CE 3020 Construction Materials Dr. Kurtis 100 Points Possible
Fall 2001 MIDTERM 1 NAME:
Honor Code Having read the Georgia Institute of Technology Academic Honor Code, I understand and accept my responsibility as a member of the Georgia Tech Community to
CE 3309 Construction Materials Dr. Kurtis 120 Points Possible
Spring 1999 MIDTERM NAME:
Honor Code Having read the Georgia Institute of Technology Academic Honor Code, I understand and accept my responsibility as a member of the Georgia Tech Community to
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Page 1
CE 3020 - Construction Materials -Fall 2011 - EXAM #1 - Solution
1. The properties of materials can be subdivided into many categories (i.e., electrical, thermal, magnetic,
etc.); for the purposes of this course, and for most civil infrastruc
Laboratory 02: Concrete Mix Proportioning
CEE 3020 Civil Engineering Materials
Submitted to:
Dr. Scott and Dr. Kurtis
by:
Jarrett Stichler
Section C-2 Group C
Julian Lizarazo-Segura
Emily Flood
Anat Revai
Abstract
Introduction
Experiment
Results
Discussio
Aggregate Properties
The objectives of these laboratory experiments are to determine specific gravity (bulk and apparent),
absorption capacity, and fineness modulus of a fine aggregate sample and to plot a gradation curve
for the sample.
Part A - Specific
CE 3020 Construction Materials Dr. Kurtis
HOMEWORK 1 SOLUTIONS
Question 1 (a) What is the definition of fineness modulus? Ans. Fineness Modulus is a measure of the fineness of an aggregate; FM can be determined by adding the cumulative percentage of mater
CEE 3020 Civil Engineering Materials
Question 1
Explain the difference between porosity and void content in an aggregate sample.
Question 2
Coarse aggregate (Gb = 2.620) is placed into a bucket and dry-rodded with a tamping rod.
A total of three iteration
Laboratory 1: Aggregate Properties
CEE 3020 Civil Engineering Materials
Submitted to:
Dr. Scott and Dr. Kurtis
By:
Kamania Ray
Section D2 Group B
Tommy
Austin
Rianin
Abstract
Introduction
Experiment
Results
Discussion
Conclusion
Technical Writing
Graphics
Optimization Methods: Introduction and Basic Concepts
1
Module 1 Lecture Notes 1
Historical Development and Model Building
Introduction
In this lecture, historical development of optimization methods is glanced through. Apart
from the major developments,
Module 7 : Design of Machine Foundations
Lecture 31 : Basics of soil dynamics [ Section 31.1: Introduction ]
Objectives
In this section you will learn the following
Dynamic loads
Degrees of freedom
Module 7 : Design of Machine Foundations
Lecture 31 : Bas
Module 5 : Design of Deep Foundations
Lecture 22 : Ultimate pile capacity [ Section 22.1 : Procedure for ultimate pile capacity : Static
analysis ]
Objectives
In this section you will learn the following
Static analysis
Piles in granular soils (sands and
Advanced Topics in Optimization
Multi Objective
Optimization
1
D Nagesh Kumar, IISc
Optimization Methods: M8L2
Introduction
Introduction
In a real world problem it is very unlikely that we will meet the
situation of single objective and multiple constrain
Module 4 : Design of Shallow Foundations
Lecture 18 : Structural designs of column and footing [ Section18.1: Footing subjected to
Concentric loading ]
Objectives
In this section you will learn the following
Design of the Column.
Design of footing
Thickne
Module 8 : Foundations in difficult ground
Lecture 36 : Improvement methods [ Section 36.1 : Ground Improvement Techniques ]
Objectives
In this section you will learn the following
Ground Improvement Techniques
Module 8 : Foundations in difficult ground
L
Linear Programming
Simplex method - I
1
D Nagesh Kumar, IISc
Optimization Methods: M3L3
Introduction and Objectives
Simplex method is the most popular method
used for the solution of Linear Programming
Problems (LPP).
Objectives
To discuss the motivation
Introduction and Basic Concepts
(i) Historical
Development and
Model Building
1
D Nagesh Kumar, IISc
Optimization Methods: M1L1
Objectives
Understand the need and origin of the optimization
methods.
Get a broad picture of the various applications of
optim
Optimization Methods: Integer Programming
- Learning Objectives
Module 7: Integer Programming
Learning Objectives
The previous modules discussed about the optimization methods using linear programming
and dynamic programming techniques with almost no limi
Optimization Methods: Advanced Topics in Optimization - Evolutionary Algorithms for
Optimization and Search
1
Lecture Notes 5
Evolutionary Algorithms for Optimization and Search
Introduction
Most real world optimization problems involve complexities like
Optimization Methods: Linear Programming- Learning Objectives
Module 3: Linear Programming
Learning Objectives
It was discussed in module 2 that optimization methods using calculus have several
limitations and thus not suitable for many practical applicat
Objectives_template
Module 2 : Theory of Earth Pressure and Bearing Capacity
Lecture 6 : Introduction [ Section 6.1 Different Theories of Earth Pressure ]
Objectives
In this section you will learn the following
Introduction
Different Theories of Earth Pre
Optimization Methods: Dynamic Programming - Introduction
1
Module 5 Lecture Notes 1
Introduction
Introduction
In some complex problems, it will be advisable to approach the problem in a sequential
manner in order to find the solution quickly. The solution
Module 4 : Design of Shallow Foundations
Lecture 16 : Introduction [ Section16.1 : Introduction ]
Objectives
In this section you will learn the following
Introduction
Different types of footings
Module 4 : Design of Shallow Foundations
Lecture 16 : Introd
Optimization using Calculus
Optimization of Functions of
Multiple Variables subject to
Equality Constraints
1
D Nagesh Kumar, IISc
Optimization Methods: M2L4
Objectives
Optimization of functions of multiple variables
subjected to equality constraints usin
Linear Programming
Simplex method - II
1
D Nagesh Kumar, IISc
Optimization Methods: M3L4
Objectives
Objectives
To discuss the Big-M method
Discussion on different types of LPP
solutions in the context of Simplex method
Discussion on maximization verses
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