scholarly journals Analysis of CBR design value selection methods on flexible pavement design: Colombia case study

2020 ◽  
Vol 9 (2) ◽  
pp. 509
Author(s):  
Otto Mora L. ◽  
Michel Murillo A. ◽  
Tiana Rosania A. ◽  
Abraham Castañeda A. ◽  
Rosa Pinto C. ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Resilient Modulus ◽  
Flexible Pavement ◽  
Pavement Design ◽  
Selection Methods ◽  
Essential Variable ◽  
The Mean ◽  
Design Value ◽  
Criterion Method

A comparative analysis was carried out to observe the variation of a flexible pavement structural thickness, due to the use of different meth-ods to calculate the CBR design value, as an essential variable to estimate the Subgrade Resilient Modulus (Mr) through an empirical corre-lation. The Asphalt Institute Method and the Mean Criterion Method were applied to calculate de Design CBR value of a homogeneous roadway division from a representative track section located in the Bolivar Department, Colombia. As a result, the Design Percentiles of the CBR design unit were expanded for the Asphalt Institute method, thus, allowing the approach of more reliable and safe designs, considering that this method limits the selection percentiles to three traffic levels.  

scholarly journals Pavement Design Comparative Analysis Using Bina Marga Design Method (Pt T-01-2002-B) And Pavement Design Manual (No.04/Se/Db/2017) (Case Study: Trisakti Port - Liang Anggang)

POROS TEKNIK ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Utami Sylvia Lestari ◽  
Nurhafni Karina Resentia
Keyword(s):  
Resilient Modulus ◽  
Design Method ◽  
Flexible Pavement ◽  
Pavement Design ◽  
Traffic Load ◽  
Main Variable ◽  
Pavement Layers ◽  
Bearing Capacity Factor

Traffic load repetition is the main variable in flexible pavement layers design. In addition, a soil bearing capacity factor is also required for determining the thickness of the flexible pavement layer so that the pavement had been designed will be in good perfomance during the that period. The determination of thickness layers using the 2002 method (Pt T-01-2002 B) is based on the traffic load during the design period and subgrade resilient modulus value. Meanwhile the 2017 method (Pavement design manual No. 04/SE/Db/2017), layers thickness was determined based on traffic load and CBR subgrade value. Based on the calculation using both methods, the pavement layers thickness with the pavement design manual 2017 method is more thick than 2002 method. While the ESAL calculation using both methods, the 2002 method value is more larger than the pavement design manual 2017 method.

scholarly journals Prediction of subgrade resilient modulus for flexible pavement design

2011 ◽  
Vol 6 (21) ◽  
pp. 4567-4576 ◽  
Author(s):  
Ridvan Ozel Mehmet ◽  
Mohajerani Abbas
Keyword(s):  
Resilient Modulus ◽  
Flexible Pavement ◽  
Pavement Design

Comparison of As-Constructed and As-Designed Flexible Pavement Layer Thicknesses

2003 ◽  
Vol 1853 (1) ◽  
pp. 165-176 ◽  
Author(s):  
Goran Mladenovic ◽  
Y. Jane Jiang ◽  
Olga Selezneva ◽  
Susanne Aref ◽  
Michael Darter
Keyword(s):  
Layer Thickness ◽  
Flexible Pavement ◽  
Target Thickness ◽  
Pavement Performance ◽  
Design Values ◽  
The Mean ◽  
Design Value ◽  
Thickness Deviation ◽  
The Individual ◽  
Distribution Type

Sound pavement design is important for improving pavement performance, but construction is equally critical. Because of variations in pavement construction, the as-constructed pavement layer thickness varies spatially within a pavement section, and the mean constructed pavement layer thickness often deviates from the designed values. The as-constructed pavement layer thicknesses are compared to their design values by using data from the newly constructed flexible pavement sections in the Long-Term Pavement Performance program. First, the distribution type of the mean layer thickness deviation was investigated, and a combined statistical test for skewness and kurtosis showed that the mean thickness deviations may be assumed to be normally distributed for a given layer type and target thickness. Typical thickness deviation summary statistics values were derived to estimate the extent of deviations from the design thicknesses. Second, the analysis showed that elevation thickness data differ from the core examination data. Furthermore, as-constructed layer thickness values were compared to their design values by looking into the percentage of the individual measurements falling into specified ranges from the design values and by using two-sided and one-sided t-tests to compare the mean constructed thicknesses and the design values. The analyses showed that the mean constructed layer thicknesses tend to be above the design value for the thinner layers and below the design value for the thicker layers for the same layer and material type. Results from the study will be useful as inputs for reliability-based design procedures.

Assurance of Learning (AOL): Case Study in a Capstone Finance Course at Indiana University South Bend Comparative Analysis of Classes O ver the Years 2017 and 2018

2019 ◽  
Vol 10 (8) ◽  
pp. 691-703
Author(s):  
Raj K. Kohli ◽  
◽  
Anurag Pant
Keyword(s):  
Comparative Analysis ◽  
Classroom Environment ◽  
Poor Performance ◽  
Assessment Process ◽  
State University ◽  
Indiana University ◽  
Assurance Of Learning ◽  
The Mean ◽  
The Impact

Assurance of Learning (AOL) has become an increasingly important dimension in Association to Advance Collegiate Schools of Business (AACSB) evaluation standards. In this case study, the authors developed and used a distinct AOL model to measure the impact on students learning in a capstone finance course at a state university in Indiana. Direct assessment of students learning is tested in closely controlled classroom environment through exam. A comparative analysis is completed using AOL developed model for the years 2017 and 2018. The findings suggest the mean non-AOL grade (85.67%) is significantly higher than the mean AOL grade (58.60%) in the year 2017. The same was observed in 2018, the mean non-AOL (85.53%) was significantly greater than the mean AOL grade (70.96). A poor performance in AOL model category indicates that the AOL model developed for this study successfully measures Assessment Process.

Sign in / Sign up

Export Citation Format

Share Document