Preparation of Geotechnical Database of Mountainous Cities With ArcGIS

Transcription

1 Preparation of Geotechnical Database of Mountainous Cities With ArcGIS Binod Tiwari, Hideaki Marui, Kiyomichi Aoyama, Pankaj Bhattarai and Gyanu Ratna Tuladhar Abstract ArcGIS was used to make existing landslide distribution maps of three towns in Niigata, Japan. Soil samples were collected from highly weathered and unweathered rocks of various geological formations to measure the variation in friction angle, consistency limits and mineralogical composition. Those point data were useful to distribute the soil strength throughout the area using DEM, prepared from high resolution scanning of topographic maps, soil strength and estimation of ground water distribution pattern based on the piezometer information. An automated 3D slope instability map was prepared based on the safety factor values and compared with the existing situation. 1. Background Landslides and mass movements are common natural disasters in the world. Although such disasters can not be prevented, effective hazard and risk management techniques can lessen the loss of the lives and properties from these disasters. Soeters and Westen (1996) have summarized various methods of slope instability zonation using GIS. Although statistical and heuristic analysis methods are well practiced in order to prepare the hazard and risk maps of the area, there are very limited researches on the appropriate techniques for the deterministic analysis. One of the major reasons for it is that it is very difficult to gather information on ground water table and soil strength properties for a large area. In most of the cases, average value of cohesion intercept (c) and internal friction angle (φ) of the soil is considered in calculation of cell based factor of safety, while carrying out the deterministic analysis for the estimation of potential landslides in large area. However, in order to use the results of the deterministic analysis in quantitative way for geotechnical design purposes, it is necessary to study for more comprehensive method of data acquisition for it. One of the possible methods for it might be to use the information from the available records, laboratory testing of the soil from various existing landslide areas and using the available strength for some other known parameters such as geological region consisting the failure zone, using available ArcGIS tools. This study is mainly focused on the appropriate use of ArcGIS tools for the comprehensive deterministic analysis of the terrain. 2. Study Area In order to select the representative study area in Japan, overall landslides occurrance and prevention situation in Japan was studies. According to the available information (Landslide

2 in Niigata Prefecture; 2003), Niigata Prefecture has highest number of recorded landslides (15% of recorded 6,809 landslides throughout Japan) and covered 24% of the recorded 314,880.7 ha of landslide area throughout Japan. Besides, Niigata Prefecture covers 9% of the total landslide potential places and 17% total potential landslide area throughout the country. Therefore, Niigata Prefecture was considered for the present study : Iwafuna 2: Nishi Kanbara 3: Higashi Kanbara 4: Naka Kanbara 5: Niigata 6: Mijima 7: Minami Kanbara 8: Koshi 9: Kariba 10: Kita Uonuma 11: Higashi Kubiki 12: Minami Uonuma 13: Naka Uonuma 14: Nishi Kubiki 15: Naka Kubiki 16: Sado 17: Awashima Figure 1 Location of Various Counties in Niigata Prefecture (Study area, Higashi Kubiki County, is highlighted) Among the total landslides (4,875 in number), which are under application of prevention works in Niigata Prefecture, 40.3% are under the Ministry of Land, Infrastructure and Transportation (MOLIT), whereas 28.1% and 31.6% are under Forestry Agency, and Prefectural and Local Governments respectively. Distribution of individual counties in Niigata Prefecture is shown in Fig. 1. According to Fukumoto (2004), 32% of the total recorded landslides in Niigata Prefecture were observed in Higashi Kubiki county. The record

3 shows that 89% of the landslides having piles as prevention works, in Niigata Prefecture and 85% in Higashi Kubiki county has been occurred along two types of mudstone formations i.e. Taruda and Sugawa formation. Those formations consists 60% area of Higashi Kubiki county. Among the total recorded landslides in Niigata Prefecture (database from 1949 to 2002), 1209 numbers (24.5%) of the landslides are located in Higashi Kubiki County, which is very high compared to the other cities/counties in Niigata (Fig. 2). Within Higashi Kubiki County, Maki Village, Yasuzuka Town, Uragawahara Village, Matsunodai Village, Matsunoyama Village and Oshima Village have covered 27.9%, 23.1%, 5.8%, 18.4%, 16.7% and 7.2% of landslides respectively (Fig. 3). Therefore, current study has been focused on Maki Village, Yasuzuka Town and Uragawara Village. However, in order to make the procedure of the study easy to understand, the database of Maki Village only has been presented here. Among 338 numbers of landslides under prevention works in Maki Village, 42% are under Ministry of Land, Infrastructure and Transportation (formerly, Ministry of Construction). This shows that the Government of Japan is spending a huge amount of its funding to prevent the landslides in Maki Village. Due to such intensive prevention plans, more than 64 numbers of landslides are being continuously monitored and a comprehensive database for the study of individual landslides is also available. This was the main reason to have this study focused on and around Maki Village. Five landslide areas in Maki Village were considered for Micro level detailed analysis for the possibility of using ArcGIS in landslide prevention planning works. The method presented in this study can be extended for the deterministic analysis of other villages and towns, which are in danger due to landslides. According to the data presented by Fukumoto (2004), 47% of the total landslides in Niigata Prefecture were occurred in March and May only i.e. during the snow melt period. Besides, in the peak rainfall month i.e. July, 15% landslide were occurred. If we divide the entire landslides occurred in Niigata Prefecture, 59.2% of the landslides have been occurred in the tertiary mudstone of Sugawa formation, followed by 30% in the tertiary mudstone of Taruda formation. This clearly shows that about 90% of the landslides in Niigata Prefecture are triggered at mudstone formation. About 81% of the landslides in Niigata, having piling works as one of the major prevention measures were triggered along the above mentioned mudstone formation. It is noteworthy that among the entire area of Maki village, the above mentioned mudstone formations covers 80 % (Fig. 4). The records of the landslides in Niigata Prefectures (2003) have shown that about half of the landslides have the influence area of ha (Fig. 5).

4 µ Legend Overall Niigata No of landslides Figure 2 Distribution of landslides, recorded from 1949 through 2002 in Niigata Prefecture (For the name of the county, Fig. 1 can be referred) 28% 24% Yasuzuka Urakawahara Matsunodai 7% 17% 18% 6% Matsunoyama Oshima Maki Figure 3 Distribution of the landslides in each village/town within Higashi Kubiki County

5 3% 1% 2% 2% 1% 1% 3% 36% 44% 7% Taruda Mudstone Sugaw a mudstone Debris and Colluvium Taruda Pumice Tuff Taruda sand and silt stone Naradate Silt and Sand stone Taruda Acidic Tuff Gravel and sand Naradate sand stone and conglomerate Hypersthene andesite Figure 4 Proportions of different geological formations within total area of Maki Village 1% 3% 23% 22% 12% 24% 15% >400 Figure 5 Distribution of landslides in Niigata Prefecture, based on their influence area 3. Available Primary Information In order to have macro level study on the identification of landslide potential area based on the deterministic analysis, following primary information were available. a. Topographical map of the 1:25,000 scale (prepared by the Topographic Survey of Japan in 1990)

6 b. Regional Geological map of 1:50,000 scale (prepared by the Geological Survey of Japan in 1994) Likewise, in order to verify the analyzed landslide potential zones, following maps were available. a. Existing landslides and slope failure area superimposed on the topographical map of the 1:50,000 scale (prepared by Yasuzuka Sabo Office in 1969) b. Existing landslides area under prevention planning by different authorities, superimposed on the topographical map of the 1:50,000 scale (prepared by Yasuzuka Sabo Office in 1988) c. Aerial photograph of 1:50,000 scale In order to conduct micro level 3D stability analysis of the landslide blocks, following primary information were available. a. Large scale topographic map of 1:1,000 scale (prepared by Yasuzuka Sabo Works) b. Cross sections of the terrain at various sections showing estimated failure zone and geological boundary (prepared by Yasuzuka Sabo Works) c. Information on the underground geological profile based on the exploratory boring logs (Yasuzuka Sabo Works) d. Information on the ground water table based on the piezometer data (Yasuzuka Sabo Works) e. Information on the extents of the landslides in each blocks based on the survey monument and inclinometer data of several years (Yasuzuka Sabo Works) 4. Methodology Two separate methods were followed in order to conduct deterministic analysis of the landslides in the study area, which are as follows. a. Overall macro level deterministic analysis of the entire study area b. Local micro level 3D stability analysis of the selected landslides Methodologies for the analysis of the both levels are described here in detail. a. Overall macro level deterministic analysis of the entire study area

7 Topographic contour Geologicalregion Soilproperties GroundwaterTable Slidingsurface Existinglandslides Landslide Geometry slope friction angle cohesion unit weight depth aspect GWT prevention works ArcGIS Spatial Analyst Vector-raster conversion Formula strengths and stresses Structure Slope units stability parameter A stability parameter B stability parameter C ArcGIS Spatial Analyst Zonal Statistics FOS in each ellipsoid Figure 6 Schematic Diagram for the data preparation and calculation for deterministic analysis Soeter and Westen (1996) has summarized three methods for deterministic analysis: infinite slope model and calculation of safety factor for each pixel; selection of a number of profiles from DEM and other parameter maps to export it to external slope stability methods; and sampling of data at predefined grid points and exportation of those data to a 3D slope stability model. For this analysis, more comprehensive method closer to the third method but inclusion of 3D safety factor calculation within the ArcGIS map itself has been considered. The method proposed by Tiwari et al. (2003), as shown below, can be advantageously used for the 3D stability analysis of the large terrain using ArcGIS tools. Duncan (1996) has presented the method of 2D and 3D stability analysis for landslides and explained their merits and demerits. Various methods of 2D stability analysis such as Fellinious method, Bishop s simplified method, Morgenstern and Price method, Spencer s method, Sharma s method, Janbu s method, Effective stress method and so on are available and various software based on FORTRAN, Basic, Auto Cad and simple Excel Spread Sheet are available in the market for the stability analysis (Huang, 1983). Hovland (1977) proposed a new approach for the 3D stability analysis of landslides. Although various FEM methods and simplified methods of 3D analysis were proposed by Chen et al. (1983 and 2001), and Hungr (1987), Hovland s method

8 is used in this analysis because of its simplicity. Modification of Hovland s method (Fig. 7) has been done as follows. CL Y Y Y Z -Y plane Plan portion of shear surface dip β Strike dip Shear surface dip β -Z Section strike normal view of sliding surface Dimensional analysis of column Z Z Directional analysis of sliding plane Fig. 7 Hovland s figure for the resolution of forces for the 3D stability analysis FS = Y c YSinθ ( ) + Cosα. Cosα xz yz Y Y γ. Z.. Y. Sinα ( γ. Z... Y. Cos( dip). Tanφ ) yz (1) Modification of Hovland s formula can be done to include pore water pressure as follows. FS = Y c YSinθ ( ) + Cosα. Cosα xz yz Y. Y. Sinθ ( γ. Z... Y. Cos( dip) γwhw.. ). Tanφ Cosα. Cosα Y γ. Z.. Y. Sinα yz xz yz (2) In order to make this equation compatible with GIS, the following modifications were done. Spatial analysis extension of ArcGIS 8.3 can be used to calculate the value of deepest slope of the sliding surface and orientation of that surface from north (aspect) automatically. The slope calculated by spatial analyst is exactly same as the value of dip, mentioned in Hovland s formula. However, Hovland mentioned the direction of Y-axis according to the direction of flow although he did not consider that in the equation. The direction of flow in the GIS based map for each zone in the landslide block can be estimated from the moving peg and inclinometer displacement data. This direction can be calculated as follows. Ψ = ( 90 ( A Ψ0 )) (3) where,

9 ψ: Strike mentioned by Hovland in degree A: Aspect of the steepest sliding surface in degree ψ ο : Orientation of the displacement vector in degree Statistical data on the analysis of many landslide blocks show that value of ψ ο is close to the value of the average aspect in most of the cases. Therefore, when the direction of the slide is not measured by regular monitoring, average aspect of the sliding surface can be taken as ψ ο. From the basic geometrics, we can derive, ( SinΨ ) α = Atan Tanβ (4) yz. ( Ψ ) α = ATan Cos Tanβ (5) xz. ( Sinα α ) θ = ACos. Sin (6) yz xz where, β: steepest slope of the sliding surface (dip in Hovland s equation and slope in GIS) Now, the Hovland s equation has been modified as, FS = Y c YSinθ ( ) + Cosα. Cosα xz yz Y. Y. Sinθ ( γ. Z... Y. Cos( β) γwhw.. ). Tanφ Cosα xz. Cosα yz (7) γ. Z.. Y. Sinα Y The main benefit of ArcGIS tools is that we can divide the whole landslide area into a number of very small cell size, as small as few centimeter size as per the desire and necessity of the analysis. Besides, equal value of and Y can be set. Now, equation (7) can be reduced into the following equation. FS c. Sinθ ( ) + Cosα. Cosα x y xz yz Y xz yz = (8) which can again be simplified as, Y Sinθ ( γ. Z. Cos( β) γwhw.. Cosα. Cosα γ. Z. Sinα yz yz ). Tanφ A + B x y Y FS = (9) C Y where, and c. Sinθ Sinθ A = Cos α Cos B = ( γ. Z. Cos( β) γwhw.. ). Tanφ xz. α yz Cosα xz. Cosα yz C = γ. Z. Sinα (10) yz

10 All components for the individual cylinders can be calculated by raster calculations using the above mentioned formula. Here, orientation of the average displacement direction of moving posts or the average aspect can be considered as Y-axis. In case where the direction of movement of the block is curved, separate axes are to be considered for each portions and analysis are to be done for each zone. In the analysis, marginal friction mentioned by Ota et al (2001) can also be considered for the boundary elements. ArcGIS Spatial Analyst can be used efficiently to calculate the value of FS for the whole block using equations 9 and 10 as explained below. Besides, FS at the mid section and at right quarter and left quarter could also be calculated automatically by summing up, using the zonal statistics function of the ArcGIS spatial analyst, for one meter strip at the corresponding locations. Fig. 7 shows the schematic diagram for the calculation of FS. Main input parameters such as ground level are provided from digitized contour maps or the readymade DEM. Geological profile is input through the underground boring data. In the absence of sufficient boring data, ellipsoidal interpolation for each geological layer can be done. Ground water table can be prepared based on the ground level and the piezometer data. Boundary of the landslides can be obtained from the distribution of the cracks, monitoring data and the landslide topography. As 2D FS calculations are based on the 1m width strip in the middle of the slide, elongated polygon vector with 1m width at the mid and quarter sections (right and left) can be input as the boundary for 2D FS. With the help of TIN of all the vector layers (features), made from 3D analyst extension, all input vector data are to be converted into the raster data. It is to be noted that the simple raster-vector conversion leads other undesirable layer. Value of c, φ and γ are set according to the type of rock (after conducting the shear test on the soil samples) and the field investigation. Average γ of a slice column can be calculated with the weightage of the depth of the rock types and γ for those rocks. This method, used in macro level is named as 3D Deterministic GIS Analysis method. As the study area is under comprehensive study by various Government Departments, the available landslide study and prevention plans have been advantageously used for the required database of the study area to conduct deterministic analysis. According to Fukumoto (2004), slip surface of more than 79% of the landslides in Niigata Prefecture have been observed to be at the contact between weathered rock and bed rock formation of the same rock type. Therefore, the soil samples from both the bed rock and the weathered rock were collected from 61 points throughout the entire study area. At least 5 samples were collected from each geological formation. The measured shear strengths were separated according to the type of formation. There was less than 20% variation in the shear strength from the average shear strength of the same formation in each formation separately. The soil sampling points are shown in Fig. 8. The ground water profile of the study area has been prepared based on the available data recorded in the piezometers in the studied landslide areas, some of which are briefed in the micro level study. Ground profile was based on digitized topographic map based DEM data. Statistical analysis was done in order to analyze the geometry of the sliding surface for each type of geological region. Fukumoto (2004) explained that 70% of the

11 landslides in Niigata has less than 10m depth. Likewise, the same citation mentioned that 56% of the landslides had less than 100m length. More than 61% of the landslides, recorded, had less than 60m width. According to the paper, more than 67% of the landslides had width to length ratio ranging from 0.3 through 0.7. Likewise, width to depth ratio for more than 70% landslides had ranged between 4 through10. Therefore, it was possible to consider the average representative size of 3D landslide block as 100m long, 50m wide and 8m deep in maximum. Assuming that the shape of the sliding surface is ellipsoidal, following equation can be written for the proposed ellipsoid to be used in 3D Deterministic GIS Analysis method. This equation can be used to set the boundary for the summation zone for zonal statistical analysis, based on 10m sized cell data, proposed in this study. µ Legend <all other values> Sampling points building stream road Village boundary Kilometer Figure 8 Soil sampling points, distributed throughout Maki Village

12 2 2 2 x y z + + = 1 (11) The value of y coordinate for 2D elliptical boundary at the surface of the GIS layer can thus be determined from defined x coordinates as follows. 2 2 x y + = 1 i.e y 2 = x (12) In order to divide the entire area into small slope units and limit the proposed algorithm within the given slope unit, ArcHydro tool of ArcGIS alongwith 3D and Spatial analyst tools were used. For the slope unit preparation, a mirror DEM was prepared using spatial analyst and 3D analyst. For this, first, a new field was given in the vector layer of ground surface contour. Each contour levels in this field were calculated as, (-1 x contour level of the original field). For example, if the value of the contour label of a vector line in the original field was 300m, the contour label of the same vector line in the mirror DEM field was -300m. ArcGIS 3D analyst was used to prepare the DEM of the mirror contour label field, which is referred hereinafter as reverse DEM. Watersheds were calculated for the flow accumulation in case of both DEM (polygon joining ridge to ridge) and mirror DEM (polygon joining valley to valley). Then, the watershed polygons of DEM were dissected by the watershed polygon of mirror polygon, which resulted into the individual slope units. The average aspect value of the entire slope unit was considered as the direction of slide in 3D analysis. All the above mentioned calculations were limited within an individual slope unit. The value of safety factor obtained by automated calculations based on the calculations of the data in each cells and summation of the shear strength and stress parameters of each ellipsoidal zones as mentioned as A, B and C in equation 10, were used for the grouping of the zone, in danger with landslides. The results obtained by this method were compared with the available landslide distribution map, prepared for the study area based on the field study and aerial photo interpretation data. In order to compare the results from different ways, the factor of safety of the terrain were calculated in the following three ways: 1. Factor of Safety of individual soil column of 10m x 10m cell size 2. Factor of safety of the proposed elliptical slide boundary within the slope units 3. Average Factor of safety of the individual slope units b. Local micro level 3D stability of the selected landslides Available detailed investigation data of the five landslide blocks, which are situated in the study area, were digitized separately as vector layers in ArcGIS in order to prepare the database for the individual 3D stability analysis of the studied landslide blocks. The equation, as shown in equation 10, as proposed by Tiwari et al. (2003) was used for this analysis too. The parameters for the individual slope stability analysis were taken from

13 the raster data, made by the vector data of the different layers, as explained above. ArcGIS was advantageously used to calculate the 3D factor of safety for the given landslide block boundary by using zonal statistics function of Spatial Analyst. For 2D factor of safety in different cross sections, the same function was used, but the summation zone was individual 1m wide polygons, aligned parallel to the required cross sections. This was helpful to find out the critical cross section in the landslide block. This quantitative approach of 3D stability analysis is named here as 3D LEM (Limit Equillibrium Model) GIS method. 5. Data Acquisition and Field Study In order to obtain the required data for stability analysis, soil samples were collected from 62 numbers of points, distributed throughout the entire study area. Soil samples were collected from each and every geological regions as far as possible. Samples were collected from the soils of weathered rocks as well as from the bedrocks to compare the strength in different weathering conditions. Fukumoto (2004) wrote, in most of the total studied landslides, sliding surfaces were observeded along the weathered rocks. Therefore, strengths of the soil samples collected from the weathered rocks were used for the analysis as the failure strength. The location of the soil sampling points were digitized in ArcGIS frame with the attribute table consisting of unit weight, water content, consistency limit, mineralogical composition, residual, fully softened and peak friction angle and shear intercepts, and weathering condition (Table 1). In order to input the position of ground water table in the area, information from piezometer data at 15 numbers of landslide area were collected from the available reports and input in the ArcGIS frame as a new layer. It is to be noted that in case of the streams, water table can be considered to be same as the ground surface. The maximum ground water table contours made by using ArcGIS Spatial and 3D analyst based on the point data provided as mentioned above was used for the 3D Deterministic GIS analysis. In order to verify the location of estimated places of danger based on the proposed method, spatial distribution of existing slope failure locations based on the available information (prepared in 1969), field survey results and aerial photo interpretation results were mapped in the same map. Table 1: Schematic of the attribute table prepared for the data base of the soil sampling points FID ID γ wc LL Ip Sand Silt Clay Main_mineral Clay_Mineral φr φc φp cr cc cp Formation Qt Sm, Il, Ka Tr Qt Sm, Il, Ka Tr In order to prepare the base map, map of the infrastructure (Fig. 9) was prepared. Contour lines were digitized from high resolution scanned topographical maps. DEM was prepared at 10m x 10m cell size based on the contour (Fig. 10). Slope map (Fig. 11) and aspect maps were prepared separately from the DEM data. According to the prepared slope map (Fig. 11), about 62% of the entire area has slope from 10 through 30 degrees (Fig. 12). The vector map of the geological region (Fig. 13) was converted to the raster map in order to distribute the soil strength parameters based on the geological region.

14 Legend stream road Village boundary Kilometer Figure 9 Maps of various infrastructures such as roads with streams, Maki Village

15 Figure 10 DEM of the study area (small portion at the top left was uncovered in this study for some specific reason)

16 Legend building slope degree stream road Village boundary > Kilometer Figure 11 Terrain slope of the study area, Maki Village

17 22% 8% 3% 17% 10% >40 40% Figure 12 Distribution of entire Maki Village based on the terrain slope in degree 6. Stability Analysis Result Raster layers for all required parameters for stability analysis as mentioned in section 4 has been prepared and a GIS algorithm was written to calculate the shear stresses and mobilized strength parameters of 10m x 10m cell size. The stability parameters for each ellipsoid within the limit of the watershed were summed up and overall safety factor for each ellipsoid were calculated. For the shear strength of the sliding surface soil, peak shear strength was considered as the proposed analysis is to estimate the potential failure zones based on the terrain information. Besides, safety factor for each 3D column of 10m x 10m plan area was calculated separately and average safety factor of the entire slope unit was also calculated based on that information. Based on the calculated factor of safety values, the entire Maki Village was divided into five different zones, as follows. Value of factor of safety < 1.2 Very High Hazard High Hazard Medium Hazard Low Hazard >4.0 Very Low Hazard Proportion of existing landslides in various geological formation are shown in Fig. 14. The average safety factors for different geological formations are presented in Fig. 15. According to the result, mudstone of two different formations and silt stones of two different formations had shown the lower factor of safety. Summation of the stability analysis parameters for each column for the observed slope failures/landslides were also done using zonal statistics function of the ArcGIS Spatial Analyst in order to verify the results from the automatically calculated potential failure zones based on the proposed

18 analysis method with the actual field situation. According to the analysis result, 83% of the existing landslides were mapped in very high and high hazard zones (Fig. 16). This can be assumed as very good agreement for such types of study. Remaining 17% of the existing landslides were plotted in the high hazard zone. µ Legend stream road Village boundary Geological region Kilometer 1: Taruda Formation Mudstone 2: Naradate Formation Silt Stone and Sand Stone 3: Sugawa Formation Mudstone 4: Taruda Formation Acidic Tuff 5: Debris and Colluvial Soil 6: Gravel and Sand 7: Taruda Formation Dacitic Crystal Pumice Tuff 8: Naradate Formation Sand Stone and Silt Stone 9: Tamugigawa Formation Interbedded Sand Stone and Silt Stone 10: Hypersthene Andesite Figure 13 Distribution of the geological formation in Maki Village

19 12% 3% 2% 36% 47% Very high High Medium Low Very Low Figure 14 Proportion of various hazard potential zones within the study area Geological region Figure 15 Average value of the factor of safety for various geological region in Maki Village The distribution of cells with calculated hazard level based on the 3D safety factor values and the existing landslides/slope failures mapped in 1969 superimposed on it has been presented in Fig. 16. It is to be noted that about 93% of the existing landslides were mapped in the mudstones of the two geological formations (Fig. 17).

20 Legend stream road landslides Village boundary Hazard Zonation Potential Danger Very high high Medium Low Very low Kilometer Figure 16 Distribution of single cell sized column based 3D stability analysis result and a part of the existing landslides/slope failures superimposed on it

21 1% 0% 0% 0% 2% 0% 1% 45% 48% Taruda Mudstone Sugawa mudstone Debris and Colluvium Taruda Pumice Tuff Taruda sand and silt stone 3% Naradate Silt and Sand stone Taruda Acidic Tuff Gravel and sand Naradate sand stone and conglomerate Hypersthene andesite Figure 17 Distribution of existing landslides in Maki Village based on the geological formation 7. Micro level study of the landslide areas using ArcGIS aided analysis method Among the landslide areas under prevention works, which are being undertaken by different ministries and the Prefectural as well as Local Governments, five landslide areas in Maki Village (Fig. 18), which are triggered on the mudstones of two geological formations have been considered as the major landslide areas and have been considered for the sample micro level study. A 1:1,000 scale topographical map (Fig. 19) of the study area along with the other maps prepared for investigation and prevention were digitized and micro level DEM of 1m x 1m cell size were prepared (Fig. 20). Based on the information from a number exploratory borings on the location of sliding surface and maximum ground water table, a separate DEM for sliding surface and the maximum ground water table for each landslide blocks were prepared in ArcGIS based main frame. Sliding zone boundaries were fixed based on the results of survey monument and inclinometer records. Summation of all shear strength and stress parameters as mentioned in equation 10, for each analysis column was done for the entire sliding zone of a landslide block as explainedabove. Unlike the macro level study, micro level study was conducted on the preexisting landslides. Therefore, residual friction angle (Fig. 21) and other shear strength parameters were used. Those data were used in order to analyze each landslide block of all landslide areas and review the prevention planning using 3D LEM GIS method. This is a new and a better method, which was not outlined by Soeters and Westen (1996) and has never been practiced yet. Various prevention works such as piles, soil nails, ground water reduction measures such as drainage borings, drainage galleries etc. were distributed spatially in the area and safety factor after application of those countermeasures were calculated automatically by using GIS algorithm based analysis technique.

22 Legend Prevention authority MOLIT Prefecture, Sabo Dept. Forestry Agency MOAFF building stream road Village boundary Kilometer Figure 18 Various landslide areas under prevention works through the central and local government level funding. Highlighted portions are the landslides under micro level study mentioned above.

23 Figure 19 Typical topographic map (digitized) of 1:1000 scale of the individual landslide block (Iwagami Landslide), used for the micro level study (the point data contains the information on sliding surface, shear strength, geological profile and ground water table)

24 Figure 20 3D view showing the sliding surface of the Iwagami landslide from different sides, observed in Arc Scene In order to verify the result of the stability calculations, a symmetrical ellipsoid was also studied using the same method. The 3D coordinate of the ellipsoid were set automatically using GIS algorithm. Besides, stability analysis result of Mukohidehara, which is located in Yasuzuka Town has also been presented here for the comparison. In case of Maki Village, data of 3 blocks in Okimi Landslide and one block each in Yosio and Iwagami Landslides have been presented. Figure 22 shows the ratio of 2D safety factor and 3D safety factor values at the sections located in the mid, and two quarter of the distance of the total width from both side edges. As the main prevention work in all of the studied landslides were ground water table reduction, necessary average depth of the ground water table to be reduced for all landslides were calculated by using the 3D LEM GIS method (Fig. 23). Besides, the possible location of anchors and piles were also spatially distributed along the landslide area and the increase in safety factor with them has been calculated. Cost for the increase in the existing safety factor by 20% through the use of different preventive measures separately or in combination were calculated in order to propose the most appropriate prevention method. It is to be noted here that in the case of macro level analysis, the meaning of absolute value of the safety factor is less important and hazard potential should be judged in relative terms, based on the calculated safety factor (which is not the indicative of the actual stability of the slope due to various uncertainties). However, in the case of micro level study, the safety factor values were calculated based on the extensive field

25 investigation data. Therefore, the safety factor value based on the 3D LEM GIS method can directly be used for the design of countermeasures. The total production cost of this method compared to the available 2D stability analysis software is not that high. However proposed 3D Deterministic GIS and 3D LEM GIS methods have far better functions, such as data base management and enhancement, 3D stability analysis and spatial distribution of prevention works and their visual and analytical analysis. Legend friction stream road Village boundary Kilometer Figure 21 Distribution of residual friction angle at sampling locations, throughout the study area

26 1.4 Calculated FS Dav/3D 2Dmid/3D 2Dmin/3D 0 Ellipsoid Okimi A Okimi C Okimi D Yosio Iwagami Mukohide C Mukohide G Figure 22 Calculated 3D safety factors for different analysis blocks in some of the studied landslides and the 2D safety factor at various sections (up) and the ratio of 2D and 3D safety factor (down) Reduction in GWT for stabilization Okimi A Okimi C Okimi D Yosio Iwagami Mukohide C Mukohide G 3D,4 mid,4 crit,4 Figure 23 Proposed ground water reduction extent by 2D and 3D method using ArcGIS method 8. Conclusion The method proposed in this study (3D Deterministic GIS and 3D LEM GIS) are comprehensive methods to conduct deterministic analysis for the landslides zoning, using ArcGIS. Due to the small scale of the data acquision in case of macro level, the stability factors should be considered in terms of relative terms only whereas the safety factor obtained during micro level study can be used for the absolute value of factor of safety during analysis. This study can be established as one of the primary works for landslide management and control in Maki Village, Niigata. The base map can be periodically maintained and enhanced to make more comprehensive map of the study area. This method can be extended to use in the other villages and regions in other parts of Japan as well as the other parts of the world for geotechnical analysis of existing and potential landslides. Besides, same concept (although has not been explained in detail due to the limitation of file size) can easily be extended for the 3D Finite Element

27 Method for stability analysis, named here as 3D FEM GIS. It is expected that this paper will be an initiation for the preparation and analysis of the comprehensive database of mountainous terrain for landslide hazard mitigation. 9. Acknowledgement The authors would like to thank Niigata Prefectural Office and Yasuzuka Sabo Office for their cooperation in providing the primary maps and reports for this study. Sabo Technical Center, Japan deserves special appreciation for their support to the part of the project by providing research fund. References Chen, R., Chameu J. (1983). Three dimensional limit equilibrium analysis of slopes. Geotechniques 33, pp Duncan, J.M Soil slope stability analysis. Landslides Investigation and Mitigation, Special Report 247, Transportation Research Board, National Academy Press, Fukumoto, Y. (2004). The substation analysis and prevention work of a landslide during movement in Niigata Prefecture. Landslides, 41 (1), pp (In Japanese, abstract in English). Hovland, H. 1977, Three dimensional slope stability analysis method, ASCE Journal of Geotechnical Division. 103: Huang, Y. (1983). Stability analysis of earth slope. Van Nostrand Reinhold. Hungr, O. (1987), An extension of Bishop s simplified method of slope stability analysis to three dimensions, Geotechnique 37 (1), pp Japan Landslide Society, Niigata Branch Landslides in Niigata Prefecture (CD Rom) Japan Landslide Society, Niigata Branch Landslides in Niigata Prefecture Japan Geological Surver Geological Map of Takada Region. Japan Topographical Survey Topographic map of Takada Area. Niigata Prefectural Government, Yasuzuka Sabo Works Distribution map of landslide area under prevention works by various agencies. Niigata Prefectural Government, Yasuzuka Sabo Works Distribution map of various clope failures/landslides (active and scarpments) in the territory of Yasuzuka Sabo Work office.

28 Soeters, R. and Westen, C. J., Slope instability recognition, analysis, and zonation, Landslides Investigation and Mitigation, Special Report 247, Transportation Research Board, National Academy Press, Tiwari, B., Marui, H., Ishibashi, A., Nakagawa, K., Bhattarai, P., and Aoyama, K Use of ArcMap for 3D stability analysis of preexisting landslides. In ESRI s 23 rd Annual International User s Conference, GIS in Engineering (0125) Author Information Dr. Binod Tiwari Post Doctoral Research Fellow Department of Civil and Environmental Engineering Virginia Polytechnic Institute and State University Blacksburg, VA USA Tel: Fax: tiwari@vt.edu Dr. Hideaki Marui Professor The Research Institute for Hazards in Snowy Areas Niigata University Igarashi , Niigata Japan Tel: Fax: maruihi@cc.niigata-u.ac.jp Dr. Kiyomichi Aoyama Professor The Research Institute for Hazards in Snowy Areas Niigata University Igarashi , Niigata Japan Tel: Fax: aoyamak@gs.niigata-u.ac.jp Pankaj Bhattarai PhD Candidate The Research Institute for Hazards in Snowy Areas Niigata University Igarashi , Niigata Japan Tel: Fax:

29 Gyanu Ratna Tuladhar PhD Candidate The Research Institute for Hazards in Snowy Areas Niigata University Igarashi , Niigata Japan Tel: Fax: