Geology Reference
In-Depth Information
1. INTRODUCTION
2005). In general, the parametric methods can
detect the damage location but require complete
measurements and extensive computations for
large structures. The non-parametric methods, on
the other hand, require less measurement and have
better adaptability to large structures but provide
a global assessment on the health status of the
structure (Gonzalez & Zapico, 2008).
Sensor performance also degrades with time
under varying environmental conditions (Koh
et al, 2003, De Oliveira et al, 2004, Elouedi et
al, 2004, Blackshire et al, 2006, Glisic & In-
audi 2007). Different degradation mechanisms
have been observed in different types of sensors
(surface-bonded or fully-embedded) under various
environmental effects such as temperature- and
moisture-cycling (Elouedi et al, 2004, Blackshire
et al, 2006, Glisic & Inaudi 2007). Sensor perfor-
mance is particularly relevant in the field of road
infrastructure where the loading conditions affect-
ing the main structure (traffic loads, temperature
cycling, etc.) also affect the sensor measurements.
Details on recent sensor technologies can be found
in (Ansari 2005, Manders et al, 2006). While sen-
sor faults are difficult to be handled using existing
SI techniques, bond graphs are capable of model-
ing both the system components and the sensors.
This enables damage detection in both structural
components and sensors. Bond graphs facilitate
also the extraction of damage signatures off-line
before sensor data collection thus providing rapid
identification of the damage location through
qualitative comparison of predicted and observed
signatures. The quantification of the damage size is
performed by analyzing the substructure contain-
ing the damaged component only, thus, reducing
the computational costs. The idea of using BG
in system identification of frame structures was
introduced by these authors (Moustafa et al, 2010).
The bond graph technique is different from
most existing SI methods since it provides: (1)
graphical-modeling tool for dynamic systems
under time-varying loads, (2) domain-independent
modeling tool for dynamic analysis and health
This chapter proposes a graphical, domain-
independent, energy-based framework that is
capable of modeling multidisciplinary systems
with interacting components from structural, me-
chanical, electrical, and hydraulic domains. This
framework is based on the bond graph (BG) theory
introduced by Paynter (1961) and developed by
Karnopp, Rosenberg and Margolis (Rosenberg &
Karnopp 1983, Karnopp & Margolis, 2006). For
example, an electrical circuit and a mechanical
system can be described with the same bond graph
model. The use of bond graphs in electrical and
mechanical engineering is well established. This
method, however, has not received significant
research attention in civil engineering. The BG
model of a dynamic system represents the system
equations of motion implicitly in a graphical form
using bond graph elements. These elements model
inertial, stiffness, damping and external forces. BG
elements include serial and parallel junctions that
govern the dynamic equilibrium of the structure
subsystems.
Civil structures deteriorate over time and
experience damage due to natural events such
as earthquakes and wind. Structural health
monitoring (SHM) is a process that aims at pro-
viding accurate and in-time information of the
structural health condition of existing structures.
A comprehensive review on recent advances in
health assessment of structures can be found in
Doebling et al (1998), Alvin et al (2003), Chang
et al (2003), Koh et al (2003), Lui & Ge (2005),
Gonzalez & Zapico (2008) and Moustafa et al
(2010). System identification (SI) techniques can
be grouped into parametric and non-parametric
methods. The parametric methods identify changes
in the structure global parameters (e.g. natural
frequencies, mode shapes and modal damping)
or in the local parameters (e.g. members stiff-
nesses and damping) to characterize the structural
damage (Doebling et al 1998, Alvin et al. 2003,
Chang et al, 2003, Koh et al, 2003, Lui & Ge,
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