In this study, an algorithm using image processing techniques is proposed to identify bolt-loosening in bolted connections of steel structures. Its basic concept is to identify rotation angles of nuts from a pictured image, and is mainly consisted of the following 3 steps: (1) taking a picture for a bolt joint, (2) segmenting the images for each nut by image processing techniques, and (3) identifying rotation angle of each nut and detecting bolt-loosening. By using the concept, an algorithm is designed for continuous monitoring and inspection of the bolt connections. As a key imageprocessing technique, Hough transform is used to identify rotation angles of nuts, and then bolt-loosening is detected by comparing the angles before and after bolt-loosening. Then the applicability of the proposed algorithm is evaluated by experimental tests for two lab-scaled models. A bolted joint model which consists of a splice plate and 8 sets of bolts and nuts with 2×4 array is used to simulate inspection of bridge connections, and a model which is consisted of a ring flange and 32 sets of bolt and nut is used to simulate continuous monitoring of bolted connections in wind turbine towers.
This paper presents a technique for local structural health monitoring (SHM) of multiple structural connections by using
multi-channel wireless impedance sensor nodes based on Imote2 platform. To achieve the objective, following approaches
are implemented. Firstly, an Imote2-based multi-channel wireless impedance sensor node is designed for automated and cost-efficient
impedance-based SHM of structural connections. Secondly, an interface washer associate with impedance
measurements is designed to monitor bearing stress which is considered as main effect on structural connections. Finally,
performances of the multi-channel wireless impedance sensor node and the interface washer are experimentally validated for
a bolted connection model. A damage monitoring method using RMSD index of electro-mechanical impedance signatures is
used to examine the strength of each individual bolted connection.
KEYWORDS: Sensors, Structural health monitoring, Ferroelectric materials, Lab on a chip, Feature extraction, Data acquisition, Liquid crystal lasers, Digital filtering, Microcontrollers, Humidity
This paper presents hybrid structural health monitoring of steel girder connections using wireless acceleration and
impedance sensor nodes based on Imote2-platform. To achieve the research objective, the feasibility of the sensor nodes
is examined about its performance for vibration-based global monitoring and impedance-based local monitoring in the
structural systems as the following approaches. First, a damage monitoring scheme is described in parallel with global
vibration-based methods and local impedance-based methods. Second, multi-scale sensor nodes that enable combined
acceleration-impedance monitoring are described on the design of hardware components and embedded software to
operate. Third, the performances of the multi-scale sensor nodes are experimentally evaluated from damage monitoring
in a lab-scaled steel girder with bolted connection joints.
KEYWORDS: Bridges, 3D modeling, System identification, Data modeling, Sensors, 3D acquisition, Safety, Systems modeling, Civil engineering, Structural health monitoring
In this study, a multi-phase model update approach for system identification of real railway bridge using vibration test
results is present. First, a multi-phase system identification scheme designed on the basis of eigenvalue sensitivity
concept is proposed. Next, the proposed multi-phase approach is evaluated from field vibration tests on Wondongcheon
bridge which is a steel girder railway bridge located in Yangsan, South Korea. On the bridge, a few natural frequencies
and mode shapes are experimentally measured under the excitation of trains, ambient vibration and free vibration. The
corresponding modal parameters are numerically calculated from a three-dimensional finite element (FE) model which is
established for the target bridge. Eigenvalue sensitivities are analyzed for potential model-updating parameters of the FE
model. Then, structural subsystems are identified phase-by-phase using the proposed model update procedure. Based on
model update results, a baseline model of the Wondongcheon railway bridge is identified.
In this study, a technique using wireless impedance sensor node and interface washer is proposed to monitor prestressforce
in PSC girder bridges. In order to achieve the goal, the following approaches are implemented. Firstly, a wireless
impedance sensor node is designed for automated and cost-efficient prestress-force monitoring. Secondly, an
impedance-based algorithm is embedded in the wireless impedance sensor node for autonomous prestress-force
monitoring. Thirdly, a prestress-force monitoring technique using an interface washer is proposed to overcome
limitations of the wireless impedance sensor node such as measureable frequency ranges with narrow band. Finally, the
feasibility and applicability of the proposed technique are evaluated in a lab-scaled PSC girder model for which several
prestress-loss scenarios are experimentally monitored by the wireless impedance sensor node.
In this study, an output-only modal analysis approach for wireless sensor nodes is proposed on the basis of assumption
that a target structure is a linear system. In order to achieve the objective, the following approaches are implemented.
Firstly, an output-only modal analysis method is selected for the wireless sensor networks. Secondly, the effect of time unsynchronization
on the modal analysis method is mathematically derived. Thirdly, a new modal analysis approach
using complex mode-shapes is proposed to extract modal parameters from unsynchronized signals. Finally, the proposed
approach is evaluated by numerical tests and experimental tests.
Acceleration and impedance signatures extracted from a structure are appealing features for a prompt diagnosis on
structural condition since those are relatively simple to measure and utilize. However, the feasibility of using them for
damage monitoring is limited when their changes go undisclosed due to uncertain temperature conditions, particularly
for large structures. In this study, temperature effect on hybrid damage monitoring of prestress concrete (PSC) girder
bridges is presented. In order to achieve the objective, the following approaches are implemented. Firstly, a hybrid
monitoring algorithm using acceleration and impedance signatures is proposed. The hybrid monitoring algorithm mainly
consists of three sequential phases: 1) the global occurrence of damage is alarmed by monitoring changes in acceleration
features, 2) the type of damage is identified as either prestress-loss or flexural stiffness-loss by identifying patterns of
impedance features, 3) the location and the extent of damage are estimated from damage index method using natural
frequency and mode shape changes. Secondly, changes in acceleration and impedance signatures were investigated under
various temperature conditions on a laboratory-scaled PSC girder model. Then the relationship between temperatures
and those signatures is analyzed to estimate and a set of empirical correlations that will be utilized for the damage
alarming and classification of PSC girder bridges. Finally, the feasibility of the proposed algorithm is evaluated by using
a lab-scaled PSC girder bridge for which acceleration and impedance signatures were measured for several damage
scenarios under uncertain temperature conditions.
In this study, a system using autonomous smart sensor nodes is developed for bridge structural health monitoring (SHM).
In order to achieve the research goal, the following tasks are implemented. Firstly, acceleration-based and impedancebased
smart sensor nodes are designed. Secondly, an autonomous operation system using smart sensor nodes is designed
for hybrid health monitoring using global and local health monitoring methods. Finally, the feasibility and applicability
of the proposed system are experimentally evaluated in a lab-scaled prestressed concrete (PSC) girder for which a set of
damage scenarios are experimentally monitored by wireless sensor nodes and embedded software.
KEYWORDS: Smart sensors, Picosecond phenomena, Sensors, Bridges, Linear filtering, Algorithm development, Signal processing, Microcontrollers, Microsoft Foundation Class Library, Performance modeling
In this study, a smart sensor node is developed for hybrid health monitoring of PSC girder bridges. Hybrid health
monitoring of those structures is to alarm damage occurrence, to classify damage-types, and to identify damage locations
and severities by measuring accelerations and impedance signals. In order to achieve the objective, the following
approaches are implemented. Firstly, a smart sensor node with wireless sensing capacity and embedded monitoring
algorithms is developed for measuring acceleration. Secondly, we design a hybrid damage monitoring scheme that
combines acceleration-based and impedance-based methods for PSC girder bridges. Finally, the performance of the
smart sensor node is evaluated using a laboratory-scale PSC girder bridge model for which acceleration and impedance
signals were measured for prestress-loss and stiffness-loss cases.
Among many damage types, prestress-loss in tendon is the major one that should be monitored in its early stage in order
to secure the safety of PSC girder bridges. This damage-type obviously change vibration characteristics, but with
apparent difference depending on sensing mechanism as well as information analysis. Recently, there have been research
efforts to develop wireless smart sensor nodes embedded damage monitoring algorithms for various sensing
mechanisms. In this study, vibration-based damage monitoring algorithms which are appropriate for the smart sensor
nodes to alarm the occurrence of prestress-loss in PSC girder bridges are presented. Firstly, two sensing mechanisms are
considered for vibration characteristics: one is acceleration and the other is electro-mechanical impedance. Also, four
acceleration-based algorithms and three impedance-based algorithms are selected to extract features from those signals.
Secondly, the performances of those selected methods are evaluated using a large-scaled PSC girder for which a set of
acceleration-impedance tests were measured for several prestress-loss scenarios by using both commercial instruments
and a wireless smart sensor node.
In this study, a vibration-based method to simultaneously predict prestress-loss and flexural crack in PSC girder bridges
is presented. Prestress-loss and flexural crack are two typical, but quite different in nature, types of damage which can be
occurred in PSC girder bridges. The following approaches are implemented to achieve the objective. Firstly, two
vibration-based damage detection techniques which can predict prestress-loss and flexural crack are described. The
techniques are prestress-loss prediction model and mode-shape-based crack detection method. In order to verify the
feasibility and practicality of the techniques, two different lab tests are performed. A free-free beam with external
unbonded tendons is used to verify the feasibility of the prestress-loss prediction model. In additional, a PSC girder with
an internal unbonded tendon is used to evaluate the practicality of the prestress-loss prediction model and the mode-shape-
based crack detection method.
Artificial neural networks (ANNs) have been increasingly utilized for structural health monitoring (SHM) due to the
advantage that it needs only a few training data to detect damage in structures. In this study, a new damage monitoring
method using a set of parallel ANNs and acceleration signals is developed for alarming locations of damage in PSC
girder bridges. First, theoretical backgrounds are described. The problem addressed in this paper is defined as the
stochastic process. In addition, a parallel ANN-algorithm using output-only acceleration responses is newly designed for
damage detection in real time. The cross-covariance of two acceleration-signals measured at two different locations is
selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns
and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility of the proposed
method is evaluated from numerical model tests on PSC beams for which accelerations were acquired before and after
several damage cases.
To develop a promising hybrid structural health monitoring system, which enables to detect damage by the dynamic response of the entire structure and more accurately locate damage with denser sensor array, a combined use of mechanical vibration and electro-mechanical impedance is proposed. For the verification of the proposed healthmonitoring scheme, a series of damage scenarios are designed to simulate various situations at which the connection joints can experience during their service life. The obtained experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the connecting states and the target damage locations. From the analysis, it is shown that the proposed hybrid health monitoring system is successful for acquiring global and local damage information on the structural joints; hence, its effectiveness is verified.
Even significant damage may cause very small changes in structural characteristics, particularly for large structures. Furthermore, these changes may go undetected due to changes in environmental and operational conditions. In this paper, the temperature-driven variability on a combined structural health monitoring (SHM) system is examined in a model plate-girder bridge. The combined SHM system consists of global vibration-based technique and local electro-mechanical impedance (EMI) based technique. First, dynamic modal parameters of the test structure are measured before and after the occurrence of flexural cracks at various temperatures. Also, EMI signatures are sensed before and after the changes in support systems at various temperatures. Next, the risk of damage-occurrence in the structure is alarmed by statistical pattern recognition of the signals. Damage-induced changes in the signals are distinguished from temperature-driven uncertainty. The effect of temperature variability is also assessed to estimate the accuracy of damage detection.
The variation of modal properties caused by temperature effects is assessed to correct modal data used for damage detection in plate-girder bridges. First, experiments on model plate-girder bridges are described. Next, the relationship between temperature and natural frequencies is estimated and a set of empirical frequency-correction formula are analyzed for the test structure. Finally, a frequency-based method is used to locate and estimate severity of damage in the test structure using experimental modal data which are adjusted by the frequency-correction formula. Here, local damage in beam-type structures is detected by using measured frequencies and analytical mode shapes.
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