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Related Concept Videos

Shrinkage in Concrete01:27

Shrinkage in Concrete

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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
406
Drying Shrinkage01:21

Drying Shrinkage

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When hardened concrete is exposed to air with a relative humidity of less than 100 percent, it begins to lose the free water within its capillaries. As this water evaporates, the water initially adsorbed onto the calcium silicate hydrates migrates towards these now empty spaces and eventually evaporates as well. Over time, as more water leaves, the volume of the concrete decreases, a phenomenon known as drying shrinkage.
A portion of this drying shrinkage can be reversed; if the concrete is...
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Carbonation Shrinkage01:24

Carbonation Shrinkage

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Atmospheric CO2 penetrates the concrete's pores and, in the presence of moisture, forms carbonic acid, which then reacts with calcium hydroxide in the hydrated cement, forming calcium carbonate. This process reduces the concrete's volume and is termed carbonation shrinkage.
The concrete's permeability is slightly reduced as calcium carbonate produced during the reaction fills its pores. Furthermore, its strength is slightly enhanced as the water released during the reaction...
469
Acid Strength and Molecular Structure03:05

Acid Strength and Molecular Structure

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Binary Acids and Bases
In the absence of any leveling effect, the acid strength of binary compounds of hydrogen with nonmetals (A) increases as the H-A bond strength decreases down a group in the periodic table. For group 17, the order of increasing acidity is HF < HCl < HBr < HI. Likewise, for group 16, the order of increasing acid strength is H2O < H2S < H2Se < H2Te. Across a row in the periodic table, the acid strength of binary hydrogen compounds increases with increasing...
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Strength of Cement01:20

Strength of Cement

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Strength tests for cement are not performed directly on neat cement paste due to difficulty in obtaining consistent, reliable specimens. Instead, cement is typically tested in the form of cement-sand mortar.
For compressive strength tests, ASTM C 109-05 standards prescribe a cement-sand mix ratio of 1:2.75 and a water/cement ratio of 0.485 for making 2-inch cubes. These cubes are mixed, cast, and cured in saturated lime water at 23°C until testing. Flexural strength testing, outlined in...
488
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

666
Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
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Measuring the Strength of Mice
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Dynamically borrowing strength from another study through shrinkage estimation.

Christian Röver1, Tim Friede1

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Statistical Methods in Medical Research
|March 2, 2019
PubMed
Summary
This summary is machine-generated.

Meta-analysis combining evidence, even from two studies, enhances estimates using shrinkage. This Bayesian approach improves results, especially for rare diseases and diverse data types.

Keywords:
Bayesian statisticsRandom-effects meta-analysisbetween-study heterogeneityposterior predictive p-valuesshrinkage estimation

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Evidence Synthesis

Background:

  • Meta-analytic methods commonly employ the normal-normal hierarchical model (NNHM) with random effects to address between-study heterogeneity.
  • This framework can derive combined estimates and 'shrinkage estimates' by borrowing strength between studies.
  • Shrinkage estimates are particularly valuable for integrating evidence from small-scale trials with larger non-randomized studies, especially in rare disease research.

Purpose of the Study:

  • To demonstrate the utility of meta-analysis even with a minimal number of studies, such as two.
  • To illustrate the application of Bayesian random-effects meta-analysis for deriving shrinkage estimates.
  • To showcase a generally applicable approach for combining diverse evidence types, including meta-analyses and individual studies.

Main Methods:

  • Utilized the normal-normal hierarchical model (NNHM) with random effects for meta-analysis.
  • Applied Bayesian random-effects meta-analysis to derive shrinkage estimates, improving estimates even with limited data.
  • Demonstrated the approach using a recent trial and clinical registry data for Creutzfeldt-Jakob disease.
  • Presented an alternative model specification avoiding a common overall mean parameter, focusing on individual study effects and differences.

Main Results:

  • A meta-analysis is feasible and beneficial even when synthesizing evidence from only two studies.
  • Shrinkage estimates derived via Bayesian random-effects meta-analysis can significantly improve individual study estimates.
  • The proposed methods effectively account for potential effect heterogeneity between studies.
  • The approach was successfully applied to data on immunosuppression following liver transplantation in children.

Conclusions:

  • Meta-analysis, particularly using Bayesian random-effects models with shrinkage, offers a robust method for evidence synthesis.
  • This approach enhances the precision of estimates, even with minimal data, making it suitable for rare diseases.
  • The methodology is versatile, capable of integrating various data sources like clinical trials, registries, and existing meta-analyses.