I-96 Case Study: Jointed Concrete Pavement Curling and Warp Presented in the Context of Pavement Asset Management

Size: px
Start display at page:

Download "I-96 Case Study: Jointed Concrete Pavement Curling and Warp Presented in the Context of Pavement Asset Management"

Transcription

1 I-96 Case Study: Jointed Concrete Pavement Curling and Warp Presented in the Context of Pavement Asset Management Christopher R. Byrum, PhD, PE

2 PAVEMENT ANALYSIS: Soil-Structure Interaction Engineering 3-D Analyses: M x = Eh ( 1 µ ) 2 x z 2 2-D Analyses: M = 2 EI d z dx 2

3 PAVEMENT PROFILE: A 2-D view of a 3-D Engineering Solution LWP RWP

4 Elevation, mm GPS3 Site ± mm LWP RWP R Curved Way Up Distance, m NOTES: 1. Highest IRI of all GPS3 pavements less than 1 years old. 2. High faulting at an early age. 3. Sandy clay subgrade (PI=14.5, w%=13), sand and gravel with 11% P2 base. 4. Built in 1984, 3-35 KESAL/yr, 22 mm PCC, no dowels. Profile Date: 6/19/1992 ProfileTime: * High Level 3:51:44 PM of Locked-In Warp and Faulting. Profile Date: June 19, 1992 Profile Time: 15:51:44 * Thermally Curved-Down at this time of day.

5 Split Profile into 2 Sub-Matrices Raw Profile X1 L1 R1 X2 L2 R2 X3 L3 R Discontinuities (cracks, joints...) = + Continuous Slab Segments Xn Ln Rn

6 What is the Curvature of This? Elevation, feet GPS Structurally Continuous WARPED SLAB Distance, feet See TRB Paper# 9261: Measuring Curvature in Concrete Slabs and Connecting the Data to Slab Modeling Theory

7 Elevation, feet All Have Same Average Curvature But Different Statistics: std. deviation, median, max., min.. Slope = SINE-WAVE PERTUBATION Distance, feet Slope = κ mean L z''( x) dx z'( L) = = L L z'()

8 κ AVG concept not valid through a working/hinging joint or crack Discontinuity Elevation Slope = Zero Slope = Zero Average Curvature Over These Spans = Zero

9 Elevation, mm Slope, l/l z = f(x) Elevation Distance, m dz dx Discrete Slope Distance, m LWP RWP Upward Translation = Rocking See TRB Paper No For how to evaluate slab tilt and pumping from profile slope data Curvature, 1/m x d z dx 2 Discrete Curvature Moment Hinge Distance, m

10 z 1/κ = R, Radius of Curvature Second Derivative x CURVATURE of f(x) = d 2 z κ 2 dx 1 R

11 Average Slab Curvature, 1/ft Comparing to National Database I-96 Site : AM 5: AM 7: AM 9: AM 11: AM 1: PM 3: PM 5: PM 7: PM 9: PM Time of Testing Search IPRF Joint Load Transfer for Final Report describing airfield test sites shown 1-AC18(22)-a 1-AC18(22)-b 2-AC17-a 2-AC17-b 3-CT16 4-AC18-a 4-AC18-b 5-AGG18 6-CT16 7-AGG17 8-AGG15 9-AGG14 1-AGG14 11-CT14 LTPP GPS3 RWP Howell I-96

12 Average Slab Curvature, 1/ft Comparing to National Database Trying to Measure 1-AC18(22)-a Curl/Warp includes the 1-AC18(22)-b difficult task of 2-AC17-a trying to 2-AC17-b measure or verify a 3-CT16 ZERO value : AM 5: AM 7: AM 9: AM 11: AM 1: PM 3: PM 5: PM 7: PM 9: PM Time of Testing 4-AC18-a 4-AC18-b 5-AGG18 6-CT16 7-AGG17 8-AGG15 9-AGG14 1-AGG14 11-CT14 LTPP GPS3 RWP Howell I-96

13 OK, Zero +/- How Much???? How precise is your method? Precision mostly related to texture size

14 For a single 18-ft slab we get: Arc lengths = # of arcs = Arc Values = 216 Arc Values from 18-ft Slab LWP 2 Wheel Paths = 432 arcs, 32 end slopes, 16 κ-strings Report: Curvature Frequency Distribution Number of Arcs Standard Deviation of Arc Curvatures Median, Avg, Max, Min Curvature Average Continuous Segment Length Fault Sizes...many other indices

15 The Central Limit Theorem- For any large population having finite standard deviation, the mean of sample size, n, approaches the mean of the population as n becomes large. Standard Error for Predicted Mean Curvature = σ κ n

16 Avg. Curvature Std. Error, ft Single Slab 5-ft Sample 9,-ft uniform section Number of Slabs Standard error values are most related to the size of pavement surface texture and surface finishing/flaw features.

17 Curvature analysis summary output table. Software calculates the curvature statistics for the raw data arcs, and then does the same arc calculations on cubic splines fit at 2.5-ft intervals through the raw data. Standard error is reduced using a spline fitting technique..72/sqrt(18) =.17? Site: R1 Middle Lane, 55-6 Profile Date: 1/23/15 Curvature Summary ProfileTime: 8:11 AM Avg. hinge spacing = 11.9 Curvature Index Value Sample Size StdDev Std Error of Mean Leave Slab Arcs Approach Slab Arcs " Arcs " Arcs " Arcs " Arcs " Arcs " Arcs " Arcs " Arcs " Spline Arcs " Spline Arcs " Spline Arcs " Spline Arcs " Spline Arcs " Spline Arcs " Spline Arcs " Spline Arcs All Arc Average All Arc Mean All Arc Median Spline Average Spline Trim Mean Spline Median Leave Slab Splines Approach Slab Splines Short Slab Index Old Byrum CI (1999) Current Byrum CI Curvature Units = ft -1 x 1

18 9,-ft length Uniformly Behaving Section 18 separate 5-ft analysis samples POB POE

19

20 R1 Middle Lane, Oct-15 8:11:.3 Elevation LWP RWP.2 Elevation, in Distance, ft. Slope, in/ft texture Curved Slabs Un-Faulted Joint Faulted Joint Slope-of-slope in slabs.75/14(12) =.45 ft -1

21 R2 Right Lane, Oct-15 6:29: Elevation, in. Elevation LWP RWP Distance, ft. Pumping/tilted slabs.1.5 Tilted Patches, approach-side uplift = 6 (.5) =.3-inch (7.6 mm) uplift Mid-slab cracks Slope, in/ft Original curved slab segments Faulted Joint, size.5(.3)25.4 = 3.8 mm

22 IRI trends per 5-ft segment 13 EB I-96 Howell 22 profiles obtained 5AM to 1PM Average IRI. in/mi Uniform Segment Average Behavior 7 6 5: 6: 7: 8: 9: 1: 11: 12: 13: 14: average 4th Order Fit to Average

23 Average Fault Size 1 EB I-96 Howell 9 Avg. Fault/Hinge Elevation Difference, mm : 6: 7: 8: 9: 1: 11: 12: 13: 14: average 4th Order Fit to Average

24 Slab Curvature Index, CI.4 EB I-96 Howell 22 profiles obtained 5AM to 1PM Median Wheelpath Curvature (1/ft x 1) Approx. Locked-In Curvature (Warp) Values = zero-temperature-gradient-moment/bending 5: 6: 7: 8: 9: 1: 11: 12: 13: 14: average 4th Order Fit to Average

25 Site-Specific Curling vs. IRI trend: Sites with longer slabs would likely have steeper sloped trends Site Data IRI, in/mi y = x R² = Average Slab Curvature, 1/ft x 1

26 Median Wheelpath Curvature, (1/ft x 1) EB I-96 East of Latson Road Slab Curvature Middle Lane 1-ft to 3-ft arc data Middle Lane 1-ft to 8-ft arc data Right Lane 1-ft to 3-ft arc data Approx. Air Temp. Howell, MI 5: 6: 7: 8: 9: 1: 11: 12: 13: 14: Time of Day Air Temp. 3 5: 6: 7: 8: 9: 1: 11: 12: 13: 14:

27 5-ft averages Slab Warp 2 d z dx dz Slab Tilting/Pumping dx 2 Approx. Locked-in Warp Slab Curvature, 1/ft x to Slab Slope Index; Pumped Volume, cft/lane/mile to Sum of Fault Sizes, mm/5 ft 1 Discontinuities Ride Quality Index 2 to IRI, in/mi to

28 Measured IRI, in/mi ft samples IRI Prediction from profile shape parameters: simple linear regression Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 396 ANOVA df SS MS F Significance F Regression E-189 Residual Total Predicted IRI, in/mi IRI breakdown data from a single uniformly behaving section Coefficients Standard Error t Stat P-value Intercept E-8 CI_median E-34 12StdevCI StdevCI E-15 Avg Flt Size E-5 #Faults ToTFlt, mm

29 I-96 Howell Regression Model IRI, in/mi Site avg. Warp Stdev Curv Faulting All IRI breakdown data from a single uniformly behaving section

30 Predicted IRI, m/km The LTPP GPS3 Profile Data Adjusted R 2 =.93 # Data Points = Measured IRI, m/km IRI breakdown data from many different pavement sections from the LTPP GPS3

31 5 4.5 Sensitivity Analysis for the IRI Regression Model IRI breakdown data from many different pavement sections -LTPP GPS3 All High IRI, m/km High High None Very Very Curved Flat Flat Avg. GPS3 JPC "Average" LTPP GPS3 Profile High High Low Faulting Avg. Slab Curvature Stdev Curvature All Parameters Low All Low 115 in/mi

32 Curling = Average Slab Curvature Change.16 ft -1 change for a total change in air temperature of about 2 degrees Fahrenheit over a 5-hour period 8AM (min. temp) to 1PM (max. temp) for testing in October. Warping = Average Slab Curvature at/near Zero-Gradient Time +.2 ft -1, with apparent locked-in warp curvatures in the 5-ft samples ranging between about +.26 and +.7 ft -1 Middle Lane IRI 85 to 115 in/mi average of about 1 in/mi (not failed at 21 yr) Truck Lane IRI = 165 to 3 in/mi average of about 2 in/mi (near terminal at 21 yr) IRI Change = 25 in/mi change for a slab curvature change (curling) of about.16 ft -1 TEST SITE SUMMARY LTPP GPS3 = 7 in/mi change for about.5 ft -1 range in CI

33 Average Slab Curvature, 1/ft Slab Comparing Curvature to National Noticeable Database by Drivers at >.15 ft -1 Perception threshold Perception threshold : AM 5: AM 7: AM 9: AM 11: AM 1: PM 3: PM 5: PM 7: PM 9: PM Time of Testing 1-AC18(22)-a 1-AC18(22)-b 2-AC17-a 2-AC17-b 3-CT16 4-AC18-a 4-AC18-b 5-AGG18 6-CT16 7-AGG17 8-AGG15 9-AGG14 1-AGG14 11-CT14 LTPP GPS3 RWP Howell I-96

New Technologies for Pavement Evaluation

New Technologies for Pavement Evaluation New Technologies for Pavement Evaluation TxDOT 3-D Pavement Survey Technology For 86 th Annual Transportation Short Course at Texas A&M University, 2012 Dr. Yaxiong (Robin) Huang, Robin.Huang@txdot.gov

More information

Pavement Preservation and the Role of Bituminous Surface Treatments A Washington State View. Minnesota Pavement Conference February 14, 2008

Pavement Preservation and the Role of Bituminous Surface Treatments A Washington State View. Minnesota Pavement Conference February 14, 2008 Pavement Preservation and the Role of Bituminous Surface Treatments A Washington State View Minnesota Pavement Conference February 14, 2008 1 The Situation 2 WSDOT policy, in essence, mandated use of BSTs

More information

Introduction Texture/Friction Measurement at Winnipeg International Airport Data Analysis Conclusions

Introduction Texture/Friction Measurement at Winnipeg International Airport Data Analysis Conclusions Texture/Friction Measurements and Analysis at Runway 13-31 of James Armstrong Richardson International Airport in Winnipeg Qingfan Liu, EIT, PhD candidate, University of Manitoba Ahmed Shalaby, PhD, P.

More information

Statistical and Econometric Methods for Transportation Data Analysis

Statistical and Econometric Methods for Transportation Data Analysis Statistical and Econometric Methods for Transportation Data Analysis Chapter 14 Ordered Probability Models Example 14.2 Ordered Discrete Data Ordered Probit with Random Effects A survey of 56 subjects

More information

Exam 4. In the above, label each of the following with the problem number. 1. The population Least Squares line. 2. The population distribution of x.

Exam 4. In the above, label each of the following with the problem number. 1. The population Least Squares line. 2. The population distribution of x. Exam 4 1-5. Normal Population. The scatter plot show below is a random sample from a 2D normal population. The bell curves and dark lines refer to the population. The sample Least Squares Line (shorter)

More information

Squirm and lateral Wander in Certain Textures

Squirm and lateral Wander in Certain Textures Squirm and lateral Wander in Certain Textures A Case Study of Diagnosis and Mitigation Bernard Igbafen Izevbekhai, P.E., Ph.D. MnDOT Research Operations Engineer Research Pays Off Seminar Series March

More information

Profilograph. Changes in Profiling Technology

Profilograph. Changes in Profiling Technology Profilograph Changes in Profiling Technology Lightweight Profiler High Speed Profiler 1 How Does It Work? Lightweight Profiler Laser Sensor 2 The Laser Sensor! Laser samples the pavement 16,000/sec! Samples

More information

IQR = number. summary: largest. = 2. Upper half: Q3 =

IQR = number. summary: largest. = 2. Upper half: Q3 = Step by step box plot Height in centimeters of players on the 003 Women s Worldd Cup soccer team. 157 1611 163 163 164 165 165 165 168 168 168 170 170 170 171 173 173 175 180 180 Determine the 5 number

More information

Lecture 8. Divided Differences,Least-Squares Approximations. Ceng375 Numerical Computations at December 9, 2010

Lecture 8. Divided Differences,Least-Squares Approximations. Ceng375 Numerical Computations at December 9, 2010 Lecture 8, Ceng375 Numerical Computations at December 9, 2010 Computer Engineering Department Çankaya University 8.1 Contents 1 2 3 8.2 : These provide a more efficient way to construct an interpolating

More information

Appendix B: Vehicle Dynamics Simulation Results

Appendix B: Vehicle Dynamics Simulation Results Appendix B: Vehicle Dynamics Simulation Results B-1 Vehicle dynamic analyses were undertaken for the three barrier types under each of the curve, shoulder, and barrier placement factors identified. This

More information

INTRODUCTION TO VOLUME MEASUREMENTS Volume measurements are needed for three different categories of pay items:

INTRODUCTION TO VOLUME MEASUREMENTS Volume measurements are needed for three different categories of pay items: INTRODUCTION TO VOLUME MEASUREMENTS Volume measurements are needed for three different categories of pay items: Earthwork --items such as borrow excavation, and subsoil excavation Concrete -- the various

More information

T otal E nvironmental Management for P aving

T otal E nvironmental Management for P aving 6 F 3 F 9 F 12 F 15 F 75% % 1% Remote Maturity Monitoring Using the Virginia Concrete Conference 11 March 25 Richmond, Virginia presented by: Dennis J. Turner, M.S.E. Project Manager, The Transtec Group,

More information

Fathom Dynamic Data TM Version 2 Specifications

Fathom Dynamic Data TM Version 2 Specifications Data Sources Fathom Dynamic Data TM Version 2 Specifications Use data from one of the many sample documents that come with Fathom. Enter your own data by typing into a case table. Paste data from other

More information

Reliability of Results Depends On:

Reliability of Results Depends On: HDM-4 Calibration Reliability of Results Depends On: How well the available data represent the real conditions to HDM How well the model s predictions fit the real behaviour and respond to prevailing conditions

More information

THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL. STOR 455 Midterm 1 September 28, 2010

THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL. STOR 455 Midterm 1 September 28, 2010 THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL STOR 455 Midterm September 8, INSTRUCTIONS: BOTH THE EXAM AND THE BUBBLE SHEET WILL BE COLLECTED. YOU MUST PRINT YOUR NAME AND SIGN THE HONOR PLEDGE

More information

Surveys and Maps for Drainage Design

Surveys and Maps for Drainage Design Surveys and Maps for Drainage Design SURVEY TYPES BENCH LEVEL Survey Used to determine the elevation of a point (1-D) PROFILE Survey Used to determine the elevations of a line (2-D) TOPOGRAPHIC Survey

More information

PE Exam Review - Surveying Demonstration Problem Solutions

PE Exam Review - Surveying Demonstration Problem Solutions PE Exam Review - Surveying Demonstration Problem Solutions I. Demonstration Problem Solutions... 1. Circular Curves Part A.... Circular Curves Part B... 9 3. Vertical Curves Part A... 18 4. Vertical Curves

More information

CS8800 WALKING PROFILER

CS8800 WALKING PROFILER CS8800 WALKING PROFILER Overview CS8800 Design and Measurement Method Operational Procedures Recent and Pending Enhancements Base Price & Options Comments on October 20009 FHWA Collections CS8800 DESIGN

More information

Edge and local feature detection - 2. Importance of edge detection in computer vision

Edge and local feature detection - 2. Importance of edge detection in computer vision Edge and local feature detection Gradient based edge detection Edge detection by function fitting Second derivative edge detectors Edge linking and the construction of the chain graph Edge and local feature

More information

Learner Expectations UNIT 1: GRAPICAL AND NUMERIC REPRESENTATIONS OF DATA. Sept. Fathom Lab: Distributions and Best Methods of Display

Learner Expectations UNIT 1: GRAPICAL AND NUMERIC REPRESENTATIONS OF DATA. Sept. Fathom Lab: Distributions and Best Methods of Display CURRICULUM MAP TEMPLATE Priority Standards = Approximately 70% Supporting Standards = Approximately 20% Additional Standards = Approximately 10% HONORS PROBABILITY AND STATISTICS Essential Questions &

More information

3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS

3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS 3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS Finding the Zeros of a Quadratic Function Examples 1 and and more Find the zeros of f(x) = x x 6. Solution by Factoring f(x) = x x 6 = (x 3)(x + )

More information

10.2 Calculus with Parametric Curves

10.2 Calculus with Parametric Curves CHAPTER 1. PARAMETRIC AND POLAR 1 1.2 Calculus with Parametric Curves Example 1. Return to the parametric equations in Example 2 from the previous section: x t +sin() y t + cos() (a) Find the cartesian

More information

Further Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables

Further Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables Further Maths Notes Common Mistakes Read the bold words in the exam! Always check data entry Remember to interpret data with the multipliers specified (e.g. in thousands) Write equations in terms of variables

More information

Exploring Pavement Texture and Surface Friction Using Soft Computing Techniques

Exploring Pavement Texture and Surface Friction Using Soft Computing Techniques Exploring Pavement Texture and Surface Friction Using Soft Computing Techniques Students: Guangwei Yang, You Zhan, Ace Fei, Allen Zhang PIs: Joshua Q. Li & Kelvin C.P. Wang School of Civil and Environmental

More information

CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY

CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY 23 CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY 3.1 DESIGN OF EXPERIMENTS Design of experiments is a systematic approach for investigation of a system or process. A series

More information

Cubic smoothing spline

Cubic smoothing spline Cubic smooting spline Menu: QCExpert Regression Cubic spline e module Cubic Spline is used to fit any functional regression curve troug data wit one independent variable x and one dependent random variable

More information

Development of Warm Mix Asphalt Policies and Specifications in Minnesota

Development of Warm Mix Asphalt Policies and Specifications in Minnesota Development of Warm Mix Asphalt Policies and Specifications in Minnesota Tim Clyne, Greg Johnson, John Garrity MnDOT 2 nd International Warm Mix Conference October 13, 2011 Acknowledgements MnDOT Greg

More information

v Prerequisite Tutorials GSSHA Modeling Basics Stream Flow GSSHA WMS Basics Creating Feature Objects and Mapping their Attributes to the 2D Grid

v Prerequisite Tutorials GSSHA Modeling Basics Stream Flow GSSHA WMS Basics Creating Feature Objects and Mapping their Attributes to the 2D Grid v. 10.1 WMS 10.1 Tutorial GSSHA Modeling Basics Developing a GSSHA Model Using the Hydrologic Modeling Wizard in WMS Learn how to setup a basic GSSHA model using the hydrologic modeling wizard Objectives

More information

ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/05 TEXTURE ANALYSIS

ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/05 TEXTURE ANALYSIS ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/ TEXTURE ANALYSIS Texture analysis is covered very briefly in Gonzalez and Woods, pages 66 671. This handout is intended to supplement that

More information

AASHTOWare BrR - SIMPLE SPAN PRESTRESSED I BEAM EXAMPLE - BR 76015

AASHTOWare BrR - SIMPLE SPAN PRESTRESSED I BEAM EXAMPLE - BR 76015 AASHTOWare BrR - SIMPLE SPAN PRESTRESSED I BEAM EXAMPLE - BR 76015 M N D O T B R I D G E S T A T E A I D Page 1 PS1 - Simple Span Prestressed I Beam Example (BrR 6.7.1) 1. From the Bridge Explorer create

More information

STATS PAD USER MANUAL

STATS PAD USER MANUAL STATS PAD USER MANUAL For Version 2.0 Manual Version 2.0 1 Table of Contents Basic Navigation! 3 Settings! 7 Entering Data! 7 Sharing Data! 8 Managing Files! 10 Running Tests! 11 Interpreting Output! 11

More information

Minitab 17 commands Prepared by Jeffrey S. Simonoff

Minitab 17 commands Prepared by Jeffrey S. Simonoff Minitab 17 commands Prepared by Jeffrey S. Simonoff Data entry and manipulation To enter data by hand, click on the Worksheet window, and enter the values in as you would in any spreadsheet. To then save

More information

Table Of Contents. Table Of Contents

Table Of Contents. Table Of Contents Statistics Table Of Contents Table Of Contents Basic Statistics... 7 Basic Statistics Overview... 7 Descriptive Statistics Available for Display or Storage... 8 Display Descriptive Statistics... 9 Store

More information

Regression Analysis and Linear Regression Models

Regression Analysis and Linear Regression Models Regression Analysis and Linear Regression Models University of Trento - FBK 2 March, 2015 (UNITN-FBK) Regression Analysis and Linear Regression Models 2 March, 2015 1 / 33 Relationship between numerical

More information

CHAPTER 9: Quadratic Equations and Functions

CHAPTER 9: Quadratic Equations and Functions CHAPTER : Quadratic Equations and Functions Notes # -: Exploring Quadratic Graphs A. Graphing ax A is a function that can be written in the form ax bx c where a, b, and c are real numbers and a 0. Examples:

More information

Chapter 6: DESCRIPTIVE STATISTICS

Chapter 6: DESCRIPTIVE STATISTICS Chapter 6: DESCRIPTIVE STATISTICS Random Sampling Numerical Summaries Stem-n-Leaf plots Histograms, and Box plots Time Sequence Plots Normal Probability Plots Sections 6-1 to 6-5, and 6-7 Random Sampling

More information

Nonparametric Risk Attribution for Factor Models of Portfolios. October 3, 2017 Kellie Ottoboni

Nonparametric Risk Attribution for Factor Models of Portfolios. October 3, 2017 Kellie Ottoboni Nonparametric Risk Attribution for Factor Models of Portfolios October 3, 2017 Kellie Ottoboni Outline The problem Page 3 Additive model of returns Page 7 Euler s formula for risk decomposition Page 11

More information

Regression on the trees data with R

Regression on the trees data with R > trees Girth Height Volume 1 8.3 70 10.3 2 8.6 65 10.3 3 8.8 63 10.2 4 10.5 72 16.4 5 10.7 81 18.8 6 10.8 83 19.7 7 11.0 66 15.6 8 11.0 75 18.2 9 11.1 80 22.6 10 11.2 75 19.9 11 11.3 79 24.2 12 11.4 76

More information

Pavement Surface Microtexture: Testing, Characterization and Frictional Interpretation

Pavement Surface Microtexture: Testing, Characterization and Frictional Interpretation Pavement Surface Microtexture: Testing, Characterization and Frictional Interpretation S h u o L i, S a m y N o u r e l d i n, K a r e n Z h u a n d Y i J i a n g Acknowledgements This project was sponsored

More information

Bivariate (Simple) Regression Analysis

Bivariate (Simple) Regression Analysis Revised July 2018 Bivariate (Simple) Regression Analysis This set of notes shows how to use Stata to estimate a simple (two-variable) regression equation. It assumes that you have set Stata up on your

More information

NCHRP Project No. NCHRP 9-44 A. Validating an Endurance Limit for HMA Pavements: Laboratory Experiment and Algorithm Development

NCHRP Project No. NCHRP 9-44 A. Validating an Endurance Limit for HMA Pavements: Laboratory Experiment and Algorithm Development NCHRP Project No. NCHRP 9-44 A Validating an Endurance Limit for HMA Pavements: Laboratory Experiment and Algorithm Development Appendix 3 Project Lab Test Results Inserted into the Mechanistic Empirical

More information

Unsupervised Learning Hierarchical Methods

Unsupervised Learning Hierarchical Methods Unsupervised Learning Hierarchical Methods Road Map. Basic Concepts 2. BIRCH 3. ROCK The Principle Group data objects into a tree of clusters Hierarchical methods can be Agglomerative: bottom-up approach

More information

Important Note - Please Read:

Important Note - Please Read: Important Note - Please Read: This tutorial requires version 6.01 or later of SAFE to run successfully. You can determine what version of SAFE you have by starting the program and then clicking the Help

More information

Stat 5303 (Oehlert): Response Surfaces 1

Stat 5303 (Oehlert): Response Surfaces 1 Stat 5303 (Oehlert): Response Surfaces 1 > data

More information

D.1 Radon Model Emanation Analysis

D.1 Radon Model Emanation Analysis D.1 Radon Model Emanation Analysis This appendix presents the radon emanation analyses performed in support of the EE/CA for the Ross-Adams Mine Site. The radon emanation analyses were used to determine

More information

Data Mining Introduction

Data Mining Introduction Data Mining Introduction University of Iowa Power Plant is conducting study on their Boiler 11 Wants to use this data to predict efficiency and classification accuracy 1 Problem Definition Data has been

More information

VCEasy VISUAL FURTHER MATHS. Overview

VCEasy VISUAL FURTHER MATHS. Overview VCEasy VISUAL FURTHER MATHS Overview This booklet is a visual overview of the knowledge required for the VCE Year 12 Further Maths examination.! This booklet does not replace any existing resources that

More information

SLStats.notebook. January 12, Statistics:

SLStats.notebook. January 12, Statistics: Statistics: 1 2 3 Ways to display data: 4 generic arithmetic mean sample 14A: Opener, #3,4 (Vocabulary, histograms, frequency tables, stem and leaf) 14B.1: #3,5,8,9,11,12,14,15,16 (Mean, median, mode,

More information

Name Class. (a) (b) (c) 2. Find the volume of the solid formed by revolving the region bounded by the graphs of

Name Class. (a) (b) (c) 2. Find the volume of the solid formed by revolving the region bounded by the graphs of Applications of Integration Test Form A. Determine the area of the region bounded by the graphs of y x 4x and y x 4. (a) 9 9 (b) 6 (c). Find the volume of the solid formed by revolving the region bounded

More information

Spline Curves. Spline Curves. Prof. Dr. Hans Hagen Algorithmic Geometry WS 2013/2014 1

Spline Curves. Spline Curves. Prof. Dr. Hans Hagen Algorithmic Geometry WS 2013/2014 1 Spline Curves Prof. Dr. Hans Hagen Algorithmic Geometry WS 2013/2014 1 Problem: In the previous chapter, we have seen that interpolating polynomials, especially those of high degree, tend to produce strong

More information

Product Catalog. AcaStat. Software

Product Catalog. AcaStat. Software Product Catalog AcaStat Software AcaStat AcaStat is an inexpensive and easy-to-use data analysis tool. Easily create data files or import data from spreadsheets or delimited text files. Run crosstabulations,

More information

Algebra 1 Notes Quarter

Algebra 1 Notes Quarter Algebra 1 Notes Quarter 3 2014 2015 Name: ~ 1 ~ Table of Contents Unit 9 Exponent Rules Exponent Rules for Multiplication page 6 Negative and Zero Exponents page 10 Exponent Rules Involving Quotients page

More information

Lab 12: Sampling and Interpolation

Lab 12: Sampling and Interpolation Lab 12: Sampling and Interpolation What You ll Learn: -Systematic and random sampling -Majority filtering -Stratified sampling -A few basic interpolation methods Data for the exercise are in the L12 subdirectory.

More information

Catholic Regional College Sydenham

Catholic Regional College Sydenham Week: School Calendar Term 1 Title: Area of Study /Outcome CONTENT Assessment 2 - B 2 nd February Substitution and transposition in linear relations, such as temperature conversion. Construction of tables

More information

For Additional Information...

For Additional Information... For Additional Information... The materials in this handbook were developed by Master Black Belts at General Electric Medical Systems to assist Black Belts and Green Belts in completing Minitab Analyses.

More information

Lecture 24: Generalized Additive Models Stat 704: Data Analysis I, Fall 2010

Lecture 24: Generalized Additive Models Stat 704: Data Analysis I, Fall 2010 Lecture 24: Generalized Additive Models Stat 704: Data Analysis I, Fall 2010 Tim Hanson, Ph.D. University of South Carolina T. Hanson (USC) Stat 704: Data Analysis I, Fall 2010 1 / 26 Additive predictors

More information

Convert cubic yard to square feet

Convert cubic yard to square feet P ford residence southampton, ny Convert cubic yard to square feet Cubic Feet to Cubic Yards (ft³ to yd³) conversion calculator for Volume conversions with additional tables and formulas. The Square Feet

More information

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening Variables Entered/Removed b Variables Entered GPA in other high school, test, Math test, GPA, High school math GPA a Variables Removed

More information

WINPRES USER MANUAL. Version 1.0. Robert Lytton Professor Texas A&M University. Charles Aubeny Associate Professor Texas A&M University

WINPRES USER MANUAL. Version 1.0. Robert Lytton Professor Texas A&M University. Charles Aubeny Associate Professor Texas A&M University WINPRES USER MANUAL Version 1.0 by Robert Lytton Professor Texas A&M University Charles Aubeny Associate Professor Texas A&M University Gyeong Taek Hong Research Assistant Texas Transportation Institute

More information

Single-Axis Lasers for Flatness and Leveling Applications. Laser Systems for Flatness and Leveling L-730/L-740 Series

Single-Axis Lasers for Flatness and Leveling Applications. Laser Systems for Flatness and Leveling L-730/L-740 Series Single-Axis Lasers for Flatness and Leveling Applications Laser Systems for Flatness and Leveling L-730/L-740 Series Why the L-730/L-740 Flatness Leveling Systems are Better Sooner or later everything

More information

EVERSERIES USER S GUIDE Pavement Analysis Computer Software and Case Studies

EVERSERIES USER S GUIDE Pavement Analysis Computer Software and Case Studies INFORMATION AND CHAPTERS IN SUPPORT OF THE BACKCALCULATION TOOL WERE EXTRACTED FROM: EVERSERIES USER S GUIDE Pavement Analysis Computer Software and Case Studies August 2005 Washington State Department

More information

Name Class Date. Using Graphs to Relate Two Quantities

Name Class Date. Using Graphs to Relate Two Quantities 4-1 Reteaching Using Graphs to Relate Two Quantities An important life skill is to be able to a read graph. When looking at a graph, you should check the title, the labels on the axes, and the general

More information

9.1 Random coefficients models Constructed data Consumer preference mapping of carrots... 10

9.1 Random coefficients models Constructed data Consumer preference mapping of carrots... 10 St@tmaster 02429/MIXED LINEAR MODELS PREPARED BY THE STATISTICS GROUPS AT IMM, DTU AND KU-LIFE Module 9: R 9.1 Random coefficients models...................... 1 9.1.1 Constructed data........................

More information

Fall CSCI 420: Computer Graphics. 4.2 Splines. Hao Li.

Fall CSCI 420: Computer Graphics. 4.2 Splines. Hao Li. Fall 2014 CSCI 420: Computer Graphics 4.2 Splines Hao Li http://cs420.hao-li.com 1 Roller coaster Next programming assignment involves creating a 3D roller coaster animation We must model the 3D curve

More information

ANALYSIS OF COMPOSITE CONCRETE SLAB ON METAL DECK

ANALYSIS OF COMPOSITE CONCRETE SLAB ON METAL DECK ANALYSIS OF COMPOSITE CONCRETE SLAB ON METAL DECK Assignment #5 Select a Vulcraft composite deck to support 150 psf service live load on a 10 ft clear span. The steel deck is to be used on a three-span

More information

D&S Technical Note 09-2 D&S A Proposed Correction to Reflectance Measurements of Profiled Surfaces. Introduction

D&S Technical Note 09-2 D&S A Proposed Correction to Reflectance Measurements of Profiled Surfaces. Introduction Devices & Services Company 10290 Monroe Drive, Suite 202 - Dallas, Texas 75229 USA - Tel. 214-902-8337 - Fax 214-902-8303 Web: www.devicesandservices.com Email: sales@devicesandservices.com D&S Technical

More information

ALGEBRA 1 NOTES. Quarter 3. Name: Block

ALGEBRA 1 NOTES. Quarter 3. Name: Block 2016-2017 ALGEBRA 1 NOTES Quarter 3 Name: Block Table of Contents Unit 8 Exponent Rules Exponent Rules for Multiplication page 4 Negative and Zero Exponents page 8 Exponent Rules Involving Quotients page

More information

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions)

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions) THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533 MIDTERM EXAMINATION: October 14, 2005 Instructor: Val LeMay Time: 50 minutes 40 Marks FRST 430 50 Marks FRST 533 (extra questions) This examination

More information

Technical Report of ISO/IEC Test Program of the M-DISC Archival DVD Media June, 2013

Technical Report of ISO/IEC Test Program of the M-DISC Archival DVD Media June, 2013 Technical Report of ISO/IEC 10995 Test Program of the M-DISC Archival DVD Media June, 2013 With the introduction of the M-DISC family of inorganic optical media, Traxdata set the standard for permanent

More information

James L. Brown, Larry J. Buttler and William P. Ezzell

James L. Brown, Larry J. Buttler and William P. Ezzell ~.,_~w._'''... SURVEY OF STRUCTURAL FAILURES IN A CONTINUOUSLY REINFORCED CONCRETE PAVEMENT By James L. Brown, Larry J. Buttler and William P. Ezzell Special Study No.. Highway Design Division, Research

More information

ITSx: Policy Analysis Using Interrupted Time Series

ITSx: Policy Analysis Using Interrupted Time Series ITSx: Policy Analysis Using Interrupted Time Series Week 5 Slides Michael Law, Ph.D. The University of British Columbia COURSE OVERVIEW Layout of the weeks 1. Introduction, setup, data sources 2. Single

More information

Computational Physics PHYS 420

Computational Physics PHYS 420 Computational Physics PHYS 420 Dr Richard H. Cyburt Assistant Professor of Physics My office: 402c in the Science Building My phone: (304) 384-6006 My email: rcyburt@concord.edu My webpage: www.concord.edu/rcyburt

More information

1 Shapes of Power Functions

1 Shapes of Power Functions MA 1165 - Lecture 06 1 Wednesday, 1/28/09 1 Shapes of Power Functions I would like you to be familiar with the shape of the power functions, that is, the functions of the form f(x) = x n, (1) for n = 1,

More information

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008 MIT OpenCourseWare http://ocw.mit.edu.83j / 6.78J / ESD.63J Control of Manufacturing Processes (SMA 633) Spring 8 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

GEO-SLOPE International Ltd, Calgary, Alberta, Canada Lysimeters

GEO-SLOPE International Ltd, Calgary, Alberta, Canada   Lysimeters 1 Introduction Lysimeters This steady state SEEP/W example illustrates how to model a lysimeter from construction of the model to interpretation of the results. Lysimeters are used to measure flow through

More information

Multiple Regression White paper

Multiple Regression White paper +44 (0) 333 666 7366 Multiple Regression White paper A tool to determine the impact in analysing the effectiveness of advertising spend. Multiple Regression In order to establish if the advertising mechanisms

More information

Robert Collins CSE598G. Intro to Template Matching and the Lucas-Kanade Method

Robert Collins CSE598G. Intro to Template Matching and the Lucas-Kanade Method Intro to Template Matching and the Lucas-Kanade Method Appearance-Based Tracking current frame + previous location likelihood over object location current location appearance model (e.g. image template,

More information

The theory of the linear model 41. Theorem 2.5. Under the strong assumptions A3 and A5 and the hypothesis that

The theory of the linear model 41. Theorem 2.5. Under the strong assumptions A3 and A5 and the hypothesis that The theory of the linear model 41 Theorem 2.5. Under the strong assumptions A3 and A5 and the hypothesis that E(Y X) =X 0 b 0 0 the F-test statistic follows an F-distribution with (p p 0, n p) degrees

More information

Chapter 2: Linear Equations and Functions

Chapter 2: Linear Equations and Functions Chapter 2: Linear Equations and Functions Chapter 2: Linear Equations and Functions Assignment Sheet Date Topic Assignment Completed 2.1 Functions and their Graphs and 2.2 Slope and Rate of Change 2.1

More information

Final project: Design problem

Final project: Design problem ME309 Homework #5 Final project: Design problem Select one of the analysis problems listed below to solve. Your solution, along with a description of your analysis process, should be handed in as a final

More information

Section 4.1: Time Series I. Jared S. Murray The University of Texas at Austin McCombs School of Business

Section 4.1: Time Series I. Jared S. Murray The University of Texas at Austin McCombs School of Business Section 4.1: Time Series I Jared S. Murray The University of Texas at Austin McCombs School of Business 1 Time Series Data and Dependence Time-series data are simply a collection of observations gathered

More information

3D Laser Imaging for Pavement Survey at 60 mph and True 1mm Resolution

3D Laser Imaging for Pavement Survey at 60 mph and True 1mm Resolution 3D Laser Imaging for Pavement Survey at 60 mph and True 1mm Resolution Kelvin C. P. Wang Oklahoma State University & WayLink kelvin.wang@okstate.edu 2013 Arizona Pavements/Materials Conference ASU MU,

More information

Current Investigations into the Effects of Texture on IRI RPUG Jareer Abdel-Qader, Emmanuel Fernando, Roger Walker

Current Investigations into the Effects of Texture on IRI RPUG Jareer Abdel-Qader, Emmanuel Fernando, Roger Walker Current Investigations into the Effects of Texture on IRI RPUG 28 Jareer Abdel-Qader, Emmanuel Fernando, Roger Walker Study Objectives Emmanuel s Presentation confirmed texture effects on IRI in both Laboratory

More information

Moving Beyond Linearity

Moving Beyond Linearity Moving Beyond Linearity Basic non-linear models one input feature: polynomial regression step functions splines smoothing splines local regression. more features: generalized additive models. Polynomial

More information

Year 10 General Mathematics Unit 2

Year 10 General Mathematics Unit 2 Year 11 General Maths Year 10 General Mathematics Unit 2 Bivariate Data Chapter 4 Chapter Four 1 st Edition 2 nd Edition 2013 4A 1, 2, 3, 4, 6, 7, 8, 9, 10, 11 1, 2, 3, 4, 6, 7, 8, 9, 10, 11 2F (FM) 1,

More information

FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu

FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu FMA901F: Machine Learning Lecture 3: Linear Models for Regression Cristian Sminchisescu Machine Learning: Frequentist vs. Bayesian In the frequentist setting, we seek a fixed parameter (vector), with value(s)

More information

Resources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes.

Resources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes. Resources for statistical assistance Quantitative covariates and regression analysis Carolyn Taylor Applied Statistics and Data Science Group (ASDa) Department of Statistics, UBC January 24, 2017 Department

More information

6. Find the equation of the plane that passes through the point (-1,2,1) and contains the line x = y = z.

6. Find the equation of the plane that passes through the point (-1,2,1) and contains the line x = y = z. Week 1 Worksheet Sections from Thomas 13 th edition: 12.4, 12.5, 12.6, 13.1 1. A plane is a set of points that satisfies an equation of the form c 1 x + c 2 y + c 3 z = c 4. (a) Find any three distinct

More information

Four equations are necessary to evaluate these coefficients. Eqn

Four equations are necessary to evaluate these coefficients. Eqn 1.2 Splines 11 A spline function is a piecewise defined function with certain smoothness conditions [Cheney]. A wide variety of functions is potentially possible; polynomial functions are almost exclusively

More information

Experiment 1 CH Fall 2004 INTRODUCTION TO SPREADSHEETS

Experiment 1 CH Fall 2004 INTRODUCTION TO SPREADSHEETS Experiment 1 CH 222 - Fall 2004 INTRODUCTION TO SPREADSHEETS Introduction Spreadsheets are valuable tools utilized in a variety of fields. They can be used for tasks as simple as adding or subtracting

More information

ST512. Fall Quarter, Exam 1. Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false.

ST512. Fall Quarter, Exam 1. Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false. ST512 Fall Quarter, 2005 Exam 1 Name: Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false. 1. (42 points) A random sample of n = 30 NBA basketball

More information

Quadratic Functions CHAPTER. 1.1 Lots and Projectiles Introduction to Quadratic Functions p. 31

Quadratic Functions CHAPTER. 1.1 Lots and Projectiles Introduction to Quadratic Functions p. 31 CHAPTER Quadratic Functions Arches are used to support the weight of walls and ceilings in buildings. Arches were first used in architecture by the Mesopotamians over 4000 years ago. Later, the Romans

More information

Hypermarket Retail Analysis Customer Buying Behavior. Reachout Analytics Client Sample Report

Hypermarket Retail Analysis Customer Buying Behavior. Reachout Analytics Client Sample Report Hypermarket Retail Analysis Customer Buying Behavior Report Tools Used: R Python WEKA Techniques Applied: Comparesion Tests Association Tests Requirement 1: All the Store Brand significance to Gender Towards

More information

OPTIMIZING HIGHWAY PROFILES FOR INDIVIDUAL COST ITEMS

OPTIMIZING HIGHWAY PROFILES FOR INDIVIDUAL COST ITEMS Dabbour E. Optimizing Highway Profiles for Individual Cost Items UDC: 656.11.02 DOI: http://dx.doi.org/10.7708/ijtte.2013.3(4).07 OPTIMIZING HIGHWAY PROFILES FOR INDIVIDUAL COST ITEMS Essam Dabbour 1 1

More information

Slope Stability Problem Session

Slope Stability Problem Session Slope Stability Problem Session Stability Analysis of a Proposed Soil Slope Using Slide 5.0 Tuesday, February 28, 2006 10:00 am - 12:00 pm GeoCongress 2006 Atlanta, GA software tools for rock and soil

More information

YEAR 12 Core 1 & 2 Maths Curriculum (A Level Year 1)

YEAR 12 Core 1 & 2 Maths Curriculum (A Level Year 1) YEAR 12 Core 1 & 2 Maths Curriculum (A Level Year 1) Algebra and Functions Quadratic Functions Equations & Inequalities Binomial Expansion Sketching Curves Coordinate Geometry Radian Measures Sine and

More information

5.1 Introduction to the Graphs of Polynomials

5.1 Introduction to the Graphs of Polynomials Math 3201 5.1 Introduction to the Graphs of Polynomials In Math 1201/2201, we examined three types of polynomial functions: Constant Function - horizontal line such as y = 2 Linear Function - sloped line,

More information

Robust Regression. Robust Data Mining Techniques By Boonyakorn Jantaranuson

Robust Regression. Robust Data Mining Techniques By Boonyakorn Jantaranuson Robust Regression Robust Data Mining Techniques By Boonyakorn Jantaranuson Outline Introduction OLS and important terminology Least Median of Squares (LMedS) M-estimator Penalized least squares What is

More information

1) Find. a) b) c) d) e) 2) The function g is defined by the formula. Find the slope of the tangent line at x = 1. a) b) c) e) 3) Find.

1) Find. a) b) c) d) e) 2) The function g is defined by the formula. Find the slope of the tangent line at x = 1. a) b) c) e) 3) Find. 1 of 7 1) Find 2) The function g is defined by the formula Find the slope of the tangent line at x = 1. 3) Find 5 1 The limit does not exist. 4) The given function f has a removable discontinuity at x

More information

General Factorial Models

General Factorial Models In Chapter 8 in Oehlert STAT:5201 Week 9 - Lecture 2 1 / 34 It is possible to have many factors in a factorial experiment. In DDD we saw an example of a 3-factor study with ball size, height, and surface

More information