The random shape and distribution of the aggregates add large inhomogeneity into the mixtures. Large material composition variability will increase the risk of damages, such as cracking and rutting. This paper introduced a digital image processing based HMA mixture material composition variability analysis method as a new material performance evaluation supplementary tool. The low temperature performances of six types of asphalt pavement materials have been evaluated through beam bending tests. Through comparing with the beam bending lab test data, it has shown that material composition variability has direct influence on the variability of the strength of the HMA beam bending test specimens, and proved the effectiveness of the proposed variability analysis method.
It is often difficult to monitor the subgrade performance of an in service highway structure due to its depth and large in size. Traditional sensors for pavement performance monitoring are usually point-wise sensors and therefore cannot fulfill the requirement for large coverage. This paper introduces a shape measurement sensor based on Fiber Bragg Grating (FBG) sensing technique for pavement structure developed by our research group. It can provide large-scale layer shape measurement using only one FBG sensing element and can bear the high compaction force and high temperature during pavement construction. The proposed sensor has been test in lab to show high accuracy in measurement. In the construction of the He-da highway in Jilin province, the proposed sensor has been applied to monitor the subgrade performance for the first time. Ten sets of the sensors with individual length of about 15 m have been embedded and the performance data have been collected twice. The embedding process and monitoring data will be introduced and discussed. The results have shown the potential of the proposed sensor for future large scale application.
Structural health monitoring has become more and more popular in application of damage diagnosis and safety assessment. Optical fiber sensors, as one of the most efficient sensing elements, for the superior advantages of long-term stability and durability, good geometrical shape-versatility, corrosion resistance and anti-electromagnetic interference, are widely used in diverse technological fields. Measurement precision of the sensor is thus emphasized and strain transfer analysis put forward to explain the action mechanism and improve the test accuracy. Theoretical derivation on strain transfer error analysis of optical fiber sensor applied to structure with local debonding interface is carried out in this paper. Cases that optical fiber sensors are bonded on the surface of structure and embedded in structure are discussed, respectively, and related error modification functions are provided. The research is meaningful for improving the precision of optical fiber sensors applied to structures with the existing of local debonding interface, which will be ultimately serve for showing true mechanical state of structures.
The large span and heterogeneous components of multi-layered pavement structure usually bring about stochastic damage, and many modern approaches, such as ground penetrating radar, integral imaging and optical fiber sensing technology, have been employed to detect the degeneration mechanism. Restricted by the cost and universality, novel elements for pavement monitoring are in high demand. Optical fiber sensing technology for high sensitivity, long stability, anti-corrosion and resistance to water erosion then is considered. Therefore, a movable FBG sensor located in flexible pipe is developed, which has long stroke inside inner wall of the hollow pipe, and a full-scale shape of the structure could be sketched just with one FBG. Theoretical and experimental methods about establishing the relationship between wavelength variable and curvature have been provided, and function about reconfiguring the coordinate is converted to a mathematic question. Move over, transfer error modification has been taken into account for modify related error. Multi-layered pavement model embedded with this sensor will be accomplished to inspect its performance in later work. The work in the paper affords a feasible method for shape monitoring and would be potentially valuable for the maintenance and inverse design of pavement structure.
This paper introduces an optical fiber based sensing system design for multi-layered pavement structural
health monitoring. The co-line and integration design of FBG (Fiber Bragg Gating) sensors and BOTDR
(Brillouin Optical Time Domain Reflectometry) sensors will ensure the large scale damage monitoring and
local high accurate strain measurement. The function of pavement structure multi-scale shape measurement
will provide real time subgrade settlement and rutting information. The sensor packaging methodology and
strain transfer problem of the system will also be discussed in this paper. Primary lab tests prove the potential
and feasibility of the practical application of the sensing system.
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.
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.
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).
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 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.
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.
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