MATLAB Modeling of SPT and Grain Size Data in Producing Soil Profile
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The document discusses the development of a MATLAB model to predict soil profiles based on GPS coordinates and validate it using known soil data, specifically in relation to the Padma multipurpose bridge project. It includes details on subsurface investigation methods, SPT (Standard Penetration Test) correlations for pile capacity, and soil classification based on grain size distribution. The model's predictions appear accurate for borehole logs and SPT values, though refinements are suggested for liquefaction potential assessments.
![Particle Size Distribution of Soil
Typical sieve analysis Graph [at APBH 13]](https://image.slidesharecdn.com/useofmatlabinidentifyingboreholeofaspecificsite-150425170212-conversion-gate02/85/MATLAB-Modeling-of-SPT-and-Grain-Size-Data-in-Producing-Soil-Profile-14-320.jpg)
![SPT contour profile:
Figure : SPT contour profile
from chainage 17600 [APBH 05] to 27600 [APBH 19], up to 19.5m depth](https://image.slidesharecdn.com/useofmatlabinidentifyingboreholeofaspecificsite-150425170212-conversion-gate02/85/MATLAB-Modeling-of-SPT-and-Grain-Size-Data-in-Producing-Soil-Profile-18-320.jpg)
MATLAB Modeling of SPT and Grain Size Data in Producing Soil Profile
- 1. MATLAB MODELLING OF SPT AND GRAIN SIZE DATA IN PRODUCING SOIL PROFILE CE 400: PROJECT AND THESIS Submitted by- Debojit Sarker Student ID: 0704015 Supervised by- Dr. Md. Zoynul Abedin Professor, Department of Civil Engineering, BUET.
- 2. Objectives: To develop a MATLAB computer model that could produce the soil-profile at a particular location using GPS coordinates or chainage location. To validate the model using known soil profile data. To use predicted borehole log in case studies of designing practical problems (e.g. Pile Capacity and Liquefaction).
- 3. Project Site Jajira Approach Road of Padma Multipurpose Bridge Project in Madaripur district
- 4. Subsurface Investigation: • Determining the nature of soil at the site and its stratification. • Obtaining disturbed and undisturbed soil samples for visual identification and appropriate laboratory tests. • Determining the depth and nature of bedrock, if and when encountered. • Performing some in situ field tests, such as Standard Penetration Test (SPT) • Assessing any special construction problems with respect to the existing structure(s) nearby • Determining the position of the R.L. & water table.
- 5. Planning for Soil Exploration Table-1 gives guidelines for initial planning of borehole spacing. In our study 15 borings were conducted within 20KM chainage at Jajira approach road of Padma multipurpose bridge project. Table 1: Spacing of Borings
- 6. Position of Boreholes APBH 05 APBH 06 APBH07 APBH08 APBH09 APBH10 APBH11 APBH12 APBH13 APBH14 APBH15 APBH16 APBH17 APBH18 APBH19 Bangladesh Geological Survey indicates that the project site Jajira of Madaripur district, in general, is underlain by recent alluvium. The Padma superficial alluvial river deposits typically comprise normally- consolidated, low strength compressible clays, or silts and fine sands of low density.
- 7. Applicability and Usefulness of In-situ Tests
- 8. Standard Penetration Test The test consists of the following: Driving the standard split-barrel sampler of dimensions a distance of 460 mm into the soil at the bottom of the boring. Counting the number of blows to drive the sampler the last two 150 mm distances ( total = 300 mm) to obtain the N number. Using a 63.5-kg driving mass (or hammer) falling “free” from a height of 760 mm. several hammer configurations are available. Standard Dimensions of Standard Split SpoonSPT arrangements
- 9. Correlations for Standard Penetration Test Cohesive Cohesionless
- 10. Correlations for Standard Penetration Test Prediction of pile capacity by SPT (after Shoospasha et. at. 2013)
- 11. Correlations for Standard Penetration Test Skin Friction of pile: Cohesive (clay): α Method Qs=α*Cu*p*ΔL Sladen (1992): α=C * ( eff/ Cu)^0.45ϭ C=0.5 for driven piles Cohesionless (sand) according to mayerhof,1976: fav=0.02*Pa*N-avg (for high displacement driven pile) fav=0.01*Pa*N-avg (for low displacement driven pile) Qs=p*L*fav (p=peremeter of pile) Pile end bearing capacity: Cohesive (clay): (Mayerhof) Qp=9*Cu*Ap (Ap=area of pile tip) Cohesionless (sand): Meyerhof(1976) Qp=qp * Ap qp=0.4*Pa*N*L/D <= 4*Pa*N (N= avg value of SPT) For Foundation design and analysis purpose (pile foundation) Clay: Visic(1977) Qp=Ap * Cu * Nc* Nc*=4/3*(ln(Irr) +1)+3.1416/2 +1 O'Neil & Reese (1999) Ir=347*(Cu/Pa) - 33 <= 300 Sand: Briaud et al. (1985) Qp=qp*Ap qp=19.7*Pa*(N60)^0.36 Clay: λ method, Vijayvergiya and focht (1972) fav= λ*( effective avgϭ +2*Cu) Qs=p*L*fav p= perimeter of pile section Sand: Briaud et al. (1985) fav= 0.224*pa*(N60 avg)^0.29 Pa = atmospheric pressure=100 KN/m^2
- 12. Correlations for Standard Penetration Test For Foundation design and analysis purpose (Seismic Soil Liquefaction)
- 13. Grain Size Distribution Soil Type Particle Size Range, mm Retained on Mesh Size/ Sieve No. Boulder Cobble Gravel: Sand: Silt Clay Coarse Medium Fine Coarse Medium Fine >300 300-75 75-19 19-9.5 9.5-4.75 4.75-2.00 2.00-0.425 0.425-0.075 0.075-0.002 <0.002 12” 3” ¾” 3/8” No. 4 No. 10 No. 40 No. 200 --- --- Engineering Classification (For particles smaller than 75mm and based on estimated weights) Coarse grained soils (More than 50% of the material retained on No. 200 sieve (0.075 mm) Gravels (More than 50% of coarse fraction retained on No. 4 sieve (4.75 mm) Clean gravels Less than 5% fines Gravel with fines More than 12% fines Sands (over 50% of coarse fraction smaller than 4.75 mm) Clean Sands Less than 5% fines Sands with fines More than 12% fines Fine grained soils (Over 50% of the material smaller than 0.075 mm) Silts & Clays WL < 50 Inorganic Organic Silts & Clays WL > 50 Inorganic Organic Soils of high organic origin
- 14. Particle Size Distribution of Soil Typical sieve analysis Graph [at APBH 13]
- 15. MATLAB MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™ .
- 16. MATLAB R2013a Win8 screenshot:
- 17. Input method for SPT profile (MS Excel Spreadsheet): chainage - depth 17600 18600 19600 20100 20600 21100 21600 24100 24582 25100 25600 26600 27100 27600 1.5 4 5 5 5 6 5 2 5 5 5 5 4 6 10 3 4 5 3 3 20 5 6 17 17 3 4 7 1 12 4.5 6 6 26 16 18 33 5 10 9 13 3 2 9 11 6 10 7 27 31 12 31 24 6 10 14 11 15 5 3 7.5 11 7 31 26 28 30 19 8 11 12 13 16 18 11 9 11 8 30 15 29 9 23 11 26 16 13 11 16 12 10.5 12 2 32 17 24 12 32 12 22 14 24 18 9 32 12 17 15 33 15 21 13 35 22 24 9 23 38 14 14 13.5 15 37 32 14 20 14 20 24 18 4 19 31 19 16 15 29 29 31 26 32 11 18 23 21 5 23 14 13 21 16.5 27 30 38 24 43 23 25 7 18 30 16 42 23 34 22 22 42 19.5 26 21 46 22 39 21 19 25
- 18. SPT contour profile: Figure : SPT contour profile from chainage 17600 [APBH 05] to 27600 [APBH 19], up to 19.5m depth
- 19. Input method for soil-profile (MS Excel Spreadsheet): APBH-05 APBH-06 APBH-07 APBH-08 APBH-09 APBH-10 APBH-11 APBH-12 APBH-13 APBH-14 Start End Avg Chainage 17600 18600 19600 20100 20600 21100 21600 24100 24582 25100 Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % Sand % Fine % 1.35 1.8 1.35 D1 86 14 92 8 94 6 90 10 86 14 94 6 15 85 18 82 8 92 14 86 2.85 3.3 3.075 D2 93 7 83 17 94 6 92 8 93 7 94 6 87 13 86 14 94 6 4 96 4.35 4.8 4.575 D3 94 6 84 16 86 14 93 7 87 13 82 18 94 6 5.85 6.3 6.075 D4 91 9 92 8 89 11 92 8 92 8 91 9 94 6 83 17 7.35 7.82 7.585 D5 84 16 88 12 88 12 94 6 90 10 94 6 93 7 87 13 8.85 9.3 9.075 D6 87 13 87 13 88 12 91 9 95 5 10.35 10.8 10.575 D7 88 12 89 11 91 9 87 13 90 10 92 8 88 12 2 98 11.85 12.3 12.075 D8 90 10 85 15 92 8 94 6 91 9 90 10 91 9 63 37 92 8 20 80 13.35 13.8 13.575 D9 85 15 89 11 87 13 89 11 91 9 86 14 63 37 17 83 14.85 15.3 15.075 D10 89 11 90 10 87 13 90 10 89 11 88 12 84 16 94 6 17 83 16.35 16.8 16.575 D11 92 8 90 10 92 8 90 10 90 10 84 16 10 90 17.85 18.3 18.075 D12 96 4 95 5 67 33 92 8 87 13 89 11 90 10 19.35 19.8 19.575 D13 94 6 88 12 88 12 92 8
- 20. Soil-profile (from chainage 17600 to 27600, up to 19.5m depth)
- 21. Predicted Borehole Log (At chainage 26100) Location Latitude (deg) 23.4009 Longitude (deg) 90.1735 0 0 cohesive very soft N/A 0 0 1.5 5 cohesive soft N/A 46.2 23 3 6 cohesive soft N/A 48.8 42 4.5 3 cohesive soft N/A 39.9 54 6 13 cohesionless medium 0.63 N/A 66 7.5 15 cohesionless medium 0.64 N/A 79 9 12 cohesionless medium 0.55 N/A 91 10.5 21 cohesionless dense 0.7 N/A 103 12 31 cohesionless dense 0.83 N/A 115 13.5 25 cohesionless dense 0.72 N/A 128 15 19 cohesionless medium 0.61 N/A 140 16.5 18 19.5 effectivestress γ (kN/m^2) 15 γ sat (kN/m^2) 18 RelativeDensity UndrainedShear Strength(kPa) GraphicLog Depth(meter) SampleType SampleNumber BlowCounts (blows/foot) SoilType Consistency Groundwater Depth (m): 2.5 Elevation(m) PWD: 5.804 Total Depth of Boring: 15 Project Number:Project: Client: Boring No. Address: Madaripur Position: Chainage: 26100 0 5 6 3 13 15 12 21 31 25 19 0 1.5 3 4.5 6 7.5 9 10.5 12 13.5 15 16.5 18 19.5
- 22. The ultimate load-carrying capacity Qu of a pile is given by the equation : Qu = Qp +Qs Where , Qp = Load carrying capacity of the pile point Qs = Frictional resistance (skin friction) derived from the soil-pile interface. Allowable pile capacity, Qa = Qu/ F.S. Estimating pile capacity (at chainage 21100)
- 23. Liquefaction Potential Index (at chainage 21100)
- 24. Summary The developed MATLAB model can predict an intermittent borehole log with reasonable accuracy. The developed model gives SPT contours that may be used to identify the soil spatial stiffness. The program yields grain size surface plots that may be used to identify the soil profile. The estimation of pile capacity suggests that the predicted borehole estimates the SPT values well. The variation in liquefaction potential suggests that the model be refined for grain size estimation.
- 25. Thank You