Improved evaluation of field experiments by accounting for inherent soil variability

2017 ◽  
Vol 89 ◽  
pp. 1-15 ◽  
Author(s):  
K. Heil ◽  
U. Schmidhalter
Keyword(s):  
Field Experiments ◽  
Soil Variability ◽  
Inherent Soil Variability

Characterization of geotechnical variability

1999 ◽  
Vol 36 (4) ◽  
pp. 612-624 ◽  
Author(s):  
Kok-Kwang Phoon ◽  
Fred H Kulhawy
Keyword(s):  
Measurement Error ◽  
Coefficient Of Variation ◽  
Field Measurements ◽  
Mean Value ◽  
Primary Sources ◽  
Soil Variability ◽  
Extensive Literature ◽  
Scale Of Fluctuation ◽  
Error Coefficient ◽  
Inherent Soil Variability

Geotechnical variability is a complex attribute that results from many disparate sources of uncertainties. The three primary sources of geotechnical uncertainties are inherent variability, measurement error, and transformation uncertainty. Inherent soil variability is modeled as a random field, which can be described concisely by the coefficient of variation (COV) and scale of fluctuation. Measurement error is extracted from field measurements using a simple additive probabilistic model or is determined directly from comparative laboratory testing programs. Based on an extensive literature review, the COV of inherent variability, scale of fluctuation, and COV of measurement error are evaluated in detail, along with the general soil type and the approximate range of mean value for which the COVs are applicable. Transformation uncertainty and overall property uncertainty are quantified in a companion paper.Key words: inherent soil variability, measurement error, coefficient of variation, scale of fluctuation, geotechnical variability.

Reliability-based analysis of settlements of shallow foundations on cohesionless soils

2020 ◽  
Author(s):  
◽  
Hashim Ghalib Al-Sumaiday
Keyword(s):  
Upper Bound ◽  
Prediction Method ◽  
Probability Of Failure ◽  
Shallow Foundations ◽  
Soil Variability ◽  
Prediction Methods ◽  
Cohesionless Soils ◽  
Settlement Prediction ◽  
Inherent Soil Variability

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Although settlements of foundations on cohesionless soils usually are small, it is important to be able to predict them because the primary issue in the design of shallow foundations on sand is the settlement requirement. Many methods for estimation of settlements in cohesionless soils have been published and evaluated. The majority of these methods rely on an empirical or semiempirical correlation with in-situ tests due to the difficulty and expense of obtaining undisturbed samples of cohesionless soils. The empiricism, in addition to the natural inherent soil variability, bring significant uncertainties into evaluation of design soil properties, and consequently to the settlement estimations. Traditional settlement analysis methodologies do not incorporate a consistent approach to account for the uncertainties and can lead to either costly design by overestimation of the settlement or a risky design by underestimation of the settlement. In contrast, a reliability-based methodology allows engineers to produce designs with a consistent level of safety that separately accounts for variability and uncertainty. The published works have extended the reliability-based methodology to settlement prediction. However, in the previous works, the uncertainties have been considered as one lumped factor and footing size has never been considered as an input variable. The research aims to extend the reliability-based methodology to settlement of shallow foundations on cohesionless soils considering the footing size and the main sources of uncertainties. It is hypothesized that incorporating the footing size in addition to the main sources of uncertainties in the reliability-based methodology will improve the reliability estimation of the settlement prediction; and consequently, improve the designs of shallow foundations on cohesionless soils. In the research described herein, six settlement prediction methods were evaluated using a database of 361 settlement case histories in terms of reliability, "the percentage of cases which the predicted settlement is equal or larger than measured settlement", and accuracy, "the ratio of the average of predicted settlement to the average of measured settlement". Sources of uncertainties associated with settlement prediction were investigated. The sources included inherent soil variability (from the natural formation of the soil), measurement uncertainty (from equipment, procedural, and random errors of the in-situ testing), transformation uncertainty (from empirical models to transform field or laboratory measurements into a design soil property), and the applied stress variability. Three probabilistic approaches were used to estimate: (i) probability of failure "the probability that the actual settlement exceeds a tolerable settlement", and (ii) settlement factors "multipliers are used in the design equations to target one of several acceptable probabilities of failure for serviceability limit state". The first approach was performed to estimate the probability of failure and the settlement factors probabilistically based on the total uncertainties of each settlement prediction method. The total uncertainties were characterized as one lumped factor by the statistics of the predicted to the measured settlements ratios. The second approach considered the main sources of uncertainty separately. A second-moment probabilistic technique was used to estimate the upper bound of the transformation uncertainty based on best- and worst- case scenarios of other uncertainty components. The estimated upper bound of the transformation uncertainty for each settlement prediction method was used in the probabilistic analysis herein. In the third approach, a framework was developed to estimate the realistic transformation uncertainty of each settlement prediction method to be used in the probabilistic analysis. A new approach to estimate the settlement is presented. The method has better accuracy and lower dispersion of the predicted to the measured settlement ratio than existing methods. The influence of soil type, size of footing, embedment depth, elevation of groundwater, and length to width ratio on both reliability and accuracy of the settlement prediction methods were examined. The width of the footing was found to be the most influential factor on the reliability and accuracy of the settlement prediction. The results support the hypothesis and show that the same amount of predicted settlement might indicate a different reliability according to the footing size. The results can be used to determine the reliability of settlement prediction in terms of probability of failure at different ranges of footing size, inherent soil variability and measurement uncertainty. The findings of this study can be used as a guide for geotechnical engineers to avoid over- or under- estimation of settlement. The results of the research described herein allow geotechnical engineers to achieve a better design of shallow foundations on cohesionless soils with a consistent level of safety that accounts for variability and uncertainty.

Electron Microscopy of the Shape of Small Metal Particles

1977 ◽  
Vol 35 ◽  
pp. 300-301
Author(s):  
M. Jose Yacaman
Keyword(s):  
Electron Microscopy ◽  
Metal Particle ◽  
Field Experiments ◽  
Size Range ◽  
Dark Field ◽  
Metal Particles ◽  
Bragg Condition ◽  
Crystal Particle ◽  
Small Metal Particle ◽  
Small Metal Particles

In the Study of small metal particles the shape is a very Important parameter. Using electron microscopy Ino and Owaga(l) have studied the shape of twinned particles of gold. In that work electron diffraction and contrast (dark field) experiments were used to produce models of a crystal particle. In this work we report a method which can give direct information about the shape of an small metal particle in the amstrong- size range with high resolution. The diffraction pattern of a sample containing small metal particles contains in general several systematic and non- systematic reflections and a two-beam condition can not be used in practice. However a N-beam condition produces a reduced extinction distance. On the other hand if a beam is out of the bragg condition the effective extinction distance is even more reduced.

Successful field experiments: Getting in, getting the data, getting published

2010 ◽  
Author(s):  
Susan M. Kochanowski ◽  
Charles F. Seifert ◽  
Gary A. Yukl ◽  
Dov Eden ◽  
Gary P. Latham
Keyword(s):  
Field Experiments

Optimizing genotype-environment-management interactions to ensure silage maize production in the Chinese Maize Belt

2020 ◽  
Vol 80 (2) ◽  
pp. 133-146
Author(s):  
L Zhang ◽  
Z Zhang ◽  
J Cao ◽  
Y Luo ◽  
Z Li
Keyword(s):  
Field Experiments ◽  
Weather Conditions ◽  
Growth Cycle ◽  
High Energy ◽  
Risk Level ◽  
Energy Yield ◽  
Maize Production ◽  
Early Date ◽  
Environment Management ◽  
Silage Maize

Grain maize production exceeds the demand for grain maize in China. Methods for harvesting good-quality silage maize urgently need a theoretical basis and reference data in order to ensure its benefits to farmers. However, research on silage maize is limited, and very few studies have focused on its energetic value and quality. Here, we calibrated the CERES-Maize model for 24 cultivars with 93 field experiments and then performed a long-term (1980-2017) simulation to optimize genotype-environment-management (G-E-M) interactions in the 4 main agroecological zones across China. We found that CERES-Maize could reproduce the growth and development of maize well under various management and weather conditions with a phenology bias of <5 d and biomass relative root mean square error values of <5%. The simulated results showed that sowing long-growth-cycle cultivars approximately 10 d in advance could yield good-quality silage. The optimal sowing dates (from late May to July) and harvest dates (from early October to mid-November) gradually became later from north to south. A high-energy yield was expected when sowing at an early date and/or with late-maturing cultivars. We found that Northeast China and the North China Plain were potential silage maize growing areas, although these areas experienced a medium or even high frost risk. Southwestern maize experienced a low risk level, but the low soil fertility limited the attainable yield. The results of this paper provide information for designing an optimal G×E×M strategy to ensure silage maize production in the Chinese Maize Belt.

Mechanisms leading to recruitment inhibition of giant kelp Macrocystis pyrifera by an understory alga

2021 ◽  
Vol 657 ◽  
pp. 59-71
Author(s):  
BA Beckley ◽  
MS Edwards
Keyword(s):  
Field Experiments ◽  
Local Community ◽  
Kelp Forest ◽  
Macrocystis Pyrifera ◽  
Forest Recovery ◽  
Kelp Forests ◽  
Giant Kelp ◽  
Combined Effects ◽  
Life Stages ◽  
Survival And Growth

The forest-forming giant kelp Macrocystis pyrifera and the communities it supports have been decreasing across their native ranges in many parts of the world. The sudden removal of giant kelp canopies by storms increases space and light for the colonization by understory macroalgae, such as Desmarestia herbacea, which can inhibit M. pyrifera recovery and alter local community composition. Understanding the mechanisms by which algae such as D. herbacea interact with M. pyrifera can provide insight into patterns of kelp forest recovery following these disturbances and can aid in predicting future community structure. This study experimentally tested the independent and combined effects of two likely competitive mechanisms by which D. herbacea might inhibit recovery of M. pyrifera in the Point Loma kelp forest in San Diego, California (USA). Specifically, we conducted field experiments to study the individual and combined effects of shade and scour by D. herbacea on the survival of M. pyrifera microscopic life stages, and the recruitment, survival, and growth of its young sporophytes. Our results show that scour had the strongest negative effect on the survival of M. pyrifera microscopic life stages and recruitment, but shade and scour both adversely affected survival and growth of these sporophytes as they grew larger. Canopy-removing storms are increasing in frequency and intensity, and this change could facilitate the rise of understory species, like D. herbacea, which might alter community succession and recovery of kelp forests.

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