Structural health monitoring of bridges in the State of Connecticut

Liu, Chengyin; Olund, Joshua K; Cardini, Alan J.; D'Attilio, Paul F; Feldblum, Eric G; DeWolf, John T.

doi:10.1007/s11803-008-1003-8

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…More modest SHM systems use a much smaller array of sensors, such as the system implemented by Liu et al (2008). This SHM system was deployed on an interstate highway in Connecticut and used 16 accelerometers, 6 tilt meters, and 14 temperature sensors.…”

Section: Acceleration-based Systems Using Ambient Vibrationmentioning

confidence: 99%

“…An example of such an approach was presented by Liu et al (2008), who developed linear relationships between temperature and the first three natural frequencies of the bridge being studied. Another method to account for temperature is to analyze the data over a prolonged period of time (i.e., years) and determine the fluctuation in the behavior over time.…”

Section: Challenges Of Long-term Continuous Monitoringmentioning

confidence: 99%

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Development of a structural health monitoring system for the life assessment of critical transportation infrastructure.

Roach¹,

Jáuregui

Daumueller

2012

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Recent structural failures such as the I-35W Mississippi River Bridge in Minnesota have underscored the urgent need for improved methods and procedures for evaluating our aging transportation infrastructure. This research seeks to develop a basis for a Structural Health Monitoring (SHM) system to provide quantitative information related to the structural integrity of metallic structures to make appropriate management decisions and ensuring public safety.This research employs advanced structural analysis and nondestructive testing (NDT) methods for an accurate fatigue analysis. Metal railroad bridges in New Mexico will be the focus since many of these structures are over 100 years old and classified as fracture-critical. The term fracture-critical indicates that failure of a single component may result in complete collapse of 4 the structure such as the one experienced by the I-35W Bridge. Failure may originate from sources such as loss of section due to corrosion or cracking caused by fatigue loading. Because standard inspection practice is primarily visual, these types of defects can go undetected due to oversight, lack of access to critical areas, or, in riveted members, hidden defects that are beneath fasteners or connection angles. Another issue is that it is difficult to determine the fatigue damage that a structure has experienced and the rate at which damage is accumulating due to uncertain history and load distribution in supporting members. A SHM system has several advantages that can overcome these limitations. SHM allows critical areas of the structure to be monitored more quantitatively under actual loading. The research needed to apply SHM to metallic structures was performed and a case study was carried out to show the potential of SHM-driven fatigue evaluation to assess the condition of critical transportation infrastructure and to guide inspectors to potential problem areas. This project combines the expertise in transportation infrastructure at New Mexico State University with the expertise at Sandia National Laboratories in the emerging field of SHM.5

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Section: Acceleration-based Systems Using Ambient Vibrationmentioning

confidence: 99%

Section: Challenges Of Long-term Continuous Monitoringmentioning

confidence: 99%

Development of a structural health monitoring system for the life assessment of critical transportation infrastructure.

Roach¹,

Jáuregui

Daumueller

2012

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“…Wrought iron was phased out as a primary construction material and replaced with more ductile steel in the early 20th century. Diagnosing the health of the historic wrought iron truss bridge structures is important as many of these structures remain in daily use and have been subjected to aging, harsh conditions, excessive loading, and unpredictable or accidental damage [1]. Field Testing -Equipment: Wireless sensor networks (WSNs) are recommended for the proposed experimental system's identification method for historical bridge structures.…”

Section: Introductionmentioning

confidence: 99%

Dynamic behaviors of historical wrought iron truss bridges: a field testing case study

et al. 2016

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The U.S. transportation infrastructure has many wrought iron truss bridges that are more than a century old and still remain in use. Understanding the structural properties and identifying the health conditions of these historical bridges are essential to deciding the maintenance or rebuild plan of the bridges. This research involved an on-site full-scale system identification test case study on the historical Old Alton Bridge (a wrought iron truss bridge built in 1884 in Denton, Texas) using a wireless sensor network. The study results demonstrate a practical and convenient experimental system identification method for historical bridge structures. The method includes the basic steps of the in-situ experiment and in-house data analysis. Various excitation methods are studied for field testing, including ambient vibration by wind load, forced vibration by human jumping load, and forced vibration by human pulling load. Structural responses of the bridge under these different excitation approaches were analyzed and compared with numerical analysis results.

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“…More recently, SHM has been applied to civil structures such as buildings and bridges. SHM has been performed on span bridges with promising results [9] and extensive bridge monitoring techniques have been developed by Wong [10].…”

mentioning

confidence: 99%

Vibration Analysis of 2.3 MW Wind Turbine Operation Using the Discrete Wavelet Transform

Bassett¹,

Carriveau

Ting³

2010

Wind Engineering

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The vibration analysis of operational response data from a 2.3 MW wind turbine is presented. Vibration signals were acquired for two unique environmental conditions with an accelerometer mounted in the turbine tower. A Daubechies 6th order (db6) wavelet was used to perform a 12-level discrete wavelet transform (DWT) revealing trends and similarities within the signals. Full operation signals were segmented into start up and steady state periods. Analysis of turbine start up revealed a common ramping of low frequency energy on the order of rotor rotational frequency. DWT plots were also utilized to reveal high-energy response features related to the mechanical start up of the turbine. Analysis of steady state signals revealed distinct low frequency periodicity evident in the 11th (0.1776Hz) and 12th (0.0888 Hz) decomposition levels. The analysis technique performed shows promise for potential integration into comprehensive structural health monitoring schemes designed to reduce downtime and improve the reliability of commercial wind turbines.

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Structural health monitoring of bridges in the State of Connecticut

Cited by 6 publications

References 7 publications

Development of a structural health monitoring system for the life assessment of critical transportation infrastructure.

Development of a structural health monitoring system for the life assessment of critical transportation infrastructure.

Dynamic behaviors of historical wrought iron truss bridges: a field testing case study

Vibration Analysis of 2.3 MW Wind Turbine Operation Using the Discrete Wavelet Transform

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