Estimating Soil Classification Via Quantitative and ... - ScienceDirect.com

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ScienceDirect Procedia Engineering 145 (2016) 860 – 867

International Conference on Sustainable Design, Engineering and Construction

Estimating soil classification via quantitative and qualitative field testing for use in constructing compressed earth blocks Jase D. Sittona, Brett A. Storya* a

Department of Civil and Environmental Engineering, Southern Methodist University, PO Box 750340 Dallas, TX 75275, USA

Abstract Compressed earth blocks (CEBs) represent a cost-effective and sustainable building material for construction in low-income areas. One challenge with CEB construction is the dependence of CEB unit strength on the character of soil, which varies based on location. Soil classification standards require laboratory testing; therefore standardized assessment of soil characteristics is difficult in the field. Typically, field tests provide primarily qualitative data collected by builders of varying experience; some crude quantitative data can also be collected. This research seeks to establish a framework for relating qualitative and quantitative field data to standardized soil classification methods. The framework is a neural evaluation system comprising artificial neural networks to compare input-output soil classification data and establish relationships between field data, laboratory data, and ultimately standardized soil classification. Soil samples from many regions around and outside of the United States were classified using results from both ASTM tests and field tests. © 2016 2015The TheAuthors. Authors.Published Published Elsevier © by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering (http://creativecommons.org/licenses/by-nc-nd/4.0/). and Construction 2015. Peer-review under responsibility of the organizing committee of ICSDEC 2016 Keywords: compressed earth blocks; earth building; soil classification; neural networks

1. Introduction Compressed earth blocks (CEBs) represent a cost-effective, sustainable, and environmentally-friendly building alternative to traditional masonry elements. CEB construction comprises low-cement, compressed local soil brick units that can be rapidly and efficiently manufactured and assembled on site. CEB construction uses local soil as the

* Corresponding author. Tel.:214-768-1991; fax: 214-768-2164. E-mail address: [email protected]

1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICSDEC 2016

doi:10.1016/j.proeng.2016.04.112

Jase D. Sitton and Brett A. Story / Procedia Engineering 145 (2016) 860 – 867

primary component, which provides advantages over traditional masonry elements including lower cost, increased energy efficiency, and lower environmental impact [1-13]. One significant challenge with prolific CEB construction is the lack of standardization in the United States and internationally [2,8-10]. The ultimate behavior (e.g. energy efficiency, structural resistance) of a CEB structure is dependent on properties of the character of soil, which varies based on location [1,2,4,5,7-10,12-14]. Soil classification standards require laboratory testing; therefore standardized assessment of soil characteristics in the field may be difficult. Typically, field tests provide primarily qualitative data collected by builders of varying experience; some crude quantitative data can also be collected [1,10,12]. Some of the soil properties of interest are particle distribution, clay content, and plasticity, which are used to assign soils a classification. Laboratory procedures to characterize these soil properties are detailed in ASTM D422 and ASTM D4318 [15,16]. The Unified Soil Classification System (USCS) is a widely used soil classification system which uses the aforementioned soil properties to assign a soil a classification. The USCS can be found in ASTM D2487 [17]. The research presented in this paper seeks to explore the relationship between qualitative and quantitative field test data and standardized laboratory soil analysis procedures in an effort to be able to accurately classify a soil based on field analysis alone. 2. Research 2.1. Soil data collection and classification In order to study how the field soil analysis compares with ASTM standardized laboratory soil analysis, soil samples have been analyzed using ASTM standard procedures in order to classify each sample according to the USCS while concurrently performing the field soil analysis tests on the samples. Classification of a soil according to the USCS requires determination of three soil properties: the soil’s particle gradation, the fines content (particles less than 75 μm in diameter), and the plasticity of the soil. The particle gradation and fines content can be determined by running the soil sample through sieve analysis (ASTM D422), which includes sifting the soil through a stack of wire mesh sieves which get finer from top to bottom [15]. The plasticity can be determined by testing the soil for its liquid limit and plastic limit. The liquid limit is defined as the water content (%) of a soil at the boundary between the semi-liquid and plastic states, while the plastic limit is defined as the water content (%) of a soil at the boundary between the plastic and semi-solid states. A soil exhibits plastic behavior if its water content is between the liquid and plastic limits; this range of plasticity is called the plasticity index, and is numerically defined as the difference between the liquid limit and the plastic limit. The plasticity index of a soil gives a measure of the plasticity of the soil that can be used for classification. The tests for the liquid and plastic limits can be found under ASTM D4318 [16]. A flowchart of ASTM and field tests run in parallel is shown in Figure 1.

Fig. 1. Soil analysis flow chart.

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The field soil analysis tests are: x x x x x

x

The Wash Test – The sample is slowly washed out of the hand with water. This gives an opportunity to observe the quantity of larger particles in the sample as any fines will be washed away first. The Pen Test – Water is added to the sample until it forms a putty. The putty is rolled into a “pen” shape. If it breaks before it can reach the diameter of a pen, the soil has low plasticity. If it can be rolled to the diameter of a pen and can support its own weight, the soil has high plasticity. The Stick Test – The soil putty is rolled into a ball and stabbed with a knife. The amount of soil that clings to the knife when it is removed is observed. Soil sticking to the knife indicates clay content. The Shine Test – The ball of soil putty is cut in half with a knife. The cross section of the ball is observed. A glossy/shine cross section indicates clay content, a dull cross section indicates higher silt or sand content. Test Tube Particle Gradation – A dry sample is dispersed in water in a test tube marked “sand”. The sample will start to slowly settle out of suspension at the bottom of the test tube. After different time increments, the portion of the sample remaining in suspension is transferred to a “silt” test tube and then to a “clay” test tube. Essentially, the test uses gravity to separate the soil sample into different groups based on particle size, and can give an approximation of the soil’s particle gradation and fines content. The Jar Test – The sample is dispersed in water inside of a jar. Once all of the sample settles out of suspension, different strata within the sample can be observed. These layers correspond to different particle sizes, although it is difficult to tell between finer sands and coarser silts and clays using this. This test has proven to be most valuable when used as a measure of the expansiveness of a soil. A soil that expands significantly after the introduction of water is likely to cause problems with shrinkage cracks if used to produce CEBs without enough stabilizer.

The names of the above tests differ by author, but the procedures are consistent [1, 10, 12]. Samples from a variety of locations both within the United States and internationally were evaluated and the results from both sets of tests (ASTM and field) were recorded side by side. 2.2. Neural network framework The complex, nonlinear relationship between USCS classification, Y, and diagnostic field test data streams, X, is difficult to ascertain deterministically. Material properties, data collection techniques, and other uncertainties contribute to difficulties in developing the relationship. Artificial neural networks (ANNs) are one method of estimating unknown relationships between input and output [18, 19, 20]. A neural network comprises neurons that are designed to roughly mimic the cognitive functions of a brain [18, 20]. ANNs are an efficient numerical tool to analyze seemingly unrelated numerical vectors or matrices and correlate underlying patterns to corresponding output vectors or matrices. ANNs have been used in structural, material, and construction engineering efforts where input/output relationships are not conducive to solutions of traditional mechanics [19, 21-23]. 3. Results and observations 3.1. Preliminary observations Some preliminary observational relationships between data obtained from the field tests and laboratory procedures were formed by the authors. Figure 2 shows a matrix that illustrates the observed correlations between field testing and ASTM testing (i.e. which field tests correspond with particular ASTM procedures). These correlations are imperfect, but are helpful to understand input/output relationships in the data. In order to study how the test tube particle gradation compared with ASTM sieve analysis, the percent fines measured in the test tube test was plotted against the percent fines measured in the sieve analysis for each soil sample. The percent fines (or fines content) for the test tube test refers to the percentage of the sample retained in both the “clay” and “silt” test tubes. For the sieve analysis, the percent fines refers to the percentage of the sample


that passes the no. 200 sieve (particles less than 75 μm in diameter). Figure 3 shows a simple scatter plot of these data points with a linear trend line; the linear regression is clearly unable to relate these methods. The test tube field test always results in a higher measured fines content than the sieve analysis. This could possibly be attributed to the inability of sieve analysis to distinguish between single large particles and clumps of smaller particles that are difficult to break apart. It is also likely that some of the discrepancy is due to the imprecise nature of the test tube test and its reliance on gravity and timing.

Fig. 2. Test relationship matrix.

Fig. 3. Percent fines comparison.

After compiling the data, a metric was created in an attempt to obtain a preliminary observation of the relationship between the more qualitative field tests (e.g. the pen, stick, and shine tests) and the Atterberg limits of a soil sample. This metric was dubbed “clay score” and is the summation of the scores from the pen, stick, and shine tests. The scores for these tests are meant to reflect whether or not the sample’s performance indicated clay content. Scores were assigned as follows: x

Pen Test o o

x

x

o Stick Test o o o Shine Test o o o

10: The sample is able to be rolled to approximately the diameter of a ball-point pen and does not break when draped over the side of the hand. 5: The sample holds together and is able to be rolled out, however it breaks before it reaches approximately the diameter of a ball-point pen or it breaks when draped over the side of the hand. 0: The sample does not hold together or is unable to be rolled out without crumbling. 10: When the knife is pulled out of the sample, there is soil clinging to the blade. 5: When the knife is pulled out of the sample, there are streaks of soil residue on the blade. 0: When the knife is pulled out of the sample, the blade is essentially clean. 10: When the soil ball is cut in half, the cross-section appears glossy and reflects light. 5: When the soil ball is cut in half, the cross-section appears somewhat glossy but does not reflect light very well. 0: When the soil ball is cut in half, the cross-section appears dull and does not reflect light.

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Figure 4 shows a plot of clay score versus the plasticity index obtained via the liquid limit and plastic limit tests for each soil sample. It appears that there is a strong correlation between a sample’s clay score and its plasticity index, although the clay score has its limits due to the large increments (5) in the score. This can be seen in Figure 4; all plasticity indexes above a certain threshold, approximately 6.0 in this case, result in a clay score of 30, which is the absolute maximum. Still, the clay score is valuable for preliminary observation of the relationship between the qualitative field tests and the laboratory tests that measure the Atterberg limits of a soil, and would likely benefit from further refinement.

Fig. 4. Clay score vs. plasticity index.

It should be noted that the results from these tests, especially the pen, stick, and shine tests, are likely to vary slightly based on the person who is performing them. For this paper, all tests were run by a graduate laboratory researcher who is not a soil expert. This possible variation in tests results based on the tester is accounted for in the next section of this paper by adding noise to the field test data obtained. 3.2. Neural network evaluation After each soil sample was classified using ASTM procedures and the USCS and preliminary observations of the correlation between ASTM analysis and field analysis were made, a neural network was trained to assign soils a USCS classification based purely on the results from field soil analysis. The input for the network is the data obtained from the field soil analysis tests. This includes the results from the test tube test (specifically the percent fines), the pen test, the stick test, the shine test, and the jar test (percent expansion). As mentioned in the previous section, for the pen, stick, and shine tests, each sample was given a score (0, 5, or 10) based on how it performed in that test. These scores were normalized to values between -1 and 1 before being input into the network. Figure 5 shows a simple input-output model of the neural network.

Fig. 5. Neural network inputs and output.


Test data from all 16 of the samples was used to train the network. The network architecture chosen in this study is a 2 layer feed-forward backpropagation neural network. Each hidden layer contained 10 neurons. The field test data was used as inputs and the USCS classifications determined by data from ASTM soil analysis was used as the target vector. Due to the fact that 16 data sets are likely not sufficient to adequately train the network, noise within a predetermined acceptable error range was added to the input data, thereby generating additional data sets to be used for training the network. This added noise also helps account for the fact that the results from the field soil analysis are dependent on the tendencies of the person performing the tests as assigning scores for the pen, stick, and shine tests are essentially judgment calls. After noise was added, there were 336 input and target training data sets. Table 1. Known classification vs. network output. Known Classification

Network Output

7

7

20

20

22

22

23

23

6

6

11

11

8

8

9

9

14

10*

10

10

10

10

10

10

11

11

9

9

21

21

9

9

During the training process, some data sets are held in order to test and validate the network. Table 1 shows a network output vector and a known output vector side by side. The input vector for the test was comprised of different field test data sets. The network output vector shows the USCS classifications returned by the network. The known classification vector contains the USCS classifications that were predetermined manually during soils testing. Analysis performed on the network during and after training showed that the network is able to assign soils a classification using only field test data with 94% accuracy. This is reflected in the data presented in Table 1, in which the network incorrectly classified only one sample. For ease of programming and training the network, each USCS classification has been assigned a number (1-23). Appendix A provides a table with the group symbol and name for each of the 23 classifications. 4. Conclusions This presented research establishes a framework for relating qualitative and quantitative field data to standardized soil classification methods. The framework is a neural evaluation system comprising neural networks to compare input-output soil classification data and establish relationships between field data, laboratory data, and ultimately standardized soil classification. A neural network was trained to assign soil classifications based solely on qualitative and quantitative field test data, and is able to do so with 94% accuracy. The incorrectly classified sample should have been classified as a fat clay (no. 14), but was instead classified as a clayey sand (no. 10). This is likely

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due to the network making an incorrect decision to classify the soil as a coarse-grained soil based on the fines content obtained from the test tube test when it should have been classified as a fine-grained soil. After this mistake, the network correctly identified the significant presence of clay in the sample and thus assigned a classification of clayey sand. The accuracy of the neural system will increase as more soil samples are analysed and more training points are provided to continually train the network. The success of the network described in this paper leads the authors to conclude that neural networks offer an efficient solution for making use of both qualitative and quantitative soil analysis data obtained in the field and are promising as tools for future research into the relationship between soil quality and CEB performance. Acknowledgements The authors would like to thank Adam and Bob De Jong from DwellEarth for providing all of the soil samples that were tested and for their continued expertise, experience, enthusiasm, and support for this project. The authors would also like to thank Dr. Eva Csaky, director of the Hunt Institute for Engineering and Humanity at SMU, for her research and financial support for this project. Appendix A. USCS classifications Network Classification

USCS Group Symbol

USCS Group Name

1

GW

Well-graded gravel

2

GP

Poorly-graded gravel

3

GM

Silty gravel

4

GC-GM

Silty clayey gravel

5

GC

Clayey gravel

6

SW

Well-graded sand

7

SP

Poorly-graded sand

8

SM

Silty sand

9

SC-SM

Silty clayey sand

10

SC

Clayey sand

11

CL

Lean clay

12

CL-ML

Silty clay

13

ML

Silt

14

CH

Fat clay

15

MH

Elastic silt

16

GW-GM

Well-graded gravel with silt

17

GW-GC

Well-graded gravel with clay

18

GP-GM

Poorly-graded gravel with silt

19

GP-GC

Poorly-graded gravel with clay

20

SW-SM

Well-graded sand with silt

21

SW-SC

Well-graded sand with clay

22

SP-SM

Poorly-graded sand with silt

23

SP-SC

Poorly-graded sand with clay


References [1] A.E. Adam, A.R.A. Agib, Compressed stabilized earth block manufacture in Sudan, UNESCO, Paris, 2001. [2] G.T.R. Allen, Strength properties of stabilized compressed earth blocks with varying soil compositions (master’s thesis), University of Colorado, Boulder, 2012. [3] M. Bachar, L. Azzouz, M. Rabehi, B. Mezgiche, Characterization of a stabilized earth concrete and the effect of incorporation of aggregates of cork on its thermo-mechanical properties: experimental study and modeling, Construction and Building Materials 74 (2015) 259-267. [4] J. Dahmen, J.F. Muñoz, Earth masonry unit: sustainable CMU alternative, International Journal of GEOMATE 6 (2014) 903-909. [5] M.C. Jiménez Delgado, I.C. Guerrero, The selection of soils for unstabilised earth building: a normative review, Construction and Building Materials 21 (2007) 237-251. [6] C. Egenti, J.M. Khatib, D. Oloke, Conceptualisation and pilot study of shelled compressed earth block for sustainable housing in Nigeria, International Journal of Sustainable Built Environment 3 (2014) 72-86. [7] B.R. Grunert, The development of a standard of care defining suitable testing of geomaterials intended for unstabilized compressed earth block construction (master’s thesis), University of Colorado, Boulder, 2008. [8] H. Guillaud, T. Joffroy, P. Odul, Compressed earth blocks: manual of design and construction, Vieweg, Eschborn, Germany, 1995. [9] A.D. Krosnowski, A proposed best practice method of defining a standard of care for stabilized compressed earthen block production (master’s thesis), University of Colorado, Boulder, 2011. [10] G. Minke, Building with earth: design and technology of a sustainable architecture, Birkhauser, Basel, Switzerland, 2006. [11] J.J. Morony, Adobe moisture absorption and temperature control, South West Texas Junior College, 2005. [12] V. Rigassi, Compressed earth blocks: manual of production, Vieweg, Eschborn, Germany, 1995. [13] F.V. Riza, I.A. Rahman, A.M.A. Zaidi, A brief review of compressed stabilized earth brick (CSEB), Proc., 2010 International Conference of Science and Social Research (CSSR 2010), Institute of Electrical and Electronics Engineers, New York, 2010. [14] J.C. Morel, A. Pkla, P. Walker, Compressive strength testing of compressed earth blocks, Construction and Building Materials 21 (2007) 303-309. [15] American Society for Testing and Materials (ASTM), ASTM D422: Standard test method for particle-size analysis of soil, in: ASTM Book of Standards Volume 04.08, West Conshohocken, PA, 2007. [16] American Society for Testing and Materials (ASTM), ASTM D4318: Standard test methods for liquid limit, plastic limit, and plasticity index of soils, in: ASTM Book of Standards Volume 04.08, West Conshohocken, PA, 2010. [17] American Society for Testing and Materials (ASTM), ASTM D2487: Standard practice for classification of soils for engineering purposes (Unified Soil Classification System), in: ASTM Book of Standards Volume 04.08, West Conshohocken, PA, 2011. [18] M.L. Minsky, S.A. Papert, Perceptrons, MIT Press, Cambridge, MA, 1969. [19] H. Adeli, Neural netorks in civil engineering: 1989ˀ2000., J. Comput-Aided Civ. Inf. Eng. 16:2 (2001) 126-142. [20] S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Pub. Comp., New Jersey, 1994. [21] Story, B.A. and Fry, G.T., A Structural A Structural Impairment Detection System Using Competitive Arrays of Artificial Neural Networks, Comp.-Aided Civil and Infrastructure Eng., 29 (2014), 180–190. [22] Story, B.A., and Fry, G.T., Methodology for Designing Diagnostic Data Streams for Use in a Structural Impairment Detection System, J. of Bridge Eng., 19(4), (2014), 10.1061/(ASCE)BE.1943-5592.0000556 , 04013020. [23]H.K. lam, U. Ekong, H. Liu, B. Xiao, H. Araujo, S.H. Ling, K.Y. Chan, A Study of Neural-Network-Based Classifiers for Material Classification, J. Neuro Comp. 144 (2014) 367-377.

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