This paper is a summary of the preliminary experimental results regarding the use of optical fiber Bragg grating (OFBG)
sensing equipment to monitor the local strain about the circumference of un-axially restricted, thick walled, unreinforced concrete vessels, developed under the moniker Simulated Carbon Ash Retention Cylinder (SCARC), subject to internal expansion pressure from ash carbon concrete (ACC). Internal pressure developed following the introduction of varying combinations of cement, fly-ash, aluminum powder, carbon-dioxide, and water to the voided region of the SCARC specimens. Seven specimens were created, and monitored, so that cracking patterns, material property variables, and OFBG strain irregularities could be investigated to begin formulating a crack location prediction and detection system.
The motivation of this study is to determine a technique to completely describe the damage state of large deformed
structures commonly found during forensic investigations. The combination of Laser Detecting and Ranging (LiDAR)
and Piezoelectric (PZT) Sensing Technologies for damage quantification is suggested to generate the full-field description of large deformation of a plate. The test subject is a 16 inch by 16 inch aluminum plate subjected to different damage scenarios. LiDAR is a static scanning laser that provides a 3-dimensional picture of the object. Smart Layer is a commercial PZT actuator/sensor network system that generates stress waves for internal damage evaluation. Both techniques were applied to the test plate after damages are introduced. In order to effectively analyze the results, the images for each test were superimposed. Frequencies that depicted the best interpretation of damage in the direct path images were superimposed with the 3-dimensional LiDAR images. Four damage scenarios were imposed on an aluminum plate including saw cuts at different depths using an electric saw. The final damage is a severe bending of the plate. The bending of the specimen produced an image that located the most severe damage directly under the left hand portion and directly above the right hand portion of the bend.
Small format aerial photography (SFAP) with low flying technique is proposed for damage evaluation of bridge decks.
High resolution images obtained using under-belly photography can be used to quantify the various bridge deck
problems. The conventional truck-mount or vehicle-mount deck imaging technologies require a large number of image
samples. Hence the physical scanning is time consuming and it is also challenging consider the size and location of a
bridge. Aerial imaging overcomes these issues, but they face different kinds of challenges that are posed by obstacles
such as shadow from trees, power lines and vehicles, signs and luminaries structures. The image resolution uncertainty,
which is a function of the pilot skills and flying conditions, may also add additional challenges to aerial imaging
technique. Hence different image processing tools have to be integrated into a single package to achieve the desired task.
This paper summarizes the challenges faced and the preliminary results are presented and discussed.
Previous visual damage detection on bridge structure based on eye-ball method is arbitrary and time-consuming for
bridge management due to its heuristic nature. Commercial remote sensing (CRS), which has remarkable applications for
geometric quantification, is suggested to supplement visual bridge inspection. Ground-based LIDAR is one of the remote
sensing tools that have been successfully used in bridge evaluation. Most of the early measurement algorithms are
developed based on the spatial information contained from the LIDAR data; this paper explores the potential of applying
another important feature of the scan data: the reflectivity, to enhance the defect detection program. The addition of
reflectivity in damage diagnostics is particularly useful for defect detection of curved surfaces. A damaged joint area and
concrete beam were selected to verify the method. The study shows that the reflectivity of the LIDAR could be used to
support the automatic defect detection in bridges by combining it with the current position-based only image processing
algorithms.
Spun-cast concrete poles have been increasingly used in power line support in U.S. during the past decade. Dynamic
behaviors of these pole structures are critical design considerations due to the wind and conductor effects. However, free
vibration of pole structures has rarely been studied and existing design guidelines do not provide clear information
associated with pole natural frequencies. To build-up knowledge in pole vibration, analyses of concrete poles of various
sizes and classes were performed numerically. The finite element models were verified with experimental modal data
from several poles. Based on the study, empirical relations between geometric parameters and pole natural frequencies
were developed, which are very basic information for health monitoring of power lines.
Deck joint is important for a bridge - Any cost-effective evaluation methods that can help trace joint movements during
frequent inspections will provide valuable data to bridge engineers. In this paper, 3D Terrestrial LiDAR and Aerial
photography are being investigated as possible joint evaluation methods. The laser scanners record 3D positions of the
surface points, generating high density point clouds. Aerial images taken by commercial DSLR cameras in a small
airplane flying at 1000 feet, generates high resolution imagery. Both techniques have sub-inch pixel resolutions.
Scanning results from bridges in both Florida and Alabama have shown that LiDAR and aerial imaging technologies are
compatible techniques and can be applied in bridge deck joint performance evaluation. Moreover, both techniques have
the potential to reduce the costs in bridge inspection.
This paper reports the outcomes of a study of the vehicle crossing effects on terrestrial LiDAR scan on highway bridges
for underclearance measurements. Ground-based or vehicle-mount terrestrial LiDAR scanners, which recreate the bridge
structure as 3D point cloud of thousands of position data points, have been found to be ideal for bridge clearance
measurements. To determine the effects of ambient overhead vehicle crossing and seasonal temperature variation on
clearance measurements, periodic monitoring of the Harris Road Bridge has been conducted. A simplistic but practical
correlation analysis is performed which shows that operational LiDAR scanning is a viable technique for bridge
clearance measurements.
Terrestrial 3D LiDAR scanner has been suggested as a remote sensing technique for existing and newly constructed
bridges. Using high resolution laser, 3D LiDAR can populate a surficial area with millions of position data points.
Bridge problems can benefit from LiDAR scan and current studies have found potential application including: bridge
clearance, static deflection measurement and damage detection. The technique is especially useful when accurate
measurement of bridge geometry cannot be achieved by traditional survey technique, especially when site topography is
prohibitive. However, resolution is still one of the main reasons that limit the application of LiDAR technology for
advance bridge monitoring. This paper discusses the reliability issues of such technique as well as the LiDAR based
bridge monitoring methodologies. Several experimental results are presented to establish the sensitivities for different
assessments.
A new skewed two span continuous steel girder bridge was constructed and opened to traffic recently. This bridge uses
high performance steel (HPS 100W) in the flanges of the negative moment region over the intermediate pier. For
construction verification and long-term structural health monitoring purposes, a finite element (FE) model was
developed for the bridge superstructure. Various field tests were performed to verify the model: 1) LiDAR scan, 2) static
truck load tests, and 3) Laser doppler vibrometer testing. LiDAR scanner was introduced to gain geometrical information
of the bridge in the real world. It was also used to measure girder deflections during load tests. The fundamental
frequency of the bridge vibration was obtained by using a Laser doppler vibrometer. Both dynamic and static
measurements are then used to update the FE model. This valid bridge superstructure FE model was provided to local
DOT bridge engineers with the completion of this study.
This paper addresses the potential applications of terrestrial 3D LiDAR scanning technologies for bridge monitoring.
High resolution ground-based optical-photonic images from LiDAR scans can provide detailed geometric information
about a bridge. Applications of simple algorithms can retrieve damage information from the geometric point cloud data,
which can be correlated to possible damage quantification including concrete mass loss due to vehicle collisions, large
permanent steel deformations, and surface erosions. However, any proposed damage detection technologies should
provide information that is relevant and useful to bridge managers for their decision making process. This paper summaries bridge issues that can be detected from the 3D LiDAR technologies, establishes the general approach in using 3D point clouds for damage evaluation and suggests possible bridge state ratings that can be used as supplements to existing bridge management systems (BMS).
Current bridge visual inspections are time-consuming, subjective, and rely heavily on personal experiences. The
resulting ratings may be inconsistent. This paper discusses using remote-sensing technologies for bridge assessment,
specifically, the use of high-resolution aerial imagery. The Small-Format Aerial Photography (SFAP) is a low-cost
solution for bridge surface imaging. Providing top-down views, the airplanes flying at 1000 ft, can allow visualization
of sub-inch (< 0.5 inch) cracks and joint openings on bridge decks or highway pavements. However, the site lighting
may influence the quality of the images; surrounding tree shades and the highway wear surface reflectivity. Several
examples of bridge evaluation using SFAP aerial photography are presented to demonstrate the capability of remote
sensing as an effective tool for bridge construction monitoring and condition assessment. Several imaging issues are
raised about analytical techniques that are necessary to ensure proper quantification of bridge problems, which include
crack detection, movement determination, heavy trucking assessment, debris detection, channel width determination and
environment assessment.
This paper addresses the potential applications of commercial remote sensing (CRS) technologies for bridge monitoring.
High resolution optical-photonic images can provide bridge damage information including through-deck collision
damages, large permanent deformations, overload cracking and surface erosions, as well as surrounding environmental
information. This paper summaries bridge issues that can be detected from high resolution remote sensing imageries
based on visual interpretation as guidance for remote sensing imagery based bridge inspection, and the development of
future automatic detection methods. A LiDAR based automatic bridge evaluation system LiBE (LiDAR Bridge
Evaluation) is introduced in this paper with an application example of a test bridge maintained by the Los Angeles
County (CA) Department of Public Works. Laser scanning techniques have also been used for bridge load testing in a
new bridge near Charlotte, NC and maintained by the North Carolina DOT. The primary results of these preliminary
tests are also presented. Remote sensing techniques are introduced as a supplement to the existing, required visual bridge
inspections.
Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to
make efficient and effective informed decisions. The management involves a multi-faceted operation that requires
the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical
assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing
and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management.
This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world
practitioners from industry, local and federal government agencies.
IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding
and enforcement of complex inspection process that can bridge the gap between evidence gathering
and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation,
representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and
ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation
system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations
that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented
Architecture (SOA) framework to compose and provide services on-demand.
IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance
and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme
events.
Infrastructure safety affects millions of U.S citizens in many ways. Among all the infrastructures, the bridge
plays a significant role in providing substantial economy and public safety. Nearly 600,000 bridges across the
U.S are mandated to be inspected every twenty-four months. Although these inspections could generate great
amount of rich data for bridge engineers to make critical maintenance decisions, processing these data has become
challenging due to the low efficiency from those traditional bridge management systems. In collaboration with
North Carolina Department of Transportation (NCDOT) and other regional DOT collaborators, we present our
knowledge integrated visual analytics bridge management system. Our system aims to provide bridge engineers a
highly interactive data exploration environment as well as knowledge pools for corresponding bridge information.
By integrating the knowledge structure with visualization system, our system could provide comprehensive
understandings of the bridge assets and enables bridge engineers to investigate potential bridge safety issues and
make maintenance decisions.
A GIS-based data management system has been proposed for pavement management due to the spatial capability in organizing
diverse geo-referenced information. The technology can be further enhanced by nondestructive distributed sensing. To target a
wide study area, this research proposes a low-cost vibro-acousto passive sensing technique that embeds within roadways for
long-term sensing. Using self-sustaining MEM sensors, the technology detects acoustic signals and use relative rating to assess
pavement conditions. The detection technique echoes the traditional chain-drag technology in that the same sound detection is
deployed. Coupling with previously established AMPIS pavement imaging and distress detection technique, the proposed system
can evolve to be a more powerful new-generation GIS-PMS. This paper introduces the system concept and describes the
philosophy behind the system and some of the challenges that we are currently attempting to solve.
To ensure the safety and integrity of tied arch bridges, it is crucial that tension levels in cables do not exceed their design levels. Currently, visual inspection is required since there are no reliable techniques that can accurately determine the tension levels of these cables. A possible approach would be to correlate the vibration measurements with the tensions in these cables. However, due to their long length, access to these cables for mounting contact sensors is not easy. An attempt has been made to use a He/Ne laser vibrometer for non-contact cable vibration measurements on a tied arch bridge. The objectives of this test are to assess the quality of vibration measurements from the He/Ne laser under regular daylight environment and to determine the vibration signatures of these bridge cables under ambient condition. The results of study indicate that using non-contact measurements, a quick and easy prediction of tension levels in bridge cables can be made. This paper presents the results of the study and presents discussion on the advantages of non-contact measurements and possible testing difficulties.
A damage detection algorithm based on the principle of curvature changes has been developed at CFC-WVU. However, the algorithm requires accurate mode shapes with adequate spatial density. Existing contact sensors can not provide adequate spatial density without adding excessive mass. Hence, non-contact scanning techniques, such as scanning laser vibrometer (SLV) has adequate sensitivity and accuracy is yet to be proven. The applicability of SLV on large structures is also questionable. To assess the suitability of using SLV for damage detection, a beam specimen has been tested using an existing system. The results confirm that damage detection using vibration measurements from SLV is successful. Due to more spatial density, the SLV data is shown to be more sensitive than the contact sensor test.
This paper presents results from testing several suspender ropes of the Delaware Memorial Bridge using vibration measurements and a non-destructive evaluation (NDE) instrument called the Axial Load Monitor (ALM). The testing consisted of measuring the frequencies of suspender ropes and determining their tension levels. Results were compared to theoretical predictions. This paper presents the results of the testing and discusses the problems associated with vibration measurements on actual bridges.
This paper presents work in progress toward the development of a bridge condition assessment system. The system combines remote laser vibration sensing technology and a strain-energy- based damage detection algorithm. The results from vibration tests conducted on laboratory specimens with different degrees of damage are presented. The vibration signatures are acquired using Scanning Laser Vibrometers (SLV). The extracted mode shapes from these tests are then used in the damage detection algorithm. The preliminary results indicate that the strain energy differences are highly sensitive to damage, and can be used to locate and distinguish progressive damages. The combination of SLV technology and the damage detection algorithm makes remote sensing attractive for the monitoring and inspection of structures. Finite element simulation of a progressive damage at a single location is also presented to illustrate the sensitivity of the algorithm to increasing damages.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.