Soil Strength Sensing for Quantifying Within-Field Variability

2002 ◽  
Author(s):  
Kenneth A. Sudduth ◽  
Newell R. Kitchen ◽  
Scott T. Drummond ◽  
Germán A. Bollero ◽  
Donald G. Bullock ◽  
...  
Keyword(s):  
Soil Strength ◽  
Field Variability

NASA Boeing 757 Cavity Field Variability Based on Boeing 757 and Boeing 707 Test Data.

1997 ◽  
Author(s):  
Michael O. Hatfield ◽  
Mark D. Johnson ◽  
Gustav J. Freyer ◽  
Michael B. Slocum
Keyword(s):  
Test Data ◽  
Cavity Field ◽  
Field Variability

Grazing winter rye cover crop in a cotton no‐till system: Soil strength and runoff

2021 ◽  
Author(s):  
Harry H. Schomberg ◽  
Dinku M. Endale ◽  
Kipling S. Balkcom ◽  
Randy L. Raper ◽  
Dwight H. Seman
Keyword(s):  
Cover Crop ◽  
Soil Strength ◽  
Winter Rye ◽  
No Till

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

scholarly journals Effect of Urease Enzyme on Shear Strength of Clay Shale

2018 ◽  
Vol 7 (4.36) ◽  
pp. 424 ◽  
Author(s):  
Maxwel Joseph Henri Nainggolan ◽  
Wiwik Rahayu ◽  
Puspita Lisdiyanti
Keyword(s):  
Shear Strength ◽  
Bearing Capacity ◽  
Triaxial Test ◽  
Clay Soil ◽  
Soil Strength ◽  
Clay Shale ◽  
Strength Performance ◽  
Enzyme Mixture ◽  
Urease Enzyme

In recent years, utilization of biotechnology in geotechnical field has rapidly grown. One of the biotechnologies being utilized is urease enzyme, a stabilization material by bio-cementation method studied in this research.  Urease enzyme is manually mixed with additional 10% of clay soil to clay shale. The objective of mixing it is to increase the bearing capacity of the clay shale. Consolidated undrained triaxial test was performed for testing the soil strength performance for samples that had undergone curing for 2, 4, and 6 weeks. The results indicated that the sample stiffens, proved by the increase of shear strength from consolidated undrained triaxial test. The shear strength value produced by the variation of the urease enzyme mixture + 10% the clay is higher than that of without the original clay shale.  

Early Pinus caribaea var. hondurensis root development. 2. Influence of soil strength

1996 ◽  
Vol 36 (7) ◽  
pp. 847 ◽  
Author(s):  
A Costantini ◽  
D Doley ◽  
HB So
Keyword(s):  
Root Development ◽  
Soil Type ◽  
Root Elongation ◽  
Primary Root ◽  
High Strength ◽  
Soil Strength ◽  
Shoot Development ◽  
Pinus Caribaea ◽  
Primary Shoot ◽  
Primary Roots

The influence of penetration resistance (PR), an easily measured indicator of soil strength, on the growth of Pinus caribaea var. hondurensis radicles and seedlings was investigated. Negative exponential relationships between PR and both radicle and primary root elongation were observed. All root elongation ceased at PR levels of 3.25 MPa. Tip diameters of radicles and primary roots were positively correlated with PR values up to 2.4 MPa, whilst numbers of primary roots, total root lengths and lengths of longest roots were all negatively correlated with PR. Hypocotyl elongation was also reduced by increasing PR, although the reductions occurred at higher PRs than those which inhibited root development. In contrast, primary shoot development was unaffected by PR levels which were sufficient to stop root elongation, but was reduced in soil with a PR of 4.8 MPa. There were significant family x soil type and family x PR interactions for radicle, hypocotyl, primary root and primary shoot development. 1f these interactions are correlated with performance in the field, then they may serve as useful indicators of family suitability to both soil type and high strength soils.

scholarly journals Comparison of FASST and SNTHERM in Three Snow Accumulation Regimes

2008 ◽  
Vol 9 (6) ◽  
pp. 1443-1463 ◽  
Author(s):  
Susan Frankenstein ◽  
Anne Sawyer ◽  
Julie Koeberle
Keyword(s):  
Surface Energy ◽  
Single Layer ◽  
Soil Strength ◽  
Snow Accumulation ◽  
Dynamic State ◽  
Energy Fluxes ◽  
One Dimensional ◽  
Surface Energy Fluxes ◽  
Snow Model ◽  
Surface Ice

Abstract Numerical experiments of snow accumulation and depletion were carried out as well as surface energy fluxes over four Cold Land Processes Experiment (CLPX) sites in Colorado using the Snow Thermal model (SNTHERM) and the Fast All-Season Soil Strength model (FASST). SNTHERM is a multilayer snow model developed to describe changes in snow properties as a function of depth and time, using a one-dimensional mass and energy balance. The model is intended for seasonal snow covers and addresses conditions found throughout the winter, from initial ground freezing in the fall to snow ablation in the spring. It has been used by many researchers over a variety of terrains. FASST is a newly developed one-dimensional dynamic state-of-the-ground model. It calculates the ground’s moisture content, ice content, temperature, and freeze–thaw profiles as well as soil strength and surface ice and snow accumulation/depletion. Because FASST is newer and not as well known, the authors wanted to determine its use as a snow model by comparing it with SNTHERM, one of the most established snow models available. It is demonstrated that even though FASST is only a single-layer snow model, the RMSE snow depth compared very favorably against SNTHERM, often performing better during the accumulation phase. The surface energy fluxes calculated by the two models were also compared and were found to be similar.

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