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Instrumentation, Modeling and Monitoring of a

Concrete Bridge from Construction through

Service

Erin Santini Bell, Ph. D., P. E. Assistant Professor, University of New Hampshire

Jesse Sipple Doctoral Student, Tufts University

Paul Lefebvre

Masters Student, University of New Hampshire

John Phelps Masters Student, Tufts University

Brian Brenner, P.E. Vice President, Fay Spofford and Thorndike

Professor of the Practice, Tufts University

Masoud Sanayei, Ph. D. Professor, Tufts University

Presentation to the Transportation Research Board Annual Meeting January 23, 2011

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Project Motivation

• Leverage current technologies

• Bridge design today is elemental

• Bridge design is complete on opening day

• “Design intelligence” is not readily available

during life of bridge

• Address long term behavior of bridges during

initial design

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Central Thesis

• How is long term design addressed in the

design process?

• Leverage advancing technology

(instrumentation, analysis, data management,

remote sensing) to improve the bridge design

process that currently focuses on opening day,

but not the 75 years that follow opening day

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Instrumentation

• Installed during the construction process

• Used to verify the design and modeling

assumptions

• Continuously used to monitor the health of the

bridge

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Structural Modeling

• Structural engineers use design programs to

aid in design process

• Designed based models with code

requirements arrive

• SAP2000®, RAM®, STADD®

– Bridge Information Modeler (BrIM™)

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Structural Baseline Modeling

Implementing a Baseline Model into the bridge design process will shift the paradigm to focus on long-term performance

Keep the Baseline Model simple for usability, while still capturing the desired level of response accuracy

The Baseline Model will be created with condition assessment in mind using input from the bridge management and design divisions

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Structural Baseline Modeling

• Takes design models a step further by

including specific elements into modeling

• Elements include

– Composite action

– Diaphragms

– Bridge rail

– Spring boundary conditions

• Goal: To make a usable model that accurately

captures bridge behavior

[Kp]

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Modeling Bearing Pads as Springs

• Stanton et al. (2004) provides

equations for axial and rotational

stiffness

• AASHTO provides equations for

elastic modulus of bearing pads

• NCHRP Report 596 –Rotation

Limits for Elastomeric Bearings

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Structural Health Monitoring

• The goal of SHM systems is to employ sensing

instruments to provide information pertaining

to the condition of the structure

• Recent advancements in technology have

made bridge structure instrumentation very

popular and relatively easy to implement

• This collected data must then be post-

processed to provide beneficial information for

bridge owners

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How do we get there?

• In current AASHTO design practices, bridges

are designed on an elemental basis

• AASHTO specifies that each structural

element is to be designed for the loads it will

experience during the life of the bridge

• Develop a “baseline” model and suggest

certain modifications to the traditional bridge

design process to take advantage of modern

computing capabilities to create a refined

baseline model

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Durham, NH

Barre,MA

Vernon Avenue over the Ware River

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Vernon Avenue Bridge

• Opened to traffic in September 2009

• Collaborative project with Fay, Spofford and Thorndike,

Inc.,Tufts University and Geocomp, INC. in cooperation with

the Massachusetts Highway Administration

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Vernon Avenue Bridge

• 6 Steel Girders

with a Reinforced

Concrete Deck

• Composite CIP

Deck

• 3 Continuous

Spans

• 150 Feet Long with

a 75 ft Center Span

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Instrumentation Plan

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Instrumentation in the Yard

Strain Gauges

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Instrumentation at the Site

Tiltmeters

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Instrumentation at the Site

Accelerometers

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Instrumentation in the Deck

Concrete Temperature

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Instrumentation in the Approach

Pressure Cells

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Instrumentation Plan

Summary

100 Strain Gauges

36 Temperature Sensors

36 Concrete Temperature

16 Accelerometers

16 Tiltmeters

3 SWP

2 Pressure Plates

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Steel Erection

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Data Acquisition by Geocomp

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Concrete Pour

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Load Test

DAQ

Truck

Tracking

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Baseline Model of the Vernon Ave Bridge

• Use construction photographs

• Field measurements

• Design documents

• SAP2000

– V14

– BriM®

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Vernon Ave Bridge Model

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Finite Element Baseline Model

• Capturing composite system behavior

• Access to accurate geometry, temperature

gradients, etc.

• Access to advanced modeling techniques and

analysis methods

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Vernon Ave Modeling Procedure

• Bridge modeled by drawing cross section

using node locations

• One layer of elements drawn and then

extruded/replicated for length of bridge

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Vernon Ave Modeling Challenges

• Negative moment regions/concrete cracking

• Deck reinforcement

• Support conditions

• Dead load deflected shape showing negative

bending regions

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Concrete Pour Data

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Comparison Between Solid Model and

Load Test Data

09:48:57 09:49:40 09:50:24 09:51:07 09:51:50 09:52:33 09:53:16 09:54:00

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

Mic

ro S

train

(ue)

time (seconds)

Moving Average-400 DB0099stoplocationcomparison SG-13

Measured

Model

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Future Work

• Refine the estimation of the bearing pad

stiffness values using finite element modeling

• Post-process collected data from the concrete

pour and controlled load test data

• Use the structural models to address design

assumptions, such as distribution factors

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Finite Element Model of the Bearing Pad

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Live Load Locations for FEM Distribution

Factor Analysis

Interior girder governing load cases

Exterior girder governing load cases

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• Accurate comparison for multiple lanes loaded (multiple presence factor = 1.0)

• DF’s for single lane loaded are low (multiple presence factor = 1.2)

• Exterior girders give close match with AASHTO lever rule

Distribution Factor Comparison

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Conclusions

• Instrumentation of a bridge during

construction required coordination with

multiple parties

• The data collected during construction is

critical in order to capture bridge behavior

• The structural models created for this bridge

will be calibrated periodically for health

monitoring

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Acknowledgement

• This works was partially funded by the NSF

PFI Grant Number 650258 and NSF CAREER

Grant Number 644683

• The authors would like to thank

– E.T.&L. Corp.

– Geocomp, INC.

– Massachusetts Highway Administration

– Town of Barre, Massachusetts

– High Steel, INC.

– Atlantic Bridge and Engineering, INC.

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Questions?