Effects of Pavement Surface Type and Sample Preparation Method on Tack Coat Interface Shear Strength

2010 ◽  
Vol 2180 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Louay N. Mohammad ◽  
Abraham Bae ◽  
Mostafa A. Elseifi ◽  
Joe Button ◽  
Nachiketa Patel
Keyword(s):  
Shear Strength ◽  
Sample Preparation ◽  
Preparation Method ◽  
Interface Shear Strength ◽  
Sample Preparation Method ◽  
Surface Type ◽  
Interface Shear ◽  
Pavement Surface ◽  
Tack Coat

A Study of Sample Preparation Method to Determine the Shear Strength Soil Treated with Lime

2015 ◽  
Vol 9 (1) ◽  
pp. 2615-2632
Author(s):  
Vahid Taebi ◽  
M HajiSotodeh ◽  
AR Mahbobi Ardakani ◽  
◽  
◽  
...  
Keyword(s):  
Shear Strength ◽  
Sample Preparation ◽  
Preparation Method ◽  
Sample Preparation Method

Development and Validation of a Model to Predict Interface Bonding Between Pavement Layers

2018 ◽  
Vol 2672 (28) ◽  
pp. 22-30 ◽  
Author(s):  
Ramendra Das ◽  
Louay N. Mohammad ◽  
Mostafa Elseifi ◽  
Wei Cao ◽  
Samuel B. Cooper
Keyword(s):  
Shear Strength ◽  
Nonlinear Model ◽  
Application Rate ◽  
Statistical Regression ◽  
Interface Bonding ◽  
Interface Shear Strength ◽  
Interface Shear ◽  
Tack Coat ◽  
Development And Validation ◽  
Overlay Construction

During the planning of overlay construction activities, a contractor and/or state agency may be interested in selecting a tack coat material and application rate that would provide adequate interface bonding at the site. The objective of this study was to develop an interface shear strength (ISS) predictive model to estimate interface bonding between the hot-mix asphalt (HMA) overlay and the underlying pavement layer. To achieve this objective, 10 field rehabilitation projects were selected in six states that included 33 in-service test sections representing different traffic and climatic conditions. The overlay construction used different types of tack coat at various residual application rates. Field cores were obtained to measure ISS using a direct shear test device, referred to as the Louisiana Interlayer Shear Strength Tester (LISST). Influencing factors to be included in the model were pavement surface type and mean texture depth, residual application rate, and characteristics of tack coat residues. The experimental data were complemented with test results from a recently completed study for model development and validation. Two types of statistical regression models (linear and nonlinear) were evaluated. Results of the analysis showed that the predicted ISS obtained from the proposed models were in good agreement with the measurements. Further, the developed nonlinear model showed better prediction accuracy than the linear model. The nonlinear model can be used effectively for optimum selections of tack coats given the project conditions. The results presented herein were part of NCHRP Project 9-40A, Field Implementation of the Louisiana Interface Shear Strength Test.

scholarly journals Development of an Interface Shear Strength Tester and a Model Predicting the Optimal Application Rate of Tack Coat

2021 ◽  
Vol 1 (1) ◽  
pp. 22-38
Author(s):  
Dowan Kim ◽  
Sungho Mun
Keyword(s):  
Shear Strength ◽  
South Korea ◽  
Predictive Model ◽  
Linear Function ◽  
Polynomial Regression ◽  
Interface Shear Strength ◽  
Interface Shear ◽  
Application Rates ◽  
Non Linear ◽  
Tack Coat

Tack coat application rates and testing conditions differ among nations and construction conditions because various tack coat materials are available. In this study, newer materials are optimized for addition to porous asphalt pavements exposed to torrential rainfall, which is common in South Korea. Interface shear strength (ISS) tests are used to define the optimum application rates (OARs) of tack coat materials generally used in South Korea, by reference to the Korean Design Standard (KDS), the Korean Construction Standard (KCS), and features of pavement construction and bonding. We performed ISS tests using asphalt mixtures with porosities of 3, 5, and 7% to explore the effect of porosity on shear strength. The ISSs associated with varying tack coat proportions were earlier determined by creating polynomial regression equations. Here, we develop a predictive model using a non-linear function to estimate the OAR of tack coat and compare our approach with the earlier polynomial regression analysis. Based on the ISSs, the golden section search method was applied to define the OARs afforded by the predictive polynomial function. We used the generalized reduced gradient algorithm to construct a nonlinear predictive function using data from the ISS tests. Finally, our comparative analysis showed that the predictive model using the non-linear function was superior to the polynomial model in terms of both error rate and predictive tendency.

To Eliminate Curtain Effect of FIB TEM Samples by a Combination of Sample Dicing and Backside Milling

2014 ◽  
Author(s):  
Jian-Shing Luo ◽  
Hsiu Ting Lee
Keyword(s):  
Sample Preparation ◽  
Focused Ion Beam ◽  
Preparation Method ◽  
Low Cost ◽  
Ion Beam ◽  
Sample Preparation Method ◽  
Site Specific ◽  
Dual Beam ◽  
Transmission Electron

Abstract Several methods are used to invert samples 180 deg in a dual beam focused ion beam (FIB) system for backside milling by a specific in-situ lift out system or stages. However, most of those methods occupied too much time on FIB systems or requires a specific in-situ lift out system. This paper provides a novel transmission electron microscopy (TEM) sample preparation method to eliminate the curtain effect completely by a combination of backside milling and sample dicing with low cost and less FIB time. The procedures of the TEM pre-thinned sample preparation method using a combination of sample dicing and backside milling are described step by step. From the analysis results, the method has applied successfully to eliminate the curtain effect of dual beam FIB TEM samples for both random and site specific addresses.

Cross-Section Sample Preparation Method for Imaging Dopant Related Anomalies Using Scanning Probe Microscopy Techniques

2014 ◽  
Author(s):  
Swaminathan Subramanian ◽  
Khiem Ly ◽  
Tony Chrastecky
Keyword(s):  
Sample Preparation ◽  
Failure Analysis ◽  
Cross Section ◽  
Scanning Probe Microscopy ◽  
Preparation Method ◽  
Fault Isolation ◽  
Scanning Probe ◽  
Sample Preparation Method ◽  
Probe Microscopy ◽  
Section Sample

Abstract Visualization of dopant related anomalies in integrated circuits is extremely challenging. Cleaving of the die may not be possible in practical failure analysis situations that require extensive electrical fault isolation, where the failing die can be submitted of scanning probe microscopy analysis in various states such as partially depackaged die, backside thinned die, and so on. In advanced technologies, the circuit orientation in the wafer may not align with preferred crystallographic direction for cleaving the silicon or other substrates. In order to overcome these issues, a focused ion beam lift-out based approach for site-specific cross-section sample preparation is developed in this work. A directional mechanical polishing procedure to produce smooth damage-free surface for junction profiling is also implemented. Two failure analysis applications of the sample preparation method to visualize junction anomalies using scanning microwave microscopy are also discussed.

scholarly journals Application of the Dehydration Homogeneous Liquid–Liquid Extraction (DHLLE) Sample Preparation Method for Fingerprinting of Honey Volatiles

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2277
Author(s):  
Piotr M. Kuś ◽  
Igor Jerković
Keyword(s):  
Carboxylic Acid ◽  
Sample Preparation ◽  
Preparation Method ◽  
Liquid Extraction ◽  
Sample Preparation Method ◽  
Phenyllactic Acid ◽  
Homogeneous Liquid ◽  
Liquid Liquid Extraction ◽  
Wide Range ◽  
Chemical Groups

Recently, we proposed a new sample preparation method involving reduced solvent and sample usage, based on dehydration homogeneous liquid–liquid extraction (DHLLE) for the screening of volatiles and semi-volatiles from honey. In the present research, the method was applied to a wide range of honeys (21 different representative unifloral samples) to determine its suitability for detecting characteristic honey compounds from different chemical classes. GC-FID/MS disclosed 130 compounds from different structural and chemical groups. The DHLLE method allowed the extraction and identification of a wide range of previously reported specific and nonspecific marker compounds belonging to different chemical groups (including monoterpenes, norisoprenoids, benzene derivatives, or nitrogen compounds). For example, DHLLE allowed the detection of cornflower honey chemical markers: 3-oxo-retro-α-ionols, 3,4-dihydro-3-oxoedulan, phenyllactic acid; coffee honey markers: theobromine and caffeine; linden honey markers: 4-isopropenylcyclohexa-1,3-diene-1-carboxylic acid and 4-(2-hydroxy-2-propanyl)cyclohexa-1,3-diene-1-carboxylic acid, as well as furan derivatives from buckwheat honey. The obtained results were comparable with the previously reported data on markers of various honey varieties. Considering the application of much lower volumes of very common reagents, DHLLE may provide economical and ecological advantages as an alternative sample preparation method for routine purposes.

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