Augmented Reality for Urban Visualization
The objective of this project is to effectively combine the qualities of different sensor types of a dynamic monitoring network to capitalize on the intrinsic redundancies of the measured data to identify the structural model parameters. Currently there is increasing activity in the area of structural health monitoring using newly emerging, dynamic sensor technologies. There is, however, no clear framework to best combine these heterogeneous measurement quantities for health monitoring purposes. In this project, this dual parameter and state estimation problem with different types of sensor measurements is formulated as a nonlinear estimation problem. In this study, the challenges that will be addressed in dealing with this nonlinear dual state and parameter estimation problem are: 1) the implementation of the approach to large structural problems with many unmeasured states and parameters to be identified and 2) determining the required sensor configurations and resolution to ensure "observability" such that the measured quantities are, indeed, useful and usable for this nonlinear estimation problem. The theoretical developments and the proposed identification approach will be experimentally validated with the laboratory model of a building structure and also with a leveraged data set from a major long-span bridge collected by the principal investigator. This study is expected to provide a validated approach to maximize the return on the use of the heterogeneous sensor networks and an important practical tool to the structural engineering community for better health monitoring, management and maintenance of critical civil infrastructure system with improved life safety. The PI has an industry/agency outreach plan, and will rapidly introduce the dual state-parameter estimation concepts in a graduate course under development. The project will also provide advanced training to graduate and undergraduate students through their direct involvements in this project.
Discharge of wastewater, sewerage and runoff from coastal cities remains the dominant sources of coastal zone pollution. The impervious nature of modern cities is only exacerbating this problem by increasing runoff from city surfaces, triggering combined sewer overflow events in cities with single-pipe wastewater conveyance systems and intensifying urban flooding. Many coastal cities, including US cities like Seattle, New York and San Francisco, are turning to urban green infrastructure (GI) to mitigate the city's role in coastal zone pollution. Urban GI, such as green roofs, green streets, advanced street-tree pits, rainwater gardens and bio-swales, introduce vegetation and perviousness back into city landscapes, thereby reducing the volume and pollutant loading of urban runoff. Urban GI, however, also has co-benefits that are equally important to coastal city sustainability. For example, increasing vegetation and perviousness within city boundaries can help cool urban environments, trap harmful air-borne particulates, increase biodiversity and promote public health and well-being. Despite the significance of these co-benefits, most current urban GI programs still focus on achieving volume reduction of storm water through passive detention and retention of rainfall or runoff. Holistic approaches to GI design that consider multiple sustainability goals are rare, and real time monitoring and active control systems that help ensure individual or networked GI meet performance goals over desired time-scales are lacking. Furthermore, how city inhabitants view, interact with, and value GI is little studied or accounted for in current urban GI programs. This project will develop and test a new framework for the next generation of urban GI that exploits the multi-functionality of GI for coastal city sustainability, builds a platform for real-time monitoring and control of urban GI networks, and takes account of the role of humans in GI stewardship and long-term functionality. The project will use the Bronx River Sewershed in New York City, where a $20 million investment in GI is planed over the next 5-years, as its living test bed. GI has its roots in several disciplines, and the project brings together expertise from these disciplines, including civil and environmental engineering, environmental science, and plant science/ horticulture. In addition, the project integrates expertise from other disciplines needed to elevate GI performance to the next level, including urban planning and design, climate science, data science, environmental microbiology, environmental law and policy, inter-agency coordination, community outreach and citizen science.The specific outcomes of the project will include: (i) new, scientific data on the holistic, environmental performance of different GI interventions in an urban, coastal environment; (ii) new models for the system level performance of networks of GI interventions; (iii) methodologies for projecting GI performance under a changing climate; (iv) a platform for remote monitoring and control of GI; (v) proposals for law and policy changes to enable US coastal cities to introduce GI at scales necessary to meet sustainability goals, and (vi) new understanding of human-GI interactions and their role in the long-term performance and maintenance of urban GI. Engagement with schools in the Bronx River Sewershed and engagement of citizens in the GI performance monitoring are both important components of the project work. The interdisciplinary project team integrates academic expertise with expertise in industry, government and non-profit organizations.
Eco-feedback systems for Building Energy Efficiency
This study focuses on the use of strong motion data recorded during earthquakes and aftershocks to provide a preliminary assessment of the structural integrity and possible damage in bridges. A system identification technique is used to determine dynamical characteristics and high-fidelity first-order linear models of a bridge from low level earthquake excitations. A finite element model is developed and updated using a genetic algorithm optimization scheme to match the frequencies identified and to simulate data from a damaging earthquake for the bridge. Here, two criteria are used to determine the state of the structure. The first criteria uses the error between the data recorded or simulated by the calibrated nonlinear finite element model and the data predicted by the linear model. The second criteria compares relative displacements of the structure with displacement thresholds identified using a pushover analysis. The use of this technique can provide an almost immediate, yet reliable, assessment of the structural health of an instrumented bridge after a seismic event. Copyright © 2011 John Wiley & Sons, Ltd.
The Role of Distributed Infrastructure in Future Cities
In this project we are developing Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and energetically self-reliant devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, walls, doors, toys, keys, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors.Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is significantly higher than the energy required to transmit a bit).The objective of the project is to design hardware, algorithms, and software to enable the realization of EnHANTs. This interdisciplinary project includes 5 PIs in the departments of Electrical Engineering and Computer Science at Columbia University with expertise in energy-harvesting devices and techniques, ultra-low power integrated circuits, and energy efficient communications and networking protocols.