This paper presents the use of a novel fiber optic accelerometer system to monitor civil engineering structures in real time. This sensor system integrates the Moire fringe phenomenon with fiber optics to achieve accurate and reliable measurements. A low-cost signal processing unit implements unique algorithms to further enhance the resolution and increase the dynamic bandwidth of the sensors. There are two major benefits in using this fiber optic accelerometer system for monitoring civil engineering structures. One is its immunity to electromagnetic (EM) interference making it suitable for difficult applications in such environments involving strong EM fields, electrical spark-induced explosion risks, and cabling problems, prohibiting the use of conventional electromagnetic accelerometers. The other benefit is its ability to measure both low- and high-amplitude vibrations with a constantly high resolution without pre-setting a gain level, as usually required in a conventional accelerometer. This benefit makes the sensor system particularly useful for real-time measurement of both ambient vibration (that is often used for structural health monitoring) and strong motion. This paper presents the prototype hardware and software development, characterization tests, and applications to real-time damage assessment, demonstrating the uniquely high performance of the Moire fringe fiber optic sensor system.

This paper is focus on the applications of EM sensor in stress measurement for steel cables used in bridges. The calibration of the EM sensors has been investigated. A new methodology makes this calibration process done either in the laboratory or in the field. Application of EM sensors on QianJiang No.4 Bridge to monitor the stresses of key hanger cables and post-tensioned cables is presented. Furthermore, a multi-EM sensor configuration has been developed to monitor the stress in a multi-strand-cable system.

The quality of test data is an important consideration in conducting field experiments on civil infrastructure. In addition to possible errors due to the experimental setup, the uncertainties due to incomplete knowledge of a structure's behavior and its interactions with the natural environment greatly affect the reliability of the system identification results. This paper discusses the uncertainties related to ambient vibration testing of a long-span steel arch bridge and possible ways to mitigate them. The consistency of the identified parameters is examined through statistical analyses.

A substructural identification methodology by the direct use of acceleration measurements with neural networks is proposed. The rationality of the substructural identification methodology employing a substructural acceleration-based emulator neural network (SAENN) and a substructural parametric evaluation neural network (SPENN) is explained. Based on the discrete time solution of the state space equation of the substructure, the theory basis for the construction of SAENN and SPENN is described. An evaluation index called root mean square of prediction difference vector (RMSPDV) corresponding to acceleration response is presented to evaluate the condition of object structure. The performance of the SAENN for acceleration forecasting and SPENN for parametric identification is examined by numerical simulations with a substructure of a 50-DOFs shear structure involving all stiffness and damping coefficient values unknown. Based on the trained SAENN and the PENN, the inter-storey stiffness and damping coefficients of the substructure are identified. Since the strategy does not require structural modes or frequencies extraction, it is computationally efficient, thus providing a possibly viable tool for structural identification and damage detection of large-scale infrastructures.

Research and development related to homeland security has emerged as one of the most challenging topics nationwide in the recent few years. Effective structural health monitoring, diagnosis, and prognosis are of great importance for the safety and reliability analysis for civil infrastructural systems. While the technologies of sensor-arrays embedded in host structures are widely employed for structural health monitoring, the key issue is how to set-up a physics-based model framework and its corresponding efficient algorithm to evaluate the quality of the host structures through the output of the sensors. It is a frontal interdisciplinary topic bridging the microstructural damage mechanics and signal estimation theory. By employing a multi-scale constitutive model of solids with damage, this paper conducts an exploratory research on the modeling and algorithm of estimating the mean value of crack density and the distribution of crack orientation of a cracked plate subjected to unidirectional tension. Simulation results reveal that the framework and algorithm provide a reasonable performance in recovering crack orientation.

Composite bridges offer many advantages compared to current steel and aluminum bridges including their lightweight and superior corrosion resistance properties. This paper presents the results of a comprehensive on-going research program to develop innovative field repair techniques for composite bridges. In this study, an innovative Diagnostic/Prognostic System (DPS) for monitoring the performance and reliability of a smart repair kit (SRK) for composite military bridges has been developed. The DPS system is founded on three technologies, namely; optical fiber sensing, remote data transmission and virtual testing. In developing this system, both laboratory and virtual tests simulating the different potential damage scenarios. In order to minimize the number of expensive full-scale tests, virtual testing technique using an advanced progressive failure simulator code (GENOA) was utilized. The results of the pre-simulated damage scenarios are stored in a secure database. Different composite patches with optical fiber sensors are being developed for different damage types. These smart patches act as health-monitoring devices for different parts of the composite bridge, especially areas surrounding the repaired portions of the bridge. In the event of local damage such as debonding of any patch due to excessive loading or lower application quality, for example, the stress distribution will change.

Rail lines are subject to many types of damage that, in the worst cases, can cause train derailments. The damage can arise from either manufacturing defects or external factors, possibly even terrorist acts to disrupt the civil infrastructure. Current rail inspection techniques require train traffic to be interrupted while workers and equipment move along the track. Moreover, a technician with rail testing experience is required to analyze the results. This paper focuses on simple proof of concept experiments to determine if impedance based structural health monitoring may be used to detect anomalies in rail tracks, and in particular broken rails. The technique applies a very low voltage (one volt) high frequency wave to a structure, measures its response and determines the location and extent of a rail break. The monitoring device is envisioned to run off of ambient vibration and thermal gradients provided by passing trains and daily thermal cycles, store the energy and utilize the stored energy periodically to inspect the track (according to the track usage schedule). If damage occurs or starts to occur, a warning signal would be transmitted to substation then broadcast to the appropriate operator listing the location and extent of the damage.

When lifeline systems such as water delivery networks are damaged, it is critical to pinpoint the location of the damage, to assess the extent of the damage, and to mitigate the damage in real-time. We propose a wireless sensor network consisting of densely populated, optimally located MEMS sensors that monitor pipe pressure and transmitting data wirelessly. Transient analysis of water pressure in the networked pipes showed that the damage sustained by the pipe creates a state of transient in the pipe pressure, which is detectable by MEMS sensors for damage localization and estimation. This is critical information to enhance post-event disaster mitigation.

In the years following the 1994 Northridge earthquake, many researchers in the earthquake community focused on the development of GIS-based loss estimation tools such as HAZUS. These highly customizable programs have many users, and different results after an event can be problematic. Online IMS (Internet Map Servers) offer a centralized system where data, model updates and results cascade to all users. INLET (Internet-based Loss Estimation Tool) is the first online real-time loss estimation system available to the emergency management and response community within Southern California. In the event of a significant earthquake, Perl scripts written to respond to USGS ShakeCast notifications will call INLET routines that use USGS ShakeMaps to estimate losses within minutes after an event. INLET incorporates extensive publicly available GIS databases and uses damage functions simplified from FEMA's HAZUS^(R) software. INLET currently estimates building damage, transportation impacts, and casualties. The online model simulates the effects of earthquakes, in the context of the larger RESCUE project, in order to test the integration of IT in evacuation routing. The simulation tool provides a "testbed" environment for researchers to model the effect that disaster awareness and route familiarity can have on traffic congestion and evacuation time.

Building inventory is a core input to risk and loss evaluation models, and as such, plays a key role in providing decision support for the disaster management community. This paper describes the extraction of detailed building inventory data, using optical remote sensing data within the new MIHEA (Mono Image Height Extraction Algorithm) tool. MIHEA is developed to extract building inventory information such as height, shape and square footage from single high-resolution remotely sensed images. Its pilot implementation in conjunction with QuickBird satellite imagery for London, United Kingdom and Long Beach, USA is described. A methodological protocol is proposed for integrating remote sensing-derived data into loss estimation tools, such as HAZUS® and INLET, to replace default datasets which offer limited accuracy at a census tract scale. The study suggests that when used in conjunction with MIHEA, remote sensing is a valuable source of building inventory information for locations around the World. Preliminary results for the integration of derived data into loss estimation tools are expected in Summer 2006.

Bridge management officials have expressed a keen interest in the use of low-cost and easy-to-install wireless sensors to record bridge responses during short-term load testing. To illustrate the suitability of wireless sensors for short-term monitoring of highway bridges, a wireless monitoring system is installed upon the Grove Street Bridge to monitor structural responses during static and dynamic load testing. Specifically, load testing of the Grove Street Bridge is conducted after its construction to validate the behavior of a novel jointless bridge deck constructed from a high-performance fiber reinforced cementitious composite (HPFRCC) material. A heterogeneous array of sensing transducers are installed in the bridge including metal foil strain gages, accelerometers and linear variable differential transducers (LVDTs). First, the acceleration response of the bridge is monitored by the wireless system during routine traffic loading. Modal parameters (modal frequencies and mode shapes) are calculated by the wireless sensors so that an analytical model of the bridge constructed in a standard commercial finite element package can be updated off-line. Next, the bridge is closed to traffic and trucks of known weight are parked on the bridge to induce static deformations. The installation strategy of the wireless monitoring system during static load testing is optimized to monitor the strain and rotation response of the HPFRCC deck. The measured static response of the deck is compared to that predicted by the updated analytical model.

There is continuing plans and actions from terrorists to use "violence to inculcate fear with intent to coerce or try to intimidate governments or societies in the pursuit of goals that are generally political, religious or ideological." (Joint Pub 3-07.2) One can characterize the types of attacks and plan to interdict terrorist actions before they become crises. This paper focuses on Radiological (RDD) and Nuclear (WMD) threats. The X-ray inspection process and the use of dual-energy imaging will interdict materials for WMDs. Listed herewith is "several major characteristics that one can exploit for the detection. First, both WMDs and RDDs are radioactive. Therefore, one can hope to detect radiation coming from the containers to identify the threat. However since uranium and plutonium are largely self-shielding and since lead can be used to shield and hide these substances, passive detection of emitted radiation can be easily defeated. An important second characteristic is that WMDs and shielded dirty bombs contain materials with very high atomic numbers. Since normal commerce rarely contains materials with atomic numbers higher than that of iron, dual-energy imaging technology can detect such materials automatically, for the successful interdiction of WMDs and dirty bombs". (Bjorkolm 2005)

Government agencies and homeland security related organizations have identified the need to develop and establish a wide range of unprecedented capabilities for providing scientific and technical forensic services to investigations involving hazardous chemical, biological, and radiological materials, including extremely dangerous chemical and biological warfare agents. Pacific Northwest National Laboratory (PNNL) has developed a prototype portable, hand-held, hazardous materials acoustic inspection prototype that provides noninvasive container interrogation and material identification capabilities using nondestructive ultrasonic velocity and attenuation measurements. Due to the wide variety of fluids as well as container sizes and materials encountered in various law enforcement inspection activities, the need for high measurement sensitivity and advanced ultrasonic measurement techniques were identified. The prototype was developed using a versatile electronics platform, advanced ultrasonic wave propagation methods, and advanced signal processing techniques. This paper primarily focuses on the ultrasonic measurement methods and signal processing techniques incorporated into the prototype. High bandwidth ultrasonic transducers combined with an advanced pulse compression technique allowed researchers to 1) obtain high signal-to-noise ratios and 2) obtain accurate and consistent time-of-flight (TOF) measurements through a variety of highly attenuative containers and fluid media. Results of work conducted in the laboratory have demonstrated that the prototype experimental measurement technique also provided information regarding container properties, which will be utilized in future container-independent measurements of hidden liquids.