and the hourly rainfall is estimated based on the comprehensive evaluation point. A method for predicting the critical rainfall of a railway embankment characterized by performing: 2. The structure / soil condition of the embankment includes height, soil quality, and embankment strength of the embankment, the structure / soil condition of the basement includes surface geology and inclination angle of the basement, and the condition of water collection / penetration is a permeability coefficient. 2. The method for predicting a collapse rainfall of a railway embankment according to claim 1, wherein the method includes a catchment topography, a vertical cross section, and a cross section. 3. The evaluation basic point is 13.14, and each power of continuous rainfall and hourly rainfall is 0.3.
The method for predicting the critical rainfall of railway embankment described. 4. The method according to claim 1, wherein the empirical rainfall is a total rainfall received from the embankment after construction. 5. The rainfall accumulated from the start of rainfall is:
2. The method according to claim 1, wherein when the rainfall is interrupted for 12 hours or more on the way, the rainfall accumulated after the interruption is the rainfall limit of the railway embankment. 6. A means for calculating the sum of the evaluation points of the structure / soil condition of the embankment, the structure / soil condition of the basement, the water collecting / penetration condition and the empirical rainfall condition, and (b) the basic point Means for obtaining the total evaluation point by adding the total evaluation points; and (c) calculating the hourly rainfall that is the maximum rainfall per unit time within 24 hours in the area where the railway embankment to be predicted exists. Means, (d) means for calculating continuous rainfall, which is the amount of rainfall accumulated from the start of rainfall in the area, and (e) power of each of the continuous rainfall and the hourly rainfall based on the comprehensive evaluation point. Means for estimating the critical rainfall of the railway embankment; (f) means for determining whether the critical rainfall of the railway embankment is reached; and (g) as a result, when the critical rainfall of the railway embankment is reached, Trains that regulate the operation of trains on embankment sections Operation management system.