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Transcript of Lab Portfolio
CEG 3011C - CRN 11780
Dirt Collection Lab
Jon went into Lakes Park off of Summerlin and Gladiolus Blvd. The weather was cloudy and appeared to about to rain. The sample is from a pine forest and has needles and other organics on top of the sample. This sample is rather dark in color. Once he collected the sample and showed me pictures of the location I was able to visualize the site for myself. The sample was unearthed from some layers of organics but still contains several different organic materials. The sample was slightly wet upon collection due to its proximity to Lakes Park and the impending rainfall.
Specific Gravity Lab
Cu and Cc
The specific value for my soil sample was calculated to be 2.39, whereas the typical values for soils 2.68-2.8. My soil’s specific gravity value falls below the typical range. This can be due to many things. One thing could be do to calculation error or even experimental error. Another reason could be that my soil is high in organic material. My site is in a pine wood forest patch so with the high amounts of organics on the ground and then it being hard to get just soil this could account for that low value. Due to this low value I will be using 2.7 for all my calculations throughout the rest of the lab. If possible I will compare the values for both 2.39 and 2.7. I chose 2.7 after reading the textbook and looking at the description of my soil and comparing with those in the book 2.7 seemed appropriate.
**all sample calculations where done using the 0.5 minute data.**
Effective Depth, L is used to calculate the soil diameter. This value is found on figure 1c below. This table uses the actual hydrometer reading to establish effective depth.
Figure 1c: Effective Depth
The K value is used to help calculate the soil diameter. The value can be determined from figure 2c. For this chart one must know the temperature as well as the specific gravity of the soil to find the K values. For my soil the temperature in the lab was found to be 21 degrees Celsius. The specific gravity used was 2.7
figure 2c K values
The soil was classified according to the Unified Soil Classification System (USCS) standards for soil. This system is based on the percent passing both the #4 and #200 sieves. The first step was to look at the values in Table 4c. These values decided if the soil is coarse or fine. Based on these values the soil was found to be coarse grained. From there these values get me almost all the value through figure 4c. From there the values in Table 5c were examined. These values helped me determine that my soil was a poorly graded sand (SP).
Before this lab was conducted an assumption that I made was that the soil was going to be a sand. This was based on the appearance of the sand only. Once a Grain Size Distribution test was conducted this in fact turned out to be true. The however is not always the case. In certain situation the soil may give a different classification then the what was to be expected. Another thing is that it tells whether the soil is poorly or well graded. In my case I would not have assumed a poorly graded soil but from the classification method used it turned out to be poorly graded. Grain Size distribution curves are a great tool when it comes to classifying a soil.
Grain Size Distribution Lab
Table 3c: Calculated values
Figure 2a: Location of sample collection site
**photo courtesy of google maps**
Table 1b: Collected values for specific gravity test
Table 2b: Collected values for water content test
Table 3b: Calculated Water content and Specific Gravity
Table 1c: Sieve Analysis Data
Table 2c: Hydrometer Analysis Data
Figure 4c: Grain Size Distribution Curve
Table 4C: Perfect passing values for classification
Figure 4c: Classification table
Figure 5c: Plasticity Chart
Visual Classification and Microscope Lab
Sample Calculations, trial 4
Figure 1e: 10x magnification
Figure 2e: 60x Magnification
Figure 3e: 200x Magnification
Figure 1a: Soil Sample Collected
Figure 1d - Raw data sheet
Table 1d - Constant variables
Table 2d - Proctor Test Results
Figure 2d - Compaction Curve
Table 4d - Typical Values
Table 3d - Results
Compaction curves are created when dry unit weight vs. water content are plotted on a scatter plot. A trend line is fitted to the points. From this curve the max dry unit weight and the optimum water levels can be found. In this case we did not reach the max dry unit weight. This means a lot of the curve had to be extended based on the few points that we had found. Even so the max unit weight was found to be 15.9 kN/m^3 with an optimum water content of 30%. From this max value the field specification of 92% can be determined. This is done simply by multiplying the max unit weight by 0.92. This gave a value of 14.6 kN/m^3. At this point on the graph draw a horizontal line, where this line hits the graph that becomes the water content range. All these values are summarized in table 3d below.
Determined in GSD lab the soil was classified as a sand. The average values for sand dry unit weights can be seen below in table 4d. My results ended up being reasonable because the values for dry unit weight seen in table 2d fall within the standard range. As for test accuracy we never reached our optimum water content in testing and it had to be determined from a best fit curve of only the rising side. So the test ended up not being very accurate but still produced values in the normal range.
This is my second time taking this class. Last spring I really struggled through this class. I had a lot of family issues going on plus taking five classes, I didn’t really take the time to learn the material as well as I could have. I had a lot of help getting through my portfolio last year. This year I have sat down and done the labs through the semester as they have come along and worked on the data myself instead of someone else doing it or helping me. This time around, I have a much better grasp on the material. This portfolio is part of the reason that I have that grasp. Learning isn’t just in the class room. As soon to be engineers, we need that hands-on experience to really learn.
For this portfolio, a soil sample was collected at the beginning of the term and several labs have been performed on that single sample. It is interesting to see how the different labs compare to one another or fill in a piece of the puzzle in trying to determine the soil type. Our soil was calculated to have a 2.39 specific gravity but that could be because of organics in the soil so throughout the rest of the calculations a standard specific gravity was used. This was the first “assumption” made in the portfolio. This was made because of the knowledge I have learned from class lecture, reading the textbook, and hands-on understanding.
The dirt collection lab was the first one. For this one, it was just collecting the soil to be examined for the semester. The first real lab conducted was specific gravity. This lab helped us get our hands dirty and start to really play with the soil. Moving on to the grain size distribution lab. This lab was one of the bigger ones. For this lab we could really see how important geotechnical engineering is. Classifying the sample based on simple lab test. This lead into the compaction lab. This lab taught us how soil reacts under pressure, which is important in geotechnical engineering. Here in Florida, most of the soil is some kind of sand, but when you get up north you start to get more clays and stuff. From the compaction, we can see what type of load causes different soils to react in different ways. This gives our dry unit weights and these values are helpful with consolidation of soils. These two concepts help show how a different soils react. Visual classification and microscope analysis were the last labs conducted. This is helpful to when trying to get a feel for the soil in the field before it is sent to the lab for testing.
After conducting these labs I have grasped some of the concepts of soil mechanics. Learning just doesn’t happen from a book. Things need to have a hands on experience to help with understand. Geotech can be tough at times but these labs are helpful. At the start of this term I learned that my cousin’s house in North Port, FL has a sink whole under it. It was interesting to hear about what the geotechnical engineer did and to see the report that was written on the house. She calls me all the time asking questions about this and that, usually I can’t answer it right away but with a little research and my growing knowledge through the semester I can help her understand a few of the things they have told her about it. One day I hope to be a geotechnical engineer.
For the microscope analysis, the sample was examined at three different magnifications, 10x, 60x, and 200x. This was done using a Digiblue Microscope. Figure 1e shows the 10x magnification of the soil. At this magnification, classification of the soil is still difficult. Some of the aspects of soil, like shape and color and things of that nature, are still hard to see. So that being said, the magnification was moved up to 60x, this can be seen in Figure 2e. This image shows much more detail. In this image, some of the particles are rounded and some have shape edges still. Most appear to be colorless as well. Increasing the magnification once more to 100x, figure 3e, an even better image, is seen. At this level I can say that the soil is sub rounded and colorless. This will help in the classification stage.
Visual classification is based on many different components: shape, color, odor and angularity. The sample had been through the oven previous to this, so it is dry. There wasn’t much of an odor coming from the soil. The sample is dark gray in color, this can be seen in figure 1a. The sample did not contain too many larger particles. When the sample was run through my fingers, it was smooth in texture. The angularity could not really be seen in the visual part of the lab. The microscope analysis helped with being able to see the angularity and this is shown in the next slide.
Based on both the visual and microscope analyses, a classification can be made. The classification was made to be a poorly graded sand. This is because the soil has a few larger particles and a few smaller particles but for the most part is made of sand particles. This was seen under the microscope. I got the same classification as in the GSD lab. For this soil, the visual analysis gave the same classification as more in depth-testing. This is not always the case but sometime for sands this is easy to see. Visual classifications can be helpful in the field. They may not always be accurate, but can provide something similar.
Grain size distribution is a means of examining and classifying soil based on the distribution of the soil size. This is done by doing both sieve and hydrometer analyses. Sieve analysis is done by running the soil through a sieve set and weighing the sieves before and after to get a percentage of soil in each. A hydrometer test is done by placing a specified amount of soil into a beaker, mixing with water. Once this is completed a hydrometer is placed in the mixture, this will give values used to find the distribution of fines in a particular sample.
Soil compaction is done so that a denser soil can be made. Compaction is the mechanical process of reducing the volume by removing air. This can be done for many reason, one would be so that a road or building could be built. By compacting the soil some of the aspects change. The unit weight and shear strength increase while the permeability, and chance of settlement decrease. This can be extremely important when building a structure. In this case a standard proctor test was performed.
Specific gravity is used in determining several aspects of a soil. It will be used on both the Grain size and Compaction labs that follow. For this lab we measure the specific gravity of the soil collected in the dirt collection lab. This was done by placing soil in a pycnometer and adding water. By knowing the specific gravity of water and several weights the specific gravity can be found.
Visual classification is simply classifying the sample based on several physical characteristics. Some examples are color, texture, shape, and odor. These results will be completely subjective seeing as not everyone sees everything in exactly the same manner. This result is just a quick baseline in the field before other tests are performed.