Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.

Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient damage (loss of preload) in a bolted joint. In this study we examine a physical experiment on a single-bolt aluminum lap joint as well as a three-dimensional physics-based simulation designed to model the behavior of guided ultrasonic waves through a similarly configured joint. A multiple bolt frame structure is also experimentally examined. In the physical experiment each signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the lap joint and sensed using an equivalent MFC patch on the opposite side of the joint. The model applies the waveform via direct nodal displacement and 'senses' the resulting displacement using an average of the nodal strain over an area equivalent to the MFC patch. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence. This damage detection algorithm is used to evaluate multiple damage levels and locations.

This article describes the development of a fiber optic accelerometer based on Fiber Bragg Gratings (FBG). The accelerometer utilizes the stiffness of the optical fiber and a lumped mass in the design. Acceleration is measured by the FBG in response to the vibration of the fiber optic mass system. The wavelength shift of FBG is proportional to the change in acceleration, and the gauge factor pertains to the shift in wavelength as a function of acceleration. Low frequency version of the accelerometer was developed for applications in monitoring bridges. The accelerometer was first evaluated in laboratory settings and then employed in a demonstration project for condition assessment of a bridge. Laboratory experiments involved evaluation of the sensitivity and resolution of measurements under a series of low frequency low amplitude conditions. The main feature of this accelerometer is single channel multiplexing capability rendering the system highly practical for application in condition assessment of bridges. This feature of the accelerometer was evaluated by using the system during ambient vibration tests of a bridge. The Frequency Domain Decomposition method was employed to identify the mode shapes and natural frequencies of the bridge. Results were compared with the data acquired from the conventional accelerometers.

Structural Health Monitoring (SHM), particularly remote monitoring, is an emerging field with great potential to help infrastructure owners obtain more and up-to-date knowledge of their structures. The methodology could provide supplemental information to guide the frequency and extent of visual inspections, and the possible need for maintenance. The instrumentation for a SHM system needs to be developed with longevity and the objectives for the system in mind. Sensors need to be selected for reliability and durability, sited where they provide the maximum information for the objectives, and where they can be accessed and replaced should the need arise over the monitoring period. With the rapid changes now occurring with sensors and software, flexibility needs to be in place to allow the system to be upgraded over time. Damage detection needs to be considered in terms of the type of damage that needs to be detected, informing maintenance requirements, and how detection can be achieved. Current vibration analysis techniques appear not yet to have achieved the necessary sensitivity for that purpose. Societal factors will influence the design of a SHM system in terms of the sophistication of the instrumentation and methodology employed.

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

Composite bridges offer many advantages compared to current steel and aluminum bridges. This paper presents the results of a comprehensive on-going research program to develop innovative Diagnostic Prognostic System (DPS) and a structural evaluation of Composite Army Bridge (CAB) system. The DPS is founded on three technologies: optical fiber sensing, remote data transmission, and virtual testing. In developing this system, both laboratory and virtual test were used in different damage scenarios. Health monitoring with DPS entailed comparing live strain data to archived strained data in various bridge locations. For field repairs, a family of composite chords was subjected to simple ramp loads in search of ultimate strength. As such, composite bridge specimens showcased their strengths, heralded the viability of virtual testing, highlighted the efficacy of field repair, and confirmed the merits of health monitoring.

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

The use of fiber reinforced polymer (FRP) reinforcement in concrete structures has been on the rise due to its advantages over conventional steel reinforcement such as corrosion. Reinforcing steel corrosion has been the primary cause of deterioration of reinforced concrete (RC) structures, resulting in tremendous annual repair costs. One application of FRP reinforcement to be further explored is its use in RC frames. Nonetheless, due to FRP's inherently elastic behavior, FRP-reinforced (FRP-RC) members exhibit low ductility and energy dissipation as well as different damage mechanisms. Furthermore, current design standards for FRP-RC structures do not address seismic design in which the beam-column joint is a key issue. During an earthquake, the safety of beam-column joints is essential to the whole structure integrity. Thus, research is needed to gain better understanding of the behavior of FRP-RC structures and their damage mechanisms under seismic loading. In this study, two full-scale beam-column joint specimens reinforced with steel and GFRP configurations were tested under quasi-static loading. The control steel-reinforced specimen was detailed according to current design code provisions. The GFRP-RC specimen was detailed in a similar scheme. The damage in the two specimens is characterized to compare their performance under simulated seismic loading.

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.

Damage detection has been proven to be a challenging task in structural health monitoring (SHM) due to the fact that damage cannot be measured. The difficulty associated with damage detection is related to electing a feature that is sensitive to damage occurrence and evolution. This difficulty increases as the damage size decreases limiting the ability to detect damage occurrence at the micron and submicron length scale. Damage detection at this length scale is of interest for sensitive structures such as aircrafts and nuclear facilities. In this paper a new photonic sensor based on photonic crystal (PhC) technology that can be synthesized at the nanoscale is introduced. PhCs are synthetic materials that are capable of controlling light propagation by creating a photonic bandgap where light is forbidden to propagate. The interesting feature of PhC is that its photonic signature is strongly tied to its microstructure periodicity. This study demonstrates that when a PhC sensor adhered to polymer substrate experiences micron or submicron damage, it will experience changes in its microstructural periodicity thereby creating a photonic signature that can be related to damage severity. This concept is validated here using a three-dimensional integrated numerical simulation.

Bridges form crucial links in the transportation network especially in high seismic risk regions. This research aims to provide a quantitative methodology for post-earthquake performance evaluation of the bridges. The experimental portion of the research involved shake table tests of a 4-span bridge which was subjected to progressively increasing amplitudes of seismic motions recorded from the Northridge earthquake. As part of this project, a high resolution long gauge fiber optic displacement sensor was developed for post-seismic evaluation of damage in the columns of the bridge. The nonlinear finite element model was developed using Opensees program to simulate the response of the bridge and the abutments to the seismic loads. The model was modified to predict the bent displacements of the bridge commensurate with the measured bent displacements obtained from experimental analysis results. Following seismic events, the tangential stiffness matrix of the whole structure is reduced due to reduction in structural strength. The nonlinear static push over analysis using current damaged stiffness matrix provides the longitudinal and transverse ultimate capacities of the bridge. Capacity loss in the transverse and longitudinal directions following the seismic events was correlated to the maximum displacements of the deck recorded during the events.