The motivation of this study is to determine a technique to completely describe the damage state of large deformed
structures commonly found during forensic investigations. The combination of Laser Detecting and Ranging (LiDAR)
and Piezoelectric (PZT) Sensing Technologies for damage quantification is suggested to generate the full-field description of large deformation of a plate. The test subject is a 16 inch by 16 inch aluminum plate subjected to different damage scenarios. LiDAR is a static scanning laser that provides a 3-dimensional picture of the object. Smart Layer is a commercial PZT actuator/sensor network system that generates stress waves for internal damage evaluation. Both techniques were applied to the test plate after damages are introduced. In order to effectively analyze the results, the images for each test were superimposed. Frequencies that depicted the best interpretation of damage in the direct path images were superimposed with the 3-dimensional LiDAR images. Four damage scenarios were imposed on an aluminum plate including saw cuts at different depths using an electric saw. The final damage is a severe bending of the plate. The bending of the specimen produced an image that located the most severe damage directly under the left hand portion and directly above the right hand portion of the bend.
Acellent Technologies, Inc. developed a smart structural health monitoring (SHM) sensor network that can autonomously assess in real time the structural stability of buildings. The sensor network uses piezoelectric actuators and sensors to characterize damage in, and monitor the
rigidity of components of the building primary structure. Additionally, temperature sensors are integrated into the proposed sensor network to
monitor the temperature of the structural components. Acellent's existing sensor network SMART Layer technology was used as the basis for
the proposed development. The modifications to our existing technology included a redesigned sensor/actuator arrangement, the development
of a SmartDAQ sensor package with the required sensor and electronics, and additional software that provides a map of the structural damage,
temperature and rigidity information. This will be useful to provide a real time assessment of the building structural integrity. The data will be
available for display to provide and early warning to first responders and emergency personnel to ensure their safety prior to entering the
A GIS-based data management system has been proposed for pavement management due to the spatial capability in organizing
diverse geo-referenced information. The technology can be further enhanced by nondestructive distributed sensing. To target a
wide study area, this research proposes a low-cost vibro-acousto passive sensing technique that embeds within roadways for
long-term sensing. Using self-sustaining MEM sensors, the technology detects acoustic signals and use relative rating to assess
pavement conditions. The detection technique echoes the traditional chain-drag technology in that the same sound detection is
deployed. Coupling with previously established AMPIS pavement imaging and distress detection technique, the proposed system
can evolve to be a more powerful new-generation GIS-PMS. This paper introduces the system concept and describes the
philosophy behind the system and some of the challenges that we are currently attempting to solve.
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.
DuraNode is a sensing system designed for structural monitoring. It can detect the damage of structural members, provide crucial intelligence information of structural integrity and activate emergency response mechanism in the initial stages of a disaster. The sensor encompasses three MEMS-type accelerometers (SD-1221) and Wi-Fi (802.11b) communication adapter. It operates on solar power and rechargeable battery making it last for long term service without battery replacement. DuraNodes can be deployed in the form of a dense wireless network to enable seamless acquisition of structural intelligence in a complex structural system. A preliminary data acquisition and signal display module with graphic user interface (GUI) has been developed for connection of access points in ad-hoc networking. To validate the performance of DuraNode in structural monitoring applications, experiments were conducted on measuring vibration of a Pedestrian bridge in UC, Irvine, and a two-column bridge bent specimen with a Shake-table test in University of Neveda, Reno. Results were compared with that from conventional wired sensors and showed that DuraNode is cost-effective for carrying out robust sensing functions in the structural safety monitoring missions.
This study investigates the reliability and accuracy of wireless micro-electromechanical-system (MEMS)-type sensors in application of bridge structural vibration monitoring. With wireless capabilities added onto the developed sensors, it becomes unnecessary for engineers to connect enormous lengths of cables in order to measure vibration on bridges for instance. We investigated two types of MEMS accelerometers: the ADXL 202E and the Silicon Design 2210. To prove the validity of measuring acceleration by using these devices with wireless communication, we succeeded on measuring a pedestrian bridge's vibration under excitation loads in the center of span. The result had been compared with the traditional cabled sensor, PCB 393C. The wireless sensors were showed to be effective and much affordable to carry out the monitoring missions in situ.
An automated in-situ road surface distress surveying and management system, AMPIS, has been developed on the basis of video images within the framework of GIS software. Video image processing techniques are introduced to acquire, process and analyze the road surface images obtained from a moving vehicle. ArcGIS platform is used to integrate the routines of image processing and spatial analysis in handling the full-scale metropolitan highway surface distress detection and data fusion/management. This makes it possible to present user-friendly interfaces in GIS and to provide efficient visualizations of surveyed results not only for the use of transportation engineers to manage road surveying documentations, data acquisition, analysis and management, but also for financial officials to plan maintenance and repair programs and further evaluate the socio-economic impacts of highway degradation and deterioration. A review performed in this study on fundamental principle of Pavement Management System (PMS) and its implementation indicates that the proposed approach of using GIS concept and its tools for PMS application will reshape PMS into a new information technology-based system that can provide convenient and efficient pavement inspection and management.
An automated in-situ road surface distress surveying and management system, AMPIS, has been developed on the basis of video images within the framework of GIS software. Video image processing techniques are introduced to acquire, process and analyze the road surface images obtained from a moving vehicle. ArcGIS platform is used to integrate the routines of image processing and spatial analysis in handling the full-scale metropolitan highway surface distress detection and data fusion/management. This makes it possible to present user-friendly interfaces in GIS and to provide efficient visualizations of surveyed results not only for the use of transportation engineers to manage road surveying documentations, data acquisition, analysis and management, but also for financial officials to plan maintenance and repair programs and further evaluate the socio-economic impacts of highway degradation and deterioration. A review performed in this study on fundamental principle of Pavement Management System (PMS) and its implementation indicates that the proposed approach of using GIS concept and its tools for PMS application will reshape PMS into a new information technology-based system providing a convenient and efficient pavement inspection and management.
Emerging image processing techniques demonstrate their potential applications in earthquake engineering, particularly in the area of system identification. In this respect, the objectives of this research are to demonstrate the underlying principle that permits system identification, non-intrusively and remotely, with the aid of video camera and, for the purpose of the proof-of-concept, to apply the principle to a system identification problem involving relative motion, on the basis of the images. In structural control, accelerations at different stories of a building are usually measured and fed back for processing and control. As an alternative, this study attempts to identify the relative motion between different stories of a building for the purpose of on-line structural control by digitizing the images taken by video camera. For this purpose, the video image of the vibration of a structure base-isolated by a friction device under shaking-table was used successfully to observe relative displacement between the isolated structure and the shaking-table. This proof-of-concept experiment demonstrates that the proposed identification method based on digital image processing can be used with appropriate modifications to identify many other engineering-wise significant quantities remotely. In addition to the system identification study in the structural dynamics mentioned above, a result of preliminary study is described involving the video imaging of state of crack damage of road and highway pavement.
Emerging digital image processing techniques are utilized to demonstrate their potential applications in earthquake engineering, particularly in the area of system identification. In this respect, the objectives of this research are to demonstrate the underlying principle that permits nonlinear system identification, non-intrusively and remotely, with the aid of CCD camera and, for the purpose of the proof-of-concept, to apply the principle to an identification problem involving friction forces, a simple but not necessarily easy problem to solve, on the basis of the image captured by a CCD camera. On the one hand, intricate micromechanic interpretation of friction phenomena is prevalent and necessary in the area of Tribology and similarly detailed analysis of friction is performed as the extension of contact problem in continuum mechanics. On the other hand, the practice in the structural dynamics dealing with the friction issue is such that the Coulomb friction model is widley used for its mathematical expedience and ease of application. In this study, the method of digital image processing is applied to the identification of friction behavior between two solids with the aid of the Coulomb friction model. For this purpose, a pendulum is used which has a metal weight hung by a metal wire from a fixed point on a slant solid board and sliding on the board. The algorithm developed for this problem can be extended to identify, simultaneously and in near real-time, the friction coefficient and the relative motion between the model structure and the base in a shaking table test of a structure base-isolated by a sliding system. The proof-of- concept experiment was successfully carried out to show that the proposed identification method based on digital image processing can be used with appropriate modifications to identify many other engineering-wise significant quantities remotely. For example, the system in principle can be used to identify the friction coefficient of friction base-isolators of model or actual buildings during strong earthquakes.