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Rapid determination of soil unconfined compressive strength using reflectance spectroscopy

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Abstract

Understanding the physical and especially mechanical properties of forest soils is very important in forest engineering operations including road construction and exploitation. Unconfined compressive strength (UCS) is one of the most important mechanical properties for evaluating the strength of adhesive soils in many engineering projects including forest roads. Measuring the USC of soil in the laboratory and in situ is difficult, time-consuming, and costly, so it is essential to explore a rapid, low-cost, and non-destructive method. The aim of this study was to estimate the UCS of forest roads using spectroscopy. The applied support vector machine (SVM) method determined the soil moisture classes (i.e., 14%, 25%, 31%, and 36%) with R2 of 0.98. Two methods of partial least squares regression (PLSR) and the normal difference index (NDI) were used to estimate the UCS at different moisture classes. The results showed that the NDI performed better than the PLSR to estimate the USC at four different moisture classes of 14% (R2 = 0.8, RMSE = 19.36, RPD = 2.61), 25% (R2 = 0.78, RMSE = 16.06, RPD = 1.64), 31% (R2 = 0.82, RMSE = 11.2, RPD = 2.28), and 36% (R2 = 0.83, RMSE = 7.49, RPD = 2.41). Also, a sampling interval method was applied to reduce spectral dimensionality and processing time. The sampling interval of 16 nm was selected using a genetic algorithm. Finally, the UCS was estimated at maximum soil moisture using the simple regression model (SRM) based on the results of NDI. Based on this current study, it has been found that using spectroscopy to estimate the UCS of soil could be considered an alternative method for developmental operations in identifying the UCS of soil due to the costly and time-consuming conventional laboratory methods.

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References

  • Ackerson JP, Demattê JAM, Morgan CLS (2015) Predicting clay content on field-moist intact tropical soils using a dried, ground VisNIR library with external parameter orthogonalization. Geoderma 259:196–204

    Article  Google Scholar 

  • Aïchi H, Fouad Y, Walter C et al (2009) Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosyst Eng 104:442–446. https://doi.org/10.1016/j.biosystemseng.2009.08.002

    Article  Google Scholar 

  • American society for testing and materials (ASTM) (1995) Standard test methods for liquid limit, plastic limit, and plasticity index of soils (D4318-05). ASTM, In, pp 1–16

    Google Scholar 

  • Bagherieh AR, Farsijani A (2014) The effect of moisture content on the shear strength parameters of plastic fine soils. Modares J Civ Eng 14

  • Barclay RT, Casias T, David AC et al (1990) State-of-the-art report on soil cement. ACI Mater J 87

  • Ben-Dor E, Banin A (1995) Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci Soc Am J 59:364–372. https://doi.org/10.2136/sssaj1995.03615995005900020014x

    Article  Google Scholar 

  • Ben-Dor E, Patkin K, Banin A, Karnieli A (2002) Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over soils in Israel. Int J Remote Sens 23:1043–1062. https://doi.org/10.1080/01431160010006962

    Article  Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê JAM et al (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Article  Google Scholar 

  • British Standard (2016) Methods of test for soils for civil engineering purposes. Part 1: general requirements and sample preparation. In: BS 1377-1990, London. https://standards.globalspec.com/std/10052867/BS 1377-1. Accessed 11 Apr 2019

  • CAMO ASA (1998) The Unscrambler User Manual. CAMO ASA Norw

    Google Scholar 

  • Castaldi F, Palombo A, Pascucci S et al (2015) Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: a case study using simulated PRISMA data. Remote Sens 7:15561–15582

    Article  Google Scholar 

  • Clark RN (1999) Spectroscopy of rocks and minerals, and principles of spectroscopy. Remote Sens Eartb Sci Man Remote Sens 3:3–58. https://doi.org/10.1111/j.1945-5100.2004.tb00079.x

    Article  Google Scholar 

  • Clark RN, Roush TL (1984) Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J Geophys Res Solid Earth 89:6329–6340

  • Clark R, Swayze G, LE Wise R et al (2007) USGS digital spectral library splib06a. US Geol Surv Digit Data Ser 231

  • Conforti M (2013) Potentiality of laboratory visible and near infrared spectroscopy for determining clay content in forest soils: a case study from high forest beech (Fagus Sylvatica) in Calabria (Southern Italy). EQA (Imola 2009) 11:49–64. https://doi.org/10.6092/issn.2281-4485/4172

  • Conforti M, Castrignanò A, Robustelli G et al (2015) Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena 124:60–67

    Article  Google Scholar 

  • Coûteaux MM, Berg B, Rovira P (2003) Near infrared reflectance spectroscopy for determination of organic matter fractions including microbial biomass in coniferous forest soils. Soil Biol Biochem 35:1587–1600. https://doi.org/10.1016/j.soilbio.2003.08.003

    Article  Google Scholar 

  • Dafalla MA (2013) Effects of clay and moisture content on direct shear tests for clay-sand mixtures. Adv Mater Sci Eng 2013

  • Das BM (2013) Advanced Soil Mechanics. Crc Press, Fourth Edition

    Book  Google Scholar 

  • Diek S, Chabrillat S, Nocita M et al (2019) Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping. Geoderma 337:607–621

    Article  Google Scholar 

  • Dufréchou G, Grandjean G, Bourguignon A (2015) Geometrical analysis of laboratory soil spectra in the short-wave infrared domain: clay composition and estimation of the swelling potential. Geoderma 243–244:92–107. https://doi.org/10.1016/j.geoderma.2014.12.014

    Article  Google Scholar 

  • Gao B-C, Goetzt AFH (1995) Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ 52:155–162

    Article  Google Scholar 

  • Ge Y, Morgan CLS, Ackerson JP (2014) VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact. Geoderma 221:61–69

    Article  Google Scholar 

  • Gitau AN, Gumbe LO, Biamah EK (2006) Influence of soil water on stress–strain behaviour of a compacting soil in semi-arid Kenya. Soil Tillage Res 89:144–154

    Article  Google Scholar 

  • Güneyli H, Rüşen T (2016) Effect of length-to-diameter ratio on the unconfined compressive strength of cohesive soil specimens. Bull Eng Geol Environ 75:793–806

    Article  Google Scholar 

  • Haubrock S, Chabrillat S, Lemmnitz C, Kaufmann H (2008) Surface soil moisture quantification models from reflectance data under field conditions. Int J Remote Sens 29:3–29

    Article  Google Scholar 

  • Hayati E, Abdi E, Saravi MM et al (2018) Soil water dynamics under different forest vegetation cover: Implications for hillslope stability. Earth Surf Process Landf 43:2106–2120. https://doi.org/10.1002/esp.4376

    Article  Google Scholar 

  • Hrubesova E, Lunackova B, Brodzki O (2016) Comparison of liquid limit of soils resulted from Casagrande test and modificated cone penetrometer methodology. Procedia Eng 142:364–370

    Article  Google Scholar 

  • Hunt GR, Salisbury JW (1970) Visible and near-infrared spectra of minerals and rocks: 1. Silicate minerals. Mod Geol 1:283–300

    Google Scholar 

  • Hyun CU, Park HD (2011) Assessment of chemical weathering of granite stone monuments using reflectance spectroscopy. Bull Eng Geol Environ 70:63–78. https://doi.org/10.1007/s10064-010-0276-0

    Article  Google Scholar 

  • Islam K, Singh B, McBratney A (2003) Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Aust J Soil Res 41:1101–1114. https://doi.org/10.1071/SR02137

    Article  Google Scholar 

  • Kahraman S, Alber M (2006) Estimating unconfined compressive strength and elastic modulus of a fault breccia mixture of weak blocks and strong matrix. Int J Rock Mech Min Sci 43:1277–1287

    Article  Google Scholar 

  • Kayabaşı A (2020) Geotechnical properties of fine-grained soils in Ankara/Turkey: an assessment of the existing empirical equations. Environ Earth Sci 79:1–27. https://doi.org/10.1007/s12665-020-09025-z

    Article  Google Scholar 

  • Kim T-H, Kim T-H, Kang G-C, Ge L (2012) Factors influencing crack-induced tensile strength of compacted soil. J Mater Civ Eng 24:315–320

    Article  Google Scholar 

  • Liu W, Baret F, Gu X et al (2003) Evaluation of methods for soil surface moisture estimation from reflectance data. Int J Remote Sens 24:2069–2083

    Article  Google Scholar 

  • Liu J, Han J, Xie J et al (2020) Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics. Spectrochim Acta Part A Mol Biomol Spectrosc 226:117639

    Article  Google Scholar 

  • Luce MS, Ziadi N, Nyiraneza J et al (2012) Near infrared reflectance spectroscopy prediction of soil nitrogen supply in humid temperate regions of Canada. Soil Sci Soc Am J 76:1454–1461

    Article  Google Scholar 

  • Luce MS, Ziadi N, Zebarth BJ et al (2014) Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy. Geoderma 232:449–458

    Article  Google Scholar 

  • Martens H, Naes T (1989) Assessment, validation and choice of calibration method. Multivar calibration:237–266

  • McMillan PF, Wolf GH, Poe BT (1992) Vibrational spectroscopy of silicate liquids and glasses. Chem Geol 96:351–366

    Article  Google Scholar 

  • Minasny B, McBratney AB, Bellon-Maurel V et al (2011) Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 167:118–124

    Article  Google Scholar 

  • Mousavi F, Abdi E, Ghalandarzadeh A et al (2020) Diffuse reflectance spectroscopy for rapid estimation of soil Atterberg limits. Geoderma 361:114083. https://doi.org/10.1016/j.geoderma.2019.114083

    Article  Google Scholar 

  • Nduwamungu C, Ziadi N, Parent L-É et al (2009) Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: a review. Can J Soil Sci 89:531–541

    Article  Google Scholar 

  • Nefeslioglu HA (2013) Evaluation of geo-mechanical properties of very weak and weak rock materials by using non-destructive techniques: ultrasonic pulse velocity measurements and reflectance spectroscopy. Eng Geol 160:8–20

    Article  Google Scholar 

  • Nocita M, Stevens A, Noon C, van Wesemael B (2013) Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma 199:37–42

    Article  Google Scholar 

  • Parthasarathy G (2006) Effect of high-pressures on the electrical resistivity of natural zeolites from Deccan Trap, Maharashtra, India. J Appl Geophys 58:321–329

    Article  Google Scholar 

  • Parthasarathy G, Kunwar AC, Srinivasan R (2001) Occurrence of moganite-rich chalcedony in Deccan flood basalts, Killari, Maharashtra, India. Eur J Mineral 13:127–134

    Article  Google Scholar 

  • Pytka J, Dabrowski J (2001) Determination of the stress–strain relationship for sandy soil in field experiments. J terramechanics 38:185–200

    Article  Google Scholar 

  • Rehman ZU, Khalid U, Farooq K, Mujtaba H (2017) Prediction of CBR value from index properties of different soils. Tech J Univ Eng Technol Taxila, Pakistan 22

  • Rienzi EA, Mijatovic B, Mueller TG et al (2014) Prediction of soil organic carbon under varying moisture levels using reflectance spectroscopy. Soil Sci Soc Am J 78:958–967

    Article  Google Scholar 

  • Rossel RAV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54

    Article  Google Scholar 

  • Rossel RAV, McGlynn RN, McBratney AB (2006) Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137:70–82. https://doi.org/10.1016/j.geoderma.2006.07.004

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec Publ 309–351

  • Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639. https://doi.org/10.1021/ac60214a047

    Article  Google Scholar 

  • Schimleck LR, Evans R, Ilic J (2001) Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy. Can J For Res 31:1671–1675

    Article  Google Scholar 

  • Shainberg I, Laflen JM, Bradford JM, Norton LD (1994) Hydraulic flow and water quality characteristics in rill erosion. Soil Sci Soc Am J 58:1007–1012

    Article  Google Scholar 

  • Singh TN, Kainthola A, Venkatesh A (2012) Correlation between point load index and uniaxial compressive strength for different rock types. Rock Mech Rock Eng 45:259–264

    Article  Google Scholar 

  • Smith GN (1986) Probability and statistics in civil engineering. Collins Prof Tech books 244

  • Sridharan A (2014) Fourth IGS-Ferroco Terzaghi Oration: 2014: Soil clay mineralogy and physico-chemical mechanisms governing the fine-grained soil behaviour. Indian Geotech J 44:371–399. https://doi.org/10.1007/s40098-014-0136-0

    Article  Google Scholar 

  • Stenberg B, Rossel RAV, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. Adv Agron 107:163–215. https://doi.org/10.1016/S0065-2113(10)07005-7

    Article  Google Scholar 

  • Summers D, Lewis M, Ostendorf B, Chittleborough D (2011) Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol Indic 11:123–131. https://doi.org/10.1016/j.ecolind.2009.05.001

    Article  Google Scholar 

  • Tian J, Philpot WD (2015) Relationship between surface soil water content, evaporation rate, and water absorption band depths in SWIR reflectance spectra. Remote Sens Environ 169:280–289

    Article  Google Scholar 

  • Tucker CJ (1978) Red and photographic infrared linear combinations for monitoring vegetation

  • Walvoort MTC, Moggré GJ, Lodder G et al (2011) Stereoselective synthesis of 2,3-diamino-2,3-dideoxy-β-d- mannopyranosyl uronates. J Organomet Chem 76:7301–7315. https://doi.org/10.1021/jo201179p

    Article  Google Scholar 

  • Wijewardana C, Alsajri FA, Irby JT et al (2019) Physiological assessment of water deficit in soybean using midday leaf water potential and spectral features. J Plant Interact 14:533–543

    Article  Google Scholar 

  • Williams M, Bond BJ, Ryan MG (2001) Evaluating different soil and plant hydraulic constraints on tree function using a model and sap flow data from ponderosa pine. Plant Cell Environ 24:679–690. https://doi.org/10.1046/j.1365-3040.2001.00715.x

    Article  Google Scholar 

  • Wold S, Sjöström M, Eriksson L, Wold S (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130. https://doi.org/10.1016/S0169-7439(01)00155-1

    Article  Google Scholar 

  • Wu Y, Chen J, Wu X et al (2005) Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Appl Geochem 20:1051–1059

    Article  Google Scholar 

  • Wu C-Y, Jacobson AR, Laba M, Baveye PC (2009) Alleviating moisture content effects on the visible near-infrared diffuse-reflectance sensing of soils. Soil Sci 174:456–465

    Article  Google Scholar 

  • Yitagesu FA, Van Freek Meer DER, Van Harald Werff DER, Zlgterman W (2008) Assessing expansive soil engineering parameters using spectroscopy. Int Geosci Remote Sens Symp 2:1255–1258. https://doi.org/10.1109/IGARSS.2008.4779230

    Article  Google Scholar 

  • Yitagesu FA, van der Meer F, van der Werff H, Zigterman W (2009) Quantifying engineering parameters of expansive soils from their reflectance spectra. Eng Geol 105:151–160. https://doi.org/10.1016/j.enggeo.2009.01.004

    Article  Google Scholar 

  • Yokoi H (1968) Relationship between soil cohesion and shear strength. Soil Sci Plant Nutr 14:89–93

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Fatemeh Mousavi, Ehsan Abdi, Parviz Fatehi & Baris Majnounian

  2. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Abbas Ghalandarzadeh

  3. Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

    Hossein Ali Bahrami

  4. Quebec Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Quebec City, QC, G1V 2J3, Canada

    Noura Ziadi

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  1. Fatemeh Mousavi
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  2. Ehsan Abdi
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Mousavi, F., Abdi, E., Fatehi, P. et al. Rapid determination of soil unconfined compressive strength using reflectance spectroscopy. Bull Eng Geol Environ 80, 3923–3938 (2021). https://doi.org/10.1007/s10064-021-02159-9

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