Structural Health Monitoring Using Smart Sensors - ideals ...

scalability to a large number of smart sensors and autonomous operation, as well as
effective damage detection capability.
Chapter 2 provides the background of this research. Smart sensors, middleware
services, and SHM are briefly reviewed. Research efforts employing smart sensors for
SHM applications are then summarized and difficulties in these attempts are addressed. In
the subsequent chapters, an SHM framework is realized on a smart sensor network that
resolves the majority of these difficulties.
Chapter 3 describes the SHM architecture developed in this research. A homogeneous
hardware configuration is selected, while smart sensor nodes are functionally
differentiated into several categories. Smart sensors, middleware services, and damage
detection algorithms used in such networks are briefly explained.
In Chapter 4, sensor boards for one of the smart sensor platforms, the Mica2, are
developed to demonstrate sensor board customizability according to SHM requirements.
Because strain sensors for the Mica2 were not available, a strain sensor board is developed
as well as an Anti-Aliasing (AA) filter board. Scale-model experiments show that these
sensor boards can facilitate accurate measurement of structural responses.
Middleware services realized as part of this research for SHM applications are
discussed in Chapter 5. Middleware services include reliable communication, modelbased
data aggregation, and synchronized sensing. These middleware services can be used
in a wide variety of civil engineering applications.
Chapter 6 discusses algorithms to be implemented on smart sensors. The DCS
algorithm has the potential to realize densely deployed smart sensor networks for SHM
because of its distributed and coordinated data processing. Algorithmic components of
DCS are briefly reviewed. The damage detection algorithm in DCS is then extended by
replacing the mass perturbation Damage Locating Vector (DLV) method with the
Stochastic Damage Locating Vector (SDLV) method. The SDLV method is shown to
simplify damage detection and reduce total power consumption. This chapter provides the
algorithmic basis for the subsequent chapters.
In Chapter 7, the DCS algorithm is implemented on the Imote2 smart sensor platform
using the middleware services and algorithms. First, numerical functions are ported to the
Imote2. Second, the capabilities of the generic sensor board are examined. Third, each of
the DCS algorithms is implemented on smart sensors, and its validity is numerically
investigated.
Chapter 8 describes experimental verification of the developed framework. Smart
sensor nodes are placed on a scale-model, three-dimensional truss. One of the bar
elements of the truss is replaced with a more slender element to simulate linear damage to
the truss. The smart sensor system measures acceleration responses of the model and
localizes damage. Calculation and communication time, the battery life, and damage
detection capability are discussed based on findings from the experiments.
Chapter 9 summarizes the research detailed in this report and presents possible
directions for future research on SHM using smart sensors.
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