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Relevant bibliographies by topics / Soil property estimation

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Academic literature on the topic 'Soil property estimation'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Soil property estimation"

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Fredlund, Delwyn G., Daichao Sheng, and Jidong Zhao. "Estimation of soil suction from the soil-water characteristic curve." Canadian Geotechnical Journal 48, no. 2 (February 2011): 186–98. http://dx.doi.org/10.1139/t10-060.

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Soil-water characteristic curves (SWCCs) are routinely used for the estimation of unsaturated soil property functions (e.g., permeability functions, water storage functions, shear strength functions, and thermal property functions). This paper examines the possibility of using the SWCC for the estimation of in situ soil suction. The paper focuses on the limitations of estimating soil suctions from the SWCC and also suggests a context under which soil suction estimations should be used. The potential range of estimated suction values is known to be large because of hysteresis between drying and wetting SWCCs. For this, and other reasons, the estimation of in situ suctions from the SWCC has been discouraged. However, a framework is suggested in this paper for estimating the median value for in situ soil suction along with a likely range of soil suction values (i.e., maximum and minimum values). The percentage error in the estimation of soil suction from the SWCC is shown to be lowest for sand soils and highest for clay soils.

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Fredlund, Delwyn G. "The 1999 R.M. Hardy Lecture: The implementation of unsaturated soil mechanics into geotechnical engineering." Canadian Geotechnical Journal 37, no. 5 (October 1, 2000): 963–86. http://dx.doi.org/10.1139/t00-026.

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The implementation of unsaturated soil mechanics into geotechnical engineering practice requires that there be a paradigm shift from classical soil mechanics methodology. The primary drawback to implementation has been the excessive costs required to experimentally measure unsaturated soil properties. The use of the soil-water characteristic curve has been shown to be the key to the implementation of unsaturated soil mechanics. Numerous techniques have been proposed and studied for the assessment of the soil-water characteristic curves. These techniques range from direct laboratory measurement to indirect estimation from grain-size curves and knowledge-based database systems. The soil-water characteristic curve can then be used for the estimation of unsaturated soil property functions. Theoretically based techniques have been proposed for the estimation of soil property functions such as (i) coefficient of permeability, (ii) water storage modulus, and (iii) shear strength. Gradually these estimations are producing acceptable procedures for geotechnical engineering practices for unsaturated soils. The moisture flux ground surface boundary condition is likewise becoming a part of the solution of most problems involving unsaturated soils. The implementation process for unsaturated soils will still require years of collaboration between researchers and practicing geotechnical engineers.Key words: unsaturated soil mechanics, soil suction, unsaturated soil property functions, negative pore-water pressure, matric suction, soil-water characteristic curve.

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Ng, Set Foong, Pei Eng Ch’ng, Yee Ming Chew, and Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties." Scientific Research Journal 11, no. 1 (June 1, 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.5416.

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Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Ng, Set Foong, Pei Eng Ch’ng, Yee Ming Chew, and Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties." Scientific Research Journal 11, no. 1 (June 1, 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.9398.

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Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Ortenzi, Sofia, Martina Mangoni, and Lucio Di Matteo. "Estimating moisture content and hydraulic properties of unsaturated sandy soils of Tiber River (Central Italy): integrating data from calibrated PR2/6 probe and hydraulic property estimator." Acque Sotterranee - Italian Journal of Groundwater 11, no. 1 (March 31, 2022): 17–25. http://dx.doi.org/10.7343/as-2022-541.

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The correct estimation of soil moisture data is essential in soil-water management and estimating the hydraulic properties of unsaturated soils. The increased use of Multi-Sensor Capacitance Probes (MCAPs) requires careful calibration. Without accurate calibration, the use of MCAPs leads to incorrect water content estimation, making them of no practical use. This work presents the specific calibration equations for the correct use of the PR2/6 profile probe on sands of different nature. As the iron oxides content of the Tiber River basin sands increases, the calibration lines slope increases, allowing the understanding of the different electromagnetic responses. As for other sands worldwide, sands with high iron oxides content show a relative high specific surface than quartz or calcareous sands, responsible for more adhesive water (e.g., high permittivity values). The water content data are integrated with a hydraulic property estimator allowing the estimation of the hydraulic conductivity of soils. Applying the manufacturer equation of the PR2/6 profile probe instead of the specific equation leads to an overestimation of the hydraulic conductivity values up to two orders of magnitude, making therefore rather incorrect the understanding of the phenomena occurring in the unsaturated zone.

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Mattikalli, N. M., E. T. Engman, L. R. Ahuja, and T. J. Jackson. "Microwave remote sensing of soil moisture for estimation of profile soil property." International Journal of Remote Sensing 19, no. 9 (January 1998): 1751–67. http://dx.doi.org/10.1080/014311698215234.

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Odgers, Nathan P., Alex B. McBratney, and Budiman Minasny. "Digital soil property mapping and uncertainty estimation using soil class probability rasters." Geoderma 237-238 (January 2015): 190–98. http://dx.doi.org/10.1016/j.geoderma.2014.09.009.

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Cho, Yongjin, Kenneth A. Sudduth, and Scott T. Drummond. "Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe." Transactions of the ASABE 60, no. 3 (2017): 683–92. http://dx.doi.org/10.13031/trans.12049.

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Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

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Zhang, Feixia, G. Ward Wilson, and D. G. Fredlund. "Permeability function for oil sands tailings undergoing volume change during drying." Canadian Geotechnical Journal 55, no. 2 (February 2018): 191–207. http://dx.doi.org/10.1139/cgj-2016-0486.

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The coefficient of permeability function is an important unsaturated soil property required when modeling seepage and contaminant transport phenomena. Inaccuracies in the estimation of the permeability function can lead to significant errors in numerical modeling results. Changes in void ratio and degree of saturation are factors that influence the permeability function. Presently available methodologies for estimating the unsaturated permeability function make the assumption that there is no volume change as soil suction is changed. As a result, volume changes are interpreted as changes in degree of saturation. The commonly used estimation techniques for the permeability function are reasonable for soils such as sands that experience little volume change as soil suction is changed. On the other hand, inaccurate results are generated when soils undergo volume change as is the case with oil sands tailings. Revisions to previous methodologies are proposed to render the estimation of the permeability function more suitable for simulating the drying process associated with soils that undergo high volume changes. The revised methodology independently analyzes the effect of volume changes (i.e., changes in void ratio) and degree of saturation changes (i.e., changes in S-SWCC (degree of saturation - soil-water characteristic curve)). Laboratory data on thickened oil sands tailings are presented and interpreted within the context of the proposed methodology.

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Mohanty, Binayak P. "Soil Hydraulic Property Estimation Using Remote Sensing: A Review." Vadose Zone Journal 12, no. 4 (November 2013): vzj2013.06.0100. http://dx.doi.org/10.2136/vzj2013.06.0100.

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Dissertations / Theses on the topic "Soil property estimation"

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Mboh, Cho Miltin [Verfasser]. "Coupled hydrogeophysical inversion for soil hydraulic property estimation from time-lapse geophysical data / Cho Miltin Mboh." Bonn : Universitäts- und Landesbibliothek Bonn, 2012. http://d-nb.info/1043056599/34.

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Book chapters on the topic "Soil property estimation"

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Sugimura, Yoshihisa, Yasuhiro Sega, Masaaki Katagiri, and Katsuhide Nishizono. "Estimating Landfill Height Considering the Heterogeneous Physical Property of Dredging Soil as Reclamation Material." In Lecture Notes in Civil Engineering, 53–61. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0077-7_6.

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"Soil and Sediment Sorption Coefficients." In Handbook of Property Estimation Methods for Chemicals, 163–210. CRC Press, 2000. http://dx.doi.org/10.1201/9781420026283-12.

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Doucette, William. "Soil and Sediment Sorption Coefficients." In Handbook of Property Estimation Methods for Chemicals. CRC Press, 2000. http://dx.doi.org/10.1201/9781420026283.ch8.

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Odgers, N., A. McBratney, and B. Minasny. "Digital soil property mapping and uncertainty estimation using soil class probability rasters." In GlobalSoilMap, 341–46. CRC Press, 2014. http://dx.doi.org/10.1201/b16500-63.

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Bekele, Daniel, Agumassie Gela, Daniel Mengistu, and Andargachew Derseh. "Remote Sensing Based Soil Moisture Estimation for Agricultural Productivity: A note from Lake Tana Sub Basin, NW Ethiopia." In Soil Moisture [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.109420.

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Soil moisture availability affects agricultural productivity and in turn food security. Estimating the moisture content of soil is imperative for proper water resource management and agricultural productivity. However, field based method is expensive and covers limited spatial variation. The advancement of remote sensing technology eases the soil moisture estimation over large geographic area. Hence, this study intended to apply the optical and thermal remote sensing data for estimating SM in the Lake Tana sub basin. Temperature vegetation dryness index (TVDI) model which is used in this study to estimate soil moisture is derived from the wet and dry edge of the LST-NDVI triangular scatterplot. The finding revealed that NDVI and LST have inverse relationship where LST decrease with increasing NDVI. Spatially, northern and north western part has experienced high LST. The estimated soil moisture result ranging from 0 to 1 where the soil moisture is higher in areas with TVDI value is near 1. Thus, soil moisture is higher in the east, and northeast part of the sub basin whereas the central, western and northwest part experienced low soil moisture. Therefore, applying remote sensing enables estimation of soil moisture across large geographical area with scarcity of field data (in-situ observations).

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Charles, Galdies, Zammit Amy, and Gauci Adam. "Soil Erosion Risk Analysis of a Small Watershed." In Soil Erosion - Risk Modeling and Management [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.111424.

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Malta is being rapidly exposed to developmental activities occurring inland and along its coastline, which in turn triggers erosion and flooding in the event of high-intensity rainfall. Most of the rainwater-containing several contaminants from urban and agricultural areas are lost as runoff into the coastal waters, which in turn have adverse environmental and socioeconomic impacts. The extent of soil erosion and runoff can be investigated starting from the watershed basin downhill till coastal waters. This study links the runoff of soil along an ecologically sensitive watershed in Malta with the use of multidisciplinary techniques. These included the estimation of soil erosivity coupled with satellite remote sensing chlorophyll-a (CHLA) and total suspended matter (TSM) in coastal waters adjacent to the mouth of the valley. This represents a novel study for the Maltese islands because it provides a precise map of soil erosion hotspots in the Ramla watershed as high as 30 ton ha−1 yr−1. Using three case studies of past torrential rain episodes, the sedimentation process resulted in a 120% and 133% increase in CHLA and TSM levels, respectively, against background levels. This information is vital for proper risk management of ecologically sensitive watershed basins.

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Conference papers on the topic "Soil property estimation"

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Di, Haibin, and Aria Abubakar. "Semi-Supervised Learning for Geotechnical Soil Property Estimation in Offshore Windfarm Sites." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211836-ms.

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Abstract Site characterization and monitoring of the subsurface formations around wind turbine locations are crucial for reliable wind farm construction, operation and maintenance. In order to extract relevant information about subsurface soils, ultrahigh-resolution (UHR) seismic survey and geotechnical cone- penetration testing (CPT) is often acquired, processed, interpreted and integrated, which could be repeated over time for site monitoring purposes. Due to the size of the area to be investigated and the manual efforts to complete multiple steps in the traditional workflow, the turnaround time for soil property estimation in a wind farm site can be quite long. In this study we implement a semi-supervised learning workflow to automate the task, which integrates URH seismic and CPT logs through two convolutional neural networks (CNNs), with one for seismic denoising and feature engineering (SDFE) and the other for seismic-CPT integration (SCI), which reduces the difficulties in CNN training due to poor data quality and small data quantity. The two components are connected by implementing the encoder of the pretrained SDFE-CNN as part of the SCI-CNN encoder. As tested on a public wind farm site, the use of deep learning leads to promising results in terms of both quality and efficiency. The proposed workflow is also extensible to include additional information, such as structure and velocity models, for further constraining the SCI-CNN. Highlights: A semi-supervised learning workflow is proposed for soil property estimation from UHR seismic and CPT tests in a wind farm site,allows estimating the essential soil properties such as cone-tip resistance from post-stack UHR seismic as tested on a real windfarm site HKZ, andreduces the turnaround time of windfarm site characterization compared to traditional workflows.

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"Session 4: Hydrogeophysics & EM soil property estimation II." In 2011 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/iwagpr.2011.5963875.

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"Session 1: Hydrogeophysics & EM soil property estimation I." In 2011 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/iwagpr.2011.5963889.

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Kulaglic, Ajla, and Burak Berk Ustundag. "Effect of soil property change on soil moisture profile estimation through data fusion." In 2015 Fourth International Conference on Agro-Geoinformatics. IEEE, 2015. http://dx.doi.org/10.1109/agro-geoinformatics.2015.7248131.

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Di, H., and A. Abubakar. "Structure-constrained windfarm soil property estimation via deep neural networks." In 84th EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202310238.

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LEE, KYOU SEUNG, DONG HOON LEE, KENNETH A. SUDDUTH, and SUN-OK CHUNG. "SAMPLING AND CALIBRATION REQUIREMENTS FOR SOIL PROPERTY ESTIMATION USING NIR SPECTROSCOPY." In Proceedings of the International Conference on ANDE 2007. World Scientific Publishing Company, 2008. http://dx.doi.org/10.1142/9789812793034_0030.

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Kyou Seung Lee, Dong Hoon Lee, Kenneth A Sudduth, Sun Ok Chung, Newell R Kitchen, and Scott T Drummond. "Sampling and Calibration Requirements for Soil Property Estimation Using Reflectance Spectroscopy." In 2008 Providence, Rhode Island, June 29 - July 2, 2008. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2008. http://dx.doi.org/10.13031/2013.24634.

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Long Huiling, Gu Xiaohe, Li Weiguo, Wang Yancang, and Xu Qingyun. "Integration MRA and MVA for cropland soil property estimation using hyperspectral reflectance." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947178.

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Wagner, W. Jacob, Ahmet Soylemezoglu, Dustin Nottage, and Katherine Driggs-Campbell. "In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics- Infused Neural Networks." In 16th European-African Regional Conference of the ISTVS. Hanover, NH, U.S.: International Society for Terrain-Vehicle Systems, 2023. http://dx.doi.org/10.56884/jdxp2382.

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Konishi, Chisato, and Genyuu Kobayashi. "An Interpretation of Various Well Logs Acquired in Unconsolidated Soil for Hydraulic Property Estimation." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2005. Environment and Engineering Geophysical Society, 2005. http://dx.doi.org/10.4133/1.2923468.

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Reports on the topic "Soil property estimation"

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Esparza and Westine. L51482 Well Casing Response to Buried Explosive Detonations. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 1985. http://dx.doi.org/10.55274/r0010272.

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Occasionally, buried explosives are used within proximity of producing oil and gas wells which increases the stresses in the casing near the explosion which may result in failure of the well. A procedure was needed for predicting the maximum stresses in producing oil and gas wells, specifically the well casing, induced by nearby, buried, explosive detonations. An extensive experimental and analytical program were funded and performed over a six (6) year period 1975-1981. The program was divided into two (2) parts: In the first part, similitude theory, empirical analyses and test data were used to derive equations for estimating maximum ground displacement and particle velocity. The ground motions provided the forcing function imparted to a buried pipeline. In the second part, similitude theory, conservation of mass and momentum, and approximate energy methods were used to derive functional relationships for the maximum pipe strains and stresses. Experimental data from more than sixty (60) field tests ere used to develop equations for estimating maximum pipe stresses induced by point and parallel line explosive sources buried in homogeneous soil media. The pipe stress and ground motion data from these experiments were used to develop an equation for computing an effective standoff distance so that the point source soil equations could be used to approximate the casing response. The large amount of data used and the wide range of these data make the solutions applicable to most blasting situations near producing oil and gas wells. This report provides comprehensive and detailed information for pipeline as well as oil and gas operators to predict the effect of buried explosives and thus the safety of a well(s) while in-service through proper assessment of stresses and guidelines for the appropriate selection of explosive charges, techniques and methods. This will avoid unexpected damages, operational costs, provide guidance for \operator qualification\" for blasting near in-service wells and minimize liabilities to the operator.

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