Bio-inspired Sensors for Structural Health Monitoring - Biotechnologies and Biomimetics for Civil Engineering - page 251

For example, a fatal rupture and explosion of a natural gas pipeline in Carlsbad,
NM in 2000 was a result of deterioration caused by corrosion and other factors
(NTSB 2003 , Peekema 2013 ). Another example is cracking in steel girders of the
Interstate I-794 Daniel Webster Hoan Bridge (Milwaukee, WI) in 2000 that caused
one of its spans to sag by more than 1.2 m (4 ft); as a result, a total of US$7.8
million was invested for repair and retrofit (Fisher et al. 2001 ). The catastrophic
collapse of the Sgt. Aubrey Cosens V.C. Memorial Bridge (Latchford, ON,
Canada) in 2003 was due to fatigue rupture of hanger connections within the arch
bridge's enclosed steel boxes (Biezma and Schanack 2007 ). As evident from these
historic events, the timely detection of damage is crucial for facilitating the nec-
essary structural repairs, ensuring optimal performance of structures, and

enhancing public safety.
Routine visual inspection by trained technicians continues to be the predomi-
nant methodology adopted for evaluating the performance and safety of existing
structural systems. For instance, the National Bridge Inspection Standards, set
forth by the U.S. Federal Highway Administration, mandates that all highway
bridges be inspected every 24 months ( FWHA 2004). Due to their inherent time-,
labor-, and cost-intensiveness (Hartle et al. 1990 ; Moore et al. 2001 ), tethered
sensing systems are sometimes used to supplement visual inspection (Zhang et al.
2007 ; Sumitro et al. 2005 ; Celebi 2006 ). While simple sensors such as acceler-
ometers and strain gages offer quantitative measures of structural response, their
high costs to install and maintain the great lengths of coaxial cables have limited
the number and density of sensors installed per structure (Celebi 2006 ). The end
result is a sparsely distributed monitoring system (relative to the size of the
structure) that is, at times, poorly scaled with structural damage, which is inher-
ently highly localized.
On the other hand, structural health monitoring (SHM) integrates structural
response data collected from distributed sensors with feature extraction algorithms
for identifying the presence of damage relative to its healthy or pristine state.
Statistical analysis is then required for relating accumulated damage to structural
functionality, resistance to various loading scenarios, and overall safety (Farrar
and Worden 2007 ). According to Farrar and Worden ( 2007 ) and Rytter ( 1993 ),
damage detection involves a five-step process of increasing complexity, namely:
(1) existence; (2) location; (3) type; (4) extent; and (5) prognosis. One of the major
considerations in which any or all of these five steps could be achieved relies on
the careful selection and deployment of sensors. These sensors need to be stra-
tegically instrumented and need to provide high signal-to-noise data that are
indicative of damage, where damage and detection threshold is defined based on
the end-user's needs.
In fact, a plethora of emerging technologies have been proposed over the past
few decades for autonomous SHM and damage detection. Examples include fiber
optics and fiber Bragg gratings for distributed strain and temperature measure-
ments (Tsuda et al. 1999 ; Kersey 1996 ), wireless sensors for densely distributed
system identification (Lynch and Loh 2006 ; Spencer Jr. et al. 2004 ), and piezo-
electric sensor and actuator arrays for active sensing and acoustic emissions